Last updated: 2026-01-27

Checks: 6 1

Knit directory: paed-airway-allTissues/

This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: uncommitted changes

The R Markdown is untracked by Git. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20230811)

The command set.seed(20230811) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 378760b

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 378760b. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .DS_Store
    Ignored:    .RData
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    analysis/.DS_Store
    Ignored:    analysis/naiveB_top10_markers_whitebg.png
    Ignored:    data/.DS_Store
    Ignored:    data/RDS/
    Ignored:    figure/.DS_Store
    Ignored:    output/.DS_Store
    Ignored:    output/CAM_results/
    Ignored:    output/CSV/.DS_Store
    Ignored:    output/CSV_v2/.DS_Store
    Ignored:    output/CSV_v2/Adenoids/.DS_Store
    Ignored:    output/CSV_v2/BAL/.DS_Store
    Ignored:    output/CSV_v2/Bronchial_brushings/.DS_Store
    Ignored:    output/CSV_v2/Nasal_brushings/.DS_Store
    Ignored:    output/CSV_v2/Tonsils/.DS_Store
    Ignored:    output/DGE/.DS_Store
    Ignored:    output/DGE/RUVseq_TonsilAtlas/.DS_Store
    Ignored:    output/DGE/RUVseq_TonsilAtlas_k1/
    Ignored:    output/DGE/RUVseq_earlyAIR_Tonsils/.DS_Store
    Ignored:    output/DGE/Tonsils/RUV_Tonsils_Double negative T/
    Ignored:    output/RDS/
    Ignored:    output/plots/

Untracked files:
    Untracked:  All_metadata_combinedplots.Rhistory
    Untracked:  analysis/03_Batch_Integration.Rmd
    Untracked:  analysis/Age_proportions.Rmd
    Untracked:  analysis/Age_proportions_AllBatches.Rmd
    Untracked:  analysis/All_Batches_QCExploratory_v2.Rmd
    Untracked:  analysis/All_Umaps_and_marker_plots.Rmd
    Untracked:  analysis/All_metadata.Rmd
    Untracked:  analysis/Annotation_BAL.Rmd
    Untracked:  analysis/Annotation_Bronchial_brushings.Rmd
    Untracked:  analysis/Annotation_Nasal_brushings.Rmd
    Untracked:  analysis/BatchCorrection_Adenoids.Rmd
    Untracked:  analysis/BatchCorrection_Nasal_brushings.Rmd
    Untracked:  analysis/BatchCorrection_Tonsils.Rmd
    Untracked:  analysis/Batch_Integration_&_Downstream_analysis.Rmd
    Untracked:  analysis/Batch_correction_&_Downstream.Rmd
    Untracked:  analysis/Cell_cycle_regression.Rmd
    Untracked:  analysis/Clustering_Tonsils_v2.Rmd
    Untracked:  analysis/Cross-tissue_analysis.Rmd
    Untracked:  analysis/DGE_RUVSeq_Adenoids.Rmd
    Untracked:  analysis/DGE_RUVSeq_Bronchial_brushings.Rmd
    Untracked:  analysis/DGE_RUVSeq_Nasal_brushings.Rmd
    Untracked:  analysis/DGE_earlyAIR_tAtlas_Naïve_Bcells_Adenoids.Rmd
    Untracked:  analysis/DGE_earlyAIR_tAtlas_Naïve_Bcells_activated.Rmd
    Untracked:  analysis/DGE_earlyAIR_tAtlas_Plasma_Bcells.Rmd
    Untracked:  analysis/DGE_earlyAIR_vs_TonsilAtlas.Rmd
    Untracked:  analysis/Explore_census.Rmd
    Untracked:  analysis/Lineage_subclustering_Bcells.Rmd
    Untracked:  analysis/Lineage_subclustering_Myeloid_cells.Rmd
    Untracked:  analysis/Lineage_subclustering_Tcells.Rmd
    Untracked:  analysis/Lineages_subclustering.Rmd
    Untracked:  analysis/Master_metadata.Rmd
    Untracked:  analysis/Pediatric_Vs_Adult_Atlases.Rmd
    Untracked:  analysis/Preprocessing_Batch1_Nasal_brushings.Rmd
    Untracked:  analysis/Preprocessing_Batch2_Tonsils.Rmd
    Untracked:  analysis/Preprocessing_Batch3_Adenoids.Rmd
    Untracked:  analysis/Preprocessing_Batch4_Bronchial_brushings.Rmd
    Untracked:  analysis/Preprocessing_Batch5_Nasal_brushings.Rmd
    Untracked:  analysis/Preprocessing_Batch6_BAL.Rmd
    Untracked:  analysis/Preprocessing_Batch7_Bronchial_brushings.Rmd
    Untracked:  analysis/Preprocessing_Batch8_Adenoids.Rmd
    Untracked:  analysis/Preprocessing_Batch9_Tonsils.Rmd
    Untracked:  analysis/TonsilsVsAdenoids.Rmd
    Untracked:  analysis/cell_cycle_regression.R
    Untracked:  analysis/testing_age_all.Rmd
    Untracked:  code/DGE_utils.R
    Untracked:  code/pseudobulk_analysis.R
    Untracked:  data/Cell_labels_Gunjan_v2/
    Untracked:  data/Cell_labels_Mel/
    Untracked:  data/Cell_labels_Mel_v2/
    Untracked:  data/Cell_labels_Mel_v3/
    Untracked:  data/Cell_labels_modified_Gunjan/
    Untracked:  data/Gene_sets/
    Untracked:  data/Hs.c2.cp.reactome.v7.1.entrez.rds
    Untracked:  data/Raw_feature_bc_matrix/
    Untracked:  data/cell_labels_Mel_v4_Dec2024/
    Untracked:  data/celltypes_Mel_GD_v3.xlsx
    Untracked:  data/celltypes_Mel_GD_v4_no_dups.xlsx
    Untracked:  data/celltypes_Mel_modified.xlsx
    Untracked:  data/celltypes_Mel_v2.csv
    Untracked:  data/celltypes_Mel_v2.xlsx
    Untracked:  data/celltypes_Mel_v2_MN.xlsx
    Untracked:  data/celltypes_for_mel_MN.xlsx
    Untracked:  data/col_palette.xlsx
    Untracked:  data/earlyAIR_sample_sheets_combined.xlsx
    Untracked:  figure/DGE_Tonsils_CD8.Rmd/
    Untracked:  metadata_export.rds
    Untracked:  metadata_export1.rds
    Untracked:  naiveB_top10_markers.png
    Untracked:  naiveB_top10_markers_whitebg.png
    Untracked:  output/CSV/All_tissues.propeller.xlsx
    Untracked:  output/CSV/Bronchial_brushings/
    Untracked:  output/CSV/Bronchial_brushings_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/
    Untracked:  output/CSV/G000231_Neeland_Adenoids.propeller.xlsx
    Untracked:  output/CSV/G000231_Neeland_Bronchial_brushings.propeller.xlsx
    Untracked:  output/CSV/G000231_Neeland_Nasal_brushings.propeller.xlsx
    Untracked:  output/CSV/G000231_Neeland_Tonsils.propeller.xlsx
    Untracked:  output/CSV/Nasal_brushings/
    Untracked:  output/CSV_v3/B_cell_lineage/
    Untracked:  output/DGE/Adenoids/
    Untracked:  output/DGE/Adenoids_B_memory/
    Untracked:  output/DGE/Adenoids_CD4/
    Untracked:  output/DGE/Adenoids_CD8/
    Untracked:  output/DGE/Adenoids_Cycling_GCB/
    Untracked:  output/DGE/Adenoids_IFN/
    Untracked:  output/DGE/Bronchial_brushings/
    Untracked:  output/DGE/DGE_Age_summary_BAL.xlsx
    Untracked:  output/DGE/Nasal_brushings/
    Untracked:  output/DGE/RUV/
    Untracked:  output/DGE/RUVseq_earlyAIR_Adenoids/
    Untracked:  output/DGE/RUVseq_earlyAIR_Bronchial_brushings/
    Untracked:  output/DGE/RUVseq_earlyAIR_Nasal_brushings/
    Untracked:  output/DGE/TonsilAtlas/
    Untracked:  output/DGE/Tonsils/DGE_Age_summary_Tonsils.xlsx
    Untracked:  output/DGE/Tonsils/RUVTonsils_Memory B cells/
    Untracked:  output/DGE/Tonsils/RUV_Tonsils_Memory B cells/
    Untracked:  output/DGE/Tonsils/Tonsils_Naïve B cells/
    Untracked:  output/DGE/Tonsils_CD4/
    Untracked:  output/DGE/Tonsils_CD8/
    Untracked:  output/DGE/Tonsils_IFN/
    Untracked:  paed_sub_L1_cells.rds

Unstaged changes:
    Deleted:    02_QC_exploratoryPlots.Rmd
    Deleted:    02_QC_exploratoryPlots.html
    Modified:   analysis/00_AllBatches_overview.Rmd
    Modified:   analysis/01_QC_emptyDrops.Rmd
    Modified:   analysis/02_QC_exploratoryPlots.Rmd
    Modified:   analysis/Adenoids.Rmd
    Modified:   analysis/Adenoids_v2.Rmd
    Modified:   analysis/Age_modeling.Rmd
    Modified:   analysis/Age_modelling_Adenoids.Rmd
    Modified:   analysis/Age_modelling_Tonsils.Rmd
    Modified:   analysis/AllBatches_QCExploratory.Rmd
    Modified:   analysis/BAL.Rmd
    Modified:   analysis/BAL_v2.Rmd
    Modified:   analysis/Bronchial_brushings.Rmd
    Modified:   analysis/Bronchial_brushings_v2.Rmd
    Modified:   analysis/DGE_TonsilAtlas.Rmd
    Modified:   analysis/DGE_Tonsils_CD8.Rmd
    Modified:   analysis/DGE_analysis_George.Rmd
    Modified:   analysis/DGE_earlyAIR_tAtlas_CD4_TCM.Rmd
    Modified:   analysis/DGE_earlyAIR_tAtlas_CD4_TFH.Rmd
    Modified:   analysis/DGE_earlyAIR_tAtlas_CD4_TN.Rmd
    Modified:   analysis/DGE_earlyAIR_tAtlas_CD8_TF.Rmd
    Modified:   analysis/DGE_earlyAIR_tAtlas_CD8_TN.Rmd
    Modified:   analysis/DGE_earlyAIR_tAtlas_DZtoLZ_GCB.Rmd
    Modified:   analysis/DGE_earlyAIR_tAtlas_Memory_Bcells.Rmd
    Modified:   analysis/DGE_earlyAIR_tAtlas_Naïve_Bcells.Rmd
    Modified:   analysis/Nasal_brushings.Rmd
    Modified:   analysis/Nasal_brushings_v2.Rmd
    Modified:   analysis/Subclustering_Adenoids.Rmd
    Modified:   analysis/Subclustering_BAL.Rmd
    Modified:   analysis/Subclustering_Bronchial_brushings.Rmd
    Modified:   analysis/Subclustering_Nasal_brushings.Rmd
    Modified:   analysis/Subclustering_Tonsils.Rmd
    Modified:   analysis/Tonsils.Rmd
    Modified:   analysis/Tonsils_v2.Rmd
    Modified:   analysis/index.Rmd
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/REACTOME-cluster-limma-c0.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/REACTOME-cluster-limma-c1.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/REACTOME-cluster-limma-c10.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/REACTOME-cluster-limma-c11.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/REACTOME-cluster-limma-c12.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/REACTOME-cluster-limma-c13.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/REACTOME-cluster-limma-c14.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/REACTOME-cluster-limma-c15.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/REACTOME-cluster-limma-c16.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/REACTOME-cluster-limma-c17.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/REACTOME-cluster-limma-c2.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/REACTOME-cluster-limma-c3.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/REACTOME-cluster-limma-c4.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/REACTOME-cluster-limma-c5.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/REACTOME-cluster-limma-c6.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/REACTOME-cluster-limma-c7.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/REACTOME-cluster-limma-c8.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/REACTOME-cluster-limma-c9.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/up-cluster-limma-c0.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/up-cluster-limma-c1.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/up-cluster-limma-c10.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/up-cluster-limma-c11.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/up-cluster-limma-c12.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/up-cluster-limma-c13.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/up-cluster-limma-c14.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/up-cluster-limma-c15.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/up-cluster-limma-c16.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/up-cluster-limma-c17.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/up-cluster-limma-c2.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/up-cluster-limma-c3.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/up-cluster-limma-c4.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/up-cluster-limma-c5.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/up-cluster-limma-c6.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/up-cluster-limma-c7.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/up-cluster-limma-c8.csv
    Modified:   output/CSV/BAL_Marker_gene_clusters.limmaTrendRNA_snn_res.0.4/up-cluster-limma-c9.csv
    Modified:   output/CSV_v2/G000231_Neeland_Nasal_brushings.propeller.xlsx
    Modified:   output/DGE/RUVseq_TonsilAtlas/DGE_RUVSeq_Age_TonsilAtlas.xlsx
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/CD4 TCM/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/CD4 TCM/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/CD4 TCM/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/CD4 TFH/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/CD4 TFH/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/CD4 TFH/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/CD4 TN/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/CD4 TN/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/CD4 TN/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/CD4 Treg-eff/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/CD4 Treg-eff/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/CD4 Treg-eff/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/CD4 Treg/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/CD4 Treg/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/CD4 Treg/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/CD4 effector/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/CD4 effector/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/CD4 effector/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/CD8 TF/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/CD8 TF/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/CD8 TF/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/CD8 TN/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/CD8 TN/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/CD8 TN/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Cycling GCB/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Cycling GCB/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Cycling GCB/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/DGE_RUVSeq_Age_Tonsils.xlsx
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/DGE_RUVSeq_Tonsils_all_results.rds
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/DZ G2Mphase/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/DZ G2Mphase/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/DZ G2Mphase/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/DZ GCB/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/DZ GCB/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/DZ GCB/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/DZ early  Sphase/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/DZ early  Sphase/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/DZ early  Sphase/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/DZ late Sphase/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/DZ late Sphase/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/DZ late Sphase/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/DZtoLZ GCB transition/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/DZtoLZ GCB transition/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/DZtoLZ GCB transition/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Double negative T/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Double negative T/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Double negative T/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Early GC-committed NBC/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Early GC-committed NBC/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Early GC-committed NBC/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Early MBC/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Early MBC/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Early MBC/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Early PC precursor/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Early PC precursor/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Early PC precursor/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Follicular dendritic cells/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Follicular dendritic cells/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Follicular dendritic cells/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/GC-commited metabolic activation/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/GC-commited metabolic activation/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/GC-commited metabolic activation/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Gamma delta T/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Gamma delta T/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Gamma delta T/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Memory B cells/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Memory B cells/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Memory B cells/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Monocytes_macrophages/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Monocytes_macrophages/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Monocytes_macrophages/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Naïve B cell-IFN/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Naïve B cell-IFN/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Naïve B cell-IFN/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Naïve B cells activated/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Naïve B cells activated/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Naïve B cells activated/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Naïve B cells/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Naïve B cells/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Naïve B cells/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Plasma B cells/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Plasma B cells/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Plasma B cells/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Pre-BCRi II/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Pre-BCRi II/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/Pre-BCRi II/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/T-IFN/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/T-IFN/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/T-IFN/up_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/TFH-LZ-GC/all_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/TFH-LZ-GC/down_res.csv
    Modified:   output/DGE/RUVseq_earlyAIR_Tonsils/TFH-LZ-GC/up_res.csv
    Deleted:    test

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

There are no past versions. Publish this analysis with wflow_publish() to start tracking its development.

Load libraries

suppressPackageStartupMessages({
  library(BiocStyle)
  library(tidyverse)
  library(here)
  library(dplyr)
  library(Seurat)
  library(clustree)
  library(paletteer)
  library(viridis)
  library(ggforce)
  library(ggridges)
  library(kableExtra)
  library(RColorBrewer)
  library(data.table)
  library(dplyr)
  library(cowplot)
  library(ggplot2)
  library(paletteer)
  library(patchwork)
  library(harmony)
  library(BiocParallel)
  library(circlize)
  library(presto)
  library(gtools)
})

T and NK cells

paed_sub <- readRDS(here("output/RDS/Lineages_RDS_combined/SEU_T_NK_Cells.rds"))

if (!"umap.sub" %in% names(paed_sub@reductions)) {
  
  paed_sub <- paed_sub |>
    FindVariableFeatures() |>
    ScaleData() |>
    RunPCA()
  
  gc()
  
paed_sub[["RNA"]]
paed_sub[["RNA"]] <- JoinLayers(paed_sub[["RNA"]])
gc()

  paed_sub <- RunUMAP(paed_sub, dims = 1:30, reduction = "pca",
                      reduction.name = "umap.sub")
  
  paed_sub <- paed_sub |>
    FindNeighbors(dims = 1:30, reduction = "pca") |>
    FindClusters(resolution = 0.5)
  gc()
  
  saveRDS(paed_sub, here("output/RDS/Lineages_RDS_combined/SEU_T_NK_Cells.rds")) 
  } else {
  message("skipping processing")
}

skipping processing

paed_sub$tissue <- factor(
  paed_sub$tissue,
  levels = c("Tonsils", "Adenoids", "Nasal_brushings",
             "Bronchial_brushings", "BAL")
)

Previous labels

Excluding counts less than 100 to avoid too many labels in the plot

#sort(table(paed_sub$cell_labels_v2), decreasing = T)

k <- names(which(table(paed_sub$cell_labels_v2) > 100))
paed_sub$cell_labels_v22 <- ifelse(
    as.character(paed_sub$cell_labels_v2) %in% k,
    as.character(paed_sub$cell_labels_v2),
    NA
)

DimPlot(
    paed_sub,
    group.by = "cell_labels_v22",
    reduction = "umap.sub",
    label = TRUE,
    repel = TRUE,
    label.size = 4.5,
    shuffle = TRUE, raster = F)

#table( paed_sub$cell_labels_v2,paed_sub$RNA_snn_res.0.5)

opt_res <- "RNA_snn_res.0.5"  
n <- nlevels(paed_sub$RNA_snn_res.0.5)
paed_sub$RNA_snn_res.0.5 <- factor(paed_sub$RNA_snn_res.0.5, levels = seq(0,n-1))
paed_sub$seurat_clusters <- NULL
paed_sub$cluster <- paed_sub$RNA_snn_res.0.5
Idents(paed_sub) <- paed_sub$cluster

DimPlot(paed_sub, reduction = "umap.sub", raster =F, repel=T, label = T)

Checking expression of known markers for CD4 T cells, CD8 T cells, NK cells, ILC, Unconventional T cells and Cycling T cells

FeaturePlot(paed_sub, 
            features = c("CD4", "CD8A", "NCAM1","KLRC1", "KIT", "TRDC", "MKI67"),
            ncol = 3,
            reduction = "umap.sub",
            raster =F, 
          # split.by = "tissue",
            repel=T, 
            label = T)

paed_sub <- FindSubCluster(paed_sub, cluster = 10, graph.name = "RNA_snn", resolution = 0.3, subcluster.name = "T_sub")

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 5818
Number of edges: 163516

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8544
Number of communities: 9
Elapsed time: 0 seconds

3 singletons identified. 6 final clusters.

Idents(paed_sub) <- paed_sub$T_sub
paed_sub <- FindSubCluster(paed_sub, cluster = 11, graph.name = "RNA_snn", resolution = 0.1, subcluster.name = "T_sub")

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 4972
Number of edges: 125405

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9281
Number of communities: 3
Elapsed time: 0 seconds

Idents(paed_sub) <- paed_sub$T_sub
paed_sub <- FindSubCluster(paed_sub, cluster = 12, graph.name = "RNA_snn", resolution = 0.2, subcluster.name = "T_sub")

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 4447
Number of edges: 146996

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8999
Number of communities: 4
Elapsed time: 0 seconds

Idents(paed_sub) <- paed_sub$T_sub
paed_sub <- FindSubCluster(paed_sub, cluster = 15, graph.name = "RNA_snn", resolution = 0.1, subcluster.name = "T_sub")

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 1721
Number of edges: 48338

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9029
Number of communities: 2
Elapsed time: 0 seconds

Idents(paed_sub) <- paed_sub$T_sub

#levels(Idents(paed_sub)) <- mixedsort(levels(Idents(paed_sub)))
#levels(paed_sub$T_sub) <- mixedsort(levels(paed_sub$T_sub))
#DimPlot(paed_sub, reduction = "umap.sub", raster =F, repel=T, label = T)

Adding CellTypist info

files  <- c(
  paeds  = here("../EarlyAir_paper/output/CellTypist_results/cellTtypist_T_NK_Paed_Covid19.csv"),
  immune = here("../EarlyAir_paper/output/CellTypist_results/cellTtypist_T_NK_Immune_low.csv"),
  lung_airway   = here("../EarlyAir_paper/output/CellTypist_results/cellTtypist_T_NK_Lung_Airway.csv"),
   lung_atlas   = here("../EarlyAir_paper/output/CellTypist_results/cellTtypist_T_NK_Lung_atlas.csv"),
  tonsil = here("../EarlyAir_paper/output/CellTypist_results/cellTtypist_T_NK_Human_Tonsil.csv")
)

for (nm in names(files)) {
  df <- read.csv(files[nm])
  paed_sub[[paste0("celltypist_", nm)]] <- df$predicted_labels
}

plot_celltype_heatmap <- function(
  seu,
  cluster_col = "T_sub",
  celltype_col,
  min_n = 100,
  title,
  scale = "row",
  fontsize = 10
) {
  seu@meta.data %>%
    dplyr::count(.data[[cluster_col]], .data[[celltype_col]]) %>%
    dplyr::filter(n >= min_n) %>%
    tidyr::pivot_wider(
      names_from = .data[[celltype_col]],
      values_from = n,
      values_fill = 0
    ) %>%
    as.data.frame() %>%
    tibble::column_to_rownames(cluster_col) %>%
    t() %>%
    pheatmap::pheatmap(
      scale = scale,
      main = title,
      fontsize = fontsize
    )
}

plot_celltype_heatmap(paed_sub,celltype_col = "celltypist_paeds",title = "CellTypist Predictions (Paed Covid Study) by Cluster")

plot_celltype_heatmap(paed_sub,celltype_col = "celltypist_immune",title = "CellTypist Predictions (Immune Low) by Cluster")

plot_celltype_heatmap(paed_sub,celltype_col = "celltypist_lung_airway",title = "CellTypist Predictions (Lung Atlas) by Cluster")

plot_celltype_heatmap( paed_sub,celltype_col = "celltypist_lung_atlas",title = "CellTypist Predictions (Lung Atlas) by Cluster")

plot_celltype_heatmap(paed_sub,celltype_col = "predicted.celltype.l2", title = "Azimuth Predictions (Tonsil Atlas) by Cluster")

plot_ct <- function(obj, col) {
  k <- names(which(table(obj[[col]][,1]) > 150))
  DimPlot(
    obj,
    group.by = col,
    reduction = "umap.sub",
    cells = colnames(obj)[obj[[col]][,1] %in% k],
    label = TRUE, repel = TRUE, label.size = 2.5,
    shuffle = TRUE, raster = FALSE
  )
}

plot_ct(paed_sub, "celltypist_paeds") + ggtitle("Paed Covid Study")

plot_ct(paed_sub, "celltypist_immune") + ggtitle("Immune low dataset")

plot_ct(paed_sub, "celltypist_lung_airway") + ggtitle("Lung Airway Study")

plot_ct(paed_sub, "celltypist_lung_atlas") + ggtitle("Lung Atlas")

plot_ct(paed_sub, "celltypist_tonsil") + ggtitle("Tonsil Atlas")

plot_ct_bar <- function(obj, col) {
  k <- names(which(table(obj[[col]][,1]) > 100))
  
  df <- obj@meta.data %>%
    filter(get(col) %in% k) %>%   
    count(cluster, !!sym(col)) %>%
    group_by(cluster) %>%
    mutate(frac = n / sum(n)) %>%
    ungroup()
  
  ggplot(df, aes(x = cluster, y = frac, fill = !!sym(col))) +
    geom_col(position = "stack") +
    scale_y_continuous(labels = scales::percent) +
    labs(x = "Cluster", y = "Fraction of cells", fill = col) +
    theme_minimal() +
    theme(axis.text.x = element_text(angle = 45, hjust = 1))
}

plot_ct_bar(paed_sub, "celltypist_paeds") + ggtitle("Paed Covid Study")

plot_ct_bar(paed_sub, "celltypist_immune") + ggtitle("Immune low dataset")

plot_ct_bar(paed_sub, "celltypist_lung_airway") + ggtitle("Lung Airway study")

plot_ct_bar(paed_sub, "celltypist_lung_atlas") + ggtitle("Lung Atlas")

plot_ct_bar(paed_sub, "celltypist_tonsil") + ggtitle("Tonsil Atlas")

Based on the prominent markers above, we subdivide the T cells into CD4 T cells, CD8 T cells, NK cells, ILC cells and Unconventional t cells, cycling T cells and Double negative T cells)

new_levels <- c(
  "0" = "CD4 T",
  "1" = "CD4 T",
  "2" = "CD4 T",
  "3" = "CD8 T",
  "4" = "CD4 T",
  "5" = "CD8 T",
  "6" = "CD8 T",
#  "6_1" = "CD8 T",
#  "6_2" = "CD4 T",
  "7" = "CD8 T",
  "8" = "CD4 T",
  "9" = "Gamma-delta T",
  "10_0" = "CD4 T",
 "10_1" = "CD4 T",
"10_2" = "CD4 T",
"10_3" = "CD4 T",
"10_4" = "CD8 T",
"10_5" = "CD4 T",
  "11_0" = "CD4 T",
  "11_1" = "CD8 T",
  "11_2" = "CD4 T",
  "12_0" = "NK cells",
  "12_1" = "MAIT cells",
  "12_2" = "NK cells",
  "12_3" = "NK cells",
  "13" = "CD4 T",
  "14" = "Double Negative T",
  "15_0" = "CD4 T",
  "15_1" = "CD8 T",
  "16" = "ILC",
  "17" = "CD4 T",
  "18" = "Cycling T",
  "19" = "Pre T"
)

paed_sub <- RenameIdents(paed_sub, new_levels)
paed_sub$cell_labels <- NULL

Level 1 labels

Idents(paed_sub) <- factor(
  Idents(paed_sub),
  levels = c(
    "Pre T","Cycling T","CD4 T","CD8 T","Gamma-delta T",
    "Double Negative T","MAIT cells","NK cells","ILC"
  )
)

paed_sub$cell_labels_l1 <- Idents(paed_sub)

DimPlot(
  paed_sub,
  reduction = "umap.sub",
  label = TRUE,
  repel = TRUE,
  label.size = 2.5, raster = F
)

Cd4 T cells

cd4_t <-  subset(paed_sub, idents = "CD4 T")
keep_cells <- paed_sub$cell_labels_l1 %in% "CD4 T"
cd4_t@assays$RNA@cells@.Data <- paed_sub@assays$RNA@cells@.Data[keep_cells, ]
cd4_t

An object of class Seurat 
18076 features across 89930 samples within 1 assay 
Active assay: RNA (18076 features, 2000 variable features)
 3 layers present: data, counts, scale.data
 4 dimensional reductions calculated: pca, umap.unintegrated, umap.l1, umap.sub

out <- here("output/RDS/Lineages_RDS_combined/SEU_CD4T_Cells.rds")

if (!file.exists(out)) {
  cd4_t <- cd4_t %>%
  FindVariableFeatures() %>%
  ScaleData() %>%
  RunPCA() 
  
  gc() 
  cd4_t <- RunUMAP(cd4_t, dims = 1:30, reduction = "pca", reduction.name = "umap.cd4T")

  meta_data_columns <- colnames(cd4_t@meta.data)
  columns_to_remove <- grep("^RNA_snn_res", meta_data_columns, value = TRUE)
  cd4_t@meta.data <- cd4_t@meta.data[, !(colnames(cd4_t@meta.data) %in% columns_to_remove)]
  resolutions <- seq(0.1, 0.5, by = 0.1)
  cd4_t <- FindNeighbors(cd4_t, dims = 1:30, reduction = "pca")
  cd4_t <- FindClusters(cd4_t, resolution = resolutions )
  
  saveRDS(cd4_t, out)

} else {
  cd4_t <- readRDS(out)
}

clustree(cd4_t, prefix = "RNA_snn_res.")

DimPlot(cd4_t, group.by = "RNA_snn_res.0.5", reduction = "umap.cd4T", label = TRUE, label.size = 2.5, repel = TRUE, raster = FALSE )

DimPlot(cd4_t, group.by = "RNA_snn_res.0.5", reduction = "umap.cd4T", label = TRUE, label.size = 2.5, repel = TRUE, raster = FALSE, split.by = "tissue" )

opt_res <- "RNA_snn_res.0.5"  
n <- nlevels(cd4_t$RNA_snn_res.0.5)
cd4_t$RNA_snn_res.0.5 <- factor(cd4_t$RNA_snn_res.0.5, levels = seq(0,n-1))
cd4_t$seurat_clusters <- NULL
cd4_t$cluster <- cd4_t$RNA_snn_res.0.5
Idents(cd4_t) <- cd4_t$cluster

k <- names(which(table(cd4_t$celltypist_paeds) > 100))
cd4_t$celltypist_paeds2 <- ifelse(cd4_t$celltypist_paeds %in% k,
                                  cd4_t$celltypist_paeds, NA)

DimPlot(
    cd4_t,
    group.by = "celltypist_paeds2",
    reduction = "umap.cd4T",
    label = TRUE,
    repel = TRUE,
    label.size = 2.5,
    shuffle = TRUE
)

plot_ct_bar(cd4_t, "celltypist_paeds")

plot_ct_bar(cd4_t, "celltypist_lung_airway")

plot_ct_bar(cd4_t, "celltypist_lung_atlas")

plot_ct_bar(cd4_t, "celltypist_immune")

#plot_ct_bar(cd4_t, "celltypist_tonsil")
plot_ct_bar(cd4_t, "predicted.celltype.l2") # Tonsil Atlas level 2

plot_ct_bar(cd4_t, "cell_labels_v2") # previous labels

Checking for known markers-

features <- list(
  ## 1. Naïve CD4 T cells
  T_naive = c("CD4", "CCR7","SELL","LEF1"),
  ## 2. Memory CD4 T cells
  T_memory = c("IL7R","PRDM1","TCF7","TMSB10", "ITGB1", "LDHB", "MAL"),
  ## 3. Effector CD4 T cells
  T_eff = c("GZMK","KLRB1","IFNG","TNF","CCL5", "HECW2","EGLN3"),
  ## 4. Regulatory CD4 T cells
  T_reg = c("FOXP3","IL2RA","CTLA4","IKZF2")
)

for (g in features) {
  print(
    FeaturePlot(
      cd4_t,                     
      features  = g,
      reduction = "umap.cd4T",     
      raster    = FALSE,
      label     = TRUE,
      ncol = 4
      #split.by  = "tissue"
    )
  )
}

cd4_t <- FindSubCluster(cd4_t, cluster = 6, graph.name = "RNA_snn", resolution = 0.1, subcluster.name = "T_sub")

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 4810
Number of edges: 126511

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9164
Number of communities: 2
Elapsed time: 0 seconds

DimPlot(cd4_t, repel = T, label = T, reduction = "umap.cd4T", group.by = "T_sub")

Idents(cd4_t) <- cd4_t$T_sub

cd4_t <- FindSubCluster(cd4_t, cluster = 9, graph.name = "RNA_snn", resolution = 0.1, subcluster.name = "T_sub")

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 3904
Number of edges: 110985

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9284
Number of communities: 2
Elapsed time: 0 seconds

DimPlot(cd4_t, repel = T, label = T, reduction = "umap.cd4T", group.by = "T_sub")

Idents(cd4_t) <- cd4_t$T_sub

new_levels <- c(
  "0" = "CD4 T Memory",
  "1" = "CD4 T Naive",
  "2" = "CD4 T Memory",
  "3" = "CD4 T Memory",
  "4" = "CD4 T Naive",
  "5" = "CD4 T Memory",
  "6_0" = "CD4 T Effector",
 "6_1" = "CD4 T Regulatory",
  "7" = "CD4 T Regulatory",
  "8" = "CD4 T Memory",
  "9_0" = "CD4 T Effector",
  "9_1" = "CD4 T Regulatory",
  "10" = "CD4 T Naive",
  "11" = "CD4 T Memory",
  "12" = "CD4 T Memory",
  "13" = "CD4 T Memory",
  "14" = "CD4 T Naive"
)

cd4_t <- RenameIdents(cd4_t, new_levels)
cd4_t$cell_labels_l2 <- Idents(cd4_t)
DimPlot(cd4_t, raster = F, reduction = "umap.cd4T", label = T, repel = T)

Subclustering CD4 T cells clusters-

cd4_t <- FindSubCluster(cd4_t, cluster = "CD4 T Naive", graph.name = "RNA_snn", resolution = 0.1, subcluster.name = "T_sub")

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 24759
Number of edges: 593426

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9329
Number of communities: 4
Elapsed time: 2 seconds

1 singletons identified. 3 final clusters.

DimPlot(cd4_t, raster = F, reduction = "umap.cd4T", label = T, group.by = "T_sub", repel = T)

Idents(cd4_t) <- cd4_t$T_sub
cd4_t$cluster <- cd4_t$T_sub

cd4_t <- FindSubCluster(cd4_t, cluster = "CD4 T Memory", graph.name = "RNA_snn", resolution = 0.1, subcluster.name = "T_sub")

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 52115
Number of edges: 1248777

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9316
Number of communities: 4
Elapsed time: 9 seconds

DimPlot(cd4_t, raster = F, reduction = "umap.cd4T", label = T, group.by = "T_sub", repel = T)

Idents(cd4_t) <- cd4_t$T_sub

cd4_t <- FindSubCluster(cd4_t, cluster = "CD4 T Effector", graph.name = "RNA_snn", resolution = 0.1, subcluster.name = "T_sub")

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 5419
Number of edges: 134909

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9325
Number of communities: 2
Elapsed time: 0 seconds

DimPlot(cd4_t, raster = F, reduction = "umap.cd4T", label = T, group.by = "T_sub", repel = T)

Idents(cd4_t) <- cd4_t$T_sub

cd4_t <- FindSubCluster(cd4_t, cluster = "CD4 T Regulatory", graph.name = "RNA_snn", resolution = 0.1, subcluster.name = "T_sub")

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 7637
Number of edges: 220702

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9425
Number of communities: 3
Elapsed time: 0 seconds

DimPlot(cd4_t, raster = F, reduction = "umap.cd4T", label = T, group.by = "T_sub", repel = T)

Idents(cd4_t) <- cd4_t$T_sub

Renaming idents

new_levels <- c(
  "CD4 T Naive_0",
    "CD4 T Naive_1",
    "CD4 T Naive_2",
   "CD4 T Memory_0",
     "CD4 T Memory_1",
     "CD4 T Memory_2",
     "CD4 T Memory_3",
"CD4 T Effector_0",
"CD4 T Effector_1",
 "CD4 T Regulatory_0",
 "CD4 T Regulatory_1",
"CD4 T Regulatory_2"

)
Seurat::Idents(cd4_t) <- factor(
  Seurat::Idents(cd4_t),
  levels = new_levels
)

cd4_t$cell_labels_l3 <- Idents(cd4_t)

table(cd4_t$cell_labels_l3, cd4_t$tissue)

                    
                     Tonsils Adenoids Nasal_brushings Bronchial_brushings   BAL
  CD4 T Naive_0         7743     6712             228                  63    44
  CD4 T Naive_1         3368     3258             237                  41    71
  CD4 T Naive_2         2189      788               6                   8     3
  CD4 T Memory_0       13920     9514             282                  27    47
  CD4 T Memory_1        8062     9491             493                  34    79
  CD4 T Memory_2        3053     1964              60                  16    16
  CD4 T Memory_3        1447     3407             127                  42    34
  CD4 T Effector_0      1763     1165             200                  35    83
  CD4 T Effector_1        76      101             507                 877   612
  CD4 T Regulatory_0    2169     1779             286                  73    89
  CD4 T Regulatory_1     413      492             489                 151   182
  CD4 T Regulatory_2     906      571              13                   5    19

df_counts <- cd4_t@meta.data |>
  count(cell_labels_l3, tissue, name = "n")
df_totals <- df_counts |>
  group_by(cell_labels_l3) |>
  summarise(total = sum(n), .groups = "drop")

ggplot(df_counts, aes(x = cell_labels_l3, y = n, fill = tissue)) +
  geom_bar(stat = "identity", position = "stack") +
  geom_text(data = df_totals,
            aes(x = cell_labels_l3, y = total, label = total),
            vjust = -0.3, size = 3, inherit.aes = FALSE) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  labs(x = "cluster at l3",
       y = "Number of cells",
       fill = "Tissue")

DE features of Cd4 T cells

The marker genes for this subclustering can be found here-

CD4T_population_subclusters

features.no.mt <- rownames(paed_sub)[!grepl("^MT-", rownames(paed_sub))]
gc()

             used    (Mb) gc trigger    (Mb) limit (Mb)   max used    (Mb)
Ncells    4495377   240.1    7334035   391.7         NA    7334035   391.7
Vcells 1609086948 12276.4 2453198976 18716.5     163840 2453173210 18716.3

paed_sub.markers <- FindAllMarkers(
  cd4_t,
  only.pos = TRUE,
  min.pct = 0.25,
  logfc.threshold = 0.25,
  features = features.no.mt
)
gc()

             used    (Mb) gc trigger    (Mb) limit (Mb)   max used    (Mb)
Ncells    4559676   243.6    7334035   391.7         NA    7334035   391.7
Vcells 1609249247 12277.6 3532782525 26953.0     163840 3527585484 26913.4

top10 <- paed_sub.markers %>%
    group_by(cluster) %>%
    top_n(n = 10, wt = avg_log2FC) %>%
    ungroup() %>%
    distinct(gene, .keep_all = TRUE) %>%
    arrange(cluster, desc(avg_log2FC))


best.wilcox.gene.per.cluster <- paed_sub.markers |>
  group_by(cluster) |>
  slice_max(avg_log2FC, n = 1) |>
  select(cluster, gene)
best.wilcox.gene.per.cluster

# A tibble: 12 × 2
# Groups:   cluster [12]
   cluster            gene    
   <fct>              <chr>   
 1 CD4 T Naive_0      NOG     
 2 CD4 T Naive_1      ANXA1   
 3 CD4 T Naive_2      SERPINE2
 4 CD4 T Memory_0     GNG4    
 5 CD4 T Memory_1     GPR183  
 6 CD4 T Memory_2     TSHR    
 7 CD4 T Memory_3     IFIT3   
 8 CD4 T Effector_0   IL26    
 9 CD4 T Effector_1   FOSL2   
10 CD4 T Regulatory_0 FOXP3   
11 CD4 T Regulatory_1 GZMK    
12 CD4 T Regulatory_2 RGS6

FeaturePlot(
  cd4_t,
  features = best.wilcox.gene.per.cluster$gene,
  reduction = "umap.cd4T",
  raster = FALSE,
  repel = TRUE,
  ncol = 1,
  label = TRUE,
  split.by = "tissue"
)

Note: CD4 T Memory_3 is potentially T-IFN

cd4_t <- ScaleData(
  cd4_t,
  features = unique(top10$gene),
  assay = "RNA",
  verbose = FALSE
)

DoHeatmap(cd4_t, features = unique(top10$gene), angle = 90) + NoLegend()

## Seurat top markers
cluster_colors <- paletteer::paletteer_d("pals::glasbey")[factor(top10$cluster)]

DotPlot(cd4_t,    
        features = unique(top10$gene),
        group.by = "cell_labels_l3",
        cols = c("azure1", "blueviolet"),
        dot.scale = 3, assay = "RNA") +
    RotatedAxis() +
    FontSize(y.text = 8, x.text = 12) +
    labs(y = element_blank(), x = element_blank()) +
    coord_flip() +
    theme(axis.text.y = element_text(color = cluster_colors)) +
    ggtitle("Top 10 marker genes per cluster (Seurat)")

marker_counts <- paed_sub.markers %>%
    filter(avg_log2FC > 1 & p_val_adj < 0.05) %>%  
    count(cluster, name = "n_markers") %>%
    arrange(desc(n_markers))

print(marker_counts)

              cluster n_markers
1    CD4 T Effector_1       184
2  CD4 T Regulatory_2       134
3    CD4 T Effector_0       102
4      CD4 T Memory_0        95
5  CD4 T Regulatory_1        88
6       CD4 T Naive_0        70
7      CD4 T Memory_3        70
8       CD4 T Naive_2        66
9      CD4 T Memory_2        64
10      CD4 T Naive_1        41
11 CD4 T Regulatory_0        39
12     CD4 T Memory_1         3

ggplot(marker_counts, aes(x = reorder(cluster, n_markers), y = n_markers)) +
    geom_col(fill = "steelblue", alpha = 0.8) +
    coord_flip() +
    labs(title = "High-confidence markers per subcluster (logFC > 1)",
         x = "Subcluster", y = "Number of markers") +
    theme_minimal()

out_markers <- here("output",
            "CSV_v3","T_cell_lineage",
            paste("Marker_genes_Reclustered_Cd4T.",opt_res, sep = ""))

dir.create(out_markers, recursive = TRUE, showWarnings = FALSE)

for (cl in unique(paed_sub.markers$cluster)) {
  cluster_data <- paed_sub.markers %>% dplyr::filter(cluster == cl)
  file_name <- here(out_markers, paste0("G000231_Neeland_", cl, ".csv"))
  if (!file.exists(file_name)) {
  write.csv(cluster_data, file = file_name)
  }
}

DE markers/gene between CD4 T sub-clusters

run_de_and_vln <- function(
  obj, ids, title_prefix,
  top_n = 10, min.pct = 0.25, logfc.threshold = 0.5,
  run_one_vs_all = TRUE, run_pairwise = TRUE,
  print_table = TRUE
) {

  res_list <- list()

  # Helper function to run DE, plot, and optionally show table
  de_plot <- function(id1, id2, comp_name) {
    res <- FindMarkers(
      obj,
      ident.1 = id1,
      ident.2 = id2,
      only.pos = TRUE,
      min.pct = min.pct,
      logfc.threshold = logfc.threshold,
      features = features.no.mt
    )

    top_genes <- res |>
      arrange(p_val_adj, desc(avg_log2FC)) |>
      head(top_n) |>
      rownames()

    res_list[[comp_name]] <<- res

    cat("\n\n####", title_prefix, ":", comp_name, "\n\n")
    print(
      VlnPlot(obj, features = top_genes, idents = unique(c(id1, id2)), pt.size = 0) +
        patchwork::plot_annotation(title = comp_name)
    )

    if (print_table) {
      print(
        kable(
          head(res, 50),
          caption = paste("Top 50 markers:", comp_name)
        )
      )
    }
  }

  # ---- One-vs-rest ----
  if (run_one_vs_all) {
    for (id in ids) {
      de_plot(id, setdiff(ids, id), paste(id, "vs_rest"))
    }
  }

  # ---- Pairwise ----
  if (run_pairwise && length(ids) > 1) {
    for (pair in combn(ids, 2, simplify = FALSE)) {
      de_plot(pair[1], pair[2], paste(pair[1], "vs", pair[2]))
    }
  }

  invisible(res_list)
}

Pairwise markers within CD4 T Naive

naive_ids <- c("CD4 T Naive_0","CD4 T Naive_1","CD4 T Naive_2")

res_all <- run_de_and_vln(
  cd4_t,
  naive_ids,
  title_prefix = "CD4 T Naive",
  run_one_vs_all = TRUE,
  run_pairwise = TRUE,
  print_table = TRUE
)

CD4 T Naive : CD4 T Naive_0 vs_rest

Top 50 markers: CD4 T Naive_0 vs_rest
	avg_log2FC	pct.1	pct.2
NOG	2.3721023	0.467	0.139
LEF1	0.8063011	0.775	0.631
SATB1	0.6715195	0.814	0.675
PDE7A	0.8266771	0.738	0.637
TRABD2A	1.3213376	0.468	0.275
FHIT	1.8145307	0.315	0.130
PECAM1	2.0529342	0.270	0.094
RASGRP2	0.7484927	0.730	0.637
RFLNB	1.4790741	0.395	0.218
OXNAD1	0.8409488	0.657	0.542
ATM	0.5734900	0.843	0.796
ACTN1	0.9552672	0.575	0.417
AIF1	1.8900242	0.282	0.118
ITGA6	1.3259496	0.390	0.234
RIN3	1.0287974	0.465	0.325
OBSCN	0.8626625	0.529	0.411
CHMP7	0.6602652	0.677	0.614
FOXP1	0.5250899	0.748	0.696
CSGALNACT1	1.2444958	0.305	0.176
PDK1	1.1510288	0.367	0.244
NOSIP	0.7214973	0.571	0.485
CAMK4	0.8243306	0.494	0.413
RCAN3	0.5807431	0.614	0.567
ABLIM1	0.5242259	0.616	0.574
NELL2	0.7086201	0.383	0.276
RNF144A	0.9442384	0.328	0.238
TMEM63A	0.5330424	0.593	0.541
LTBP3	0.5545770	0.546	0.481
NUCB2	0.6823880	0.406	0.328
ZNF609	0.7763345	0.390	0.321
CRLF3	0.6093957	0.486	0.445
BACH2	0.8679767	0.278	0.202
NAA16	0.9725942	0.295	0.228
INPP4A	0.5598757	0.468	0.431
SAP25	0.7768572	0.328	0.275
PGGHG	0.5543266	0.447	0.405
SAMD3	0.6603815	0.296	0.232
IL17RA	0.6666897	0.383	0.341
HERC3	0.5942640	0.407	0.369
LDLRAP1	0.5586224	0.406	0.366
DGKD	0.8178502	0.255	0.202
FLT3LG	0.5184941	0.471	0.450
CD55	0.6285506	0.348	0.307
SUSD3	0.5090834	0.422	0.403
TNRC6C	0.5457036	0.365	0.339
ZMYND8	0.6615226	0.287	0.255
ADPRM	0.6781973	0.273	0.241
MARCKSL1	0.6197628	0.297	0.266
ANXA2R	0.6542459	0.254	0.221
TMEM71	0.5843542	0.302	0.274

CD4 T Naive : CD4 T Naive_1 vs_rest

Top 50 markers: CD4 T Naive_1 vs_rest
	avg_log2FC	pct.1	pct.2
AHNAK	3.6279751	0.687	0.092
MAF	3.3865267	0.576	0.063
S100A4	2.5716562	0.617	0.178
CCR4	4.4461486	0.468	0.031
SYNE2	1.6182663	0.776	0.377
PYHIN1	2.2011354	0.493	0.111
KLF6	1.8236419	0.589	0.224
CRIP1	1.7869948	0.697	0.335
KLRB1	4.9961620	0.379	0.020
DUSP1	1.7818792	0.617	0.271
IL10RA	1.5698429	0.555	0.224
ANXA1	2.1198692	0.481	0.150
ATP2B4	1.4958171	0.549	0.229
GPR183	1.3314528	0.625	0.312
ITGB1	3.0277785	0.361	0.058
CASP8	1.1748969	0.602	0.301
OPTN	1.5411787	0.481	0.182
CAPN2	1.7202835	0.446	0.150
CXCR5	3.5957708	0.324	0.030
S100A11	2.0261629	0.410	0.118
PREX1	2.4614988	0.356	0.068
IKZF3	1.2490304	0.587	0.301
GPRIN3	1.3449576	0.522	0.237
ST8SIA1	2.8029895	0.331	0.048
FLNA	1.1141420	0.631	0.355
DUSP16	1.9318290	0.389	0.118
SMAD3	1.4055149	0.446	0.184
NBEAL2	1.8162631	0.377	0.118
TRIB2	1.0755851	0.663	0.407
ITGA4	1.3319972	0.565	0.312
ELOVL5	1.0978740	0.529	0.282
HNRNPLL	1.8366466	0.347	0.103
MAP3K5	2.1585144	0.316	0.072
IL2RB	1.3534463	0.431	0.189
ADAM19	3.9849296	0.258	0.023
NABP1	1.4854734	0.379	0.145
RORA	1.3397061	0.435	0.204
TIGIT	3.0947947	0.253	0.024
TRADD	1.6420354	0.348	0.121
NPDC1	3.5068383	0.250	0.024
CLDND1	1.4389694	0.393	0.173
STOM	2.8983131	0.253	0.040
IL32	1.0382522	0.860	0.649
PIEZO1	2.1025969	0.266	0.067
TP53INP1	2.1465369	0.254	0.061
CD84	1.6336222	0.292	0.100
ATXN1	1.6981000	0.268	0.084
PLP2	1.7621141	0.263	0.081
IL7R	0.8740955	0.866	0.722
FXYD5	0.7105736	0.947	0.822

CD4 T Naive : CD4 T Naive_2 vs_rest

Top 50 markers: CD4 T Naive_2 vs_rest
	avg_log2FC	pct.1	pct.2
CHI3L2	2.4339340	0.730	0.247
ID2	1.9345547	0.605	0.177
SESN3	1.6254290	0.728	0.303
GBP2	1.8352629	0.790	0.368
CD38	1.8368873	0.603	0.184
SUSD4	2.5924605	0.487	0.090
MYB	4.3940542	0.411	0.024
CHST2	1.9802415	0.494	0.115
SIRPG	1.1360642	0.709	0.353
PTGER4	1.6404102	0.553	0.202
SRGN	1.5189087	0.836	0.500
NAB2	2.0632024	0.432	0.100
NFATC1	1.8544503	0.527	0.201
DUSP2	2.7250682	0.440	0.117
SERPINE2	2.5754668	0.360	0.053
IRF4	4.2294455	0.327	0.035
EGR3	5.5051391	0.303	0.017
CTLA4	2.1163965	0.352	0.077
SNX9	1.9554623	0.370	0.109
COL6A3	3.6280223	0.286	0.031
RGS3	1.8262307	0.346	0.097
P2RX5	1.6994262	0.364	0.106
MAN1C1	1.1938482	0.636	0.299
MATK	1.9413785	0.275	0.067
MAN1A1	1.7443329	0.274	0.065
EGR1	2.7454152	0.279	0.072
C9orf16	1.1477649	0.571	0.254
IKZF2	1.8652699	0.252	0.062
BICDL1	1.3378200	0.576	0.268
FAM184A	1.5100587	0.302	0.089
CBLB	1.3410068	0.552	0.261
SREBF2	1.1496252	0.564	0.265
LDLRAD4	1.2244296	0.412	0.157
CD28	0.8224311	0.851	0.591
ID3	1.2842256	0.486	0.214
INPP5F	1.7732391	0.284	0.091
IL2RB	0.9986978	0.504	0.223
PTPN7	1.0226019	0.575	0.280
PELI1	1.2620958	0.336	0.117
CD27	0.7731521	0.859	0.640
BATF	1.6237304	0.338	0.124
HBS1L	1.0103618	0.715	0.414
PKIA	1.1573587	0.381	0.148
ETHE1	1.1523504	0.341	0.124
EPHX2	1.0639413	0.455	0.200
HIF1A	1.1863080	0.357	0.139
ACTG1	0.5960489	0.954	0.859
BTBD11	1.1833603	0.270	0.089
GBP5	1.3181278	0.330	0.127
RCC2	1.0078787	0.332	0.124

CD4 T Naive : CD4 T Naive_0 vs CD4 T Naive_1

Top 50 markers: CD4 T Naive_0 vs CD4 T Naive_1
	avg_log2FC	pct.1	pct.2
NOG	3.2620582	0.467	0.074
TRABD2A	2.0366885	0.468	0.170
PECAM1	4.5665918	0.270	0.017
ACTN1	1.4332199	0.575	0.323
LEF1	1.3157113	0.775	0.525
FHIT	2.4799947	0.315	0.075
SATB1	1.2464990	0.814	0.576
CCR7	0.8461482	0.809	0.679
AIF1	2.6450212	0.282	0.063
OXNAD1	0.9798236	0.657	0.500
ATM	0.6626084	0.843	0.766
PDE7A	0.8396662	0.738	0.619
FOXP1	0.8022245	0.748	0.635
RIN3	1.3649192	0.465	0.250
RFLNB	1.4723179	0.395	0.204
TGFBR2	0.8100656	0.714	0.605
BACH2	1.9639532	0.278	0.102
NUCB2	1.3431748	0.406	0.232
CHI3L2	1.8690275	0.302	0.130
OBSCN	0.9779104	0.529	0.365
RASGRP2	0.6881347	0.730	0.645
PDK1	1.3664417	0.367	0.202
LTBP3	0.9096030	0.546	0.399
NELL2	1.1272844	0.383	0.210
ITGA6	1.2452821	0.390	0.239
CD7	0.5840507	0.724	0.615
NOSIP	0.8032539	0.571	0.459
RGS10	0.9353517	0.475	0.344
ABLIM1	0.7171427	0.616	0.522
CHMP7	0.6497308	0.677	0.595
MAN1C1	1.2140333	0.343	0.205
PGGHG	1.0208995	0.447	0.321
CSGALNACT1	1.1214703	0.305	0.182
SAMD3	1.0025532	0.296	0.171
CHD3	0.5871107	0.643	0.583
CAMK4	0.7821684	0.494	0.401
NAA16	1.1943587	0.295	0.183
RNF144A	1.0142234	0.328	0.212
BCL11B	0.5058892	0.696	0.659
ZNF609	0.8584159	0.390	0.284
RCAN3	0.5260915	0.614	0.558
CRLF3	0.6559297	0.486	0.417
PRMT2	0.5105870	0.598	0.542
CD55	0.8826563	0.348	0.256
MAL	0.6103971	0.517	0.434
SFMBT2	0.9989590	0.259	0.167
FOXO1	0.5306661	0.586	0.542
TMEM131L	0.5871497	0.526	0.470
SAP25	0.8150157	0.328	0.250
DNMT3A	0.6073635	0.460	0.400

CD4 T Naive : CD4 T Naive_0 vs CD4 T Naive_2

Top 50 markers: CD4 T Naive_0 vs CD4 T Naive_2
	avg_log2FC	pct.1	pct.2
KLF2	1.3921423	0.801	0.488
IL7R	1.7465761	0.773	0.473
CXCR4	1.5025773	0.799	0.521
TXNIP	0.9874165	0.956	0.879
RIPOR2	1.0472164	0.792	0.628
RASGRP2	0.8996428	0.730	0.619
ZFP36L2	0.8496049	0.799	0.741
TMEM63A	1.2047905	0.593	0.440
RASA3	1.4453160	0.518	0.341
CD40LG	0.9725948	0.542	0.335
LTB	0.5983799	0.895	0.828
PDE7A	0.7967692	0.738	0.680
CDC42SE1	0.6782648	0.788	0.771
SAMHD1	0.9142205	0.659	0.568
RASSF3	1.4684172	0.466	0.310
NOG	1.3204539	0.467	0.291
ITGA6	1.5329811	0.390	0.222
RFLNB	1.4944949	0.395	0.251
TSC22D3	0.9398629	0.621	0.543
BCL9L	0.8624398	0.617	0.563
SMARCA2	1.0955117	0.496	0.387
CSGALNACT1	1.5791340	0.305	0.162
TRAF3IP3	0.5995746	0.744	0.721
EMP3	0.8298702	0.568	0.476
TLE5	0.8226437	0.595	0.552
CHMP7	0.6850051	0.677	0.660
RBL2	0.5570781	0.743	0.743
FLT3LG	1.1013338	0.471	0.390
DYRK2	0.7480807	0.596	0.552
SORL1	0.9582563	0.501	0.429
SUN2	0.5000645	0.761	0.737
RCAN3	0.7165800	0.614	0.589
KLF3	1.4271249	0.311	0.198
CAMK4	0.9273976	0.494	0.439
ADD3	0.7422015	0.585	0.572
OXNAD1	0.5613129	0.657	0.640
SIGIRR	0.7284239	0.564	0.536
SUSD3	1.0264081	0.422	0.355
INPP4A	0.8694615	0.468	0.404
S1PR1	0.8374800	0.472	0.405
ITGB7	1.2195467	0.312	0.223
STK38	0.7951686	0.501	0.459
CD47	0.6334843	0.599	0.618
P2RY8	1.3996263	0.255	0.164
ARHGEF18	0.9677789	0.408	0.353
FGD3	0.6665784	0.561	0.556
NLRP1	0.5217347	0.654	0.650
SELPLG	0.9050379	0.432	0.382
CYTH1	0.5671486	0.607	0.624
TOB1	1.2108978	0.341	0.275

CD4 T Naive : CD4 T Naive_1 vs CD4 T Naive_2

Top 50 markers: CD4 T Naive_1 vs CD4 T Naive_2
	avg_log2FC	pct.1	pct.2
AHNAK	3.9649436	0.687	0.104
IL10RA	3.0401503	0.555	0.123
CCR4	3.6386528	0.468	0.054
DUSP1	2.8888836	0.617	0.204
ANXA1	3.7016419	0.481	0.072
IL7R	2.4392177	0.866	0.473
KLF2	1.5348213	0.857	0.488
CRIP1	2.2971957	0.697	0.333
ZFP36L2	1.2067570	0.898	0.741
LTB	1.0808121	0.951	0.828
SYNE2	1.5139808	0.776	0.497
CASP8	1.8838991	0.602	0.275
MAF	1.5517883	0.576	0.204
KLRB1	3.7407992	0.379	0.059
EMP3	1.2845952	0.728	0.476
IL32	1.0693215	0.860	0.716
CXCR4	1.4188153	0.773	0.521
CAPN2	2.5572011	0.446	0.134
KLF6	1.8294803	0.589	0.304
TXNIP	0.8607182	0.957	0.879
S1PR1	1.3958084	0.670	0.405
S100A11	2.6946591	0.410	0.118
S100A4	1.6710471	0.617	0.342
SAMHD1	1.1041272	0.762	0.568
OPTN	2.0294359	0.481	0.196
ITGB1	3.0555914	0.361	0.092
CXCR5	2.9526163	0.324	0.061
RIPOR2	0.8885831	0.811	0.628
FXYD5	0.6287705	0.947	0.892
S100A10	1.8547375	0.532	0.287
TSC22D3	1.2077497	0.730	0.543
SELPLG	1.3830806	0.612	0.382
PYHIN1	1.5243773	0.493	0.215
RASA3	1.3986946	0.580	0.341
S100A6	1.2913626	0.642	0.447
NBEAL2	2.2735695	0.377	0.129
SH3BP5	2.7278914	0.315	0.085
PREX1	2.2471011	0.356	0.117
ITGB7	1.6779659	0.464	0.223
ATP2B4	1.4146231	0.549	0.327
CDC25B	1.4355995	0.539	0.316
WIPF1	0.6423304	0.887	0.820
CD40LG	0.8922374	0.582	0.335
TTC39C	1.2714217	0.554	0.348
P2RY8	1.8677636	0.390	0.164
MYO1G	1.8763575	0.397	0.176
FLNA	1.1221137	0.631	0.453
GSTK1	0.8891825	0.729	0.632
RASSF3	1.3370691	0.514	0.310
TMEM173	1.2395378	0.546	0.341

Pairwise markers within CD4 T Memory

ids <- c("CD4 T Memory_0","CD4 T Memory_1","CD4 T Memory_2", "CD4 T Memory_3" )

res_all <- run_de_and_vln(
  cd4_t,
  ids,
  title_prefix = "CD4 T Memory",
  run_one_vs_all = TRUE,
  run_pairwise = TRUE,
  print_table = TRUE
)

CD4 T Memory : CD4 T Memory_0 vs_rest

Top 50 markers: CD4 T Memory_0 vs_rest
	avg_log2FC	pct.1	pct.2
PDCD1	1.9655240	0.780	0.263
BCL6	1.8855284	0.645	0.203
TOX2	1.4302695	0.814	0.388
TRIM8	1.4928958	0.714	0.296
TIGIT	1.2776070	0.809	0.428
STK39	1.0370397	0.794	0.441
POU2AF1	2.0296239	0.514	0.167
FKBP5	1.1198127	0.682	0.342
SREBF2	1.0693405	0.674	0.340
TOX	1.1402844	0.592	0.264
RAB27A	0.8335340	0.721	0.401
CDK5R1	0.9169774	0.639	0.320
KCNK5	2.0641866	0.407	0.092
ASAP1	0.9832914	0.594	0.279
KIAA1324	1.5395261	0.488	0.176
CHI3L2	1.0698329	0.646	0.334
THADA	1.2527101	0.672	0.361
FAM43A	1.8348412	0.457	0.146
C9orf16	0.8918575	0.608	0.307
PTPN7	0.8072043	0.648	0.352
ITM2A	1.1157157	0.873	0.578
CXCR5	1.1839930	0.842	0.551
ICOS	0.8014014	0.801	0.513
NFATC1	1.3429053	0.561	0.273
RNF19A	0.7784412	0.708	0.420
ID3	1.4200317	0.533	0.248
SH2D1A	1.0096053	0.685	0.404
MAGEH1	1.2537573	0.529	0.249
RTP5	2.9045717	0.321	0.043
ST8SIA1	0.6913587	0.739	0.462
COTL1	0.6474749	0.811	0.535
ZNF703	1.8356918	0.373	0.100
DHRS7	0.8700134	0.561	0.290
CD200	2.7765525	0.360	0.092
CD82	1.1309979	0.545	0.278
SUSD6	0.8108144	0.589	0.323
TPP1	0.8167619	0.580	0.315
TBC1D4	0.6164270	0.702	0.437
SMCO4	1.4282104	0.415	0.151
IGFBP4	1.9058154	0.345	0.081
PHACTR2	0.7345814	0.544	0.282
GNG4	3.0674324	0.309	0.049
PFKFB3	0.8513792	0.552	0.293
IL21	1.4608631	0.382	0.126
P2RY8	1.4631231	0.442	0.186
H2AFZ	1.0229378	0.490	0.235
GSE1	1.1468844	0.421	0.166
PPP1CC	1.1002077	0.467	0.213
IL2RB	0.5747098	0.752	0.498
MAF	0.5752942	0.945	0.694

CD4 T Memory : CD4 T Memory_1 vs_rest

Top 50 markers: CD4 T Memory_1 vs_rest
	avg_log2FC	pct.1	pct.2
IL7R	2.0883990	0.731	0.352
VIM	1.3748465	0.504	0.217
GPR183	1.6776717	0.601	0.316
RCAN3	1.5286578	0.364	0.197
AAK1	1.3209103	0.486	0.323
CCR7	0.8106844	0.402	0.248
PDE7A	1.3891794	0.514	0.370
PLAC8	0.9170063	0.377	0.242
HELB	1.4314706	0.296	0.171
OBSCN	1.2877865	0.427	0.303
GIMAP5	0.9210526	0.518	0.414
TXNIP	1.0820702	0.829	0.736
DYRK2	0.9837891	0.547	0.476
GIMAP7	0.7671319	0.825	0.757
GIMAP4	0.6141508	0.780	0.744
ZFP36L2	0.7285587	0.789	0.755
ATM	0.6922899	0.708	0.723
FYB1	0.5499841	0.879	0.867
CYTIP	0.6645642	0.773	0.773
BTG1	0.7715049	0.834	0.834
TSC22D3	1.0563920	0.482	0.396
KLRB1	1.1493234	0.353	0.252
MGAT4A	1.1893421	0.322	0.231
JUN	1.4054211	0.381	0.300
TMEM63A	1.1736412	0.302	0.214
LDHB	0.6456054	0.618	0.631
FOS	0.9852987	0.549	0.523
CD96	0.8895031	0.421	0.351
TGFBR2	0.6969843	0.554	0.546
TRAT1	0.8415905	0.438	0.386
MAL	0.9146533	0.271	0.185
GLIPR1	1.1719472	0.321	0.254
LEPROTL1	0.6220475	0.581	0.605
CAMK4	1.0741012	0.297	0.222
TTC39C	0.8481541	0.417	0.369
DUSP1	0.8721728	0.521	0.506
OXNAD1	0.9610825	0.363	0.310
DTX1	1.1245088	0.282	0.213
ACAP1	0.5126373	0.642	0.692
SIGIRR	0.8065617	0.423	0.393
LPAR6	0.9537437	0.316	0.258
KLF6	0.6738820	0.519	0.525
GIMAP1	0.8118544	0.384	0.338
SAMHD1	0.6025788	0.375	0.309
AHNAK	0.8357461	0.345	0.296
BCL9L	0.7861076	0.360	0.319
LY9	0.7051504	0.444	0.433
TMEM173	0.9012315	0.321	0.279
CD48	0.5316240	0.572	0.611
NLRP1	0.6526725	0.468	0.480

CD4 T Memory : CD4 T Memory_2 vs_rest

Top 50 markers: CD4 T Memory_2 vs_rest
	avg_log2FC	pct.1	pct.2
PRDM1	4.4157990	0.533	0.028
TENT5C	2.5106941	0.624	0.153
TNFRSF1B	2.2626289	0.750	0.296
CTLA4	1.5489647	0.734	0.311
CEP128	1.8760232	0.692	0.294
DUSP16	1.3432249	0.680	0.295
CCND2	1.3324064	0.668	0.284
RCSD1	1.0066931	0.610	0.235
RGS1	1.6452329	0.572	0.215
LGMN	2.0538512	0.509	0.154
OGDH	0.9248956	0.772	0.427
LAG3	3.4957953	0.436	0.095
TPM4	1.2514544	0.552	0.214
FAM53B	1.1564028	0.568	0.234
CCR5	4.8666679	0.335	0.014
CD38	1.1401536	0.574	0.256
PMVK	1.0987001	0.534	0.218
IL21	1.5365429	0.526	0.212
C9orf16	0.7464875	0.724	0.414
NDUFV2	0.8263620	0.687	0.378
NAP1L4	0.8959780	0.839	0.534
SELPLG	0.9718657	0.528	0.227
CSF1	2.7212606	0.362	0.063
ARPC1B	0.8526477	0.711	0.414
SLAMF1	0.9173093	0.551	0.255
CKLF	0.8511437	0.690	0.398
TYMP	1.8697323	0.387	0.096
SH2D2A	1.5140501	0.408	0.121
RUNX2	2.5860561	0.328	0.044
CDC25B	1.2408231	0.447	0.163
ABRACL	0.8453225	0.706	0.428
YWHAQ	1.0436657	0.480	0.209
ODC1	1.2431686	0.436	0.167
FURIN	0.9911612	0.555	0.287
CARHSP1	0.8654876	0.490	0.222
TSHR	2.2856565	0.335	0.068
KLRB1	1.1262164	0.525	0.261
CDKL2	1.9700666	0.352	0.092
LMNB1	0.9128235	0.445	0.190
TRIB1	1.5411450	0.364	0.110
CXCR6	5.5032575	0.259	0.006
TXNDC17	0.9781924	0.413	0.160
PITPNM2	1.5808767	0.371	0.120
FLNA	1.1449881	0.433	0.183
PTMS	1.7150690	0.339	0.096
TNFRSF18	1.5711452	0.350	0.119
ABCC1	0.7627868	0.376	0.147
SOCS3	1.1951321	0.374	0.146
SPATS2L	1.1129249	0.362	0.134
CHST2	1.6559539	0.298	0.071

CD4 T Memory : CD4 T Memory_3 vs_rest

Top 50 markers: CD4 T Memory_3 vs_rest
	avg_log2FC	pct.1	pct.2
IFI44L	4.606444	0.856	0.093
IFI44	3.472982	0.828	0.202
MX1	3.588784	0.797	0.200
OAS1	4.054041	0.701	0.109
XAF1	3.289719	0.731	0.171
LY6E	3.107732	0.822	0.272
IFI6	3.941676	0.692	0.155
ISG15	3.536067	0.758	0.246
OAS3	4.059808	0.600	0.094
SAMD9L	3.179043	0.619	0.163
MX2	3.039837	0.638	0.185
CMPK2	4.455865	0.500	0.052
OAS2	2.452203	0.845	0.417
IFIT1	6.054737	0.438	0.013
EIF2AK2	2.850498	0.568	0.157
RSAD2	5.601066	0.421	0.020
IRF7	2.304399	0.694	0.303
STAT1	2.364577	0.841	0.457
DDX60	3.090176	0.469	0.091
HERC6	3.539233	0.441	0.066
HELZ2	3.059521	0.464	0.111
TRIM22	2.018599	0.757	0.429
KLF2	3.555600	0.379	0.053
PARP9	2.494540	0.473	0.149
HERC5	3.871179	0.342	0.042
DTX3L	2.087077	0.484	0.195
UBE2L6	1.989638	0.547	0.259
EPSTI1	1.752149	0.624	0.348
PARP14	1.995531	0.488	0.217
GBP1	3.419349	0.328	0.063
IFIT3	6.730584	0.269	0.005
USP18	5.113992	0.276	0.015
PARP12	1.957049	0.447	0.194
SAMHD1	1.631810	0.560	0.308
APOL6	1.751942	0.522	0.276
SELL	1.555318	0.651	0.409
DDX60L	2.145001	0.377	0.142
SP110	1.434031	0.636	0.407
SP100	1.400316	0.705	0.477
IFIH1	2.197988	0.353	0.131
CCR7	1.485494	0.502	0.280
EMP3	2.250635	0.326	0.108
SAMD9	1.857310	0.394	0.177
PARP10	1.801902	0.396	0.181
IFI35	2.428202	0.311	0.100
TRIM25	1.969694	0.361	0.150
IFIT5	2.370213	0.287	0.077
DDX58	2.123507	0.331	0.128
RNF213	1.354490	0.801	0.602
PDE3B	2.049132	0.316	0.120

CD4 T Memory : CD4 T Memory_0 vs CD4 T Memory_1

Top 50 markers: CD4 T Memory_0 vs CD4 T Memory_1
	avg_log2FC	pct.1	pct.2
PDCD1	2.6398093	0.780	0.159
TOX2	1.7282242	0.814	0.308
TRIM8	1.5984881	0.714	0.234
BCL6	1.9333747	0.645	0.166
TIGIT	1.5182139	0.809	0.376
POU2AF1	2.6207536	0.514	0.084
RAB27A	1.2650768	0.721	0.307
C9orf16	1.3870980	0.608	0.196
STK39	1.0475990	0.794	0.397
CHI3L2	1.3331703	0.646	0.257
TOX	1.2745688	0.592	0.205
FKBP5	1.1696398	0.682	0.300
FAM43A	2.4816967	0.457	0.080
SREBF2	1.0594317	0.674	0.299
KIAA1324	1.8676883	0.488	0.113
PTPN7	1.0387569	0.648	0.275
RNF19A	0.9408915	0.708	0.351
THADA	1.2816389	0.672	0.317
SH2D1A	1.2094046	0.685	0.331
IL21	3.6693301	0.382	0.028
TPP1	1.0599234	0.580	0.228
COTL1	0.8597149	0.811	0.461
ICOS	0.9923818	0.801	0.457
MAGEH1	1.4961256	0.529	0.186
CTLA4	1.6205026	0.474	0.133
ITM2A	1.2186048	0.873	0.532
CD82	1.3454521	0.545	0.208
P2RY8	2.0328667	0.442	0.106
SUSD6	0.9446271	0.589	0.253
ID3	1.5607387	0.533	0.203
KCNK5	2.0137477	0.407	0.077
UCP2	0.9535070	0.764	0.436
ASAP1	0.8045342	0.594	0.267
ORMDL3	0.8197098	0.656	0.330
NFATC1	1.4054580	0.561	0.237
IL2RB	0.8103258	0.752	0.428
TBC1D4	0.7208790	0.702	0.380
CDK5R1	0.7588777	0.639	0.319
ZNF703	2.2985886	0.373	0.055
CTSA	0.6693583	0.655	0.337
SAT1	0.8414878	0.607	0.292
EZR	0.6591923	0.709	0.398
PPP1CC	1.1906582	0.467	0.157
CXCR5	1.2789645	0.842	0.532
RTP5	3.6381976	0.321	0.013
SMCO4	1.5659733	0.415	0.109
PPP1R16B	0.6909509	0.583	0.277
H2AFZ	1.1282889	0.490	0.185
RGS3	1.1631191	0.480	0.175
PFKFB3	0.8456384	0.552	0.248

CD4 T Memory : CD4 T Memory_0 vs CD4 T Memory_2

Top 50 markers: CD4 T Memory_0 vs CD4 T Memory_2
	avg_log2FC	pct.1	pct.2
TCF7	1.7338627	0.962	0.436
BCL6	2.1009688	0.645	0.288
CDK5R1	1.5540757	0.639	0.344
KCNK5	2.5704172	0.407	0.127
CD40LG	1.5098135	0.708	0.433
ASAP1	1.7095642	0.594	0.327
IL7R	2.6993303	0.359	0.094
ST8SIA1	1.3867949	0.739	0.481
FKBP5	1.3050167	0.682	0.439
SREBF2	1.2764118	0.674	0.469
TRIM8	1.2888416	0.714	0.514
CXCR4	0.9920802	0.834	0.640
STK39	0.9635389	0.794	0.637
ITPKB	1.0386812	0.864	0.731
PVALB	3.7650186	0.258	0.027
NTRK3	3.2338027	0.276	0.045
ZBTB10	1.9670786	0.403	0.165
PDCD1	0.8638022	0.780	0.640
SESN1	2.4858292	0.356	0.124
IL16	0.6317948	0.888	0.796
ST6GAL1	0.9292892	0.694	0.562
DHRS7	1.2938340	0.561	0.378
ATM	0.7802520	0.750	0.632
RTP5	2.2778606	0.321	0.113
THADA	1.0764138	0.672	0.543
CXCL13	2.1696532	0.257	0.058
KIAA0319L	1.1602131	0.577	0.411
SERTAD2	1.3955845	0.505	0.332
CXCR5	0.7602336	0.842	0.750
ZFP36L1	0.6417469	0.857	0.783
IGFBP4	1.7752505	0.345	0.152
ITM2A	0.6246311	0.873	0.782
LTBP3	0.8990438	0.649	0.494
CPM	2.0819745	0.285	0.111
P2RX5	1.7234776	0.413	0.261
RASGRF2	1.8684683	0.280	0.106
SEMA4D	0.8969429	0.588	0.464
IRF2BP2	1.1244083	0.492	0.344
TMEM123	0.8438785	0.623	0.516
ANKRD13A	1.5554173	0.398	0.256
CD200	1.9818385	0.360	0.195
SLAMF6	0.9979384	0.560	0.442
TIGIT	0.5272765	0.809	0.679
FYB1	0.6291951	0.880	0.846
ELK4	0.7611220	0.651	0.561
LDLRAD4	1.1006543	0.465	0.320
ID3	1.1581368	0.533	0.399
NFATC1	0.9472768	0.561	0.432
TOX2	0.6202381	0.814	0.767
CHD3	0.5868990	0.722	0.663

CD4 T Memory : CD4 T Memory_0 vs CD4 T Memory_3

Top 50 markers: CD4 T Memory_0 vs CD4 T Memory_3
	avg_log2FC	pct.1	pct.2
TOX2	1.5677045	0.814	0.290
PDCD1	1.7231492	0.780	0.258
TIGIT	1.4910643	0.809	0.363
CXCR5	1.3620375	0.842	0.418
TRIM8	1.3552489	0.714	0.298
MAF	1.1923778	0.945	0.544
STK39	1.0758088	0.794	0.399
BCL6	1.5549330	0.645	0.252
ST8SIA1	1.1871095	0.739	0.346
RAB27A	1.1419075	0.721	0.353
ICOS	1.1258483	0.801	0.434
PTPN7	1.3242153	0.648	0.288
TOX	1.2996912	0.592	0.236
KIAA1324	1.9994772	0.488	0.141
PCNX2	1.0587539	0.670	0.331
GLCCI1	0.9240964	0.723	0.384
CDK5R1	0.9987043	0.639	0.301
POU2AF1	1.5253025	0.514	0.178
THADA	1.3418072	0.672	0.337
ITM2A	1.3714627	0.873	0.538
IL2RB	0.8765364	0.752	0.420
RNF19A	0.9692053	0.708	0.376
FAM43A	1.7697783	0.457	0.133
C9orf16	1.1370695	0.608	0.285
NFATC1	1.6051432	0.561	0.242
CTSB	0.8510663	0.813	0.494
RAP1A	0.8637871	0.770	0.456
SH2D1A	1.0905376	0.685	0.373
IL21	2.8664341	0.382	0.072
IKZF3	0.7642132	0.905	0.611
KCNK5	1.8354033	0.407	0.113
COTL1	0.8055122	0.811	0.520
SRGN	1.4631113	0.938	0.650
CD200	3.0548126	0.360	0.091
ASAP1	1.0939939	0.594	0.273
CD82	1.2997243	0.545	0.240
SREBF2	0.9181488	0.674	0.355
CTLA4	1.4224855	0.474	0.183
SMCO4	1.5768926	0.415	0.137
MAGEH1	1.3165317	0.529	0.245
CD2	0.6441464	0.912	0.700
TBC1D4	0.8383933	0.702	0.392
ZNF703	1.6836104	0.373	0.115
PFKFB3	1.0438710	0.552	0.257
RTP5	1.9914762	0.321	0.080
ID3	1.2401840	0.533	0.258
TRPS1	0.9013577	0.621	0.319
PHACTR2	0.9966381	0.544	0.246
CD4	0.6404434	0.895	0.678
GNG4	2.2346194	0.309	0.077

CD4 T Memory : CD4 T Memory_1 vs CD4 T Memory_2

Top 50 markers: CD4 T Memory_1 vs CD4 T Memory_2
	avg_log2FC	pct.1	pct.2
IL7R	5.0461303	0.731	0.094
TCF7	1.7231617	0.916	0.436
PLAC8	2.8517307	0.377	0.122
CD40LG	1.6588247	0.631	0.433
TXNIP	1.7671836	0.829	0.664
CXCR4	1.3507989	0.776	0.640
ATM	1.4462545	0.708	0.632
FYB1	1.0861211	0.879	0.846
FXYD5	0.8114486	0.859	0.887
BTG1	1.2496642	0.834	0.807
GPR183	1.4175197	0.601	0.427
ZFP36L2	0.8897197	0.789	0.784
GIMAP7	0.7395040	0.825	0.811
TMEM63A	3.3267253	0.302	0.091
PDE7A	1.7466680	0.514	0.398
TTC39C	1.8704109	0.417	0.245
CYTIP	0.7600372	0.773	0.828
PNRC1	0.7027890	0.808	0.866
VIM	0.7232063	0.504	0.288
OBSCN	1.9400853	0.427	0.271
IKZF1	0.5770858	0.822	0.882
TGFBR2	1.2541419	0.554	0.507
LPAR6	2.2055485	0.316	0.166
RASGRF2	2.3786983	0.256	0.106
GIMAP4	0.6240274	0.780	0.783
TOMM7	0.7962758	0.684	0.770
DYRK2	1.0752607	0.547	0.516
CAMK4	2.0414673	0.297	0.175
CCR7	1.2611445	0.402	0.272
TMC8	0.6315106	0.728	0.814
DGKA	0.5211362	0.785	0.863
TSC22D3	1.2711222	0.482	0.427
THEMIS	1.1489160	0.455	0.397
RCAN3	1.4628054	0.364	0.288
LY9	1.1537256	0.444	0.409
IL16	0.5073606	0.737	0.796
NOP53	0.9572861	0.554	0.624
MGAT4A	1.5185830	0.322	0.244
SLC7A6	1.2459499	0.429	0.414
KLF6	0.9604509	0.519	0.533
RBL2	0.7852025	0.583	0.652
EVI2B	0.9003588	0.532	0.556
STK17B	0.5207842	0.696	0.782
AHNAK	1.1918189	0.345	0.270
EEF1G	0.5447194	0.702	0.818
DTX1	1.8378371	0.282	0.218
SELL	0.8264035	0.454	0.402
FAM102A	0.6783148	0.602	0.675
ST8SIA1	0.9447196	0.489	0.481
JUN	1.5336297	0.381	0.344

CD4 T Memory : CD4 T Memory_1 vs CD4 T Memory_3

Top 50 markers: CD4 T Memory_1 vs CD4 T Memory_3
	avg_log2FC	pct.1	pct.2
GPR183	1.2569261	0.601	0.349
FYB1	0.5497570	0.879	0.830
KLRB1	1.6521264	0.353	0.164
IL7R	0.6814422	0.731	0.584
RESF1	0.5324118	0.835	0.741
CD40LG	0.7432155	0.631	0.498
EVI2B	0.8294245	0.532	0.405
GLIPR1	1.2343759	0.321	0.181
ST8SIA1	0.7450343	0.489	0.346
TLK1	0.7654678	0.495	0.390
STT3B	0.6971989	0.512	0.429
PCNX2	0.7357713	0.429	0.331
NABP1	0.6755613	0.448	0.353
JUN	0.9443522	0.381	0.288
PHTF2	0.7472820	0.354	0.274
OBSCN	0.6062670	0.427	0.340
LY9	0.5368093	0.444	0.368
LPAR6	0.6759944	0.316	0.240
GPR174	0.6852448	0.322	0.250
CD84	0.6454349	0.330	0.260
ATP2B4	0.5496587	0.361	0.291
CKLF	0.6963580	0.310	0.248
IVNS1ABP	0.5132961	0.418	0.359
FOS	0.6116254	0.549	0.519
TGFBR1	0.7013812	0.262	0.200
TMX4	0.6865156	0.282	0.225
ST8SIA4	0.5741966	0.360	0.306
HNRNPLL	0.5127569	0.365	0.316
GPX4	0.5587744	0.323	0.282
ARHGAP35	0.6067141	0.271	0.232
MGAT5	0.5699331	0.294	0.254
MDFIC	0.5200499	0.269	0.229

CD4 T Memory : CD4 T Memory_2 vs CD4 T Memory_3

Top 50 markers: CD4 T Memory_2 vs CD4 T Memory_3
	avg_log2FC	pct.1	pct.2
CTLA4	2.3976964	0.734	0.183
TNFRSF1B	2.7338295	0.750	0.206
TENT5C	2.8985595	0.624	0.103
CEP128	2.6489733	0.692	0.188
PRDM1	3.5969783	0.533	0.050
TOX2	0.9474664	0.767	0.290
IL21	3.5436532	0.526	0.072
CKLF	1.6011040	0.690	0.248
C9orf16	1.3745627	0.724	0.285
MAF	1.9170753	0.981	0.544
DUSP16	1.3445759	0.680	0.246
RAB27A	1.3852688	0.784	0.353
OGDH	1.1009967	0.772	0.346
CTSA	1.2306903	0.776	0.356
LGMN	2.8770490	0.509	0.097
NAP1L4	1.2243239	0.839	0.431
IL2RB	1.0863211	0.825	0.420
PTPN7	1.3851403	0.690	0.288
LAG3	3.9648805	0.436	0.067
KLRB1	1.9811255	0.525	0.164
IKZF3	1.1446042	0.962	0.611
CCR5	3.9694046	0.335	0.019
TSHR	3.3574001	0.335	0.033
SRGN	1.4921829	0.930	0.650
CD4	0.9927045	0.933	0.678
IL32	1.1790007	0.946	0.737
PFN1	0.9018111	0.986	0.884
PTMS	2.8204624	0.339	0.045
CDKL2	2.7106710	0.352	0.052
ODC1	2.1175272	0.436	0.095
TNFRSF18	2.7023032	0.350	0.053
RUNX2	2.4205528	0.328	0.041
TRIB1	2.2286907	0.364	0.061
FAM53B	1.3487245	0.568	0.184
PTPRJ	1.4099018	0.552	0.180
ARPC1B	1.1750021	0.711	0.336
PMVK	1.3451141	0.534	0.167
WSB2	1.4421403	0.555	0.190
PKM	1.3823704	0.612	0.250
CXCR6	4.1404507	0.259	0.016
FURIN	1.4621592	0.555	0.196
CSF1	2.3193170	0.362	0.071
YWHAQ	1.5479935	0.480	0.141
PITPNM1	0.9968724	0.711	0.307
SPOCK2	0.9268890	0.879	0.602
CTSC	1.1245382	0.637	0.257
CARHSP1	1.3041944	0.490	0.147
ICOS	1.0382403	0.788	0.434
TXNDC17	1.4886046	0.413	0.100
BATF	1.4830814	0.483	0.151

Pairwise markers within CD4 T Effector

ids <- c("CD4 T Effector_0","CD4 T Effector_1" )

res_all <- run_de_and_vln(
  cd4_t,
  ids,
  title_prefix = "CD4 T Effector",
  run_one_vs_all = TRUE,
  run_pairwise = TRUE,
  print_table = TRUE
)

CD4 T Effector : CD4 T Effector_0 vs_rest

Top 50 markers: CD4 T Effector_0 vs_rest
	avg_log2FC	pct.1	pct.2
KLRB1	1.7649720	0.772	0.333
RESF1	1.5826746	0.819	0.373
COL5A3	1.6627788	0.640	0.212
SIRPG	1.9112105	0.554	0.153
CD2	0.9178858	0.931	0.658
CHI3L2	3.2022933	0.399	0.068
CD4	1.0482067	0.892	0.612
PHTF2	1.4131561	0.631	0.233
EIF1	1.2706675	0.673	0.270
TBC1D4	2.0654149	0.449	0.107
GLCCI1	1.5755421	0.553	0.178
PNRC1	0.9760021	0.848	0.536
CD3D	0.8933402	0.912	0.662
RAP1A	1.1972723	0.652	0.277
ZFP36L1	0.9884743	0.827	0.503
PITHD1	1.9873222	0.404	0.091
TRAM1	1.3514324	0.559	0.202
IL6R	1.5661036	0.493	0.153
NMRK1	1.4614371	0.522	0.177
TAF7	1.4714432	0.503	0.165
TRAC	0.7106710	0.948	0.796
CD3G	1.0367342	0.715	0.353
FCMR	1.1014167	0.656	0.294
ZBTB38	1.7968998	0.398	0.101
EVI2B	1.4214418	0.519	0.190
SESN1	2.9444452	0.311	0.054
LBH	1.0381157	0.742	0.409
MAP3K4	1.2288682	0.561	0.227
LTBP3	1.3193197	0.478	0.159
VPS13C	0.9879497	0.713	0.371
GIMAP5	0.9879183	0.667	0.309
SLC38A1	1.0050406	0.686	0.334
ICOS	1.0353513	0.677	0.336
TLK1	1.1682034	0.567	0.233
PCNX2	1.2694017	0.494	0.177
COL6A3	3.1849281	0.259	0.031
LNPEP	1.1263106	0.614	0.277
GIMAP4	0.9067454	0.778	0.469
RNF19A	1.0952405	0.609	0.278
IFNAR2	1.2137582	0.529	0.213
ERBIN	1.0556645	0.598	0.260
TAB2	0.9590423	0.701	0.365
ZMYM2	1.2670514	0.443	0.145
TRPS1	1.4087616	0.391	0.115
GIMAP7	0.7520530	0.840	0.549
RABGAP1L	0.8432305	0.756	0.414
SENP6	1.3202539	0.402	0.118
PTPN13	1.4225109	0.391	0.117
GPR183	0.9331389	0.676	0.353
ITM2A	1.0892768	0.535	0.225

CD4 T Effector : CD4 T Effector_1 vs_rest

Top 50 markers: CD4 T Effector_1 vs_rest
	avg_log2FC	pct.1	pct.2
KLF2	3.9358263	0.477	0.077
VIM	1.3906148	0.892	0.679
CORO1A	1.0367485	0.906	0.903
FOSL2	3.5099365	0.388	0.100
TMSB10	1.1531460	0.867	0.802
LTB	1.7895423	0.847	0.754
S100A4	1.1631333	0.839	0.761
CFL1	1.1258006	0.825	0.754
CD74	1.6439346	0.732	0.636
CD44	0.9541085	0.886	0.849
PFN1	0.8234945	0.946	0.921
PIM1	1.8312394	0.620	0.411
MYADM	3.1623209	0.332	0.088
EMP3	1.7828003	0.583	0.363
CRIP1	1.5201754	0.740	0.636
CYBA	1.2051607	0.759	0.667
EEF2	0.9139576	0.873	0.845
ANXA1	2.1886695	0.390	0.145
ARL4C	1.2840720	0.686	0.552
CAPN2	1.7516855	0.506	0.291
S100A6	1.1207117	0.732	0.624
FMNL1	0.9251514	0.793	0.742
LY6E	1.5373134	0.645	0.509
FLNA	1.2954350	0.657	0.526
AHNAK	1.2553346	0.706	0.563
ADAM19	1.2132461	0.699	0.603
TAPBP	1.0087549	0.755	0.701
TAP1	1.1966843	0.659	0.556
ARHGEF1	0.7613520	0.841	0.822
LSP1	0.9186143	0.773	0.731
ISG15	2.2393171	0.467	0.297
GPR132	2.2624802	0.329	0.143
OAS3	2.3850757	0.342	0.158
FLT3LG	1.3912563	0.561	0.426
PXN	2.3052299	0.293	0.120
IFI6	2.2708245	0.376	0.201
SH3BGRL3	0.8579399	0.713	0.671
SKI	1.2663910	0.555	0.444
ARHGAP45	0.9885972	0.653	0.588
ADAM8	1.9284469	0.314	0.155
ACTN4	1.3201322	0.525	0.415
IRF7	1.6757345	0.457	0.323
AKNA	0.6068417	0.837	0.847
S100A11	1.3800350	0.429	0.270
CDKN1B	0.9386780	0.730	0.725
SOCS3	1.9051362	0.357	0.200
JUND	0.8546504	0.794	0.772
BCL3	1.5704276	0.439	0.298
CNN2	0.9454476	0.642	0.593
NIBAN1	2.0869749	0.262	0.118

CD4 T Effector : CD4 T Effector_0 vs CD4 T Effector_1

Top 50 markers: CD4 T Effector_0 vs CD4 T Effector_1
	avg_log2FC	pct.1	pct.2
KLRB1	1.7649720	0.772	0.333
RESF1	1.5826746	0.819	0.373
COL5A3	1.6627788	0.640	0.212
SIRPG	1.9112105	0.554	0.153
CD2	0.9178858	0.931	0.658
CHI3L2	3.2022933	0.399	0.068
CD4	1.0482067	0.892	0.612
PHTF2	1.4131561	0.631	0.233
EIF1	1.2706675	0.673	0.270
TBC1D4	2.0654149	0.449	0.107
GLCCI1	1.5755421	0.553	0.178
PNRC1	0.9760021	0.848	0.536
CD3D	0.8933402	0.912	0.662
RAP1A	1.1972723	0.652	0.277
ZFP36L1	0.9884743	0.827	0.503
PITHD1	1.9873222	0.404	0.091
TRAM1	1.3514324	0.559	0.202
IL6R	1.5661036	0.493	0.153
NMRK1	1.4614371	0.522	0.177
TAF7	1.4714432	0.503	0.165
TRAC	0.7106710	0.948	0.796
CD3G	1.0367342	0.715	0.353
FCMR	1.1014167	0.656	0.294
ZBTB38	1.7968998	0.398	0.101
EVI2B	1.4214418	0.519	0.190
SESN1	2.9444452	0.311	0.054
LBH	1.0381157	0.742	0.409
MAP3K4	1.2288682	0.561	0.227
LTBP3	1.3193197	0.478	0.159
VPS13C	0.9879497	0.713	0.371
GIMAP5	0.9879183	0.667	0.309
SLC38A1	1.0050406	0.686	0.334
ICOS	1.0353513	0.677	0.336
TLK1	1.1682034	0.567	0.233
PCNX2	1.2694017	0.494	0.177
COL6A3	3.1849281	0.259	0.031
LNPEP	1.1263106	0.614	0.277
GIMAP4	0.9067454	0.778	0.469
RNF19A	1.0952405	0.609	0.278
IFNAR2	1.2137582	0.529	0.213
ERBIN	1.0556645	0.598	0.260
TAB2	0.9590423	0.701	0.365
ZMYM2	1.2670514	0.443	0.145
TRPS1	1.4087616	0.391	0.115
GIMAP7	0.7520530	0.840	0.549
RABGAP1L	0.8432305	0.756	0.414
SENP6	1.3202539	0.402	0.118
PTPN13	1.4225109	0.391	0.117
GPR183	0.9331389	0.676	0.353
ITM2A	1.0892768	0.535	0.225

Pairwise markers within CD4 T Regulatory

ids <- c("CD4 T Regulatory_0","CD4 T Regulatory_1","CD4 T Regulatory_2" )

res_all <- run_de_and_vln(
  cd4_t,
  ids,
  title_prefix = "CD4 T Reg",
  run_one_vs_all = TRUE,
  run_pairwise = TRUE,
  print_table = TRUE
)

CD4 T Reg : CD4 T Regulatory_0 vs_rest

Top 50 markers: CD4 T Regulatory_0 vs_rest
	avg_log2FC	pct.1	pct.2
LEF1	3.6110373	0.521	0.085
KLF2	2.9790443	0.532	0.108
TCF7	2.4181124	0.571	0.189
RTKN2	3.0998620	0.446	0.087
SELL	1.4990095	0.749	0.473
FCRL3	3.1786729	0.378	0.069
IKZF2	2.2869582	0.474	0.152
TXNIP	1.2515904	0.858	0.681
MAL	2.0826104	0.466	0.159
SORL1	2.7890218	0.337	0.067
ID3	2.8979790	0.319	0.064
SESN3	2.0144551	0.519	0.264
CXCR4	1.1108627	0.709	0.442
FOXP3	1.0963911	0.698	0.419
TIGIT	1.1077375	0.737	0.506
TMEM63A	1.6406465	0.460	0.209
STAM	1.7759374	0.422	0.179
TTC39C	1.5568705	0.434	0.193
BIRC3	1.4041934	0.494	0.255
TSC22D3	1.0521870	0.666	0.468
PDE3B	1.9920875	0.282	0.094
IL7R	0.7702843	0.525	0.284
MAGEH1	1.6601587	0.385	0.218
STK38	1.3538497	0.398	0.227
RASA3	1.3624759	0.399	0.229
RIPOR2	1.3176121	0.473	0.317
LSR	1.7019943	0.263	0.106
DTX1	1.7156163	0.290	0.132
LDHB	0.8604696	0.656	0.549
TMEM123	1.2798908	0.401	0.243
DGKA	0.5951602	0.769	0.662
SERPINB9	0.9579966	0.434	0.258
HAPLN3	1.2699813	0.317	0.159
TRAF1	0.8187706	0.581	0.438
TIAM1	1.1246824	0.387	0.230
FOXO1	0.7187533	0.633	0.518
LPAR6	1.4141501	0.277	0.138
RIN3	0.9210313	0.445	0.297
USP15	0.8586866	0.551	0.436
MAP3K1	1.0634129	0.397	0.259
EEF1G	0.6000033	0.736	0.668
ATM	0.7829834	0.600	0.495
DENND11	1.0860678	0.334	0.201
RACK1	0.5928728	0.699	0.613
LTBP3	0.9042953	0.406	0.280
RCAN3	0.8024890	0.499	0.395
IVNS1ABP	0.6956523	0.514	0.394
PDK1	1.2755396	0.283	0.172
ESYT2	0.7201429	0.484	0.370
RASGRP2	1.0025516	0.359	0.249

CD4 T Reg : CD4 T Regulatory_1 vs_rest

Top 50 markers: CD4 T Regulatory_1 vs_rest
	avg_log2FC	pct.1	pct.2
CCR5	3.0482706	0.743	0.187
GZMK	6.0939195	0.520	0.019
GZMA	6.1868969	0.499	0.018
CXCR6	2.0297026	0.770	0.312
CCL5	6.7886656	0.472	0.014
CCR2	4.6360620	0.345	0.021
CCL4	6.2416090	0.306	0.007
F2R	4.3698355	0.305	0.024
CXCR3	2.1274166	0.497	0.151
LAG3	1.4750135	0.629	0.241
CD38	2.5222210	0.482	0.157
CST7	2.3500186	0.512	0.192
SYTL2	3.3436191	0.252	0.036
THEMIS	2.4431078	0.355	0.089
CD6	1.2267337	0.812	0.622
PLAAT4	1.4596216	0.717	0.464
PDCD1	2.1417519	0.427	0.154
CHST12	2.0907641	0.433	0.159
CD2	0.9231965	0.880	0.782
DTHD1	2.3323255	0.264	0.061
STOM	2.6097392	0.271	0.073
SRGN	0.8029889	0.906	0.776
MAF	0.8322957	0.891	0.619
WIPF1	0.7068633	0.889	0.841
TBX21	2.4059168	0.255	0.074
PTMS	1.6621551	0.409	0.183
IKZF3	0.8070964	0.794	0.592
RUNX2	1.8454674	0.342	0.136
HAVCR2	2.0473882	0.362	0.162
APOBEC3G	1.9383772	0.343	0.142
BICDL1	1.3754035	0.515	0.295
DUSP4	2.0606088	0.334	0.140
ADAM19	1.4889676	0.460	0.247
NFATC2	1.2007161	0.570	0.370
CD81	1.5940383	0.370	0.181
ITGAL	0.7890083	0.753	0.651
CNN2	0.9196081	0.696	0.563
STK17A	1.0262428	0.611	0.455
APOBEC3C	1.3160270	0.449	0.261
PFN1	0.5519834	0.935	0.914
CLEC2D	0.7435078	0.779	0.682
DRAP1	0.9912192	0.627	0.473
COTL1	1.1602035	0.573	0.430
CD96	0.9288930	0.580	0.403
GIMAP5	0.9027676	0.587	0.414
CYBA	0.9559303	0.713	0.594
ITGA4	1.0146613	0.519	0.342
RASAL3	0.9475953	0.614	0.466
GIMAP4	0.6864012	0.736	0.652
CD99	0.9078249	0.590	0.438

CD4 T Reg : CD4 T Regulatory_2 vs_rest

Top 50 markers: CD4 T Regulatory_2 vs_rest
	avg_log2FC	pct.1	pct.2
IL1R1	3.2620595	0.640	0.076
FURIN	2.1050092	0.808	0.257
LGMN	2.8119672	0.659	0.139
COL5A3	3.5069478	0.550	0.051
ADTRP	2.2952197	0.680	0.195
PCDH1	3.5411374	0.452	0.045
IL1R2	2.9573110	0.437	0.054
RGS6	4.1105955	0.288	0.016
MAF	1.2363902	0.983	0.605
FAM174B	2.5428010	0.393	0.064
CTSH	2.6404873	0.340	0.054
CCR4	1.3696625	0.781	0.338
MAST4	1.7917832	0.543	0.163
INPP1	2.1450004	0.427	0.101
CCL20	2.6170632	0.274	0.033
ZC2HC1A	2.3601494	0.394	0.087
LAG3	1.8575763	0.647	0.250
GPR25	1.6071292	0.373	0.078
ZC3H12D	1.2553758	0.812	0.455
PIM2	0.9713748	0.943	0.685
CDKL2	2.4566733	0.301	0.056
IKZF3	0.9891702	0.906	0.571
LAPTM5	0.6843710	0.988	0.916
PTMS	1.3668660	0.510	0.166
CCR6	1.7368773	0.385	0.098
TRIB2	1.1755143	0.847	0.531
CHI3L2	1.6227323	0.530	0.196
ZNRF1	1.1559424	0.659	0.278
RORA	1.1331721	0.758	0.387
CERK	1.1840231	0.686	0.330
CMTM6	1.5155448	0.571	0.247
RGS1	1.0387502	0.836	0.504
VPS13C	1.0478348	0.795	0.482
PHTF2	1.0641836	0.749	0.398
TNIP3	1.8919130	0.373	0.113
TNFRSF1B	1.0689642	0.890	0.631
GPR183	1.0651120	0.744	0.394
KLRB1	1.2603358	0.452	0.158
OSTF1	1.0555962	0.678	0.345
PBXIP1	0.8497188	0.853	0.530
IL6R	1.0119040	0.706	0.368
SLAMF1	0.9696155	0.723	0.385
IL2RA	1.0292780	0.659	0.324
MICAL2	1.2732241	0.440	0.162
KAT2B	1.1234750	0.643	0.329
CD2	0.6295501	0.956	0.766
F5	1.1826745	0.499	0.214
IL10	1.4108944	0.267	0.072
UCP2	0.8523442	0.899	0.704
FAS	1.0474950	0.554	0.259

CD4 T Reg : CD4 T Regulatory_0 vs CD4 T Regulatory_1

Top 50 markers: CD4 T Regulatory_0 vs CD4 T Regulatory_1
	avg_log2FC	pct.1	pct.2
FOXP3	3.6200779	0.698	0.108
SELL	2.1228633	0.749	0.291
LEF1	3.2810742	0.521	0.096
RTKN2	4.3731576	0.446	0.035
IKZF2	3.3555250	0.474	0.067
KLF2	2.7876994	0.532	0.125
IL7R	2.2040026	0.525	0.102
LTB	1.2787427	0.879	0.580
TCF7	2.3673521	0.571	0.177
TXNIP	1.4449772	0.858	0.594
BIRC3	2.5927035	0.494	0.119
SESN3	2.4869275	0.519	0.169
FCRL3	3.2105145	0.378	0.063
MAL	2.1430083	0.466	0.134
STAM	2.3407678	0.422	0.108
TMEM63A	2.0026088	0.460	0.144
TSC22D3	1.3174219	0.666	0.367
TIGIT	1.1543994	0.737	0.450
CXCR4	1.0748904	0.709	0.397
IL2RA	2.1427751	0.405	0.116
ID3	2.6293848	0.319	0.064
TTC39C	1.6854765	0.434	0.159
DTX1	2.8878498	0.290	0.057
SORL1	2.1568416	0.337	0.096
MAP3K1	1.8540021	0.397	0.152
CCR4	1.6405351	0.411	0.151
DGKA	0.8229669	0.769	0.562
LSR	2.5505731	0.263	0.052
FOXO1	1.0074858	0.633	0.399
EEF1G	0.9125433	0.736	0.562
MAGEH1	2.0415501	0.385	0.163
LDHB	1.0262893	0.656	0.459
PHACTR2	1.9126388	0.308	0.104
TRAF1	0.9829432	0.581	0.355
TMEM123	1.4940720	0.401	0.187
PNRC1	0.6283886	0.820	0.648
LPAR6	2.1197709	0.277	0.083
EIF1	0.8223152	0.671	0.454
PDK1	2.1684604	0.283	0.091
USP15	1.0912219	0.551	0.342
RIN3	1.1435960	0.445	0.223
LTBP3	1.3029855	0.406	0.187
IVNS1ABP	1.0097404	0.514	0.289
DUSP1	1.2057142	0.528	0.313
RIPOR2	1.3575426	0.473	0.270
SLC12A6	1.4948800	0.373	0.173
BTG1	0.5496204	0.888	0.749
SGPP1	1.3140595	0.395	0.199
DENND11	1.4178545	0.334	0.143
GLCCI1	1.0309086	0.497	0.302

CD4 T Reg : CD4 T Regulatory_0 vs CD4 T Regulatory_2

Top 50 markers: CD4 T Regulatory_0 vs CD4 T Regulatory_2
	avg_log2FC	pct.1	pct.2
LEF1	4.1084715	0.521	0.072
KLF2	3.2328254	0.532	0.089
TCF7	2.4777288	0.571	0.203
SORL1	4.2064757	0.337	0.034
FCRL3	3.1405336	0.378	0.077
RTKN2	2.3601145	0.446	0.147
MAL	2.0159172	0.466	0.188
PDE3B	4.0288678	0.282	0.034
ID3	3.2780819	0.319	0.065
TXNIP	1.0585629	0.858	0.781
CXCR4	1.1528904	0.709	0.493
TIGIT	1.0561610	0.737	0.570
IKZF2	1.6110082	0.474	0.250
SELL	1.0129800	0.749	0.680
TTC39C	1.4222016	0.434	0.232
SESN3	1.6149961	0.519	0.373
RASA3	1.7408643	0.399	0.210
TMEM63A	1.3145199	0.460	0.284
SERPINB9	1.2742539	0.434	0.254
STAM	1.3214891	0.422	0.260
CD96	1.1900389	0.446	0.278
STK38	1.4673226	0.398	0.240
ZFP36L2	0.9117202	0.642	0.543
BCL9L	1.2299829	0.440	0.300
HAPLN3	1.4966066	0.317	0.172
TSC22D3	0.7991551	0.666	0.583
RIPOR2	1.2728966	0.473	0.371
ATM	0.8641210	0.600	0.541
TIAM1	1.0654158	0.387	0.268
ITM2A	1.1060603	0.470	0.380
MAGEH1	1.3204229	0.385	0.282
TMEM123	1.0683356	0.401	0.307
LDHB	0.6918093	0.656	0.651
ITPKB	0.8018309	0.557	0.501
ITGB7	1.2505732	0.287	0.177
CD48	0.6457067	0.633	0.584
RASGRP2	1.1857513	0.359	0.262
IL16	0.5063878	0.697	0.688
LIMD2	0.5176747	0.723	0.722
FAM102A	0.6333823	0.617	0.590
BIRC3	0.6899625	0.494	0.411
TRAF1	0.6515374	0.581	0.534
LSR	1.1089205	0.263	0.168
ARHGEF18	1.2725311	0.282	0.201
C12orf57	0.5952274	0.593	0.557
ST8SIA1	1.1091361	0.257	0.166
RGCC	1.1614354	0.264	0.178
PCED1B	0.9519935	0.387	0.316
SFI1	0.9570060	0.336	0.255
SEMA4D	0.9424527	0.345	0.271

CD4 T Reg : CD4 T Regulatory_1 vs CD4 T Regulatory_2

Top 50 markers: CD4 T Regulatory_1 vs CD4 T Regulatory_2
	avg_log2FC	pct.1	pct.2
GZMK	6.9249832	0.520	0.016
GZMA	5.5530801	0.499	0.032
CCL5	6.5572716	0.472	0.020
CCR5	1.9463574	0.743	0.421
PLAAT4	1.8475958	0.717	0.464
CCL4	4.5488567	0.306	0.019
CXCR3	2.3644759	0.497	0.167
CD96	1.8947177	0.580	0.278
CCR2	3.2960151	0.345	0.057
CST7	2.2479090	0.512	0.240
THEMIS	2.5397247	0.355	0.093
CLEC2D	1.0717252	0.779	0.667
TRAT1	2.4883271	0.340	0.105
F2R	2.6406874	0.305	0.083
PDCD1	1.9773432	0.427	0.202
BICDL1	1.6899670	0.515	0.303
PRF1	3.0690392	0.306	0.099
CD38	1.8624039	0.482	0.275
ADA2	2.0449166	0.338	0.125
CXCR6	0.9898317	0.770	0.649
ZFP36L2	1.2309536	0.675	0.543
CHST12	1.8334511	0.433	0.224
WIPF1	0.5925470	0.889	0.914
ITGA4	1.5054485	0.519	0.328
DUSP4	2.3607268	0.334	0.155
ITGAL	0.7738370	0.753	0.735
NFATC2	1.2153129	0.570	0.443
CNN2	0.9268627	0.696	0.622
STOM	2.2956858	0.271	0.107
ST8SIA1	1.6665420	0.344	0.166
APOBEC3G	1.7874088	0.343	0.175
SYTL2	1.9925069	0.252	0.098
SPN	0.9940746	0.632	0.564
DRAP1	0.9486620	0.627	0.563
CBLB	1.0506044	0.525	0.404
LY6E	1.1064257	0.627	0.567
ARHGEF1	0.5456904	0.855	0.876
STK17A	0.8951501	0.611	0.559
ATXN1	1.3339084	0.424	0.295
LSP1	0.7418617	0.777	0.771
BST2	1.3104259	0.481	0.377
TBX21	1.8812933	0.255	0.124
COTL1	1.0431124	0.573	0.516
CYBA	0.8638183	0.713	0.693
CD99	0.8504894	0.590	0.531
C16orf54	1.3069411	0.342	0.219
TAP1	0.7830821	0.651	0.624
ARRB2	1.2458112	0.359	0.244
LYST	1.1773356	0.455	0.372
AKNA	0.5088662	0.855	0.884

Cd8 T cells

cd8_t <-  subset(paed_sub, idents = "CD8 T")
keep_cells <- paed_sub$cell_labels_l1 %in% "CD8 T"
cd8_t@assays$RNA@cells@.Data <- paed_sub@assays$RNA@cells@.Data[keep_cells, ]
cd8_t

An object of class Seurat 
18076 features across 38149 samples within 1 assay 
Active assay: RNA (18076 features, 2000 variable features)
 3 layers present: data, counts, scale.data
 4 dimensional reductions calculated: pca, umap.unintegrated, umap.l1, umap.sub

out <- here("output/RDS/Lineages_RDS_combined/SEU_CD8T_Cells.rds")

if (!file.exists(out)) {
  cd8_t <- cd8_t %>%
  FindVariableFeatures() %>%
  ScaleData() %>%
  RunPCA() 
  
  gc() 
  cd8_t <- RunUMAP(cd8_t, dims = 1:30, reduction = "pca", reduction.name = "umap.cd8T")

  meta_data_columns <- colnames(cd8_t@meta.data)
  columns_to_remove <- grep("^RNA_snn_res", meta_data_columns, value = TRUE)
  cd8_t@meta.data <- cd8_t@meta.data[, !(colnames(cd8_t@meta.data) %in% columns_to_remove)]
  resolutions <- seq(0.1, 0.5, by = 0.1)
  cd8_t <- FindNeighbors(cd8_t, dims = 1:30, reduction = "pca")
  cd8_t <- FindClusters(cd8_t, resolution = resolutions )
  
  saveRDS(cd8_t, out)

} else {
  cd8_t <- readRDS(out)
}

clustree(cd8_t, prefix = "RNA_snn_res.")

DimPlot(cd8_t, group.by = "RNA_snn_res.0.1", reduction = "umap.cd8T", label = TRUE, label.size = 2.5, repel = TRUE, raster = FALSE )

DimPlot(cd8_t, group.by = "RNA_snn_res.0.1", reduction = "umap.cd8T", label = TRUE, label.size = 2.5, repel = TRUE, raster = FALSE, split.by = "tissue" )

opt_res <- "RNA_snn_res.0.1"  
n <- nlevels(cd8_t$RNA_snn_res.0.1)
cd8_t$RNA_snn_res.0.1 <- factor(cd8_t$RNA_snn_res.0.1, levels = seq(0,n-1))
cd8_t$seurat_clusters <- NULL
cd8_t$cluster <- cd8_t$RNA_snn_res.0.1
Idents(cd8_t) <- cd8_t$cluster

confirming cluster 1 is naive Cd8 T

FeaturePlot(cd8_t, features = c("CCR7", "SELL", "LEF1"), ncol = 3, reduction = "umap.cd8T", label = T)

FeaturePlot(cd8_t, features = c("NELL2", "CD8A","TRABD2A","CCR7","OXNAD1"), ncol = 3, reduction = "umap.cd8T", label = T)

FeaturePlot(cd8_t, features = c("CCR7", "CD69"), ncol = 3, reduction = "umap.cd8T", label = T)

FeaturePlot(cd8_t, features = c("CCR7", "SELL", "CD27", "CD28", "IL7R"), ncol = 3, reduction = "umap.cd8T", label = T)

FeaturePlot(cd8_t, features = c("ITGB1", "GZMK", "CX3CR1", "CD44"), ncol = 3, reduction = "umap.cd8T", label = T)

Idents(cd8_t) <- "RNA_snn_res.0.1"
new.cluster.ids <- c("CD8 T Memory", 
                     "CD8 T Naive", 
                     "CD8 TRM/EM",
                     "CD8 T Memory"
                     )
names(new.cluster.ids) <- levels(cd8_t)
cd8_t <- RenameIdents(cd8_t, new.cluster.ids)
cd8_t$cell_labels_l2 <- Idents(cd8_t)

Subclustering CD8 T cells clusters-

cd8_t <- FindSubCluster(cd8_t, cluster = "CD8 T Memory", graph.name = "RNA_snn", resolution = 0.1, subcluster.name = "T_sub")

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 21043
Number of edges: 612607

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9306
Number of communities: 4
Elapsed time: 2 seconds

DimPlot(cd8_t, raster = F, reduction = "umap.cd8T", label = T, group.by = "T_sub", repel = T)

Idents(cd8_t) <- cd8_t$T_sub

cd8_t <- FindSubCluster(cd8_t, cluster = "CD8 T Naive", graph.name = "RNA_snn", resolution = 0.1, subcluster.name = "T_sub")

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 9105
Number of edges: 280470

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9142
Number of communities: 2
Elapsed time: 1 seconds

DimPlot(cd8_t, raster = F, reduction = "umap.cd8T", label = T, group.by = "T_sub", repel = T)

Idents(cd8_t) <- cd8_t$T_sub

cd8_t <- FindSubCluster(cd8_t, cluster = "CD8 TRM/EM", graph.name = "RNA_snn", resolution = 0.1, subcluster.name = "T_sub")

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 8001
Number of edges: 233699

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9009
Number of communities: 2
Elapsed time: 0 seconds

DimPlot(cd8_t, raster = F, reduction = "umap.cd8T", label = T, group.by = "T_sub", repel = T)

Idents(cd8_t) <- cd8_t$T_sub

Renaming idents

new_levels <- c(
    "CD8 T Naive_0",
    "CD8 T Naive_1",
   "CD8 T Memory_0",
     "CD8 T Memory_1",
     "CD8 T Memory_2",
     "CD8 T Memory_3",
"CD8 TRM/EM_0",
"CD8 TRM/EM_1"
)
Seurat::Idents(cd8_t) <- factor(
  Seurat::Idents(cd8_t),
  levels = new_levels
)

cd8_t$cell_labels_l3 <- Idents(cd8_t)

table(cd8_t$cell_labels_l3, cd8_t$tissue)

                
                 Tonsils Adenoids Nasal_brushings Bronchial_brushings  BAL
  CD8 T Naive_0     4285     3828              68                  22   15
  CD8 T Naive_1      408      476               3                   0    0
  CD8 T Memory_0     127      252            7934                1990  547
  CD8 T Memory_1      72      222            4731                1894  467
  CD8 T Memory_2      21       16              36                  67 1414
  CD8 T Memory_3      23       67             905                 184   74
  CD8 TRM/EM_0      4276     2942             345                  64   24
  CD8 TRM/EM_1       121      187              39                   2    1

df_counts <- cd8_t@meta.data |>
  count(cell_labels_l3, tissue, name = "n")
df_totals <- df_counts |>
  group_by(cell_labels_l3) |>
  summarise(total = sum(n), .groups = "drop")

ggplot(df_counts, aes(x = cell_labels_l3, y = n, fill = tissue)) +
  geom_bar(stat = "identity", position = "stack") +
  geom_text(data = df_totals,
            aes(x = cell_labels_l3, y = total, label = total),
            vjust = -0.3, size = 3, inherit.aes = FALSE) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  labs(x = "cluster at l3",
       y = "Number of cells",
       fill = "Tissue")

DE features of Cd8 T cells

The marker genes for this subclustering can be found here-

CD8T_population_subclusters

gc()

             used    (Mb) gc trigger    (Mb) limit (Mb)   max used    (Mb)
Ncells    4676111   249.8   10019789   535.2         NA   10019789   535.2
Vcells 1667906233 12725.2 3537421340 26988.4     163840 3537421340 26988.4

paed_sub.markers <- FindAllMarkers(
  cd8_t,
  features = features.no.mt,
  only.pos = TRUE,
  min.pct = 0.25,
  logfc.threshold = 0.25
)

Calculating cluster CD8 T Naive_0

Calculating cluster CD8 T Naive_1

Calculating cluster CD8 T Memory_0

Calculating cluster CD8 T Memory_1

Calculating cluster CD8 T Memory_2

Calculating cluster CD8 T Memory_3

Calculating cluster CD8 TRM/EM_0

Calculating cluster CD8 TRM/EM_1

gc()

             used    (Mb) gc trigger    (Mb) limit (Mb)   max used    (Mb)
Ncells    4675193   249.7   10019789   535.2         NA   10019789   535.2
Vcells 1667887310 12725.0 3537421340 26988.4     163840 3537421340 26988.4

top10 <- paed_sub.markers %>%
    group_by(cluster) %>%
    top_n(n = 10, wt = avg_log2FC) %>%
    ungroup() %>%
    distinct(gene, .keep_all = TRUE) %>%
    arrange(cluster, desc(avg_log2FC))


best.wilcox.gene.per.cluster <- paed_sub.markers |>
  group_by(cluster) |>
  slice_max(avg_log2FC, n = 1) |>
  select(cluster, gene)
best.wilcox.gene.per.cluster

# A tibble: 8 × 2
# Groups:   cluster [8]
  cluster        gene   
  <fct>          <chr>  
1 CD8 T Naive_0  NOG    
2 CD8 T Naive_1  IFIT3  
3 CD8 T Memory_0 ALOX5AP
4 CD8 T Memory_1 ATP8B4 
5 CD8 T Memory_2 CISH   
6 CD8 T Memory_3 ZBTB16 
7 CD8 TRM/EM_0   CXCR5  
8 CD8 TRM/EM_1   LAMP3

FeaturePlot(
  cd8_t,
  features = best.wilcox.gene.per.cluster$gene,
  reduction = "umap.cd8T",
  raster = FALSE,
  repel = TRUE,
  ncol = 1,
  label = TRUE,
  split.by = "tissue"
)

Note: CD4 T Memory_3 is potentially T-IFN

cd8_t <- ScaleData(
  cd8_t,
  features = unique(top10$gene),
  assay = "RNA",
  verbose = FALSE
)

Warning: Different features in new layer data than already exists for
scale.data

DoHeatmap(cd8_t, features = unique(top10$gene), angle = 90) + NoLegend()

## Seurat top markers
cluster_colors <- paletteer::paletteer_d("pals::glasbey")[factor(top10$cluster)]

DotPlot(cd8_t,    
        features = unique(top10$gene),
        group.by = "cell_labels_l3",
        cols = c("azure1", "blueviolet"),
        dot.scale = 3, assay = "RNA") +
    RotatedAxis() +
    FontSize(y.text = 8, x.text = 12) +
    labs(y = element_blank(), x = element_blank()) +
    coord_flip() +
    theme(axis.text.y = element_text(color = cluster_colors)) +
    ggtitle("Top 10 marker genes per cluster (Seurat)")

Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.

marker_counts <- paed_sub.markers %>%
    filter(avg_log2FC > 1 & p_val_adj < 0.05) %>%  
    count(cluster, name = "n_markers") %>%
    arrange(desc(n_markers))

print(marker_counts)

         cluster n_markers
1  CD8 T Naive_1       162
2  CD8 T Naive_0       155
3   CD8 TRM/EM_1       114
4 CD8 T Memory_1        68
5 CD8 T Memory_3        52
6   CD8 TRM/EM_0        44
7 CD8 T Memory_2        38
8 CD8 T Memory_0        29

ggplot(marker_counts, aes(x = reorder(cluster, n_markers), y = n_markers)) +
    geom_col(fill = "steelblue", alpha = 0.8) +
    coord_flip() +
    labs(title = "High-confidence markers per subcluster (logFC > 1)",
         x = "Subcluster", y = "Number of markers") +
    theme_minimal()

out_markers <- here("output",
            "CSV_v3","T_cell_lineage",
            paste("Marker_genes_Reclustered_Cd8T.",opt_res, sep = ""))

dir.create(out_markers, recursive = TRUE, showWarnings = FALSE)

for (cl in unique(paed_sub.markers$cluster)) {
  cluster_data <- paed_sub.markers %>% dplyr::filter(cluster == cl)
  file_name <- here(out_markers, paste0("G000231_Neeland_", cl, ".csv"))
  if (!file.exists(file_name)) {
  write.csv(cluster_data, file = file_name)
  }
}

Pairwise markers within CD8 T Naive

naive_ids <- c("CD8 T Naive_0","CD8 T Naive_1")

res_all <- run_de_and_vln(
  cd8_t,
  naive_ids,
  title_prefix = "CD8 T Naive",
  run_one_vs_all = TRUE,
  run_pairwise = TRUE,
  print_table = TRUE
)

CD8 T Naive : CD8 T Naive_0 vs_rest

Top 50 markers: CD8 T Naive_0 vs_rest
	avg_log2FC	pct.1	pct.2	p_val_adj
CD52	0.6429317	0.834	0.711	0.0000000
SPOCK2	0.7710545	0.736	0.590	0.0000000
NELL2	0.6593796	0.804	0.645	0.0000000
KLRK1	0.6573124	0.794	0.612	0.0000000
VIM	0.5797152	0.841	0.725	0.0000000
APBB1IP	0.6850899	0.662	0.522	0.0000000
CD96	0.5332530	0.789	0.709	0.0000000
CD248	0.8561925	0.543	0.382	0.0000000
KLRC4	1.4786896	0.272	0.113	0.0000000
IL7R	0.5777033	0.819	0.715	0.0000000
CD27	0.5796326	0.723	0.611	0.0000000
TBC1D10C	0.5858791	0.730	0.613	0.0000000
PDE7A	0.5651752	0.732	0.632	0.0000000
OXNAD1	0.5106936	0.740	0.654	0.0000000
PIK3IP1	0.6176177	0.645	0.522	0.0000000
PECAM1	0.6515123	0.556	0.411	0.0000000
CHMP7	0.5448229	0.653	0.526	0.0000000
TSPAN32	1.1784593	0.300	0.166	0.0000000
DDIT4	0.6554269	0.594	0.479	0.0000000
TSC22D3	0.5978378	0.668	0.573	0.0000000
UCP2	0.5158131	0.713	0.613	0.0000000
RASA3	0.6272000	0.554	0.427	0.0000000
SIGIRR	0.5766638	0.559	0.440	0.0000000
NOG	0.7970832	0.405	0.271	0.0000000
TNRC6C	0.7749054	0.428	0.304	0.0000000
YPEL3	0.8543608	0.369	0.251	0.0000000
PLEC	0.7733844	0.359	0.232	0.0000000
C12orf57	0.5202583	0.636	0.542	0.0000000
LDLRAP1	0.6423483	0.473	0.354	0.0000000
EIF3L	0.5380881	0.571	0.475	0.0000000
S100A6	0.5649504	0.518	0.407	0.0000000
ARRB2	0.5748324	0.478	0.375	0.0000001
CRTAM	0.8677446	0.265	0.165	0.0000003
CCNL2	0.5527657	0.544	0.477	0.0000007
LIME1	0.9121722	0.283	0.192	0.0000010
CCDC141	0.8147461	0.295	0.201	0.0000034
FCGBP	0.8712735	0.313	0.225	0.0000074
RGCC	0.7278376	0.335	0.240	0.0000118
KCNAB2	0.5262352	0.452	0.357	0.0000203
TMEM204	0.6363989	0.347	0.249	0.0000209
SIRPG	0.5132783	0.472	0.384	0.0000359
AP1M1	0.5130681	0.462	0.374	0.0000409
TSPAN14	0.5442164	0.431	0.343	0.0000609
STMN3	0.7323899	0.297	0.211	0.0000730
TNFRSF1A	0.7459482	0.307	0.220	0.0000876
AOAH	0.7505015	0.273	0.187	0.0001328
CABIN1	0.6665353	0.287	0.200	0.0001440
C16orf54	0.5296180	0.483	0.414	0.0001729
TXK	0.5705751	0.412	0.327	0.0002022
CCM2	0.6338437	0.319	0.236	0.0003617

CD8 T Naive : CD8 T Naive_1 vs_rest

Top 50 markers: CD8 T Naive_1 vs_rest
	avg_log2FC	pct.1	pct.2
IFI44L	4.089792	0.949	0.165
OAS1	5.010041	0.825	0.076
MX1	4.518293	0.941	0.195
IFIT1	6.153574	0.762	0.031
IFI44	3.572518	0.936	0.216
RSAD2	5.365762	0.753	0.064
OAS3	4.341243	0.816	0.127
XAF1	3.130677	0.897	0.230
ISG15	4.090492	0.897	0.242
IFI6	4.203868	0.877	0.228
CMPK2	4.280377	0.718	0.081
EPSTI1	3.471274	0.754	0.148
SAMD9L	3.157257	0.815	0.215
IRF7	2.962806	0.869	0.270
IFIT3	6.991976	0.601	0.012
HELZ2	3.648056	0.660	0.103
MX2	2.833979	0.832	0.279
EIF2AK2	2.898302	0.710	0.190
OAS2	2.897867	0.962	0.443
DDX60	2.646927	0.737	0.221
HERC6	3.313869	0.599	0.100
IFIT2	5.333460	0.511	0.032
HERC5	4.953677	0.508	0.034
TRIM22	2.289053	0.926	0.476
USP18	4.385878	0.487	0.040
STAT1	2.343611	0.956	0.518
LY6E	2.303852	0.940	0.513
PLSCR1	3.645294	0.470	0.064
LAMP3	5.539675	0.380	0.013
OASL	5.351756	0.361	0.012
PARP9	2.549136	0.684	0.176
IFI16	2.218688	0.797	0.339
IFIH1	2.848298	0.548	0.132
DTX3L	2.175428	0.728	0.265
PARP14	2.270933	0.708	0.251
ZBP1	3.173437	0.354	0.050
DDX60L	2.450863	0.607	0.182
IFI35	2.752360	0.485	0.111
DDX58	2.683299	0.568	0.162
SAMD9	2.463870	0.570	0.162
GBP1	2.387246	0.722	0.295
LGALS9	3.123157	0.342	0.051
UBE2L6	2.065988	0.741	0.306
TRIM25	2.336092	0.565	0.159
TNFSF10	3.450816	0.481	0.121
BST2	1.995011	0.719	0.292
STAT2	2.309400	0.579	0.185
IFITM3	3.495885	0.280	0.037
APOL6	1.906970	0.791	0.398
HSH2D	2.482121	0.406	0.090

CD8 T Naive : CD8 T Naive_0 vs CD8 T Naive_1

Top 50 markers: CD8 T Naive_0 vs CD8 T Naive_1
	avg_log2FC	pct.1	pct.2	p_val_adj
CD52	0.6429317	0.834	0.711	0.0000000
SPOCK2	0.7710545	0.736	0.590	0.0000000
NELL2	0.6593796	0.804	0.645	0.0000000
KLRK1	0.6573124	0.794	0.612	0.0000000
VIM	0.5797152	0.841	0.725	0.0000000
APBB1IP	0.6850899	0.662	0.522	0.0000000
CD96	0.5332530	0.789	0.709	0.0000000
CD248	0.8561925	0.543	0.382	0.0000000
KLRC4	1.4786896	0.272	0.113	0.0000000
IL7R	0.5777033	0.819	0.715	0.0000000
CD27	0.5796326	0.723	0.611	0.0000000
TBC1D10C	0.5858791	0.730	0.613	0.0000000
PDE7A	0.5651752	0.732	0.632	0.0000000
OXNAD1	0.5106936	0.740	0.654	0.0000000
PIK3IP1	0.6176177	0.645	0.522	0.0000000
PECAM1	0.6515123	0.556	0.411	0.0000000
CHMP7	0.5448229	0.653	0.526	0.0000000
TSPAN32	1.1784593	0.300	0.166	0.0000000
DDIT4	0.6554269	0.594	0.479	0.0000000
TSC22D3	0.5978378	0.668	0.573	0.0000000
UCP2	0.5158131	0.713	0.613	0.0000000
RASA3	0.6272000	0.554	0.427	0.0000000
SIGIRR	0.5766638	0.559	0.440	0.0000000
NOG	0.7970832	0.405	0.271	0.0000000
TNRC6C	0.7749054	0.428	0.304	0.0000000
YPEL3	0.8543608	0.369	0.251	0.0000000
PLEC	0.7733844	0.359	0.232	0.0000000
C12orf57	0.5202583	0.636	0.542	0.0000000
LDLRAP1	0.6423483	0.473	0.354	0.0000000
EIF3L	0.5380881	0.571	0.475	0.0000000
S100A6	0.5649504	0.518	0.407	0.0000000
ARRB2	0.5748324	0.478	0.375	0.0000001
CRTAM	0.8677446	0.265	0.165	0.0000003
CCNL2	0.5527657	0.544	0.477	0.0000007
LIME1	0.9121722	0.283	0.192	0.0000010
CCDC141	0.8147461	0.295	0.201	0.0000034
FCGBP	0.8712735	0.313	0.225	0.0000074
RGCC	0.7278376	0.335	0.240	0.0000118
KCNAB2	0.5262352	0.452	0.357	0.0000203
TMEM204	0.6363989	0.347	0.249	0.0000209
SIRPG	0.5132783	0.472	0.384	0.0000359
AP1M1	0.5130681	0.462	0.374	0.0000409
TSPAN14	0.5442164	0.431	0.343	0.0000609
STMN3	0.7323899	0.297	0.211	0.0000730
TNFRSF1A	0.7459482	0.307	0.220	0.0000876
AOAH	0.7505015	0.273	0.187	0.0001328
CABIN1	0.6665353	0.287	0.200	0.0001440
C16orf54	0.5296180	0.483	0.414	0.0001729
TXK	0.5705751	0.412	0.327	0.0002022
CCM2	0.6338437	0.319	0.236	0.0003617

Pairwise markers within CD8 T Memory

ids <- c("CD8 T Memory_0","CD8 T Memory_1","CD8 T Memory_2", "CD8 T Memory_3" )

res_all <- run_de_and_vln(
  cd8_t,
  ids,
  title_prefix = "CD8 T Memory",
  run_one_vs_all = TRUE,
  run_pairwise = TRUE,
  print_table = TRUE
)

CD8 T Memory : CD8 T Memory_0 vs_rest

Top 50 markers: CD8 T Memory_0 vs_rest
	avg_log2FC	pct.1	pct.2	p_val_adj
CD6	1.2429580	0.602	0.311	0e+00
CD8A	0.7977304	0.919	0.678	0e+00
CD2	0.8200010	0.800	0.653	0e+00
GRAP2	1.4444664	0.386	0.164	0e+00
THEMIS	1.1303877	0.507	0.275	0e+00
SPOCK2	0.7217716	0.548	0.326	0e+00
PAG1	1.1467872	0.383	0.190	0e+00
CD5	1.2215874	0.337	0.159	0e+00
SIT1	1.4492777	0.265	0.108	0e+00
CXCR6	0.5583129	0.791	0.679	0e+00
JUN	0.7568351	0.585	0.427	0e+00
MGAT4A	1.0509042	0.292	0.141	0e+00
CD74	0.5516723	0.797	0.711	0e+00
LAT	0.7780465	0.519	0.374	0e+00
COTL1	0.6525690	0.596	0.468	0e+00
BCL11B	0.7201053	0.493	0.355	0e+00
GZMM	1.0373066	0.292	0.164	0e+00
ALOX5AP	0.6172555	0.615	0.500	0e+00
ITM2C	0.7406850	0.474	0.347	0e+00
FKBP11	0.8463362	0.366	0.241	0e+00
CCR5	0.6053830	0.548	0.439	0e+00
FYB1	0.5067270	0.630	0.532	0e+00
JAML	0.5644695	0.546	0.433	0e+00
GIMAP4	0.8075917	0.331	0.214	0e+00
ITGB2	0.7332893	0.279	0.173	0e+00
STAT1	0.8449952	0.369	0.270	0e+00
ORMDL3	0.6469568	0.394	0.298	0e+00
PLEKHF1	0.5663697	0.433	0.342	0e+00
UCP2	0.5336800	0.475	0.387	0e+00
GBP5	0.5729444	0.427	0.338	0e+00
APOL3	0.7476716	0.264	0.180	0e+00
LBH	0.5558657	0.423	0.341	0e+00
BCL9L	0.6142142	0.353	0.269	0e+00
SH3KBP1	0.6559173	0.280	0.204	0e+00
CXCR3	0.5544752	0.376	0.299	0e+00
ARRB2	0.5157930	0.381	0.306	0e+00
GIMAP5	0.5314806	0.264	0.198	0e+00
GZMH	0.6675383	0.254	0.194	0e+00
MXD4	0.5356001	0.319	0.262	0e+00
ISG15	0.6287612	0.309	0.263	0e+00
IFI6	0.6162004	0.304	0.274	1e-07

CD8 T Memory : CD8 T Memory_1 vs_rest

Top 50 markers: CD8 T Memory_1 vs_rest
	avg_log2FC	pct.1	pct.2
GNLY	2.4132612	0.651	0.176
CSF1	1.9342218	0.624	0.205
KLRC3	2.2083494	0.515	0.131
RIN3	1.5990493	0.630	0.256
HOPX	1.4419134	0.654	0.301
PIK3AP1	1.8628358	0.499	0.152
KLRC1	1.8419687	0.478	0.146
KLRC4	1.2711636	0.603	0.283
CTSW	1.3476149	0.587	0.284
CD7	1.1173934	0.889	0.600
CLNK	1.7253457	0.386	0.109
FCRL6	1.5118944	0.437	0.163
ENTPD1	1.6345378	0.406	0.146
IL2RB	0.9952386	0.867	0.625
NCR1	3.0299575	0.273	0.033
TRDC	3.5891313	0.270	0.033
ATP8B4	3.3254759	0.250	0.025
IKZF2	2.2603396	0.273	0.054
PLCG2	2.6688880	0.251	0.040
DGKD	1.5042357	0.301	0.104
APOBR	1.0180554	0.566	0.316
KLRD1	0.9446612	0.534	0.285
ZNF683	0.8220569	0.647	0.393
TRGC2	1.0351340	0.456	0.236
CD63	0.9702097	0.507	0.278
AOAH	0.8369536	0.569	0.332
SEMA4D	0.9038500	0.545	0.318
BCAS4	1.2899782	0.290	0.115
NMUR1	0.9146206	0.460	0.244
CDIP1	1.3995872	0.260	0.098
LITAF	0.9393850	0.475	0.270
RAB3GAP1	1.1738412	0.317	0.146
ATP6V0A2	0.9467695	0.411	0.222
CCL4	1.6740371	0.419	0.244
ADCY3	0.9464424	0.308	0.149
ITGA1	0.6901678	0.559	0.367
PIK3R1	0.8804541	0.407	0.235
PRF1	0.5866359	0.724	0.553
PTPN6	0.6325839	0.579	0.392
CTSA	0.9344630	0.347	0.190
CD244	0.7758980	0.380	0.216
NKG7	0.5115263	0.794	0.631
SCML4	0.8713423	0.328	0.177
ABCA2	0.6045079	0.551	0.372
GZMB	0.8568270	0.488	0.322
PTPN22	0.5661982	0.567	0.396
MATK	0.7031089	0.394	0.236
DAPK2	0.5916035	0.456	0.292
METRNL	0.8137526	0.300	0.165
KIAA1671	0.7536755	0.310	0.171

CD8 T Memory : CD8 T Memory_2 vs_rest

Top 50 markers: CD8 T Memory_2 vs_rest
	avg_log2FC	pct.1	pct.2
CISH	3.3116635	0.741	0.194
BCL2	3.3401526	0.616	0.119
TXNIP	1.6737752	0.863	0.572
AHNAK	1.6627080	0.789	0.522
PIM1	1.8798503	0.643	0.310
VIM	1.0730359	0.921	0.750
CD44	1.3903125	0.761	0.481
KLF6	1.6023679	0.647	0.357
CRIP1	1.4782433	0.746	0.548
S100A10	1.8318854	0.445	0.202
ANXA1	1.6171229	0.560	0.353
MYADM	2.0378326	0.340	0.134
PLP2	2.1821548	0.288	0.103
S100A11	1.7053262	0.409	0.211
IVNS1ABP	1.4008154	0.522	0.336
FLT3LG	1.6497543	0.355	0.163
BHLHE40	1.5519681	0.419	0.220
SAMHD1	1.5083391	0.448	0.252
ADGRE5	1.0912974	0.654	0.496
EMP3	1.3026489	0.477	0.295
SYNE2	1.0924810	0.588	0.447
SLC1A5	1.9617468	0.250	0.106
LTB	1.5753091	0.377	0.199
S100A6	0.8114988	0.680	0.605
ITGB2	1.2135021	0.375	0.216
CD48	1.0767428	0.488	0.360
LGALS1	1.3565855	0.300	0.159
CBFB	1.3348078	0.342	0.219
MDFIC	1.3126625	0.340	0.221
FXYD5	0.8424624	0.535	0.433
TSC22D3	0.9594301	0.415	0.283
ERN1	1.3465332	0.254	0.142
CXCR4	0.6981067	0.526	0.383
KAT2B	1.4243768	0.252	0.146
CCNL2	0.9358992	0.467	0.368
OXNAD1	1.0752421	0.369	0.260
ANXA2	1.1696035	0.328	0.223
EEF2	0.6239444	0.642	0.599
TMEM43	1.2235340	0.281	0.178
ITGA4	1.0362131	0.367	0.273
SLC3A2	1.1536869	0.300	0.206
ARL4C	0.6790031	0.515	0.437
TAGAP	0.8242631	0.369	0.276
CD99	0.6056809	0.584	0.544
CYTH1	0.8663946	0.371	0.296
CCND3	0.9094912	0.315	0.234
OGT	0.5723401	0.528	0.482
XBP1	1.0496011	0.284	0.214
PLEKHO1	0.8253736	0.371	0.305
ADAM19	0.8934832	0.301	0.228

CD8 T Memory : CD8 T Memory_3 vs_rest

Top 50 markers: CD8 T Memory_3 vs_rest
	avg_log2FC	pct.1	pct.2
MAF	3.0963547	0.736	0.129
CD4	4.6910223	0.528	0.026
IL7R	2.8267326	0.594	0.136
SPOCK2	1.7798035	0.856	0.414
CD40LG	4.3943640	0.376	0.016
SCART1	4.5915417	0.333	0.026
RORC	3.2209162	0.313	0.038
COL5A1	2.7937505	0.313	0.045
ZBTB16	4.8910223	0.259	0.010
KLRB1	2.1272157	0.501	0.158
PLXND1	2.6565044	0.267	0.046
CCR6	2.2314794	0.358	0.087
LTB	1.7833403	0.547	0.191
SLAMF1	2.0242492	0.410	0.119
ERN1	1.9316109	0.429	0.133
HIPK2	2.2894480	0.318	0.084
IFI44	1.5369636	0.551	0.235
PRR5	2.0807673	0.367	0.122
TNFRSF25	1.9746102	0.325	0.097
RORA	1.4115537	0.589	0.295
CERK	1.7269771	0.386	0.138
RUNX2	1.8407398	0.332	0.107
MGAT4A	1.4696430	0.460	0.204
PBXIP1	1.5428860	0.434	0.188
DUSP1	1.7112752	0.457	0.211
PDCD1	1.7321947	0.333	0.125
PHTF2	1.8467804	0.283	0.096
RGS1	1.8835742	0.390	0.169
S100A4	0.7490337	0.828	0.637
CXCR6	0.7431039	0.864	0.728
AQP3	1.5835350	0.270	0.104
LAPTM5	0.6369856	0.873	0.780
FURIN	1.4214729	0.362	0.175
TNFAIP3	0.9938249	0.609	0.401
PDE4D	1.3272363	0.339	0.165
PRDM1	1.2800028	0.396	0.225
JAML	0.6939272	0.654	0.481
CD6	0.7127737	0.621	0.451
PIM2	0.9563257	0.381	0.225
CEBPD	1.1345812	0.304	0.163
IL18RAP	1.1536663	0.271	0.139
CDC42EP3	1.1606317	0.265	0.140
BTG1	0.5887936	0.741	0.623
MAN1A1	0.9271076	0.381	0.244
GIMAP5	0.9059988	0.363	0.224
GIMAP7	0.6329395	0.581	0.443
DUSP16	1.0324726	0.305	0.178
MAEA	0.9493618	0.319	0.188
FNBP1	0.5752563	0.617	0.487
IL12RB1	1.0282615	0.287	0.166

CD8 T Memory : CD8 T Memory_0 vs CD8 T Memory_1

Top 50 markers: CD8 T Memory_0 vs CD8 T Memory_1
	avg_log2FC	pct.1	pct.2
CD6	1.8867731	0.602	0.249
SPOCK2	1.7905073	0.548	0.229
THEMIS	1.4788601	0.507	0.243
GRAP2	2.1334527	0.386	0.123
CD5	2.2809714	0.337	0.104
CD2	1.0895926	0.800	0.632
MGAT4A	2.2444107	0.292	0.083
PAG1	1.6291475	0.383	0.155
CD8A	0.5657106	0.919	0.784
LAPTM5	0.6811479	0.825	0.729
SIT1	1.9525005	0.265	0.086
S100A4	0.8088532	0.692	0.566
CXCR6	0.6398173	0.791	0.717
BCL11B	1.0123159	0.493	0.332
CD44	0.8312968	0.537	0.371
ITGB2	1.4815528	0.279	0.125
LAT	0.9246406	0.519	0.370
GIMAP4	1.3051914	0.331	0.175
S100A6	0.6969918	0.647	0.532
JUN	0.7464429	0.585	0.454
CCR5	0.8061409	0.548	0.437
ARL4C	0.7860800	0.488	0.356
FKBP11	0.9980469	0.366	0.235
JAML	0.6713852	0.546	0.435
GZMM	0.9892423	0.292	0.176
RORA	0.9482442	0.348	0.236
CXCR4	0.7822475	0.424	0.306
FYB1	0.5019169	0.630	0.563
ADAM19	1.0025788	0.262	0.159
GBP5	0.7162096	0.427	0.332
GIMAP7	0.6508290	0.486	0.400
STAT1	0.8935483	0.369	0.279
BCL9L	0.7666989	0.353	0.266
GIMAP5	0.8825261	0.264	0.177
SH3KBP1	0.8615228	0.280	0.196
ITM2C	0.5743955	0.474	0.391
AHNAK	0.5139661	0.556	0.487
ARRB2	0.6802012	0.381	0.298
LBH	0.6369176	0.423	0.346
APOL3	0.8623798	0.264	0.181
PLEC	0.7424407	0.329	0.247
SYNE2	0.5246652	0.476	0.406
AAK1	0.6807822	0.331	0.251
UCP2	0.5394175	0.475	0.407
GIMAP1	0.8135030	0.266	0.188
GPSM3	0.6598896	0.349	0.272
ORMDL3	0.6169032	0.394	0.320
TNIK	0.7296541	0.263	0.188
PLEKHF1	0.5353738	0.433	0.367
ITK	0.6285623	0.313	0.248

CD8 T Memory : CD8 T Memory_0 vs CD8 T Memory_2

Top 50 markers: CD8 T Memory_0 vs CD8 T Memory_2
	avg_log2FC	pct.1	pct.2
LYZ	3.1350971	0.049	0.415
CD8A	1.2847758	0.919	0.508
CXCR6	1.6981949	0.791	0.345
CCL5	1.1113185	0.962	0.665
CD3D	1.4863653	0.812	0.442
PLAAT4	1.8820118	0.675	0.252
DDIT4	2.4341355	0.557	0.129
CD7	1.6784814	0.672	0.259
CD74	1.3118554	0.797	0.585
JUN	1.8531758	0.585	0.223
ACTB	0.7465220	0.944	0.828
CD3G	1.5724527	0.564	0.225
GZMB	2.7057347	0.378	0.072
WFDC2	0.7620346	0.379	0.060
COTL1	1.3724485	0.596	0.266
DAPK2	2.7665238	0.335	0.048
ALOX5AP	1.3185154	0.615	0.322
NMUR1	4.0677275	0.281	0.018
ITGA1	2.0220756	0.413	0.113
PLEKHF1	1.7915249	0.433	0.125
JAML	1.3569840	0.546	0.248
EVL	0.6954798	0.869	0.678
SPN	1.0899849	0.632	0.329
GZMA	1.1501810	0.640	0.362
ABI3	1.9124331	0.382	0.109
ABCA2	1.5732521	0.416	0.142
SASH3	1.0659924	0.623	0.344
NKG7	0.9164247	0.698	0.403
CD52	0.8870064	0.715	0.494
LAG3	1.3692144	0.512	0.241
ZNRF1	2.1140398	0.316	0.080
SEPTIN1	0.8267980	0.702	0.425
JAK3	1.2094211	0.462	0.196
ITGB7	1.1941901	0.482	0.223
PFN1	0.6186346	0.874	0.718
IL2RB	0.8029783	0.667	0.405
IKZF3	0.8302622	0.655	0.405
TRBC2	0.7753408	0.793	0.615
ITM2C	1.2458690	0.474	0.232
MVB12B	1.8545320	0.289	0.077
TAPBP	0.7945264	0.653	0.393
TRAC	0.6218032	0.825	0.647
TBC1D10C	0.8895108	0.617	0.364
CD2	0.6748166	0.800	0.634
FAM53B	1.6046742	0.312	0.101
CD6	0.8128857	0.602	0.354
RGS3	1.7422065	0.268	0.072
ARHGEF1	0.6167294	0.810	0.599
SRGN	0.7492385	0.643	0.417
USP20	1.1294961	0.411	0.184

CD8 T Memory : CD8 T Memory_0 vs CD8 T Memory_3

Top 50 markers: CD8 T Memory_0 vs CD8 T Memory_3
	avg_log2FC	pct.1	pct.2
CD8A	2.4106847	0.919	0.267
CCL5	1.6164277	0.962	0.686
ZNF683	5.4666112	0.453	0.014
NKG7	1.8174864	0.698	0.330
CD74	1.3889893	0.797	0.532
KLRK1	2.5502973	0.482	0.105
AOAH	2.9668542	0.385	0.064
KLRC4	4.1985667	0.323	0.020
COTL1	1.5310467	0.596	0.292
KLRD1	3.7642082	0.325	0.031
CD7	1.2590879	0.672	0.403
CXCR3	1.7942012	0.376	0.124
GZMB	1.9018661	0.378	0.145
ITM2C	1.3698010	0.474	0.231
CTSW	2.0021191	0.312	0.091
GZMH	2.5340122	0.254	0.055
ALOX5AP	0.8761106	0.615	0.421
ZYX	0.9575389	0.528	0.330
TMSB10	0.7678606	0.654	0.469
GZMA	0.8305371	0.640	0.462
IL32	0.5609145	0.818	0.720
NLRC5	0.8638514	0.532	0.347
TIGIT	1.2795136	0.364	0.188
IDH2	1.0319852	0.422	0.243
RIN3	1.4514777	0.284	0.127
IL2RB	0.6044168	0.667	0.533
FYB1	0.5323859	0.630	0.462
MATK	1.3069044	0.253	0.113
CST7	0.8930311	0.379	0.225
HOPX	1.1077060	0.334	0.187
TAP1	0.7128315	0.565	0.433
USP20	0.8750125	0.411	0.259
PLAAT4	0.5719145	0.675	0.566
IFI6	1.7131148	0.304	0.178
CNN2	0.7036657	0.490	0.342
LY6E	0.7973412	0.539	0.433
THEMIS	0.6184859	0.507	0.368
TYMP	1.1854965	0.278	0.153
ABCA2	0.6572942	0.416	0.275
GGA2	0.9068845	0.334	0.207
FASLG	0.8321247	0.365	0.241
SLA2	0.8476477	0.348	0.223
ITGA1	0.7430749	0.413	0.287
GRAP2	0.7320401	0.386	0.261
PRF1	0.6620252	0.590	0.474
AHNAK	0.5023378	0.556	0.432
CD63	0.8625182	0.300	0.188
GZMM	0.8377591	0.292	0.180
FAM102A	0.6424684	0.377	0.255
DAPK2	0.7633856	0.335	0.223

CD8 T Memory : CD8 T Memory_1 vs CD8 T Memory_2

Top 50 markers: CD8 T Memory_1 vs CD8 T Memory_2
	avg_log2FC	pct.1	pct.2
CD7	2.5921991	0.889	0.259
CCL5	1.2920135	0.982	0.665
LYZ	2.8896618	0.059	0.415
IL2RB	1.6779501	0.867	0.405
GNLY	3.3524415	0.651	0.124
CSF1	3.1253513	0.624	0.101
HOPX	2.2624976	0.654	0.161
NMUR1	4.7710290	0.460	0.018
DDIT4	2.3720500	0.600	0.129
PLAAT4	1.7923932	0.707	0.252
RIN3	2.0249839	0.630	0.170
ITGA1	2.5233364	0.559	0.113
PIK3AP1	2.8753694	0.499	0.066
GZMB	3.3048954	0.488	0.072
ENTPD1	4.9452282	0.406	0.014
DAPK2	3.1492338	0.456	0.048
KLRC1	2.6695342	0.478	0.069
KLRC3	2.1276617	0.515	0.103
CD3D	1.2173283	0.798	0.442
ABCA2	2.0110141	0.551	0.142
FCRL6	2.8103778	0.437	0.058
APOBR	1.9309270	0.566	0.165
NKG7	1.2447944	0.794	0.403
AOAH	1.7329386	0.569	0.184
KLRC4	1.4195519	0.603	0.217
EVL	0.7969011	0.910	0.678
SRGN	1.1764377	0.752	0.417
PRF1	1.3488459	0.724	0.364
GZMA	1.7358668	0.661	0.362
CD244	2.6395028	0.380	0.053
CXCR6	1.0583776	0.717	0.345
ZNF683	1.1996478	0.647	0.275
CLNK	2.1609289	0.386	0.066
SEMA4D	1.4714817	0.545	0.189
CTSW	1.2565524	0.587	0.240
RGS3	2.1937844	0.372	0.072
PTPN6	1.3207146	0.579	0.243
WFDC2	0.5069867	0.363	0.060
CD3G	1.3111097	0.564	0.225
FASLG	1.7435598	0.469	0.152
PTPN22	1.2899435	0.567	0.230
CD63	1.5164885	0.507	0.194
NCR1	3.3685876	0.273	0.021
TRGC2	1.5197729	0.456	0.145
P2RY11	2.1375753	0.344	0.071
ITGB7	1.2204262	0.541	0.223
ATP6V0A2	1.6472970	0.411	0.116
ABI3	1.7437469	0.394	0.109
ACTB	0.5841621	0.946	0.828
KLRD1	1.0616780	0.534	0.208

CD8 T Memory : CD8 T Memory_1 vs CD8 T Memory_3

Top 50 markers: CD8 T Memory_1 vs CD8 T Memory_3
	avg_log2FC	pct.1	pct.2
ZNF683	6.0926902	0.647	0.014
CD7	2.1728056	0.889	0.403
CCL5	1.7971227	0.982	0.686
KLRC4	5.3030491	0.603	0.020
GNLY	4.6777952	0.651	0.090
NKG7	2.1458560	0.794	0.330
CD8A	1.8449740	0.784	0.267
RIN3	2.9058562	0.630	0.127
KLRD1	4.5489854	0.534	0.031
KLRC3	5.6399366	0.515	0.016
AOAH	3.5839952	0.569	0.064
CTSW	3.2451217	0.587	0.091
IL2RB	1.4793886	0.867	0.533
HOPX	2.3890082	0.654	0.187
KLRK1	2.8037388	0.572	0.105
KLRC1	3.8750658	0.478	0.041
FCRL6	4.6468963	0.437	0.026
CLNK	3.2374149	0.386	0.046
PIK3AP1	2.1557990	0.499	0.144
GZMB	2.5010269	0.488	0.145
CD63	1.7065845	0.507	0.188
SEMA4D	1.4826247	0.545	0.237
PLCG2	6.2098969	0.251	0.004
CD74	1.0525495	0.768	0.532
BCAS4	3.3802788	0.290	0.031
LITAF	1.6645304	0.475	0.188
MATK	1.9386209	0.394	0.113
ATP8B4	4.0892963	0.250	0.017
PRF1	1.1185148	0.724	0.474
GZMA	1.4162228	0.661	0.462
ITGA1	1.2443358	0.559	0.287
TIGIT	1.6636966	0.453	0.188
NCR1	2.8680785	0.273	0.042
TRDC	3.2494333	0.270	0.043
DGKD	2.4354664	0.301	0.062
METRNL	2.5331660	0.300	0.063
ABCA2	1.0950562	0.551	0.275
NMUR1	1.2562921	0.460	0.205
RAB3GAP1	1.9560245	0.317	0.091
COTL1	1.0926862	0.541	0.292
ZYX	0.9611735	0.573	0.330
CST7	1.1397567	0.468	0.225
IL32	0.6494064	0.838	0.720
KIAA1671	1.7086548	0.310	0.093
FASLG	1.3000072	0.469	0.241
SFMBT2	1.0796785	0.485	0.241
TRGC2	1.1895236	0.456	0.219
IDH2	1.1151146	0.484	0.243
CCL4	1.8459125	0.419	0.199
DAPK2	1.1460956	0.456	0.223

CD8 T Memory : CD8 T Memory_2 vs CD8 T Memory_3

Top 50 markers: CD8 T Memory_2 vs CD8 T Memory_3
	avg_log2FC	pct.1	pct.2	p_val_adj
CISH	3.5782948	0.741	0.160	0e+00
AHNAK	1.9599826	0.789	0.432	0e+00
VIM	1.3095518	0.921	0.682	0e+00
BCL2	2.5116301	0.616	0.197	0e+00
TXNIP	1.5111047	0.863	0.595	0e+00
PIM1	1.8704530	0.643	0.316	0e+00
CRIP1	1.5432852	0.746	0.512	0e+00
ANXA1	2.0482615	0.560	0.273	0e+00
ZNF683	4.8930424	0.275	0.014	0e+00
IVNS1ABP	2.0302228	0.522	0.236	0e+00
KLF6	1.4097697	0.647	0.383	0e+00
ADGRE5	1.3033839	0.654	0.437	0e+00
S100A10	1.9420688	0.445	0.179	0e+00
MYADM	2.4341262	0.340	0.110	0e+00
LGALS1	2.5570638	0.300	0.074	0e+00
KLRK1	2.3106494	0.336	0.105	0e+00
EMP3	1.5835882	0.477	0.246	0e+00
CD44	0.8726417	0.761	0.642	0e+00
CD8A	1.1259088	0.508	0.267	0e+00
PLP2	2.1692608	0.288	0.097	0e+00
SAMHD1	1.4365669	0.448	0.251	0e+00
S100A11	1.5485036	0.409	0.218	0e+00
SYNE2	1.0721188	0.588	0.440	0e+00
BHLHE40	1.4052221	0.419	0.246	0e+00
SLC1A5	2.1798399	0.250	0.090	0e+00
ITGB2	1.3520463	0.375	0.200	0e+00
CD99	0.8873606	0.584	0.471	0e+00
TMSB10	0.7791568	0.602	0.469	0e+00
CBFB	1.4836680	0.342	0.192	0e+00
APOBEC3G	1.7739597	0.267	0.127	0e+00
MDFIC	1.4344690	0.340	0.204	0e+00
CD48	0.9611129	0.488	0.366	0e+00
XBP1	1.5989630	0.284	0.153	0e+00
ANXA2	1.3110178	0.328	0.197	0e+00
MAP4	1.1325194	0.378	0.251	0e+00
TMEM43	1.3951259	0.281	0.154	0e+00
OGT	0.7881167	0.528	0.427	0e+00
FLT3LG	0.9689007	0.355	0.236	0e+00
GSTK1	0.7568020	0.504	0.417	0e+00
CCNL2	0.8149514	0.467	0.377	0e+00
IRF7	1.2109631	0.286	0.181	0e+00
EEF2	0.5433987	0.642	0.596	0e+00
S100A6	0.5068240	0.680	0.662	0e+00
SLC3A2	1.1342739	0.300	0.198	0e+00
CYLD	1.1919502	0.292	0.197	0e+00
TAGAP	0.8456611	0.369	0.268	0e+00
IDS	0.9383903	0.364	0.273	0e+00
SOCS1	1.0583469	0.308	0.213	0e+00
PRMT2	1.0183007	0.319	0.235	2e-07
NKG7	0.9010617	0.403	0.330	4e-07

Pairwise markers within CD8 TRM/EM

ids <- c("CD8 TRM/EM_0","CD8 TRM/EM_1" )

res_all <- run_de_and_vln(
  cd8_t,
  ids,
  title_prefix = "CD8 TRM/EM",
  run_one_vs_all = TRUE,
  run_pairwise = TRUE,
  print_table = TRUE
)

CD8 TRM/EM : CD8 TRM/EM_0 vs_rest

Top 50 markers: CD8 TRM/EM_0 vs_rest
	p_val	avg_log2FC	pct.1	pct.2	p_val_adj
NELL2	0.0000000	1.4317691	0.313	0.137	0.0000000
UBA52	0.0000000	0.5599817	0.702	0.569	0.0000022
KLRG1	0.0000000	0.8334049	0.536	0.383	0.0000047
GPX4	0.0000000	1.2725297	0.266	0.117	0.0000156
LTB	0.0000000	1.0360945	0.404	0.257	0.0000293
EEF1G	0.0000000	0.6034989	0.586	0.457	0.0008720
FXYD5	0.0000001	0.5700317	0.616	0.489	0.0009293
S100A4	0.0000001	0.9494463	0.308	0.171	0.0010834
PDE7A	0.0000001	0.6371478	0.461	0.314	0.0013517
C12orf57	0.0000001	0.7486554	0.457	0.320	0.0018816
SUGP2	0.0000003	0.9457454	0.319	0.191	0.0061402
ESYT2	0.0000004	0.8413329	0.364	0.237	0.0073932
MATK	0.0000008	0.7874652	0.320	0.197	0.0144460
NOP53	0.0000010	0.5684310	0.503	0.374	0.0184875
NFKB2	0.0000014	0.8335988	0.253	0.137	0.0257083
CD27	0.0000016	0.5008812	0.646	0.560	0.0283540
SLC7A6	0.0000030	0.8376522	0.301	0.189	0.0547510
IL7R	0.0000035	0.8947560	0.346	0.229	0.0641359
EVI2B	0.0000048	0.5912796	0.477	0.360	0.0872763
PTPN7	0.0000051	0.7132180	0.366	0.249	0.0923670
USP20	0.0000120	0.7423418	0.259	0.149	0.2164030
EEF1B2	0.0000189	0.5756356	0.321	0.214	0.3417440
YPEL3	0.0000394	0.7690227	0.333	0.237	0.7127719
TNIK	0.0000416	0.6837923	0.300	0.200	0.7522054
FBL	0.0000422	0.9121884	0.255	0.163	0.7622778
EIF4B	0.0000433	0.6518818	0.353	0.254	0.7825486
USP9X	0.0000574	0.5775571	0.325	0.217	1.0000000
NUCB2	0.0000607	0.6937517	0.327	0.234	1.0000000
PLEC	0.0000869	0.6631028	0.298	0.203	1.0000000
MDFIC	0.0000878	0.5085977	0.299	0.194	1.0000000
CHMP7	0.0001593	0.9015741	0.260	0.177	1.0000000
UNC13D	0.0001615	0.5600790	0.324	0.229	1.0000000
FBXL3	0.0001660	0.6329179	0.279	0.186	1.0000000
KAT6B	0.0002066	0.5453099	0.317	0.217	1.0000000
CRTAM	0.0002322	0.5782623	0.315	0.223	1.0000000
OXNAD1	0.0002402	0.6087634	0.273	0.180	1.0000000
KLRC4	0.0002404	0.7661449	0.267	0.180	1.0000000
PIK3IP1	0.0002470	0.5300839	0.319	0.226	1.0000000
SLC4A7	0.0002598	0.5447564	0.268	0.177	1.0000000
MPRIP	0.0003209	0.5578327	0.309	0.217	1.0000000
RIN3	0.0003282	0.5799150	0.298	0.209	1.0000000
SORL1	0.0004141	0.6134065	0.284	0.197	1.0000000
SERF2	0.0004483	0.5933928	0.253	0.166	1.0000000
GSK3B	0.0004940	0.5796039	0.262	0.177	1.0000000
HIST1H1D	0.0005079	0.5857983	0.349	0.254	1.0000000
PATJ	0.0005623	0.7256277	0.277	0.203	1.0000000
EIF3B	0.0005654	0.5781233	0.295	0.209	1.0000000
SMAP2	0.0006498	0.5346144	0.366	0.283	1.0000000
SATB1	0.0010624	0.5501630	0.285	0.206	1.0000000
EIF3L	0.0011622	0.5398901	0.303	0.223	1.0000000

CD8 TRM/EM : CD8 TRM/EM_1 vs_rest

Top 50 markers: CD8 TRM/EM_1 vs_rest
	avg_log2FC	pct.1	pct.2
IFI44L	4.101500	0.791	0.097
RSAD2	4.274118	0.631	0.057
IFIT1	5.533610	0.577	0.019
IFIT3	5.308743	0.306	0.012
CMPK2	3.765476	0.649	0.087
OAS1	3.575301	0.806	0.155
MX1	3.767896	0.854	0.193
IFI44	3.400310	0.837	0.177
IFI6	3.901246	0.846	0.210
OAS3	3.636156	0.723	0.146
HERC5	3.705989	0.520	0.069
ISG15	3.445055	0.817	0.228
LAMP3	4.449669	0.269	0.017
MX2	3.166275	0.717	0.181
IFIT2	4.069887	0.349	0.037
XAF1	2.591783	0.809	0.274
USP18	3.632118	0.389	0.048
OAS2	2.393703	0.897	0.426
IRF7	2.500232	0.789	0.269
LY6E	2.159562	0.914	0.497
HERC6	2.760039	0.477	0.093
HELZ2	2.570211	0.629	0.180
EIF2AK2	2.483106	0.669	0.211
EPSTI1	2.563135	0.614	0.173
TRIM22	1.822821	0.886	0.528
SAMD9L	2.402153	0.723	0.313
STAT1	2.191248	0.774	0.358
ISG20	2.033433	0.777	0.401
IFIT5	2.479603	0.394	0.087
TYMP	2.469460	0.443	0.113
IFI35	2.267144	0.491	0.144
IFIH1	2.281831	0.486	0.151
PARP9	2.138141	0.520	0.181
PLSCR1	2.377586	0.400	0.116
ZBP1	2.045817	0.469	0.160
DDX60	2.095494	0.494	0.186
BST2	1.740890	0.666	0.343
OASL	1.914803	0.517	0.194
PARP12	1.829910	0.551	0.225
RNF213	1.127644	0.911	0.763
LGALS9	2.516324	0.269	0.060
SAMD9	1.985663	0.474	0.181
STAT2	2.086030	0.454	0.168
IFI16	1.252442	0.786	0.543
HSH2D	2.269223	0.323	0.088
LAP3	2.149751	0.380	0.120
DTX3L	1.663514	0.597	0.283
RTP4	2.267795	0.286	0.071
PARP14	1.692117	0.620	0.325
DDX60L	2.050035	0.440	0.164

CD8 TRM/EM : CD8 TRM/EM_0 vs CD8 TRM/EM_1

Top 50 markers: CD8 TRM/EM_0 vs CD8 TRM/EM_1
	p_val	avg_log2FC	pct.1	pct.2	p_val_adj
NELL2	0.0000000	1.4317691	0.313	0.137	0.0000000
UBA52	0.0000000	0.5599817	0.702	0.569	0.0000022
KLRG1	0.0000000	0.8334049	0.536	0.383	0.0000047
GPX4	0.0000000	1.2725297	0.266	0.117	0.0000156
LTB	0.0000000	1.0360945	0.404	0.257	0.0000293
EEF1G	0.0000000	0.6034989	0.586	0.457	0.0008720
FXYD5	0.0000001	0.5700317	0.616	0.489	0.0009293
S100A4	0.0000001	0.9494463	0.308	0.171	0.0010834
PDE7A	0.0000001	0.6371478	0.461	0.314	0.0013517
C12orf57	0.0000001	0.7486554	0.457	0.320	0.0018816
SUGP2	0.0000003	0.9457454	0.319	0.191	0.0061402
ESYT2	0.0000004	0.8413329	0.364	0.237	0.0073932
MATK	0.0000008	0.7874652	0.320	0.197	0.0144460
NOP53	0.0000010	0.5684310	0.503	0.374	0.0184875
NFKB2	0.0000014	0.8335988	0.253	0.137	0.0257083
CD27	0.0000016	0.5008812	0.646	0.560	0.0283540
SLC7A6	0.0000030	0.8376522	0.301	0.189	0.0547510
IL7R	0.0000035	0.8947560	0.346	0.229	0.0641359
EVI2B	0.0000048	0.5912796	0.477	0.360	0.0872763
PTPN7	0.0000051	0.7132180	0.366	0.249	0.0923670
USP20	0.0000120	0.7423418	0.259	0.149	0.2164030
EEF1B2	0.0000189	0.5756356	0.321	0.214	0.3417440
YPEL3	0.0000394	0.7690227	0.333	0.237	0.7127719
TNIK	0.0000416	0.6837923	0.300	0.200	0.7522054
FBL	0.0000422	0.9121884	0.255	0.163	0.7622778
EIF4B	0.0000433	0.6518818	0.353	0.254	0.7825486
USP9X	0.0000574	0.5775571	0.325	0.217	1.0000000
NUCB2	0.0000607	0.6937517	0.327	0.234	1.0000000
PLEC	0.0000869	0.6631028	0.298	0.203	1.0000000
MDFIC	0.0000878	0.5085977	0.299	0.194	1.0000000
CHMP7	0.0001593	0.9015741	0.260	0.177	1.0000000
UNC13D	0.0001615	0.5600790	0.324	0.229	1.0000000
FBXL3	0.0001660	0.6329179	0.279	0.186	1.0000000
KAT6B	0.0002066	0.5453099	0.317	0.217	1.0000000
CRTAM	0.0002322	0.5782623	0.315	0.223	1.0000000
OXNAD1	0.0002402	0.6087634	0.273	0.180	1.0000000
KLRC4	0.0002404	0.7661449	0.267	0.180	1.0000000
PIK3IP1	0.0002470	0.5300839	0.319	0.226	1.0000000
SLC4A7	0.0002598	0.5447564	0.268	0.177	1.0000000
MPRIP	0.0003209	0.5578327	0.309	0.217	1.0000000
RIN3	0.0003282	0.5799150	0.298	0.209	1.0000000
SORL1	0.0004141	0.6134065	0.284	0.197	1.0000000
SERF2	0.0004483	0.5933928	0.253	0.166	1.0000000
GSK3B	0.0004940	0.5796039	0.262	0.177	1.0000000
HIST1H1D	0.0005079	0.5857983	0.349	0.254	1.0000000
PATJ	0.0005623	0.7256277	0.277	0.203	1.0000000
EIF3B	0.0005654	0.5781233	0.295	0.209	1.0000000
SMAP2	0.0006498	0.5346144	0.366	0.283	1.0000000
SATB1	0.0010624	0.5501630	0.285	0.206	1.0000000
EIF3L	0.0011622	0.5398901	0.303	0.223	1.0000000

NK cells

nk <-  subset(paed_sub, idents = "NK cells")
keep_cells <- paed_sub$cell_labels_l1 %in% "NK cells"
nk@assays$RNA@cells@.Data <- paed_sub@assays$RNA@cells@.Data[keep_cells, ]
nk

An object of class Seurat 
18076 features across 3532 samples within 1 assay 
Active assay: RNA (18076 features, 2000 variable features)
 3 layers present: data, counts, scale.data
 4 dimensional reductions calculated: pca, umap.unintegrated, umap.l1, umap.sub

out <- here("output/RDS/Lineages_RDS_combined/SEU_NK_Cells.rds")

if (!file.exists(out)) {
  nk <- nk %>%
  FindVariableFeatures() %>%
  ScaleData() %>%
  RunPCA() 
  
  gc() 
  nk <- RunUMAP(nk, dims = 1:30, reduction = "pca", reduction.name = "umap.nk")

  meta_data_columns <- colnames(nk@meta.data)
  columns_to_remove <- grep("^RNA_snn_res", meta_data_columns, value = TRUE)
  nk@meta.data <- nk@meta.data[, !(colnames(nk@meta.data) %in% columns_to_remove)]
  resolutions <- seq(0.1, 0.5, by = 0.1)
  nk <- FindNeighbors(nk, dims = 1:30, reduction = "pca")
  nk <- FindClusters(nk, resolution = resolutions )
  
  saveRDS(nk, out)

} else {
  nk <- readRDS(out)
}

  clustree(nk, prefix = "RNA_snn_res.")

DimPlot(nk, group.by = "RNA_snn_res.0.5", reduction = "umap.nk", label = TRUE, label.size = 2.5, repel = TRUE, raster = FALSE )

DimPlot(nk, group.by = "RNA_snn_res.0.5", reduction = "umap.nk", label = TRUE, label.size = 2.5, repel = TRUE, raster = FALSE, split.by = "tissue" )

opt_res <- "RNA_snn_res.0.5"  
n <- nlevels(nk$RNA_snn_res.0.5)
nk$RNA_snn_res.0.5 <- factor(nk$RNA_snn_res.0.5, levels = seq(0,n-1))
nk$seurat_clusters <- NULL
nk$cluster <- nk$RNA_snn_res.0.5
Idents(nk) <- nk$cluster

confirming NK cluster

FeaturePlot(nk, features = c("NCAM1", "KLRC1","IL18RAP","IL2RB","KLRF1","GNLY", "FCER1G", "TRDC", "NKG7", "CD160"), ncol = 3, reduction = "umap.nk", label = T, repel = T)

plot_ct_bar(nk, "celltypist_paeds") + ggtitle("Paed_Covid study")

plot_ct_bar(nk, "celltypist_lung_airway") + ggtitle("Lung Airway atlas")

plot_ct_bar(nk, "celltypist_lung_atlas") + ggtitle("Lung atlas")

plot_ct_bar(nk, "celltypist_immune") + ggtitle("Immune low dataset")

plot_ct_bar(nk, "celltypist_tonsil") + ggtitle("Tonsil atlas")

plot_ct_bar(nk, "cell_labels_v2") + ggtitle("Previous labels")

Using markers from tonsil atlas glossary for cd56hi (KLRF1, NCAM1, SELL, TCF7, GZMK and XCL1. ) and cd56low (KLRF1, FCGR3A, CX3CR1, TBX21, and PRF1). Ref- (Boldt et al., 2014; Pfefferle et al., 2019; Poli et al., 2009)

FeaturePlot(nk, features = c( "SELL", "NCAM1","FCGR3A", "CX3CR1" ), ncol = 1, split.by = "tissue", reduction = "umap.nk", label = T, repel = T)

Idents(nk) <- "RNA_snn_res.0.5"
new.cluster.ids <- c("NK cd56hi", 
                     "NK cd56hi", 
                     "NK cd56hi",
                     "NK cd56hi",
                     "NK cd56lo",
                     "NK cd56lo"
                     )
names(new.cluster.ids) <- levels(nk)
nk <- RenameIdents(nk, new.cluster.ids)
nk$cell_labels_l2 <- Idents(nk)

Subclustering NK cells clusters-

nk <- FindSubCluster(nk, cluster = "NK cd56hi", graph.name = "RNA_snn", resolution = 0.1, subcluster.name = "T_sub")

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 2843
Number of edges: 113272

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9095
Number of communities: 2
Elapsed time: 0 seconds

DimPlot(nk, raster = F, reduction = "umap.nk", label = T, group.by = "T_sub", repel = T)

Idents(nk) <- nk$T_sub
nk$cluster <- nk$T_sub

nk <- FindSubCluster(nk, cluster = "NK cd56lo", graph.name = "RNA_snn", resolution = 0.1, subcluster.name = "T_sub")

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 689
Number of edges: 27321

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9191
Number of communities: 2
Elapsed time: 0 seconds

DimPlot(nk, raster = F, reduction = "umap.nk", label = T, group.by = "T_sub", repel = T)

Idents(nk) <- nk$T_sub

Renaming idents

new_levels <- c(
     "NK cd56hi_0",
     "NK cd56hi_1",
     "NK cd56lo_0",
     "NK cd56lo_1"
)
Seurat::Idents(nk) <- factor(
  Seurat::Idents(nk),
  levels = new_levels
)

nk$cell_labels_l3 <- Idents(nk)

table(nk$cell_labels_l3, nk$tissue)

             
              Tonsils Adenoids Nasal_brushings Bronchial_brushings BAL
  NK cd56hi_0      43      135             883                 294 251
  NK cd56hi_1     439      464             222                  48  64
  NK cd56lo_0      73       90             177                  26  14
  NK cd56lo_1      37       26             204                  18  24

df_counts <- nk@meta.data |>
  count(cell_labels_l3, tissue, name = "n")
df_totals <- df_counts |>
  group_by(cell_labels_l3) |>
  summarise(total = sum(n), .groups = "drop")

ggplot(df_counts, aes(x = cell_labels_l3, y = n, fill = tissue)) +
  geom_bar(stat = "identity", position = "stack") +
  geom_text(data = df_totals,
            aes(x = cell_labels_l3, y = total, label = total),
            vjust = -0.3, size = 3, inherit.aes = FALSE) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  labs(x = "cluster at l3",
       y = "Number of cells",
       fill = "Tissue")

DE features of NK cells

The marker genes for this subclustering can be found here-

NK_population_subclusters

gc()

             used    (Mb) gc trigger    (Mb) limit (Mb)   max used    (Mb)
Ncells    4679400   250.0   10019789   535.2         NA   10019789   535.2
Vcells 1622891532 12381.7 3537421340 26988.4     163840 3537421340 26988.4

paed_sub.markers <- FindAllMarkers(
  nk,
  features = features.no.mt,
  only.pos = TRUE,
  min.pct = 0.25,
  logfc.threshold = 0.25
)

Calculating cluster NK cd56hi_0

Calculating cluster NK cd56hi_1

Calculating cluster NK cd56lo_0

Calculating cluster NK cd56lo_1

gc()

             used    (Mb) gc trigger    (Mb) limit (Mb)   max used    (Mb)
Ncells    4678414   249.9   10019789   535.2         NA   10019789   535.2
Vcells 1622879374 12381.6 3537421340 26988.4     163840 3537421340 26988.4

top10 <- paed_sub.markers %>%
    group_by(cluster) %>%
    top_n(n = 10, wt = avg_log2FC) %>%
    ungroup() %>%
    distinct(gene, .keep_all = TRUE) %>%
    arrange(cluster, desc(avg_log2FC))


best.wilcox.gene.per.cluster <- paed_sub.markers |>
  group_by(cluster) |>
  slice_max(avg_log2FC, n = 1) |>
  select(cluster, gene)
best.wilcox.gene.per.cluster

# A tibble: 4 × 2
# Groups:   cluster [4]
  cluster     gene 
  <fct>       <chr>
1 NK cd56hi_0 AREG 
2 NK cd56hi_1 GZMK 
3 NK cd56lo_0 CD3G 
4 NK cd56lo_1 SPON2

FeaturePlot(
  nk,
  features = best.wilcox.gene.per.cluster$gene,
  reduction = "umap.nk",
  raster = FALSE,
  repel = TRUE,
  ncol = 1,
  label = TRUE,
  split.by = "tissue"
)

nk <- ScaleData(
  nk,
  features = unique(top10$gene),
  assay = "RNA",
  verbose = FALSE
)

Warning: Different features in new layer data than already exists for
scale.data

DoHeatmap(nk, features = unique(top10$gene), angle = 90) + NoLegend()

## Seurat top markers
cluster_colors <- paletteer::paletteer_d("pals::glasbey")[factor(top10$cluster)]

DotPlot(nk,    
        features = unique(top10$gene),
        group.by = "cell_labels_l3",
        cols = c("azure1", "blueviolet"),
        dot.scale = 3, assay = "RNA") +
    RotatedAxis() +
    FontSize(y.text = 8, x.text = 12) +
    labs(y = element_blank(), x = element_blank()) +
    coord_flip() +
    theme(axis.text.y = element_text(color = cluster_colors)) +
    ggtitle("Top 10 marker genes per cluster (Seurat)")

Warning: Scaling data with a low number of groups may produce misleading
results

Warning: Vectorized input to `element_text()` is not officially supported.
ℹ Results may be unexpected or may change in future versions of ggplot2.

marker_counts <- paed_sub.markers %>%
    filter(avg_log2FC > 1 & p_val_adj < 0.05) %>%  
    count(cluster, name = "n_markers") %>%
    arrange(desc(n_markers))

print(marker_counts)

      cluster n_markers
1 NK cd56lo_0       118
2 NK cd56lo_1       102
3 NK cd56hi_0        43
4 NK cd56hi_1        23

ggplot(marker_counts, aes(x = reorder(cluster, n_markers), y = n_markers)) +
    geom_col(fill = "steelblue", alpha = 0.8) +
    coord_flip() +
    labs(title = "High-confidence markers per subcluster (logFC > 1)",
         x = "Subcluster", y = "Number of markers") +
    theme_minimal()

out_markers <- here("output", 
            "CSV_v3", "T_cell_lineage",
            paste("Marker_genes_Reclustered_NK.",opt_res, sep = ""))

dir.create(out_markers, recursive = TRUE, showWarnings = FALSE)

for (cl in unique(paed_sub.markers$cluster)) {
  cluster_data <- paed_sub.markers %>% dplyr::filter(cluster == cl)
  file_name <- here(out_markers, paste0("G000231_Neeland_", cl, ".csv"))
  if (!file.exists(file_name)) {
  write.csv(cluster_data, file = file_name)
  }
}

Pairwise markers within NK

ids <- c("NK cd56hi_0","NK cd56hi_1","NK cd56lo_0", "NK cd56lo_1")

res_all <- run_de_and_vln(
  nk,
  ids,
  title_prefix = "NK",
  run_one_vs_all = TRUE,
  run_pairwise = TRUE,
  print_table = TRUE
)

NK : NK cd56hi_0 vs_rest

Top 50 markers: NK cd56hi_0 vs_rest
	avg_log2FC	pct.1	pct.2
GNLY	0.9635459	0.955	0.843
ITGAX	1.5924644	0.807	0.510
TNFRSF18	2.0340875	0.664	0.355
CD7	0.7856426	0.906	0.823
DDIT4	1.0522767	0.756	0.643
AREG	2.5707331	0.364	0.148
SLC16A3	1.5336129	0.530	0.349
BHLHE40	1.2258474	0.672	0.555
COTL1	1.2158336	0.603	0.482
TCF7	0.8263857	0.754	0.586
OAS3	2.4685941	0.356	0.182
SLC7A5	2.2074234	0.273	0.113
LY6E	0.8131478	0.771	0.756
IFI6	2.2956061	0.433	0.288
IFI44L	1.7696652	0.352	0.185
HAPLN3	1.9376872	0.318	0.158
FES	1.9916444	0.365	0.217
CD300A	0.6467131	0.807	0.776
SOCS1	1.4035974	0.502	0.390
ISG15	2.3758763	0.451	0.333
IRF7	1.5321791	0.495	0.387
CTSW	0.5486797	0.846	0.790
MX1	2.0844371	0.328	0.183
BCL3	1.2945546	0.452	0.323
IFI44	1.5524959	0.382	0.247
KIR2DL4	1.6355629	0.309	0.171
METRNL	1.1733319	0.460	0.328
CMPK2	2.1133313	0.273	0.141
FURIN	1.3169598	0.468	0.361
IRF8	1.6957839	0.448	0.365
ISG20	1.4545205	0.505	0.447
ZNF683	1.1989704	0.337	0.200
ECE1	1.3176874	0.367	0.247
TYMP	1.4770338	0.284	0.162
CD74	0.5518358	0.793	0.801
CAPG	1.1788197	0.407	0.300
FOSL2	1.2709003	0.502	0.431
MX2	1.8580702	0.261	0.151
SH2D2A	0.9682112	0.517	0.450
CD38	1.0960373	0.465	0.367
IFITM3	1.5521447	0.341	0.234
CD63	0.8801965	0.502	0.425
ADGRG3	1.3031317	0.271	0.166
APOBR	0.6527515	0.647	0.611
GZMB	1.7909518	0.534	0.516
DUSP2	0.9898990	0.514	0.452
ADCY3	1.1745832	0.303	0.205
TCIRG1	0.7106430	0.590	0.561
IFI35	1.3322944	0.288	0.205
SBNO2	0.9203707	0.440	0.389

NK : NK cd56hi_1 vs_rest

Top 50 markers: NK cd56hi_1 vs_rest
	avg_log2FC	pct.1	pct.2
KLRF1	1.5573653	0.741	0.299
GZMK	1.7221521	0.570	0.196
TRDC	0.9690601	0.866	0.607
KLRB1	1.5444432	0.542	0.200
PDCD4	0.9134262	0.821	0.499
KLRK1	0.8698496	0.850	0.632
SPTSSB	1.5872196	0.379	0.106
PNRC1	0.8479078	0.842	0.578
EIF1	1.1116061	0.581	0.250
TC2N	1.2135025	0.466	0.180
CMC1	1.3809471	0.353	0.107
SMURF2	1.0834193	0.459	0.174
SESN1	1.4963536	0.319	0.094
RESF1	0.8468906	0.686	0.363
GPR183	1.0829600	0.502	0.215
CLDND1	1.0051534	0.670	0.382
HELZ	1.1089372	0.444	0.176
KLRD1	0.6503707	0.889	0.728
CXCR6	1.6119864	0.317	0.106
AKT3	0.9950517	0.383	0.138
GLCCI1	0.8912384	0.458	0.193
SLC38A2	0.9851124	0.546	0.277
BTG1	0.5793703	0.900	0.763
FOSB	1.1567669	0.379	0.153
STT3B	0.7848774	0.566	0.275
ATP8B4	0.7341912	0.513	0.235
TMEM123	1.0347217	0.357	0.134
ETS1	0.5945653	0.836	0.595
EOMES	0.8208624	0.546	0.277
GNAQ	0.8616725	0.390	0.156
IL18RAP	0.8434395	0.535	0.275
CD244	0.8394934	0.475	0.222
AHR	1.3451976	0.262	0.083
SLC38A1	0.6466217	0.694	0.407
OCIAD1	0.9300849	0.377	0.155
IL18R1	1.1091435	0.292	0.100
ATM	0.6700163	0.736	0.474
RBMS1	0.8270045	0.387	0.160
DYRK2	0.6918165	0.580	0.300
CLK1	0.6725852	0.581	0.302
DENND4C	0.9781347	0.353	0.141
ZBTB16	0.9938360	0.406	0.187
STK17A	0.5945822	0.778	0.518
IL7R	1.0522505	0.465	0.241
SELL	1.1969111	0.607	0.491
PCM1	0.7097132	0.525	0.268
DDX3X	0.9005694	0.386	0.172
JUN	1.1940077	0.483	0.264
SPRY2	1.0073283	0.357	0.151
TAF7	0.7497056	0.482	0.238

NK : NK cd56lo_0 vs_rest

Top 50 markers: NK cd56lo_0 vs_rest
	avg_log2FC	pct.1	pct.2
CD3D	5.4626399	0.876	0.034
CD3G	6.0217708	0.655	0.016
THEMIS	5.7692517	0.421	0.011
CD5	6.0162392	0.395	0.009
CD6	4.2894935	0.542	0.038
CX3CR1	2.6206366	0.729	0.096
TRAC	3.7427856	0.753	0.158
FGFBP2	2.8211700	0.689	0.106
CD8A	3.8598951	0.674	0.127
GZMH	2.9102081	0.721	0.153
SIT1	5.6428377	0.268	0.004
KLRG1	3.0734217	0.592	0.111
SYNE2	2.2176616	0.821	0.257
GRAP2	3.1662385	0.426	0.058
CCL5	1.7768141	0.942	0.459
BCL11B	2.7048710	0.518	0.103
ADGRG1	1.8879933	0.776	0.263
ZEB2	1.9983800	0.784	0.285
LAG3	2.6669350	0.384	0.054
TOB1	3.0512877	0.424	0.074
KLF2	1.5149220	0.921	0.475
S100A4	2.0531443	0.745	0.306
IL32	1.4073109	0.895	0.424
TRBC2	2.3219907	0.834	0.494
FLNA	1.5450936	0.858	0.490
SYNE1	1.7632797	0.726	0.281
CD3E	1.5680540	0.813	0.411
S100A6	1.6878688	0.747	0.307
ADRB2	2.1601660	0.521	0.145
GNG2	1.7818132	0.587	0.176
S1PR5	1.6149960	0.511	0.128
CD52	1.5310381	0.871	0.566
FCRL6	1.7791501	0.605	0.213
B2M	0.5844334	1.000	0.996
C12orf75	2.2721660	0.389	0.095
ARL4C	1.3255903	0.850	0.510
P2RY8	1.9324808	0.476	0.144
AHNAK	1.2251219	0.882	0.529
FAM53B	1.9637690	0.453	0.132
CST7	1.1619249	0.882	0.620
ANXA1	1.4166302	0.708	0.339
LIME1	2.5741703	0.276	0.053
CAPN2	1.5229013	0.639	0.276
PYHIN1	1.2720305	0.713	0.344
ATXN1	1.7209839	0.439	0.133
PRSS23	2.1487258	0.358	0.099
RAP1GAP2	1.9983582	0.353	0.093
LAPTM5	0.9981412	0.895	0.687
MYO1G	1.4831338	0.666	0.337
TGFBR3	1.6360026	0.458	0.157

NK : NK cd56lo_1 vs_rest

Top 50 markers: NK cd56lo_1 vs_rest
	avg_log2FC	pct.1	pct.2
SPON2	4.1171917	0.718	0.089
CX3CR1	3.4509349	0.728	0.110
S1PR5	3.0110761	0.686	0.120
ADGRG1	2.6136091	0.845	0.267
FCGR3A	2.7810247	0.770	0.207
FGFBP2	2.8904136	0.625	0.125
EFHD2	1.7048356	0.958	0.680
SYNE1	1.8989219	0.773	0.286
ZEB2	1.9944156	0.777	0.297
NKG7	0.9143751	1.000	0.945
PRSS23	3.0417412	0.453	0.096
PRDM1	2.3499142	0.466	0.106
KLF2	1.5006227	0.893	0.488
RAP1GAP2	2.6495064	0.424	0.092
ADRB2	2.2348141	0.531	0.152
CLIC3	1.7879075	0.706	0.278
ARL4C	1.5078077	0.867	0.516
PRF1	0.9604919	0.994	0.856
GNG2	2.0008168	0.553	0.189
CD247	1.2006334	0.919	0.678
TBX21	1.4174244	0.754	0.446
ITGB2	1.2930273	0.851	0.626
FLNA	1.3243035	0.796	0.504
THEMIS2	1.8432631	0.557	0.216
FCRL6	1.6094895	0.589	0.223
CYBA	1.2198372	0.880	0.632
SYNE2	1.5100079	0.644	0.286
PXN	1.6441867	0.660	0.318
CST7	1.1439294	0.851	0.628
NMUR1	1.7502498	0.508	0.186
PYHIN1	1.4696601	0.696	0.353
CCL4	1.2567280	0.521	0.191
PLEK	1.6866859	0.628	0.323
GZMM	1.4159001	0.589	0.271
AOAH	1.2262500	0.738	0.442
PRR5L	1.5961778	0.544	0.250
TXNIP	0.8091283	0.955	0.806
LPCAT1	1.2404869	0.706	0.441
RORA	1.6416646	0.421	0.155
TNFRSF1A	1.5388022	0.453	0.178
HIPK2	1.6597651	0.472	0.209
MAF	2.0092964	0.311	0.096
CHST2	1.5065002	0.576	0.311
LBH	1.4449718	0.531	0.254
RASGRP2	1.6731049	0.515	0.259
TGFBR3	1.6985583	0.408	0.168
GZMH	1.3271666	0.453	0.191
TMEM71	1.8556843	0.327	0.114
MYBL1	1.7762530	0.411	0.177
OSBPL5	1.7559132	0.333	0.123

NK : NK cd56hi_0 vs NK cd56hi_1

Top 50 markers: NK cd56hi_0 vs NK cd56hi_1
	avg_log2FC	pct.1	pct.2	p_val_adj
GNLY	0.7627458	0.955	0.848	0.00e+00
ITGAX	1.1931980	0.807	0.617	0.00e+00
TNFRSF18	1.4619009	0.664	0.523	0.00e+00
DDIT4	1.0929682	0.756	0.665	0.00e+00
WFDC2	0.9146616	0.366	0.110	0.00e+00
IFI6	2.6680008	0.433	0.252	0.00e+00
OAS3	2.5619326	0.356	0.196	0.00e+00
BHLHE40	1.0931371	0.672	0.614	0.00e+00
ISG15	2.6498944	0.451	0.319	0.00e+00
GZMB	2.1765202	0.534	0.416	0.00e+00
TMSB10	0.7559275	0.776	0.756	0.00e+00
METRNL	1.4992032	0.460	0.313	0.00e+00
LY6E	0.8272690	0.771	0.778	0.00e+00
AREG	2.0986468	0.364	0.212	0.00e+00
FOSL2	1.6132037	0.502	0.395	0.00e+00
SLC16A3	1.2411910	0.530	0.438	0.00e+00
ZNF683	1.5890405	0.337	0.181	0.00e+00
ISG20	1.6970192	0.505	0.435	0.00e+00
MX1	2.1737925	0.328	0.188	0.00e+00
SH2D2A	1.2796797	0.517	0.432	0.00e+00
IRF7	1.5503206	0.495	0.408	0.00e+00
CD300A	0.6018980	0.807	0.808	0.00e+00
CD74	0.6503757	0.793	0.828	0.00e+00
CFL1	0.5683340	0.776	0.809	0.00e+00
IFI44L	1.6395725	0.352	0.220	0.00e+00
AKNA	0.5475574	0.827	0.888	0.00e+00
SLC7A5	1.9282968	0.273	0.144	0.00e+00
CMPK2	2.2616764	0.273	0.149	0.00e+00
COTL1	0.9016050	0.603	0.588	0.00e+00
CD38	1.3235908	0.465	0.371	0.00e+00
MX2	2.0073329	0.261	0.154	0.00e+00
IFI44	1.4338753	0.382	0.286	0.00e+00
BCL3	1.1668055	0.452	0.373	0.00e+00
SOCS1	1.1431800	0.502	0.460	0.00e+00
TYMP	1.4484319	0.284	0.171	0.00e+00
RUNX3	0.8533951	0.570	0.546	0.00e+00
FES	1.6645361	0.365	0.293	0.00e+00
OAS1	1.8842186	0.265	0.168	0.00e+00
HAPLN3	1.4358012	0.318	0.227	0.00e+00
IFI35	1.5261624	0.288	0.203	0.00e+00
TAPBP	0.5272999	0.714	0.774	1.00e-07
PRF1	0.6854746	0.820	0.880	2.00e-07
S100A6	0.8744806	0.328	0.233	8.00e-07
FURIN	1.0051297	0.468	0.442	1.30e-06
CHST12	0.6945107	0.588	0.618	1.40e-06
TAP2	0.9241543	0.460	0.437	2.20e-06
SYK	0.5967236	0.191	0.337	2.30e-06
DRAP1	0.6408093	0.609	0.654	7.90e-06
DUSP2	0.9816449	0.514	0.496	1.47e-05
ECE1	0.9929637	0.367	0.306	1.79e-05

NK : NK cd56hi_0 vs NK cd56lo_0

Top 50 markers: NK cd56hi_0 vs NK cd56lo_0
	avg_log2FC	pct.1	pct.2
IL2RB	2.7595549	0.963	0.679
CD7	2.4660976	0.906	0.534
ITGAX	4.1762779	0.807	0.158
GNLY	1.7111853	0.955	0.771
TNFRSF18	6.1266653	0.664	0.032
FCER1G	4.6732994	0.631	0.047
TCF7	2.0997360	0.754	0.384
TYROBP	3.1199183	0.576	0.095
CTSW	1.4192714	0.846	0.684
SLC16A3	3.3230478	0.530	0.105
KLRC1	2.8633474	0.514	0.124
NCAM1	4.7541622	0.418	0.037
CD300A	1.3627237	0.807	0.624
CAPG	5.0604716	0.407	0.032
NCR1	2.4846267	0.537	0.158
IRF8	4.3510365	0.448	0.076
DDIT4	1.5705410	0.756	0.497
AREG	7.4943160	0.364	0.005
FES	5.7525973	0.365	0.011
FURIN	3.1612562	0.468	0.126
SH2D1B	4.0398801	0.366	0.034
APOBR	1.7441431	0.647	0.361
CD44	1.2395485	0.804	0.695
FOS	2.4944118	0.575	0.276
DTX1	5.0219327	0.342	0.026
BHLHE40	1.6069457	0.672	0.411
TXK	2.7896338	0.432	0.111
KIR2DL4	6.2720057	0.309	0.011
KLRC3	1.9068851	0.545	0.239
SRGN	0.9530188	0.875	0.855
PLCG2	3.1244366	0.441	0.153
HAPLN3	4.1282517	0.318	0.034
ATP8B4	4.8280710	0.301	0.026
TRDC	0.8613179	0.688	0.268
SOCS1	2.3374260	0.502	0.242
ADGRG3	5.6523748	0.271	0.008
IFITM3	3.4697752	0.341	0.063
GSN	3.6420120	0.300	0.042
IFI44L	2.8113682	0.352	0.084
KLHL6	2.8179461	0.364	0.103
XCL2	3.6639196	0.321	0.066
COTL1	1.5317758	0.603	0.400
LTBP3	2.4171123	0.384	0.118
HVCN1	4.2328160	0.273	0.029
GFOD1	2.1372669	0.430	0.171
CD63	1.6952780	0.502	0.242
TCIRG1	1.5306015	0.590	0.392
CHST2	2.7623135	0.329	0.076
RIN3	1.2984973	0.627	0.466
ITM2C	2.6184931	0.364	0.121

NK : NK cd56hi_0 vs NK cd56lo_1

Top 50 markers: NK cd56hi_0 vs NK cd56lo_1
	avg_log2FC	pct.1	pct.2	p_val_adj
TCF7	3.1830196	0.754	0.165	0e+00
TNFRSF18	4.2797863	0.664	0.078	0e+00
CD44	2.1436379	0.804	0.411	0e+00
GNLY	1.1162135	0.955	0.909	0e+00
ITGAX	2.0094265	0.807	0.518	0e+00
COTL1	3.1094857	0.603	0.162	0e+00
CD7	1.2003904	0.906	0.783	0e+00
IL2RB	0.8946725	0.963	0.935	0e+00
CAPG	4.7661550	0.407	0.013	0e+00
ITM2C	4.6562878	0.364	0.023	0e+00
FOS	2.2394676	0.575	0.278	0e+00
DTX1	3.8100691	0.342	0.042	0e+00
CXCR3	2.4643504	0.401	0.091	0e+00
ADCY3	5.0677076	0.303	0.023	0e+00
HAPLN3	3.6649384	0.318	0.036	0e+00
AREG	3.2030665	0.364	0.071	0e+00
KIR2DL4	3.5653372	0.309	0.029	0e+00
CASK	3.1542480	0.321	0.061	0e+00
BHLHE40	1.3762318	0.672	0.495	0e+00
KLRC1	1.5344230	0.514	0.236	0e+00
KLHL6	2.3427761	0.364	0.094	0e+00
CD2	1.7897218	0.535	0.304	0e+00
SLC16A3	1.5667627	0.530	0.291	0e+00
SLC7A5	3.5504051	0.273	0.039	0e+00
ADGRG3	3.2045306	0.271	0.036	0e+00
IL7R	2.9485107	0.278	0.042	0e+00
TNFSF10	2.1347517	0.384	0.129	0e+00
GPR183	3.1867743	0.258	0.036	0e+00
ZNF683	2.0974325	0.337	0.094	0e+00
SOCS1	1.7326345	0.502	0.294	0e+00
MROH1	2.7872089	0.306	0.081	0e+00
CRTAM	3.2147751	0.281	0.058	0e+00
BCL3	1.5572886	0.452	0.223	0e+00
OAS3	2.2777705	0.356	0.136	0e+00
FXYD5	0.9564604	0.730	0.631	0e+00
TRAF5	1.4640735	0.425	0.204	0e+00
RAB3GAP1	2.5378209	0.255	0.055	0e+00
IRF8	1.7631289	0.448	0.243	0e+00
ECE1	1.9156375	0.367	0.162	0e+00
KLRC3	1.2822337	0.545	0.353	0e+00
NCAM1	1.5649691	0.418	0.207	0e+00
FES	1.8207151	0.365	0.162	0e+00
SLC7A6	1.6306553	0.384	0.178	0e+00
ACAP1	0.8816851	0.663	0.502	0e+00
HVCN1	2.0354298	0.273	0.078	0e+00
PDCD4	1.2127037	0.532	0.353	0e+00
UNC93B1	1.7925243	0.294	0.097	0e+00
IFITM3	2.1285061	0.341	0.155	0e+00
XCL2	1.8855011	0.321	0.126	0e+00
CD74	0.6280517	0.793	0.728	2e-07

NK : NK cd56hi_1 vs NK cd56lo_0

Top 50 markers: NK cd56hi_1 vs NK cd56lo_0
	avg_log2FC	pct.1	pct.2
IL2RB	2.734264	0.983	0.679
FCER1G	4.697998	0.744	0.047
CD7	2.029654	0.922	0.534
KLRF1	3.816683	0.741	0.089
SH2D1B	4.354024	0.597	0.034
TRDC	1.664352	0.866	0.268
TXK	3.087328	0.655	0.111
KLRC1	3.062632	0.655	0.124
TYROBP	2.904630	0.642	0.095
NCR1	2.440570	0.688	0.158
ATP8B4	5.149312	0.513	0.026
TNFRSF18	4.664765	0.523	0.032
NCAM1	4.596190	0.517	0.037
ITGAX	2.983080	0.617	0.158
PLCG2	2.449727	0.655	0.153
FOS	2.647600	0.678	0.276
KLRC3	2.007561	0.695	0.239
PDCD4	1.510630	0.821	0.476
CAPG	4.482101	0.454	0.032
TCF7	1.739380	0.753	0.384
DTX1	4.704041	0.439	0.026
XCL2	3.697926	0.465	0.066
KLRD1	1.075979	0.889	0.668
SPTSSB	7.135077	0.379	0.005
CD44	1.144775	0.897	0.695
IRF8	3.000984	0.484	0.076
LTBP3	2.422517	0.521	0.118
KLHL6	2.631775	0.493	0.103
BTG1	0.966499	0.900	0.729
GNAQ	3.693575	0.390	0.045
GSN	4.180076	0.375	0.042
CXXC5	2.697700	0.418	0.066
ITM2C	3.170957	0.463	0.121
DLL1	4.881411	0.336	0.018
GZMK	1.813765	0.570	0.211
SMURF2	2.532144	0.459	0.108
IL18RAP	1.878670	0.535	0.182
SPRY2	3.689242	0.357	0.039
SYK	3.428934	0.337	0.026
HOPX	1.273112	0.686	0.308
TRAF5	1.582032	0.582	0.242
CLDND1	1.426949	0.670	0.355
HVCN1	3.278774	0.328	0.029
SLC16A3	2.081857	0.438	0.105
FES	4.088061	0.293	0.011
IL12RB2	2.819564	0.366	0.061
GFOD1	1.777367	0.502	0.171
CTSW	1.030512	0.810	0.684
FURIN	2.156127	0.442	0.126
RIN3	1.147286	0.732	0.466

NK : NK cd56hi_1 vs NK cd56lo_1

Top 50 markers: NK cd56hi_1 vs NK cd56lo_1
	avg_log2FC	pct.1	pct.2
CD44	2.0488644	0.897	0.411
TCF7	2.8226640	0.753	0.165
GZMK	4.8569824	0.570	0.023
PDCD4	1.8931548	0.821	0.353
GPR183	3.9540214	0.502	0.036
ITM2C	5.2087518	0.463	0.023
FOS	2.3926557	0.678	0.278
CAPG	4.1877846	0.454	0.013
IL2RB	0.8693816	0.983	0.935
TRDC	1.2258280	0.866	0.599
TNFRSF18	2.8178854	0.523	0.078
CD2	2.1604633	0.676	0.304
IL7R	3.8882241	0.465	0.042
COTL1	2.2078807	0.588	0.162
CASK	3.1598205	0.469	0.061
CLDND1	1.9031530	0.670	0.262
KLRC1	1.7337071	0.655	0.236
DTX1	3.4921771	0.439	0.042
SPTSSB	4.7831664	0.379	0.013
KLHL6	2.1566051	0.493	0.094
BTG1	0.8983236	0.900	0.715
SMURF2	2.4496821	0.459	0.100
ATP8B4	1.8770414	0.513	0.149
KLRC3	1.3829093	0.695	0.353
CXCR6	5.2830178	0.317	0.013
SELL	2.0303390	0.607	0.350
TRAF5	1.3842242	0.582	0.204
TBL1XR1	1.8754740	0.475	0.126
CXCR3	2.0079696	0.441	0.091
XCL2	1.9195073	0.465	0.126
GNAQ	2.2210164	0.390	0.071
LTB	2.5117626	0.427	0.110
STT3B	1.4828080	0.566	0.210
SPRY2	2.8750856	0.357	0.052
CD7	0.7639463	0.922	0.783
PNRC1	0.8247013	0.842	0.625
STK17A	1.0464263	0.778	0.485
STRBP	2.5046401	0.344	0.045
KLRK1	0.8336911	0.850	0.660
ADCY3	4.4320005	0.293	0.023
RAB3GAP1	2.4256762	0.344	0.055
PATJ	2.4917312	0.335	0.049
FUT8	2.6472645	0.333	0.049
RUNX2	2.6009510	0.325	0.042
CRTAM	2.6111758	0.346	0.058
RESF1	1.0639006	0.686	0.359
DNMT3A	1.5028589	0.484	0.155
FOSB	2.4486495	0.379	0.091
NCAM1	1.4069969	0.517	0.207
SLC38A2	1.3604312	0.546	0.236

NK : NK cd56lo_0 vs NK cd56lo_1

Top 50 markers: NK cd56lo_0 vs NK cd56lo_1
	avg_log2FC	pct.1	pct.2	p_val_adj
CD3D	4.8121477	0.876	0.049	0.0000000
TRAC	4.4544090	0.753	0.081	0.0000000
CD3G	4.1288542	0.655	0.058	0.0000000
CD52	2.2098790	0.871	0.388	0.0000000
CD3E	2.0657916	0.813	0.220	0.0000000
CD8A	3.5132926	0.674	0.136	0.0000000
IL32	1.6124781	0.895	0.405	0.0000000
THEMIS	6.5814431	0.421	0.006	0.0000000
CD5	7.0396775	0.395	0.006	0.0000000
CD6	2.7118380	0.542	0.120	0.0000000
TRBC2	1.9555148	0.834	0.531	0.0000000
CCL5	1.0421169	0.942	0.650	0.0000000
CD27	5.0549852	0.321	0.013	0.0000000
KLRG1	1.8453725	0.592	0.217	0.0000000
CD2	1.6095582	0.642	0.304	0.0000000
LAG3	2.2538522	0.384	0.065	0.0000000
TOB1	2.3752977	0.424	0.100	0.0000000
TRGC2	2.1823778	0.495	0.172	0.0000000
SIT1	5.0436695	0.268	0.010	0.0000000
CCR5	5.3134266	0.250	0.006	0.0000000
CD84	3.8783300	0.258	0.016	0.0000000
TMSB4X	0.5020123	0.992	0.981	0.0000000
CNN2	1.2006178	0.647	0.340	0.0000000
VIM	1.2256877	0.711	0.437	0.0000000
ZNF683	2.0030819	0.350	0.094	0.0000000
GZMH	0.9954123	0.721	0.453	0.0000000
LY9	1.6127613	0.345	0.091	0.0000000
S100A10	1.2577216	0.529	0.243	0.0000000
TC2N	1.6772186	0.434	0.178	0.0000000
GPR171	2.0978150	0.271	0.052	0.0000000
GRAP2	1.4104413	0.426	0.175	0.0000000
S100A6	0.8208658	0.747	0.495	0.0000000
LAPTM5	0.6463738	0.895	0.761	0.0000000
CD44	0.9040894	0.695	0.411	0.0000000
COTL1	1.5777099	0.400	0.162	0.0000001
RESF1	0.9533270	0.603	0.359	0.0000002
ANXA1	0.8931180	0.708	0.485	0.0000004
LCK	0.7462358	0.639	0.359	0.0000025
BCL11B	1.0768484	0.518	0.285	0.0000038
PLCG1	1.2264092	0.355	0.139	0.0000075
PTMS	1.6513395	0.255	0.071	0.0000080
TCF7	1.0832837	0.384	0.165	0.0000157
CLEC2D	0.6532796	0.763	0.576	0.0000407
ARHGAP15	0.7812397	0.650	0.440	0.0000478
IKZF3	0.6576730	0.716	0.515	0.0001053
CD48	0.8210215	0.608	0.421	0.0001121
ANXA5	1.4166942	0.253	0.078	0.0001185
GIMAP4	0.6567529	0.718	0.537	0.0001287
RPTOR	1.1387905	0.334	0.136	0.0001854
EIF3H	1.2330103	0.329	0.136	0.0001886

Overview of l1,l2 and l3

paed_sub$cell_labels_l2 <- as.character(paed_sub$cell_labels_l1)
paed_sub$cell_labels_l3 <- as.character(paed_sub$cell_labels_l2)

for (obj in list(cd4_t, cd8_t, nk)) {
  cells <- intersect(colnames(paed_sub), colnames(obj))
  paed_sub$cell_labels_l2[cells] <- as.character(obj$cell_labels_l2[cells])
  paed_sub$cell_labels_l3[cells] <- as.character(obj$cell_labels_l3[cells])
}

paed_sub$cell_labels_l2 <- factor(paed_sub$cell_labels_l2)
paed_sub$cell_labels_l3 <- factor(paed_sub$cell_labels_l3)

DimPlot(paed_sub, reduction = "umap.sub", raster = F, repel = T, label = T, group.by = "lineage")

DimPlot(paed_sub, reduction = "umap.sub", raster = F, repel = T, label = T, group.by = "cell_labels_l1")

DimPlot(paed_sub, reduction = "umap.sub", raster = F, repel = T, label = T, group.by = "cell_labels_l3")

paed_sub$cell_labels_l2 <- factor(
  paed_sub$cell_labels_l2,
  levels = c(
    "Pre T","Cycling T",
    "CD4 T Naive","CD8 T Naive",
    "CD4 T Memory","CD8 T Memory","CD8 TRM/EM",
    "CD4 T Effector","CD4 T Regulatory",
    "Gamma-delta T","Double Negative T","MAIT cells",
    "NK cd56hi","NK cd56lo","ILC"
  )
)
DimPlot(paed_sub, reduction = "umap.sub", raster = F, repel = T, label = T, group.by = "cell_labels_l2")

DimPlot(paed_sub, reduction = "umap.sub", raster = F, repel = T, label = T, group.by = "cell_labels_l2", split.by = "tissue")

paed_sub$cell_labels_l3 <- factor(
  paed_sub$cell_labels_l3,
  levels = c(
    "Pre T","Cycling T",
    "CD4 T Naive_0","CD4 T Naive_1","CD4 T Naive_2",
    "CD8 T Naive_0","CD8 T Naive_1",
    "CD4 T Memory_0","CD4 T Memory_1","CD4 T Memory_2","CD4 T Memory_3",
    "CD8 T Memory_0","CD8 T Memory_1","CD8 T Memory_2","CD8 T Memory_3",
    "CD8 TRM/EM_0","CD8 TRM/EM_1",
    "CD4 T Effector_0","CD4 T Effector_1",
    "CD4 T Regulatory_0","CD4 T Regulatory_1","CD4 T Regulatory_2",
    "Gamma-delta T","Double Negative T","MAIT cells",
    "NK cd56hi_0","NK cd56hi_1","NK cd56lo_0","NK cd56lo_1",
    "ILC"
  )
)

DimPlot(paed_sub, reduction = "umap.sub", raster = F, repel = T, label = T, group.by = "cell_labels_l3")

paed_sub@meta.data %>%
  count(cell_labels_l3, tissue) %>%
  group_by(cell_labels_l3) %>%
  mutate(total = sum(n)) %>%
  ungroup() %>%
  #mutate(cell_labels_l3 = reorder(cell_labels_l3, total)) %>%
  ggplot(aes(x = cell_labels_l3, y = n, fill = tissue)) +
  geom_col() +
  geom_text(
    data = function(df) distinct(df, cell_labels_l3, total),
    aes(x = cell_labels_l3, y = total, label = total),
    inherit.aes = FALSE,
    hjust = -0.1,
    size = 3
  ) +
  coord_flip() +
  theme_classic() +
  labs(
    x = NULL,
    y = "Cell count",
    fill = "Tissue",
    title = "Cell counts by L3 cell type (stacked by tissue)"
  )

paed_sub@meta.data %>%
    count(cell_labels_l3, donor) %>%
    group_by(cell_labels_l3) %>%
    mutate(prop = n / sum(n)) %>%  # proportion per L3
    ungroup() %>%
    ggplot(aes(x = cell_labels_l3, y = prop, fill = donor)) +
    geom_col(width = 0.9) +
    scale_y_continuous(labels = scales::percent) +
    scale_fill_viridis_d(option = "turbo", guide = guide_legend(ncol = 2)) +
    theme_classic() +
    theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
    labs(
        x = NULL,
        y = "Proportion",
        fill = "Donor",
        title = "Donor composition per L3 cell type"
    )

Save RDS

#saveRDS(paed_sub, "../EarlyAir_paper/output/RDS/Annotated_lineages/SEU_T_NK_Cells_annotated.rds")

Session Info

sessionInfo()

R version 4.3.3 (2024-02-29)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 15.7.3

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Australia/Melbourne
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] gtools_3.9.5        presto_1.0.0        circlize_0.4.16    
 [4] BiocParallel_1.36.0 harmony_1.2.3       Rcpp_1.0.14        
 [7] patchwork_1.3.0     cowplot_1.1.3       data.table_1.17.2  
[10] RColorBrewer_1.1-3  kableExtra_1.4.0    ggridges_0.5.6     
[13] ggforce_0.4.2       viridis_0.6.5       viridisLite_0.4.2  
[16] paletteer_1.6.0     clustree_0.5.1      ggraph_2.2.1       
[19] Seurat_5.0.3        SeuratObject_5.1.0  sp_2.2-0           
[22] here_1.0.1          lubridate_1.9.4     forcats_1.0.0      
[25] stringr_1.5.1       dplyr_1.1.4         purrr_1.0.4        
[28] readr_2.1.5         tidyr_1.3.1         tibble_3.2.1       
[31] ggplot2_3.5.2       tidyverse_2.0.0     BiocStyle_2.30.0   
[34] workflowr_1.7.1    

loaded via a namespace (and not attached):
  [1] RcppAnnoy_0.0.22       splines_4.3.3          later_1.4.2           
  [4] prismatic_1.1.2        polyclip_1.10-7        fastDummies_1.7.5     
  [7] lifecycle_1.0.4        rprojroot_2.0.4        globals_0.17.0        
 [10] processx_3.8.6         lattice_0.22-5         MASS_7.3-60.0.1       
 [13] backports_1.5.0        magrittr_2.0.3         limma_3.58.1          
 [16] plotly_4.10.4          sass_0.4.10            rmarkdown_2.29        
 [19] jquerylib_0.1.4        yaml_2.3.10            httpuv_1.6.16         
 [22] sctransform_0.4.2      spam_2.11-1            spatstat.sparse_3.1-0 
 [25] reticulate_1.42.0      pbapply_1.7-2          abind_1.4-8           
 [28] Rtsne_0.17             tweenr_2.0.3           git2r_0.36.2          
 [31] ggrepel_0.9.6          irlba_2.3.5.1          listenv_0.9.1         
 [34] spatstat.utils_3.1-4   pheatmap_1.0.12        goftest_1.2-3         
 [37] RSpectra_0.16-2        spatstat.random_3.4-1  fitdistrplus_1.2-2    
 [40] parallelly_1.44.0      svglite_2.2.1          leiden_0.4.3.1        
 [43] codetools_0.2-19       xml2_1.3.8             tidyselect_1.2.1      
 [46] shape_1.4.6.1          farver_2.1.2           matrixStats_1.5.0     
 [49] spatstat.explore_3.4-3 jsonlite_2.0.0         tidygraph_1.3.1       
 [52] progressr_0.15.1       survival_3.5-8         systemfonts_1.2.3     
 [55] tools_4.3.3            ica_1.0-3              glue_1.8.0            
 [58] gridExtra_2.3          xfun_0.52              withr_3.0.2           
 [61] BiocManager_1.30.25    fastmap_1.2.0          callr_3.7.6           
 [64] digest_0.6.37          timechange_0.3.0       R6_2.6.1              
 [67] mime_0.13              textshaping_1.0.1      colorspace_2.1-1      
 [70] scattermore_1.2        tensor_1.5             spatstat.data_3.1-6   
 [73] utf8_1.2.5             generics_0.1.4         graphlayouts_1.2.2    
 [76] httr_1.4.7             htmlwidgets_1.6.4      whisker_0.4.1         
 [79] uwot_0.2.3             pkgconfig_2.0.3        gtable_0.3.6          
 [82] lmtest_0.9-40          htmltools_0.5.8.1      dotCall64_1.2         
 [85] scales_1.4.0           png_0.1-8              spatstat.univar_3.1-3 
 [88] knitr_1.50             rstudioapi_0.17.1      tzdb_0.5.0            
 [91] reshape2_1.4.4         checkmate_2.3.2        nlme_3.1-164          
 [94] cachem_1.1.0           zoo_1.8-14             GlobalOptions_0.1.2   
 [97] KernSmooth_2.23-22     vipor_0.4.7            parallel_4.3.3        
[100] miniUI_0.1.2           ggrastr_1.0.2          pillar_1.10.2         
[103] grid_4.3.3             vctrs_0.6.5            RANN_2.6.2            
[106] promises_1.3.2         xtable_1.8-4           cluster_2.1.6         
[109] beeswarm_0.4.0         evaluate_1.0.3         cli_3.6.5             
[112] compiler_4.3.3         rlang_1.1.6            future.apply_1.11.3   
[115] labeling_0.4.3         rematch2_2.1.2         ps_1.9.1              
[118] ggbeeswarm_0.7.2       getPass_0.2-4          plyr_1.8.9            
[121] fs_1.6.6               stringi_1.8.7          deldir_2.0-4          
[124] lazyeval_0.2.2         spatstat.geom_3.4-1    Matrix_1.6-5          
[127] RcppHNSW_0.6.0         hms_1.1.3              future_1.40.0         
[130] statmod_1.5.0          shiny_1.10.0           ROCR_1.0-11           
[133] igraph_2.1.4           memoise_2.0.1          bslib_0.9.0

sessionInfo()

R version 4.3.3 (2024-02-29)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 15.7.3

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Australia/Melbourne
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] gtools_3.9.5        presto_1.0.0        circlize_0.4.16    
 [4] BiocParallel_1.36.0 harmony_1.2.3       Rcpp_1.0.14        
 [7] patchwork_1.3.0     cowplot_1.1.3       data.table_1.17.2  
[10] RColorBrewer_1.1-3  kableExtra_1.4.0    ggridges_0.5.6     
[13] ggforce_0.4.2       viridis_0.6.5       viridisLite_0.4.2  
[16] paletteer_1.6.0     clustree_0.5.1      ggraph_2.2.1       
[19] Seurat_5.0.3        SeuratObject_5.1.0  sp_2.2-0           
[22] here_1.0.1          lubridate_1.9.4     forcats_1.0.0      
[25] stringr_1.5.1       dplyr_1.1.4         purrr_1.0.4        
[28] readr_2.1.5         tidyr_1.3.1         tibble_3.2.1       
[31] ggplot2_3.5.2       tidyverse_2.0.0     BiocStyle_2.30.0   
[34] workflowr_1.7.1    

loaded via a namespace (and not attached):
  [1] RcppAnnoy_0.0.22       splines_4.3.3          later_1.4.2           
  [4] prismatic_1.1.2        polyclip_1.10-7        fastDummies_1.7.5     
  [7] lifecycle_1.0.4        rprojroot_2.0.4        globals_0.17.0        
 [10] processx_3.8.6         lattice_0.22-5         MASS_7.3-60.0.1       
 [13] backports_1.5.0        magrittr_2.0.3         limma_3.58.1          
 [16] plotly_4.10.4          sass_0.4.10            rmarkdown_2.29        
 [19] jquerylib_0.1.4        yaml_2.3.10            httpuv_1.6.16         
 [22] sctransform_0.4.2      spam_2.11-1            spatstat.sparse_3.1-0 
 [25] reticulate_1.42.0      pbapply_1.7-2          abind_1.4-8           
 [28] Rtsne_0.17             tweenr_2.0.3           git2r_0.36.2          
 [31] ggrepel_0.9.6          irlba_2.3.5.1          listenv_0.9.1         
 [34] spatstat.utils_3.1-4   pheatmap_1.0.12        goftest_1.2-3         
 [37] RSpectra_0.16-2        spatstat.random_3.4-1  fitdistrplus_1.2-2    
 [40] parallelly_1.44.0      svglite_2.2.1          leiden_0.4.3.1        
 [43] codetools_0.2-19       xml2_1.3.8             tidyselect_1.2.1      
 [46] shape_1.4.6.1          farver_2.1.2           matrixStats_1.5.0     
 [49] spatstat.explore_3.4-3 jsonlite_2.0.0         tidygraph_1.3.1       
 [52] progressr_0.15.1       survival_3.5-8         systemfonts_1.2.3     
 [55] tools_4.3.3            ica_1.0-3              glue_1.8.0            
 [58] gridExtra_2.3          xfun_0.52              withr_3.0.2           
 [61] BiocManager_1.30.25    fastmap_1.2.0          callr_3.7.6           
 [64] digest_0.6.37          timechange_0.3.0       R6_2.6.1              
 [67] mime_0.13              textshaping_1.0.1      colorspace_2.1-1      
 [70] scattermore_1.2        tensor_1.5             spatstat.data_3.1-6   
 [73] utf8_1.2.5             generics_0.1.4         graphlayouts_1.2.2    
 [76] httr_1.4.7             htmlwidgets_1.6.4      whisker_0.4.1         
 [79] uwot_0.2.3             pkgconfig_2.0.3        gtable_0.3.6          
 [82] lmtest_0.9-40          htmltools_0.5.8.1      dotCall64_1.2         
 [85] scales_1.4.0           png_0.1-8              spatstat.univar_3.1-3 
 [88] knitr_1.50             rstudioapi_0.17.1      tzdb_0.5.0            
 [91] reshape2_1.4.4         checkmate_2.3.2        nlme_3.1-164          
 [94] cachem_1.1.0           zoo_1.8-14             GlobalOptions_0.1.2   
 [97] KernSmooth_2.23-22     vipor_0.4.7            parallel_4.3.3        
[100] miniUI_0.1.2           ggrastr_1.0.2          pillar_1.10.2         
[103] grid_4.3.3             vctrs_0.6.5            RANN_2.6.2            
[106] promises_1.3.2         xtable_1.8-4           cluster_2.1.6         
[109] beeswarm_0.4.0         evaluate_1.0.3         cli_3.6.5             
[112] compiler_4.3.3         rlang_1.1.6            future.apply_1.11.3   
[115] labeling_0.4.3         rematch2_2.1.2         ps_1.9.1              
[118] ggbeeswarm_0.7.2       getPass_0.2-4          plyr_1.8.9            
[121] fs_1.6.6               stringi_1.8.7          deldir_2.0-4          
[124] lazyeval_0.2.2         spatstat.geom_3.4-1    Matrix_1.6-5          
[127] RcppHNSW_0.6.0         hms_1.1.3              future_1.40.0         
[130] statmod_1.5.0          shiny_1.10.0           ROCR_1.0-11           
[133] igraph_2.1.4           memoise_2.0.1          bslib_0.9.0

Lineages clustering: T and NK cells

T and NK cell subclustering

Gunjan Dixit

January 27, 2026

Load libraries

T and NK cells

Previous labels

Adding CellTypist info

Level 1 labels

Cd4 T cells

Subclustering CD4 T cells clusters-

Renaming idents

DE features of Cd4 T cells

DE markers/gene between CD4 T sub-clusters

Pairwise markers within CD4 T Naive

CD4 T Naive : CD4 T Naive_0 vs_rest

CD4 T Naive : CD4 T Naive_1 vs_rest

CD4 T Naive : CD4 T Naive_2 vs_rest

CD4 T Naive : CD4 T Naive_0 vs CD4 T Naive_1

CD4 T Naive : CD4 T Naive_0 vs CD4 T Naive_2

CD4 T Naive : CD4 T Naive_1 vs CD4 T Naive_2

Pairwise markers within CD4 T Memory

CD4 T Memory : CD4 T Memory_0 vs_rest

CD4 T Memory : CD4 T Memory_1 vs_rest

CD4 T Memory : CD4 T Memory_2 vs_rest

CD4 T Memory : CD4 T Memory_3 vs_rest

CD4 T Memory : CD4 T Memory_0 vs CD4 T Memory_1

CD4 T Memory : CD4 T Memory_0 vs CD4 T Memory_2

CD4 T Memory : CD4 T Memory_0 vs CD4 T Memory_3

CD4 T Memory : CD4 T Memory_1 vs CD4 T Memory_2

CD4 T Memory : CD4 T Memory_1 vs CD4 T Memory_3

CD4 T Memory : CD4 T Memory_2 vs CD4 T Memory_3

Pairwise markers within CD4 T Effector

CD4 T Effector : CD4 T Effector_0 vs_rest

CD4 T Effector : CD4 T Effector_1 vs_rest

CD4 T Effector : CD4 T Effector_0 vs CD4 T Effector_1

Pairwise markers within CD4 T Regulatory

CD4 T Reg : CD4 T Regulatory_0 vs_rest

CD4 T Reg : CD4 T Regulatory_1 vs_rest

CD4 T Reg : CD4 T Regulatory_2 vs_rest

CD4 T Reg : CD4 T Regulatory_0 vs CD4 T Regulatory_1

CD4 T Reg : CD4 T Regulatory_0 vs CD4 T Regulatory_2

CD4 T Reg : CD4 T Regulatory_1 vs CD4 T Regulatory_2

Cd8 T cells

Subclustering CD8 T cells clusters-

Renaming idents

DE features of Cd8 T cells

Pairwise markers within CD8 T Naive

CD8 T Naive : CD8 T Naive_0 vs_rest

CD8 T Naive : CD8 T Naive_1 vs_rest

CD8 T Naive : CD8 T Naive_0 vs CD8 T Naive_1

Pairwise markers within CD8 T Memory

CD8 T Memory : CD8 T Memory_0 vs_rest

CD8 T Memory : CD8 T Memory_1 vs_rest

CD8 T Memory : CD8 T Memory_2 vs_rest

CD8 T Memory : CD8 T Memory_3 vs_rest

CD8 T Memory : CD8 T Memory_0 vs CD8 T Memory_1

CD8 T Memory : CD8 T Memory_0 vs CD8 T Memory_2

CD8 T Memory : CD8 T Memory_0 vs CD8 T Memory_3

CD8 T Memory : CD8 T Memory_1 vs CD8 T Memory_2

CD8 T Memory : CD8 T Memory_1 vs CD8 T Memory_3

CD8 T Memory : CD8 T Memory_2 vs CD8 T Memory_3

Pairwise markers within CD8 TRM/EM

CD8 TRM/EM : CD8 TRM/EM_0 vs_rest

CD8 TRM/EM : CD8 TRM/EM_1 vs_rest

CD8 TRM/EM : CD8 TRM/EM_0 vs CD8 TRM/EM_1

NK cells

Subclustering NK cells clusters-

Renaming idents

DE features of NK cells

Pairwise markers within NK

NK : NK cd56hi_0 vs_rest

NK : NK cd56hi_1 vs_rest

NK : NK cd56lo_0 vs_rest

NK : NK cd56lo_1 vs_rest

NK : NK cd56hi_0 vs NK cd56hi_1

NK : NK cd56hi_0 vs NK cd56lo_0

NK : NK cd56hi_0 vs NK cd56lo_1

NK : NK cd56hi_1 vs NK cd56lo_0

NK : NK cd56hi_1 vs NK cd56lo_1