Age-related changes in cell-type compositions

Propeller Analysis

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Last updated: 2024-06-21

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Knit directory: paed-airway-allTissues/

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    Modified:   analysis/02_QC_exploratoryPlots.Rmd
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File	Version	Author	Date	Message
Rmd	0a358ce	Gunjan Dixit	2024-05-24	Added QC analysis
html	0a358ce	Gunjan Dixit	2024-05-24	Added QC analysis
Rmd	14f8a5e	Gunjan Dixit	2024-05-24	Added age analysis
html	14f8a5e	Gunjan Dixit	2024-05-24	Added age analysis

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Introduction

This analysis investigates age-related changes in cell-type proportions across different tissues of the earlyAIR project. Cell-type proportions are determined using the propeller package. Linear models are applied to transformed proportions and raw proportions to examine the effect of continuous age, sex, and batch.

suppressPackageStartupMessages({
  library(here)
  library(glue)
  library(patchwork)
  library(Seurat)
  library(dplyr)
  library(tidyverse)
  library(gridExtra)
  library(paletteer)
  library(viridis)
  library(ggforce)
  library(tidyverse)
  library(scran)
  library(ggridges)
  library(speckle)
  library(edgeR)
  library(kableExtra)
  library(dplyr)
  library(limma)
  library(knitr)
  library(openxlsx)
})

Load data

data_path <- here("output/RDS/AllBatches_Azimuth_noDoublets_SEUs/")
rds_files <- list.files(data_path, pattern = "\\.rds$", full.names = TRUE)

tissues <- c("Nasal_brushings", "Tonsils", "Adenoids", "Bronchial_brushings", "Nasal_brushings", "BAL", "Bronchial_brushings", "Adenoids", "Tonsils" )

seurat_objects <- lapply(rds_files, function(file) {
  seu <- readRDS(file)
  seu$tissue <- tools::file_path_sans_ext(basename(file))
  return(seu)
})

names(seurat_objects) <- tissues

merge_batches <- function(tissue_name, batches) {
  objs_to_merge <- seurat_objects[batches]
  merged <- Reduce(function(x, y) merge(x, y, add.cell.ids = batches), objs_to_merge)
  return(merged)
}

# Merge batches for each tissue
adenoids <- merge_batches("Adenoids", c(3, 8))
tonsils <- merge_batches("Tonsils", c(2, 9))
nasal_brushings <- merge_batches("Nasal_brushings", c(1, 5))
bronchial_brushings <- seurat_objects[[which(tissues == "Bronchial_brushings")[1]]]
bal <- seurat_objects[[which(tissues == "BAL")[1]]]


merged_list <- list(
   "Adenoids" = adenoids,
  "Tonsils" = tonsils,
  "Nasal_brushings" = nasal_brushings,
  "Bronchial_brushings" = bronchial_brushings,
  "BAL" = bal
)
merged_list

## For BAL (Samples aggregated into one)
#head(merged_list[["BAL"]]@meta.data)
merged_list[["BAL"]]@meta.data$Sample <- sub("_\\d+$", "", merged_list[["BAL"]]@meta.data$Sample)

#head(merged_list[["BAL"]]@meta.data)

Sample-wise Proportions of batch-integrated data

data_path <- here("~/projects/paed-airway-atlas/airway-atlas-allTissues/paed-airway-allTissues/output/RDS/AllBatches_Clustering_SEUs/")
batch_corrected <- list.files(data_path, pattern = "\\.rds$", full.names = TRUE)

ordered_tissues <- c("Adenoids", "Tonsils", "Nasal_brushings", "Bronchial_brushings", "BAL")

batch_corrected <- batch_corrected[order(match(sapply(strsplit(basename(batch_corrected), "_"), `[`, 3), ordered_tissues))]

palette1 <- paletteer::paletteer_d("ggthemes::Classic_20")
palette2 <- paletteer::paletteer_d("Polychrome::light")
combined_palette <- unique(c(palette1, palette2))

labels <- c("Broad_cell_label_1", "Broad_cell_label_2", "Broad_cell_label_3", "cell_labels")

for (file in batch_corrected) {
  tissue_obj <- readRDS(file)
  tissue_name <- unique(tissue_obj$tissue)
  batch_name <- unique(tissue_obj$batch_name)
  tissue_plots <- list()
  
  for (label in labels) {
    label_plots <- list()
    
    for (batch in unique(tissue_obj$batch_name)) {
      batch_data <- tissue_obj@meta.data %>%
        filter(batch_name == batch)
      
      p1 <- ggplot(batch_data, aes(x = !!sym(label), fill = !!sym(label))) +
        geom_bar() +
        geom_text(aes(label = ..count..), stat = "count", vjust = -0.5, colour = "black", size = 2) +
        scale_y_log10() +
        theme(axis.text.x = element_blank(), axis.title.x = element_blank(), axis.ticks.x = element_blank()) +
        labs(y = "No. Cells (log scale)", title = paste(tissue_name, "-", batch)) + NoLegend()
      
      p2 <- batch_data %>%
        select(!!sym(label), Sample) %>%
        group_by(!!sym(label), Sample) %>%
        summarise(num = n(), .groups = 'drop') %>%
        mutate(prop = num / sum(num) * 100) %>%
        ggplot(aes(x = !!sym(label), y = prop, fill = Sample)) + 
        geom_bar(stat = "identity") +
        theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 8)) +
        labs(y = "% Cells", fill = "Sample", title = paste(tissue_name, "-", batch)) + 
        scale_fill_manual(values = combined_palette)
      
      combined_plot <- (p1 / p2) + 
        theme(legend.text = element_text(size = 8), legend.key.size = unit(3, "mm"))
      
      label_plots[[batch]] <- combined_plot
    }
    
    combined_label_plots <- wrap_plots(label_plots, ncol = length(unique(tissue_obj$batch_name)))
    tissue_plots[[label]] <- combined_label_plots
  }
  
  cat('### ', tissue_name, '\n')
  print(tissue_plots)
}

gc()

Sample-wise Proportions without batch-integration

for (tissue_name in names(merged_list)) {
  tissue_obj <- merged_list[[tissue_name]]
  
  tissue_plots <- list()
  labels <- c("Broad_cell_label_1", "Broad_cell_label_2", "Broad_cell_label_3")

  for (label in labels) {
    label_plots <- list()
    
    for (batch in unique(tissue_obj$batch_name)) {
      batch_data <- tissue_obj@meta.data %>%
        filter(batch_name == batch)
      
      p1 <- ggplot(batch_data, aes(x = !!sym(label), fill = !!sym(label))) +
        geom_bar() +
        geom_text(aes(label = ..count..), stat = "count", vjust = -0.5, colour = "black", size = 2) +
        scale_y_log10() +
        theme(axis.text.x = element_blank(), axis.title.x = element_blank(), axis.ticks.x = element_blank()) +
        labs(y = "No. Cells (log scale)", title = paste(tissue_name, "-", batch)) + NoLegend()
      
      p2 <- batch_data %>%
        select(!!sym(label), Sample) %>%
        group_by(!!sym(label), Sample) %>%
        summarise(num = n(), .groups = 'drop') %>%
        mutate(prop = num / sum(num) * 100) %>%
        ggplot(aes(x = !!sym(label), y = prop, fill = Sample)) + 
        geom_bar(stat = "identity") +
        theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 8)) +
        labs(y = "% Cells", fill = "Sample", title = paste(tissue_name, "-", batch)) + 
        scale_fill_manual(values = combined_palette)
      
      combined_plot <- (p1 / p2) + 
        theme(legend.text = element_text(size = 8), legend.key.size = unit(3, "mm"))
      
      label_plots[[batch]] <- combined_plot
    }
    
    combined_label_plots <- wrap_plots(label_plots, ncol = length(unique(tissue_obj$batch_name)))
    tissue_plots[[label]] <- combined_label_plots
  }
  
  cat('### ', tissue_name, '\n')
  print(tissue_plots)
}

Calculate Proportions using Propeller

data_path <- here("~/projects/paed-airway-atlas/airway-atlas-allTissues/paed-airway-allTissues/output/RDS/AllBatches_Clustering_SEUs")
batch_corrected <- list.files(data_path, pattern = "\\.rds$", full.names = TRUE)


tissues <- c("Adenoids",   "BAL", "Bronchial_brushings", "Nasal_brushings", "Tonsils")

seurat_objects <- lapply(batch_corrected, function(file) {
  seu <- readRDS(file)
  seu$tissue <- tools::file_path_sans_ext(basename(file))
  return(seu)
})

names(seurat_objects) <- tissues

for (tissue_name in names(seurat_objects)) {
  tissue_obj <- seurat_objects[[tissue_name]]
  
  props <- getTransformedProps(clusters = tissue_obj$cell_labels,
                               sample = tissue_obj$Sample, transform = "asin")
  
  cat('### ', tissue_name, '\n')
  # Plot Cell Type Mean Variance
  p1 <- plotCellTypeMeanVar(props$Counts)
  
  # Plot Cell Type Proportions Mean Variance
  p2 <- plotCellTypePropsMeanVar(props$Counts)
  p1 / p2
 
  print(knitr::kable(props$Proportions, caption = "Cell-type proportions in samples"))
}

Adenoids

Past versions of unnamed-chunk-6-1.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24
14f8a5e	Gunjan Dixit	2024-05-24

Past versions of unnamed-chunk-6-2.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24
14f8a5e	Gunjan Dixit	2024-05-24

Cell-type proportions in samples

	eAIR001	eAIR003	eAIR004	eAIR006	eAIR007	eAIR008	eAIR010	eAIR012	eAIR013	eAIR014	eAIR015	eAIR016	eAIR019	eAIR020	eAIR021	eAIR023	eAIR024	eAIR025	eAIR026	eAIR027	eAIR028	eAIR031	eAIR032	eAIR033	eAIR037	eAIR038	eAIR039	eAIR041	eAIR042	eAIR043	eAIR055	eAIR056
naïve B cells	0.3024634	0.2954311	0.0819335	0.2738669	0.1921956	0.1742466	0.2123181	0.2783908	0.1799141	0.1930647	0.2950285	0.1638024	0.1432012	0.1962645	0.1745396	0.3093500	0.1248548	0.1408228	0.3041978	0.0880373	0.2355917	0.0500194	0.1433584	0.1751133	0.1214560	0.2135794	0.1313186	0.2047907	0.3054930	0.0956383	0.4420691	0.1449383
memory B cells	0.0595572	0.1147372	0.1789510	0.2138380	0.0603274	0.1852055	0.1013514	0.1464368	0.2137487	0.2021243	0.1255094	0.1248657	0.0838625	0.0948710	0.2129704	0.1379910	0.1454704	0.2260021	0.0704428	0.0595546	0.0614273	0.1031408	0.0853801	0.1611042	0.1046278	0.1608438	0.2180248	0.1672421	0.1848267	0.2170868	0.0941019	0.2308642
CM CD4 T cells and/or pre TFH cells	0.0505145	0.1590519	0.2386013	0.0964320	0.0814084	0.1432877	0.1148649	0.0919540	0.1245972	0.1252734	0.0766096	0.2121375	0.1079483	0.0919063	0.1232986	0.1578033	0.1297909	0.2571203	0.1032202	0.1662351	0.1152665	0.1500582	0.0825397	0.1211372	0.0770075	0.1486486	0.2926785	0.1616314	0.0920756	0.2278912	0.0841764	0.2227160
Germinal centre B cell: DZ-LZ transition and/or LZ and/or LZ-DZ	0.1393826	0.0546204	0.0946178	0.1142719	0.1374748	0.1079452	0.1314969	0.1170115	0.1074114	0.1152765	0.1138957	0.1441998	0.2798336	0.1844056	0.1293034	0.0229406	0.0659117	0.0822785	0.1089707	0.1455205	0.1244806	0.1108957	0.0436090	0.1021838	0.1871227	0.0230719	0.0586436	0.1327147	0.0792436	0.1288515	0.0763504	0.1718519
T-follicular helper and/or CD4 naïve	0.0823199	0.0762625	0.1271855	0.0450820	0.1096658	0.1079452	0.0867983	0.0696552	0.0754565	0.0612309	0.0741646	0.0639098	0.0411649	0.0919063	0.0732586	0.0990615	0.1123693	0.0870253	0.1311098	0.1201450	0.1046070	0.0387747	0.1565581	0.2175525	0.0808487	0.2313777	0.0962403	0.0681916	0.0684376	0.0920368	0.0704333	0.0140741
Cycling GM B cells	0.0654818	0.0450017	0.0466232	0.0511090	0.1294012	0.0421918	0.0841996	0.0673563	0.0496778	0.0830990	0.0647922	0.0537057	0.0687541	0.0945746	0.0444355	0.0076469	0.0191638	0.0255802	0.0370903	0.0813050	0.0467931	0.2656068	0.0279031	0.0140091	0.0711542	0.0112063	0.0143911	0.0267587	0.0216119	0.0484194	0.0313037	0.0246914
T-follicular helper and/or T-follicular memory and/or CD4 Treg	0.0489554	0.0669873	0.0562221	0.0388139	0.0616730	0.0506849	0.0587318	0.0422989	0.0306122	0.0634177	0.0399348	0.0464554	0.0394132	0.0462496	0.0212170	0.0229406	0.0313589	0.0203059	0.0770558	0.1077162	0.0802168	0.0542846	0.0131997	0.0391430	0.0568868	0.0342782	0.0386760	0.0565386	0.0229626	0.0586234	0.0332124	0.0469136
Naive B cell activated	0.0492672	0.0553075	0.0329105	0.0527965	0.0318457	0.0287671	0.0563929	0.0358621	0.0341031	0.0399875	0.0686634	0.0330290	0.0194876	0.0183813	0.0416333	0.0691693	0.0499419	0.0450949	0.0451409	0.0761264	0.0402891	0.0321830	0.0237260	0.0733416	0.0170112	0.0688860	0.0347185	0.0476910	0.0531292	0.0272109	0.0815041	0.0281481
NK cells and/or NK- T cells and/or gamma delta T cells	0.0224509	0.0250773	0.0411382	0.0226615	0.0228751	0.0339726	0.0163721	0.0183908	0.0518260	0.0221806	0.0313773	0.0512889	0.0523319	0.0302401	0.0360288	0.0667362	0.0508130	0.0635549	0.0238643	0.0315898	0.0220416	0.0360605	0.0917293	0.0436753	0.0523139	0.0672380	0.0545062	0.0636599	0.0562810	0.0362145	0.0271044	0.0380247
DZ B cells (early or late phase)	0.0520736	0.0195809	0.0308536	0.0306172	0.0603274	0.0312329	0.0418399	0.0370115	0.0429646	0.0412371	0.0332111	0.0424275	0.0632801	0.0969463	0.0392314	0.0100799	0.0238095	0.0245253	0.0316274	0.0445365	0.0467931	0.0504071	0.0142022	0.0160692	0.0568868	0.0052736	0.0206872	0.0325852	0.0254390	0.0346138	0.0202329	0.0355556
interferon-activated naïve B cells	0.0589336	0.0223291	0.0133699	0.0161524	0.0459744	0.0386301	0.0298857	0.0402299	0.0093985	0.0118713	0.0307661	0.0008056	0.0142325	0.0026682	0.0260208	0.0038234	0.0818815	0.0013186	0.0209891	0.0165717	0.0220416	0.0065917	0.1547201	0.0016481	0.0881654	0.0016480	0.0032380	0.0019422	0.0418730	0.0008003	0.0015270	0.0009877
interferon-activated T cells	0.0121609	0.0065270	0.0102845	0.0024108	0.0076250	0.0063014	0.0046778	0.0105747	0.0021482	0.0024992	0.0061125	0.0016112	0.0094154	0.0005929	0.0096077	0.0027807	0.1263066	0.0013186	0.0094882	0.0124288	0.0074074	0.0007755	0.1067669	0.0016481	0.0535943	0.0016480	0.0032380	0.0017264	0.0130572	0.0014006	0.0005726	0.0007407
monocytes/macrophages	0.0115373	0.0171762	0.0102845	0.0103664	0.0199596	0.0087671	0.0161123	0.0071264	0.0126208	0.0096845	0.0073350	0.0123523	0.0192687	0.0106730	0.0172138	0.0076469	0.0069686	0.0044831	0.0080506	0.0108752	0.0088528	0.0255913	0.0188805	0.0041203	0.0074995	0.0062624	0.0026983	0.0069055	0.0108059	0.0058023	0.0078259	0.0061728
innate lymphocytes	0.0077954	0.0133975	0.0157696	0.0057859	0.0031397	0.0112329	0.0064969	0.0055172	0.0072503	0.0059356	0.0052975	0.0120838	0.0091964	0.0056330	0.0164131	0.0152937	0.0078397	0.0105485	0.0115009	0.0062144	0.0028907	0.0124079	0.0185464	0.0123609	0.0042071	0.0121951	0.0181687	0.0099266	0.0092301	0.0110044	0.0064898	0.0064198
activated DC3 (aDC3)?	0.0062364	0.0079011	0.0017141	0.0077146	0.0134559	0.0041096	0.0072765	0.0103448	0.0067132	0.0031240	0.0103912	0.0104726	0.0137946	0.0166024	0.0160128	0.0114703	0.0046458	0.0034283	0.0034503	0.0098395	0.0122855	0.0046530	0.0015038	0.0037083	0.0029267	0.0065920	0.0014391	0.0041001	0.0031517	0.0018007	0.0009544	0.0160494
plasma cells	0.0090427	0.0085881	0.0051423	0.0098843	0.0089706	0.0104110	0.0059771	0.0126437	0.0061762	0.0096845	0.0103912	0.0072503	0.0087585	0.0059294	0.0084067	0.0069517	0.0072590	0.0010549	0.0037378	0.0062144	0.0052394	0.0263668	0.0041771	0.0037083	0.0045729	0.0019776	0.0055765	0.0034527	0.0049527	0.0018007	0.0074442	0.0054321
pre-T cells	0.0171500	0.0006870	0.0010285	0.0024108	0.0008971	0.0041096	0.0145530	0.0016092	0.0375940	0.0028116	0.0000000	0.0000000	0.0072257	0.0023718	0.0000000	0.0000000	0.0000000	0.0000000	0.0025877	0.0067323	0.0556459	0.0000000	0.0000000	0.0000000	0.0056704	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000
plasmacytoid DCs	0.0015591	0.0072140	0.0092561	0.0036162	0.0031397	0.0076712	0.0028586	0.0022989	0.0034909	0.0034364	0.0030562	0.0016112	0.0032844	0.0011859	0.0044035	0.0083420	0.0017422	0.0034283	0.0046003	0.0041429	0.0014453	0.0166731	0.0100251	0.0024722	0.0027437	0.0029664	0.0041374	0.0047475	0.0015759	0.0050020	0.0009544	0.0007407
follicular dendritic cells	0.0009355	0.0017176	0.0037710	0.0014465	0.0087464	0.0016438	0.0044179	0.0032184	0.0029538	0.0018744	0.0014262	0.0053706	0.0072257	0.0056330	0.0024019	0.0010428	0.0002904	0.0010549	0.0011501	0.0025893	0.0032520	0.0096937	0.0008354	0.0012361	0.0038412	0.0019776	0.0008994	0.0041001	0.0040522	0.0038015	0.0026723	0.0022222
neutrophils	0.0006236	0.0000000	0.0000000	0.0002411	0.0002243	0.0000000	0.0012994	0.0013793	0.0000000	0.0009372	0.0008150	0.0107411	0.0015327	0.0011859	0.0016013	0.0378867	0.0008711	0.0000000	0.0002875	0.0010357	0.0003613	0.0011632	0.0005013	0.0045323	0.0001829	0.0000000	0.0000000	0.0000000	0.0000000	0.0004002	0.0108799	0.0009877
mast cells	0.0003118	0.0024047	0.0003428	0.0002411	0.0004485	0.0005479	0.0015593	0.0004598	0.0002685	0.0006248	0.0010187	0.0018797	0.0021896	0.0008894	0.0012010	0.0010428	0.0002904	0.0007911	0.0011501	0.0015536	0.0025294	0.0046530	0.0018379	0.0012361	0.0007317	0.0003296	0.0007196	0.0008632	0.0018010	0.0006002	0.0001909	0.0024691
preB cells	0.0012473	0.0000000	0.0000000	0.0002411	0.0002243	0.0010959	0.0005198	0.0002299	0.0010741	0.0006248	0.0002037	0.0000000	0.0045982	0.0008894	0.0008006	0.0000000	0.0084204	0.0002637	0.0002875	0.0010357	0.0005420	0.0000000	0.0000000	0.0000000	0.0005487	0.0000000	0.0000000	0.0004316	0.0000000	0.0010004	0.0000000	0.0000000

BAL

Past versions of unnamed-chunk-6-3.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24
14f8a5e	Gunjan Dixit	2024-05-24

Past versions of unnamed-chunk-6-4.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24
14f8a5e	Gunjan Dixit	2024-05-24

Cell-type proportions in samples

	eAIR011_1	eAIR011_2	eAIR035_1	eAIR035_2	eAIR036_1	eAIR036_2	eAIR045_1	eAIR045_2	eAIR046_1	eAIR046_2	eAIR054_1	eAIR054_2	eAIR057_1	eAIR057_2	eAIR059_1	eAIR059_2
macro-alveolar	0.0174989	0.0213831	0.0151976	0.0236584	0.4021385	0.4722103	0.1839879	0.1941109	0.7855007	0.7703286	0.1497006	0.1305903	0.1344196	0.1012195	0.0241611	0.0222717
macro-lipid	0.6598378	0.6815287	0.7082067	0.7132141	0.0278940	0.0258509	0.0537764	0.0554603	0.0150972	0.0113951	0.2335329	0.1985689	0.3862865	0.2512195	0.1829978	0.1698218
macro-monocyte-derived-or-interstitial	0.0738370	0.0605096	0.0924012	0.1027121	0.2408182	0.2244722	0.1631420	0.1695254	0.0508221	0.0573072	0.3692615	0.3649374	0.0672098	0.0865854	0.3803132	0.3413140
CD8 T cells	0.0546308	0.0491356	0.0401216	0.0288517	0.1022780	0.0831538	0.1172205	0.1183533	0.0437967	0.0544308	0.0319361	0.0393560	0.0543109	0.1085366	0.0523490	0.0662584
CD4 T cells	0.0452411	0.0382166	0.0170213	0.0155799	0.0567178	0.0374838	0.0764350	0.0786164	0.0215247	0.0277686	0.0079840	0.0214669	0.0726409	0.1426829	0.0425056	0.0501114
B cells	0.0418267	0.0436761	0.0085106	0.0109636	0.0701999	0.0491168	0.1498489	0.1392224	0.0068759	0.0081867	0.0199601	0.0357782	0.0305499	0.0451220	0.0125280	0.0111359
macro-proliferating	0.0469484	0.0491356	0.0218845	0.0253895	0.0288238	0.0336062	0.0129909	0.0154374	0.0500747	0.0464653	0.0039920	0.0125224	0.0095044	0.0036585	0.0058166	0.0055679
ciliated epithelial cells	0.0170721	0.0150136	0.0401216	0.0386613	0.0213854	0.0198190	0.0818731	0.0737564	0.0079223	0.0058635	0.0718563	0.0572451	0.0529532	0.0621951	0.0362416	0.0378619
macro-CCL	0.0000000	0.0000000	0.0000000	0.0011541	0.0046490	0.0064627	0.0299094	0.0317324	0.0004484	0.0003319	0.0059880	0.0107335	0.0115411	0.0060976	0.2000000	0.2282851
unknown	0.0179257	0.0163785	0.0115502	0.0034622	0.0037192	0.0021542	0.0888218	0.0854774	0.0112108	0.0117270	0.0039920	0.0035778	0.0278344	0.0353659	0.0326622	0.0423163
secretory epithelial cells	0.0042680	0.0036397	0.0310030	0.0276976	0.0190609	0.0159414	0.0151057	0.0160091	0.0029895	0.0030977	0.0638723	0.0876565	0.1215207	0.1121951	0.0143177	0.0083519
cycling T cells	0.0153649	0.0145587	0.0018237	0.0017311	0.0144119	0.0172340	0.0141994	0.0131504	0.0016442	0.0008851	0.0059880	0.0071556	0.0040733	0.0024390	0.0017897	0.0022272
plasmacytoid DC	0.0017072	0.0027298	0.0024316	0.0023081	0.0065086	0.0120638	0.0042296	0.0025729	0.0002990	0.0005532	0.0179641	0.0232558	0.0013578	0.0012195	0.0129754	0.0139198
basal epithelial cells	0.0017072	0.0018198	0.0079027	0.0034622	0.0000000	0.0000000	0.0018127	0.0025729	0.0016442	0.0013276	0.0119760	0.0071556	0.0251188	0.0390244	0.0013423	0.0005568
plasma B cells	0.0021340	0.0022748	0.0018237	0.0011541	0.0013947	0.0004308	0.0066465	0.0040023	0.0001495	0.0003319	0.0019960	0.0000000	0.0006789	0.0024390	0.0000000	0.0000000

Bronchial_brushings

Past versions of unnamed-chunk-6-5.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24
14f8a5e	Gunjan Dixit	2024-05-24

Past versions of unnamed-chunk-6-6.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24
14f8a5e	Gunjan Dixit	2024-05-24

Cell-type proportions in samples

	eAIR009	eAIR010	eAIR012	eAIR013	eAIR014	eAIR016	eAIR018	eAIR020	eAIR021	eAIR022	eAIR024	eAIR025	eAIR026	eAIR027	eAIR028	eAIR031
macrophages	0.4777019	0.0864048	0.1256757	0.1549398	0.4597278	0.0787021	0.1259690	0.0922935	0.0223958	0.2356198	0.2796574	0.2511468	0.0078405	0.0424779	0.1948052	0.2252027
CD8 T cells	0.1647248	0.2277946	0.3891892	0.2340503	0.1198970	0.1463583	0.1560078	0.4397785	0.2682292	0.0492156	0.1678801	0.0160550	0.2177419	0.1084071	0.0012987	0.0305677
goblet/club/basal cells	0.0634793	0.0809668	0.0851351	0.1319723	0.0856933	0.2782188	0.1967054	0.0724504	0.1453125	0.1876346	0.1721627	0.1846330	0.1944444	0.0840708	0.1649351	0.2108546
monocyte and neutrophil-like	0.0903978	0.1305136	0.0297297	0.0583303	0.1125414	0.0089748	0.0910853	0.0461467	0.1442708	0.0938173	0.0882227	0.2500000	0.1756272	0.2101770	0.1740260	0.0991890
ciliated cells	0.0550422	0.0882175	0.2689189	0.1604083	0.0592129	0.4176735	0.2189922	0.1227503	0.1041667	0.2060904	0.1331906	0.1903670	0.1418011	0.0743363	0.4506494	0.3231441
B cells	0.0369626	0.1589124	0.0378378	0.1148378	0.0386171	0.0334829	0.0862403	0.1084449	0.0770833	0.0895109	0.0149893	0.0080275	0.1178315	0.0942478	0.0000000	0.0131004
CD4 T cells	0.0566493	0.1595166	0.0256757	0.0415603	0.0581096	0.0144978	0.0193798	0.0290725	0.0473958	0.0175331	0.0252677	0.0114679	0.0152330	0.0212389	0.0000000	0.0018715
neutrophils	0.0108477	0.0012085	0.0013514	0.0222384	0.0000000	0.0044874	0.0281008	0.0544532	0.1489583	0.0944325	0.0312634	0.0000000	0.0495072	0.0004425	0.0000000	0.0000000
monocytes	0.0008035	0.0078550	0.0000000	0.0010937	0.0165502	0.0003452	0.0000000	0.0000000	0.0031250	0.0009228	0.0034261	0.0022936	0.0004480	0.3203540	0.0000000	0.0031192
mast cells	0.0024106	0.0078550	0.0216216	0.0535910	0.0308937	0.0058681	0.0251938	0.0041532	0.0145833	0.0107659	0.0526767	0.0688073	0.0103047	0.0075221	0.0000000	0.0492826
proliferating T/NK	0.0224990	0.0320242	0.0040541	0.0204156	0.0088268	0.0031067	0.0319767	0.0184587	0.0140625	0.0092279	0.0072805	0.0068807	0.0259857	0.0163717	0.0051948	0.0218341
plasmacytoid DCs	0.0008035	0.0126888	0.0027027	0.0010937	0.0044134	0.0006904	0.0155039	0.0059991	0.0067708	0.0018456	0.0029979	0.0034404	0.0318100	0.0168142	0.0077922	0.0118528
ionocytes	0.0004018	0.0030211	0.0067568	0.0029165	0.0044134	0.0072489	0.0029070	0.0013844	0.0000000	0.0009228	0.0111349	0.0068807	0.0015681	0.0035398	0.0000000	0.0031192
mesothelial cells	0.0164725	0.0006042	0.0013514	0.0010937	0.0000000	0.0003452	0.0009690	0.0000000	0.0026042	0.0021532	0.0098501	0.0000000	0.0000000	0.0000000	0.0012987	0.0043668
plasma B cells	0.0008035	0.0024169	0.0000000	0.0014583	0.0011033	0.0000000	0.0009690	0.0046147	0.0010417	0.0003076	0.0000000	0.0000000	0.0098566	0.0000000	0.0000000	0.0024953

Nasal_brushings

Past versions of unnamed-chunk-6-7.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24
14f8a5e	Gunjan Dixit	2024-05-24

Past versions of unnamed-chunk-6-8.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24
14f8a5e	Gunjan Dixit	2024-05-24

Cell-type proportions in samples

	eAIR003	eAIR004	eAIR005	eAIR006	eAIR007	eAIR008	eAIR009	eAIR010	eAIR011	eAIR013	eAIR014	eAIR016	eAIR017	eAIR018	eAIR019	eAIR020	eAIR021	eAIR022	eAIR023	eAIR024	eAIR025	eAIR026	eAIR027	eAIR028	eAIR030	eAIR031	eAIR032	eAIR033	eAIR037	eAIR038	eAIR042	eAIR047
CD8 T cells	0.0976220	0.2217659	0.3738318	0.2816242	0.0996399	0.7310606	0.4419396	0.2388132	0.2393661	0.1926033	0.3207547	0.0718542	0.1064426	0.2384287	0.1853408	0.4575107	0.2500772	0.3386454	0.1813631	0.0967742	0.0122750	0.0685835	0.2667221	0.0649401	0.0008803	0.0124378	0.0267261	0.2416251	0.2313938	0.2319289	0.3355388	0.1589958
B cells	0.0012516	0.0287474	0.0404984	0.3019261	0.1404562	0.0833333	0.0233943	0.0894942	0.0558799	0.0249760	0.0987791	0.0897301	0.0336134	0.0328867	0.3104575	0.0291845	0.1216425	0.3282426	0.6777050	0.0164223	0.0016367	0.0296960	0.0577482	0.1741399	0.0044014	0.0024876	0.0133630	0.4236161	0.1428958	0.0143023	0.0223693	0.0120874
goblet/club/basal cells	0.3591990	0.0061602	0.4922118	0.2675690	0.4669868	0.0672348	0.2384092	0.4139105	0.3261051	0.5374640	0.3235294	0.2323870	0.5462185	0.4199147	0.1783380	0.2257511	0.3491818	0.1367862	0.0581440	0.0563050	0.4705401	0.0525572	0.3444121	0.3254735	0.3855634	0.8097015	0.5534521	0.1420765	0.2167794	0.1785852	0.3260870	0.4923291
ciliated cells	0.5081352	0.5585216	0.0498442	0.0546590	0.1368547	0.0369318	0.2065079	0.1211089	0.2685571	0.1383285	0.0965594	0.2723449	0.1372549	0.1763094	0.0774977	0.1364807	0.1642482	0.0861000	0.0489026	0.0023460	0.4402619	0.1640349	0.1445783	0.0096637	0.5290493	0.0049751	0.0055679	0.0845807	0.1899865	0.5133359	0.1468179	0.3100883
NK-T cells	0.0075094	0.0020534	0.0124611	0.0317543	0.0420168	0.0227273	0.0508294	0.0418288	0.0350292	0.0139289	0.0774140	0.0045566	0.0056022	0.0630329	0.0205415	0.0163090	0.0182155	0.0064188	0.0003851	0.1771261	0.0016367	0.2667924	0.0627337	0.0421337	0.0000000	0.0012438	0.0044543	0.0080779	0.1491204	0.0173947	0.0453686	0.0013947
monocyte and neutrophil-like	0.0025031	0.0266940	0.0155763	0.0182197	0.0348139	0.0170455	0.0061676	0.0165370	0.0442035	0.0163305	0.0249723	0.0126183	0.0532213	0.0173569	0.0074697	0.0746781	0.0379747	0.0070828	0.0007701	0.2049853	0.0220949	0.1143059	0.0411300	0.0519907	0.0184859	0.0559701	0.1469933	0.0144928	0.0059540	0.0092772	0.0415879	0.0046490
viral-activated cells	0.0075094	0.0000000	0.0031153	0.0005206	0.0096038	0.0009470	0.0004254	0.0092412	0.0008340	0.0038425	0.0044395	0.0000000	0.0000000	0.0137028	0.0009337	0.0008584	0.0018524	0.0002213	0.0000000	0.3595308	0.0000000	0.2679708	0.0078936	0.0023193	0.0000000	0.0223881	0.0055679	0.0002376	0.0154263	0.0003865	0.0267801	0.0000000
neutrophils	0.0000000	0.0010267	0.0062305	0.0020822	0.0096038	0.0018939	0.0002127	0.0082685	0.0050042	0.0004803	0.0185905	0.0000000	0.0924370	0.0076127	0.0014006	0.0154506	0.0237728	0.0044267	0.0003851	0.0020528	0.0008183	0.0037709	0.0186955	0.2642056	0.0000000	0.0000000	0.0022272	0.0161559	0.0010825	0.0000000	0.0034657	0.0004649
proliferating epithelial cells	0.0000000	0.0010267	0.0031153	0.0062467	0.0108043	0.0018939	0.0197788	0.0252918	0.0016681	0.0302594	0.0102664	0.0133193	0.0140056	0.0185749	0.0018674	0.0017167	0.0108058	0.0033201	0.0000000	0.0052786	0.0090016	0.0124912	0.0245118	0.0365288	0.0457746	0.0534826	0.0256125	0.0102162	0.0083897	0.0042520	0.0141777	0.0088331
plasma B cells	0.0000000	0.0061602	0.0000000	0.0145757	0.0060024	0.0246212	0.0004254	0.0131323	0.0000000	0.0000000	0.0088790	0.0073607	0.0000000	0.0000000	0.1069094	0.0000000	0.0006175	0.0542275	0.0173277	0.0000000	0.0000000	0.0000000	0.0033236	0.0042520	0.0008803	0.0000000	0.0000000	0.0411024	0.0230041	0.0000000	0.0000000	0.0000000
proliferating T/NK	0.0012516	0.0010267	0.0031153	0.0176991	0.0060024	0.0066288	0.0089324	0.0131323	0.0075063	0.0028818	0.0044395	0.0084122	0.0000000	0.0030451	0.0966387	0.0231760	0.0114233	0.0214697	0.0042357	0.0214076	0.0008183	0.0049493	0.0099709	0.0115964	0.0017606	0.0074627	0.0111359	0.0130672	0.0078484	0.0015462	0.0176434	0.0000000
unknown	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0008340	0.0024015	0.0002775	0.2800561	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000
mast cells	0.0000000	0.0657084	0.0000000	0.0005206	0.0000000	0.0009470	0.0017014	0.0029183	0.0058382	0.0312200	0.0083241	0.0007010	0.0028011	0.0009135	0.0028011	0.0000000	0.0012349	0.0048694	0.0026954	0.0228739	0.0180033	0.0054207	0.0083091	0.0021260	0.0052817	0.0024876	0.1525612	0.0007128	0.0021651	0.0255122	0.0138626	0.0032543
ionocytes	0.0150188	0.0164271	0.0000000	0.0015617	0.0132053	0.0028409	0.0006380	0.0029183	0.0091743	0.0028818	0.0013873	0.0038556	0.0084034	0.0009135	0.0060691	0.0137339	0.0027786	0.0042054	0.0069311	0.0026393	0.0098200	0.0018855	0.0074782	0.0079242	0.0035211	0.0024876	0.0122494	0.0002376	0.0037889	0.0030924	0.0028355	0.0074384
plasmacytoid DCs	0.0000000	0.0287474	0.0000000	0.0005206	0.0024010	0.0018939	0.0004254	0.0029183	0.0000000	0.0009606	0.0005549	0.0024536	0.0000000	0.0018270	0.0000000	0.0051502	0.0058660	0.0004427	0.0000000	0.0296188	0.0073650	0.0073062	0.0008309	0.0009664	0.0008803	0.0236318	0.0400891	0.0028510	0.0016238	0.0003865	0.0015753	0.0004649
melanocyte	0.0000000	0.0359343	0.0000000	0.0005206	0.0216086	0.0000000	0.0002127	0.0004864	0.0000000	0.0014409	0.0008324	0.0003505	0.0000000	0.0054811	0.0037348	0.0000000	0.0003087	0.0035414	0.0011552	0.0026393	0.0057283	0.0002357	0.0016618	0.0017395	0.0035211	0.0012438	0.0000000	0.0009503	0.0005413	0.0000000	0.0018904	0.0000000

Tonsils

Past versions of unnamed-chunk-6-9.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24
14f8a5e	Gunjan Dixit	2024-05-24

Past versions of unnamed-chunk-6-10.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24
14f8a5e	Gunjan Dixit	2024-05-24

Cell-type proportions in samples

	eAIR002	eAIR004	eAIR006	eAIR007	eAIR008	eAIR010	eAIR012	eAIR013	eAIR014	eAIR016	eAIR017	eAIR018	eAIR019	eAIR020	eAIR021	eAIR022	eAIR024	eAIR026	eAIR027	eAIR031	eAIR032	eAIR034	eAIR037	eAIR040	eAIR042	eAIR043	eAIR044	eAIR048	eAIR050	eAIR051	eAIR055	eAIR056
naïve B cells	0.3812593	0.2196210	0.2189838	0.2051616	0.3004658	0.1997536	0.2632775	0.1382269	0.1101920	0.2129964	0.2473748	0.1290418	0.0934486	0.1555418	0.1968415	0.2256278	0.3394707	0.1516937	0.2679669	0.1978055	0.2877030	0.0981139	0.0968620	0.1856578	0.2524734	0.1128423	0.1464435	0.1504905	0.1459596	0.1748828	0.2992940	0.1954155
CM CD4 T cells and/or pre TFH cells	0.1006445	0.1574136	0.1243229	0.1411841	0.1137422	0.1040854	0.0991196	0.1307912	0.1497784	0.1421982	0.2051696	0.1056066	0.1446625	0.1002794	0.0800902	0.1340473	0.1371692	0.1428571	0.1586962	0.1103203	0.1535963	0.1799057	0.1122801	0.2122574	0.1415525	0.2271010	0.2077283	0.1448549	0.2020202	0.2048735	0.1477559	0.1673352
Germinal centre B cell: DZ-LZ transition and/or LZ and/or LZ-DZ	0.1311353	0.1250836	0.1697189	0.0923878	0.1199534	0.1597208	0.1178642	0.1199237	0.0986706	0.1466105	0.0795638	0.2272323	0.2311274	0.1974542	0.2053017	0.1872230	0.0708861	0.2306333	0.1338499	0.0747331	0.0581593	0.1282191	0.1052059	0.0264198	0.0616438	0.1355052	0.0703913	0.0434147	0.0670034	0.0886598	0.0335350	0.1002865
memory B cells	0.0369360	0.1302118	0.0595822	0.0724355	0.1207298	0.0398276	0.1167282	0.0890372	0.0977843	0.0549539	0.0775444	0.0344112	0.0425673	0.0493636	0.0679639	0.0468981	0.1272727	0.0379971	0.0537002	0.1651839	0.1040990	0.1559666	0.0613096	0.1272466	0.1236682	0.0979698	0.1417672	0.0632436	0.1117845	0.0980319	0.0796773	0.1308500
naïve CD4 T cells	0.0629648	0.0622074	0.0799587	0.0934721	0.0481366	0.1178403	0.0443056	0.1061964	0.1270310	0.0459286	0.0500808	0.0412341	0.0269371	0.0484322	0.0800902	0.0347120	0.0568470	0.0350515	0.0641197	0.0762159	0.0583140	0.0658324	0.0448032	0.1356937	0.0591705	0.0675165	0.0824514	0.1644751	0.1060606	0.0361762	0.1106909	0.0259790
Cycling GM B cells	0.0523054	0.0742475	0.0794429	0.0800260	0.0423137	0.0845822	0.0715706	0.0955195	0.0685377	0.1075010	0.0383683	0.1323050	0.1030928	0.1080410	0.0733221	0.0801329	0.0451093	0.1310751	0.0961795	0.1070581	0.0685228	0.0781647	0.0926900	0.0289360	0.0702055	0.1019830	0.0854049	0.0667919	0.0843434	0.1190253	0.0360565	0.0758357
DZ B cells (early or late phase)	0.0877541	0.0637681	0.0949188	0.0652787	0.0652174	0.0995689	0.0590741	0.0844614	0.0437223	0.0738067	0.0470517	0.1423910	0.1143997	0.1409500	0.1144952	0.1203840	0.0326812	0.1054492	0.0558376	0.0708778	0.0501160	0.0634748	0.0653002	0.0129403	0.0376712	0.0533522	0.0386414	0.0250470	0.0425926	0.0620431	0.0189107	0.0651385
CD8 T cells	0.0123946	0.0185061	0.0281145	0.0346996	0.0151398	0.0217614	0.0184607	0.0629171	0.0463811	0.0521460	0.0648223	0.0157223	0.0658464	0.0298044	0.0191765	0.0472674	0.0324511	0.0170839	0.0122896	0.0361803	0.0635731	0.0252086	0.0631235	0.0656003	0.0753425	0.0259679	0.0462712	0.0809852	0.0575758	0.0575445	0.0655572	0.0668577
Regulatory CD4 T cells	0.0146257	0.0283166	0.0299200	0.0290609	0.0308618	0.0373640	0.0278330	0.0465205	0.0573117	0.0238668	0.0454362	0.0222486	0.0329232	0.0298044	0.0442752	0.0343427	0.0269275	0.0371134	0.0374032	0.0483393	0.0397525	0.0433442	0.0272084	0.0702732	0.0618341	0.0472144	0.0425794	0.0705489	0.0599327	0.0423618	0.0529501	0.0605540
Naive B cell activated	0.0557759	0.0376812	0.0345628	0.0351334	0.0362966	0.0234038	0.0454416	0.0158246	0.0262925	0.0377056	0.0512924	0.0192821	0.0133023	0.0294939	0.0346870	0.0336041	0.0695052	0.0170839	0.0309912	0.0438909	0.0477958	0.0119695	0.0067114	0.0434939	0.0348174	0.0280925	0.0206744	0.0229597	0.0126263	0.0346767	0.0683308	0.0324737
naïve CD8 T cells	0.0180962	0.0258640	0.0304359	0.0570375	0.0364907	0.0371587	0.0332292	0.0491897	0.0407681	0.0216606	0.0056543	0.0109760	0.0076488	0.0114871	0.0214326	0.0073855	0.0140391	0.0182622	0.0251135	0.0145314	0.0131477	0.0308306	0.0362779	0.0330697	0.0211187	0.0141643	0.0283042	0.0653308	0.0240741	0.0142455	0.0292486	0.0106972
activated DC3 (aDC3)?	0.0049579	0.0120401	0.0118648	0.0043375	0.0085404	0.0090331	0.0119284	0.0106768	0.0091581	0.0204573	0.0068659	0.0151290	0.0222813	0.0170754	0.0169205	0.0199409	0.0098964	0.0223859	0.0240449	0.0088968	0.0106729	0.0230323	0.0137856	0.0152768	0.0197869	0.0302172	0.0265813	0.0185765	0.0274411	0.0269916	0.0153807	0.0336199
monocytes/macrophages	0.0012395	0.0095875	0.0059324	0.0199523	0.0081522	0.0104701	0.0090883	0.0101049	0.0457903	0.0230646	0.0163570	0.0471670	0.0079814	0.0086930	0.0155104	0.0088626	0.0046030	0.0091311	0.0106866	0.0080071	0.0032483	0.0081610	0.0172320	0.0082674	0.0072298	0.0127479	0.0103372	0.0160718	0.0107744	0.0082474	0.0098336	0.0051576
interferon-activated naïve B cells	0.0027268	0.0024526	0.0041269	0.0325309	0.0027174	0.0207350	0.0258449	0.0040038	0.0109306	0.0030084	0.0042407	0.0284782	0.0053209	0.0086930	0.0059222	0.0025849	0.0039125	0.0117820	0.0037403	0.0035587	0.0051044	0.0313747	0.0950481	0.0007189	0.0036149	0.0007082	0.0019690	0.0085577	0.0084175	0.0013121	0.0045386	0.0022923
plasma cells	0.0242935	0.0066890	0.0028372	0.0028193	0.0050466	0.0036953	0.0045442	0.0101049	0.0076809	0.0042118	0.0260501	0.0029665	0.0049884	0.0180068	0.0073322	0.0025849	0.0144994	0.0064801	0.0053433	0.0038553	0.0058778	0.0152339	0.0067114	0.0095255	0.0064688	0.0110954	0.0091066	0.0062617	0.0067340	0.0073102	0.0047907	0.0078319
interferon-activated T cells	0.0017353	0.0008919	0.0023214	0.0078074	0.0009705	0.0022583	0.0235728	0.0055291	0.0020679	0.0008022	0.0042407	0.0011866	0.0073163	0.0015523	0.0028201	0.0007386	0.0009206	0.0029455	0.0024045	0.0017794	0.0029389	0.0174102	0.1091964	0.0014378	0.0007610	0.0018886	0.0004922	0.0043832	0.0070707	0.0009372	0.0027736	0.0005731
follicular dendritic cells	0.0004958	0.0122631	0.0054166	0.0065062	0.0054348	0.0108807	0.0028401	0.0043851	0.0197932	0.0044124	0.0012116	0.0038564	0.0372464	0.0043465	0.0042301	0.0044313	0.0027618	0.0053019	0.0021373	0.0065243	0.0003094	0.0018136	0.0237620	0.0034148	0.0081811	0.0160529	0.0127984	0.0227510	0.0038721	0.0020619	0.0015129	0.0028653
double negative T cells	0.0012395	0.0035674	0.0041269	0.0091087	0.0106755	0.0057483	0.0124964	0.0080076	0.0135894	0.0032090	0.0052504	0.0083061	0.0043232	0.0043465	0.0042301	0.0040620	0.0057537	0.0082474	0.0045418	0.0068209	0.0095901	0.0023576	0.0088881	0.0053918	0.0070396	0.0089707	0.0120601	0.0064705	0.0092593	0.0119963	0.0022693	0.0036294
NK cells and/or NK- T cells and/or gamma delta T cells	0.0054536	0.0028986	0.0025793	0.0032531	0.0038820	0.0014371	0.0048282	0.0019066	0.0097489	0.0060168	0.0048465	0.0035598	0.0036581	0.0015523	0.0019741	0.0018464	0.0027618	0.0044183	0.0026717	0.0074140	0.0034029	0.0141458	0.0068928	0.0093458	0.0019026	0.0037771	0.0093527	0.0058443	0.0077441	0.0014995	0.0103379	0.0049666
neutrophils	0.0022310	0.0020067	0.0067062	0.0021687	0.0027174	0.0065695	0.0017041	0.0005720	0.0059084	0.0020056	0.0147415	0.0047464	0.0256069	0.0083825	0.0014100	0.0022157	0.0006904	0.0002946	0.0000000	0.0029656	0.0116009	0.0001814	0.0010883	0.0003595	0.0013318	0.0007082	0.0009845	0.0029221	0.0000000	0.0031865	0.0052950	0.0038204
Pre-T follicular helper / CD4 Treg	0.0004958	0.0015608	0.0018055	0.0047712	0.0013587	0.0036953	0.0002840	0.0041945	0.0038405	0.0016045	0.0010097	0.0026698	0.0043232	0.0006209	0.0016920	0.0007386	0.0016110	0.0032401	0.0066791	0.0023725	0.0018561	0.0019949	0.0047161	0.0017973	0.0020928	0.0021246	0.0022151	0.0058443	0.0033670	0.0024367	0.0005043	0.0017192
plasmacytoid DCs	0.0009916	0.0026756	0.0018055	0.0008675	0.0032997	0.0004106	0.0019881	0.0019066	0.0041359	0.0014039	0.0008078	0.0008899	0.0003326	0.0012419	0.0002820	0.0003693	0.0000000	0.0011782	0.0016030	0.0023725	0.0003094	0.0029017	0.0007256	0.0025162	0.0007610	0.0000000	0.0007384	0.0039658	0.0011785	0.0007498	0.0002521	0.0009551
epithelial cells	0.0002479	0.0004459	0.0005159	0.0000000	0.0178571	0.0000000	0.0005680	0.0000000	0.0000000	0.0002006	0.0000000	0.0002966	0.0006651	0.0086930	0.0000000	0.0000000	0.0002301	0.0002946	0.0000000	0.0002966	0.0003094	0.0003627	0.0001814	0.0003595	0.0013318	0.0000000	0.0027074	0.0002087	0.0001684	0.0007498	0.0005043	0.0011461
unknown	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0034081	0.0000000	0.0008863	0.0102286	0.0020194	0.0002966	0.0000000	0.0161441	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000

Sources of variations- Sex

dims <- list(c(1,2), c(2:3), c(3,4), c(4,5))

for (tissue_name in names(seurat_objects)) {
  tissue_obj <- seurat_objects[[tissue_name]]
  props <- getTransformedProps(clusters = tissue_obj$cell_labels,
                               sample = tissue_obj$Sample, transform = "asin")
  plot_list <- list()
  
  for (data_type in c("Counts", "TransformedProps")) {
    p <- vector("list", length(dims))
    
    for(i in 1:length(dims)) {
      mds <- plotMDS(if (data_type == "Counts") props$Counts else props$TransformedProps,
                     gene.selection = "common",
                     plot = FALSE, dim.plot = dims[[i]])
      
      data.frame(x = mds$x, 
                 y = mds$y,
                 sample = rownames(mds$distance.matrix.squared)) %>%
        left_join(tissue_obj@meta.data %>%
                   dplyr::select(Sample,
                                 batch_name, 
                                 age_years, 
                                 sex), 
                 by = c("sample" = "Sample")) %>%
        distinct() -> dat
      
      p[[i]] <- ggplot(dat, aes(x = x, y = y, 
                                shape = batch_name,
                                color = as.factor(sex))) +
        geom_point(size = 3) +
        labs(x = glue("Principal Component {dims[[i]][1]}"),
             y = glue("Principal Component {dims[[i]][2]}"),
             colour = "Sex",
             shape = "Batch") +
        theme(legend.direction = "horizontal",
              legend.text = element_text(size = 8),
              legend.title = element_text(size = 9),
              axis.text = element_text(size = 8),
              axis.title = element_text(size = 9))
    }
    
    title_suffix <- if (data_type == "Counts") "Cell-type Counts (Sex)" else "Transformed Proportions (Sex)"
    plot_list[[data_type]] <- wrap_plots(p, cols = 2) + plot_annotation(title = paste0(tissue_name, ": ", title_suffix)) +
      plot_layout(guides = "collect") &
      theme(legend.position = "bottom")
  
    }
  
  cat(paste('### ', tissue_name, '\n', sep = ""))
  print(plot_list$Counts)
  print(plot_list$TransformedProps)
  cat("\n\n")
}

Adenoids

Past versions of unnamed-chunk-7-1.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24
14f8a5e	Gunjan Dixit	2024-05-24

Past versions of unnamed-chunk-7-2.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24
14f8a5e	Gunjan Dixit	2024-05-24

BAL

Past versions of unnamed-chunk-7-3.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24
14f8a5e	Gunjan Dixit	2024-05-24

Past versions of unnamed-chunk-7-4.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24
14f8a5e	Gunjan Dixit	2024-05-24

Bronchial_brushings

Past versions of unnamed-chunk-7-5.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24
14f8a5e	Gunjan Dixit	2024-05-24

Past versions of unnamed-chunk-7-6.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24
14f8a5e	Gunjan Dixit	2024-05-24

Nasal_brushings

Past versions of unnamed-chunk-7-7.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24
14f8a5e	Gunjan Dixit	2024-05-24

Past versions of unnamed-chunk-7-8.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24
14f8a5e	Gunjan Dixit	2024-05-24

Tonsils

Past versions of unnamed-chunk-7-9.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24
14f8a5e	Gunjan Dixit	2024-05-24

Past versions of unnamed-chunk-7-10.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24
14f8a5e	Gunjan Dixit	2024-05-24

Sources of variations- Age in years

dims <- list(c(1,2), c(2:3), c(3,4), c(4,5))

for (tissue_name in names(seurat_objects)) {
  tissue_obj <- seurat_objects[[tissue_name]]
  props <- getTransformedProps(clusters = tissue_obj$cell_labels,
                               sample = tissue_obj$Sample, transform = "asin")
  
  plot_list <- list()
  
  for (data_type in c("Counts", "TransformedProps")) {
    p <- vector("list", length(dims))
    
    for(i in 1:length(dims)) {
      mds_data <- if (data_type == "Counts") props$Counts else props$TransformedProps
      mds <- plotMDS(mds_data,
                     gene.selection = "common",
                     plot = FALSE, dim.plot = dims[[i]])
      
      data.frame(x = mds$x, 
                 y = mds$y,
                 sample = rownames(mds$distance.matrix.squared)) %>%
        left_join(tissue_obj@meta.data %>%
                   dplyr::select(Sample,
                                 batch_name, 
                                 age_years, 
                                 sex), 
                 by = c("sample" = "Sample")) %>%
        distinct() -> dat
        
    p[[i]] <- ggplot(dat, aes(x = x, y = y, 
                            shape = as.factor(batch_name),
                            color = age_years))+
    geom_point(size = 3) +
    labs(x = glue("Principal Component {dims[[i]][1]}"),
         y = glue("Principal Component {dims[[i]][2]}"),
         colour = "age_years",
         shape = "Batch") +
    theme(legend.direction = "horizontal",
          legend.text = element_text(size = 8),
          legend.title = element_text(size = 9),
          axis.text = element_text(size = 8),
          axis.title = element_text(size = 9))+
    scale_colour_viridis_c(option = "plasma")
    }
    
    title_suffix <- if (data_type == "Counts") "Cell-type Counts (Age in years)" else "Transformed Proportions (Age in years)"
    plot_list[[data_type]] <- wrap_plots(p, cols = 2) + plot_annotation(title = paste0(tissue_name, ": ", title_suffix)) +
      plot_layout(guides = "collect") &
      theme(legend.position = "bottom")
  }
  
  cat(paste('### ', tissue_name, '\n', sep = ""))
  print(plot_list$Counts)
  print(plot_list$TransformedProps)
  cat("\n\n")
}

Adenoids

Past versions of unnamed-chunk-8-1.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24

Past versions of unnamed-chunk-8-2.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24

BAL

Past versions of unnamed-chunk-8-3.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24

Past versions of unnamed-chunk-8-4.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24

Bronchial_brushings

Past versions of unnamed-chunk-8-5.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24

Past versions of unnamed-chunk-8-6.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24

Nasal_brushings

Past versions of unnamed-chunk-8-7.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24

Past versions of unnamed-chunk-8-8.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24

Tonsils

Past versions of unnamed-chunk-8-9.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24

Past versions of unnamed-chunk-8-10.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24

Sources of variations- Log transformed Age

dims <- list(c(1,2), c(2:3), c(3,4), c(4,5))

for (tissue_name in names(seurat_objects)) {
  tissue_obj <- seurat_objects[[tissue_name]]
  props <- getTransformedProps(clusters = tissue_obj$cell_labels,
                               sample = tissue_obj$Sample, transform = "asin")
  
  plot_list <- list()
  
  for (data_type in c("Counts", "TransformedProps")) {
    p <- vector("list", length(dims))
    
    for(i in 1:length(dims)) {
      mds_data <- if (data_type == "Counts") props$Counts else props$TransformedProps
      mds <- plotMDS(mds_data,
                     gene.selection = "common",
                     plot = FALSE, dim.plot = dims[[i]])
      
      data.frame(x = mds$x, 
                 y = mds$y,
                 sample = rownames(mds$distance.matrix.squared)) %>%
        left_join(tissue_obj@meta.data %>%
                   dplyr::select(Sample,
                                 batch_name, 
                                 age_years, 
                                 sex), 
                 by = c("sample" = "Sample")) %>%
        distinct() -> dat
        p[[i]] <- ggplot(dat, aes(x = x, y = y, 
                            colour = log2(age_years)))+
    #geom_text(aes(label = str_remove_all(sample, "sample_")), size = 2.5) +
    geom_text(aes(label = str_remove_all(age_years, "sample_")), size = 3.5) +
    labs(x = glue("Principal Component {dims[[i]][1]}"),
         y = glue("Principal Component {dims[[i]][2]}"),
         colour = "Log2 Age") +
    theme(legend.direction = "horizontal",
          legend.text = element_text(size = 8),
          legend.title = element_text(size = 9),
          axis.text = element_text(size = 8),
          axis.title = element_text(size = 9)) +
    scale_colour_viridis_c(option = "plasma")
    }
    
    title_suffix <- if (data_type == "Counts") "Cell-type Counts (Log-transformed Age)" else "Transformed Proportions (Log-transformed Age)"
    plot_list[[data_type]] <- wrap_plots(p, cols = 2) + plot_annotation(title = paste0(tissue_name, ": ", title_suffix)) +
      plot_layout(guides = "collect") &
      theme(legend.position = "bottom")
  }
  
  cat(paste('### ', tissue_name, '\n', sep = ""))
  print(plot_list$Counts)
  print(plot_list$TransformedProps)
  cat("\n\n")
}

Adenoids

Past versions of unnamed-chunk-9-1.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24

Past versions of unnamed-chunk-9-2.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24

BAL

Past versions of unnamed-chunk-9-3.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24

Past versions of unnamed-chunk-9-4.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24

Bronchial_brushings

Past versions of unnamed-chunk-9-5.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24

Past versions of unnamed-chunk-9-6.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24

Nasal_brushings

Past versions of unnamed-chunk-9-7.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24

Past versions of unnamed-chunk-9-8.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24

Tonsils

Past versions of unnamed-chunk-9-9.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24

Past versions of unnamed-chunk-9-10.png

Version	Author	Date
0a358ce	Gunjan Dixit	2024-05-24

Model as continuous age

Adenoids, Tonsils and Nasal Brushings with two batches each-

for (tissue_name in c("Adenoids", "Tonsils", "Nasal_brushings")) {
  tissue_obj <- seurat_objects[[tissue_name]]
  props <- getTransformedProps(clusters = tissue_obj$cell_labels,
                               sample = tissue_obj$Sample, transform = "asin")
  
  tissue_obj@meta.data <- tissue_obj@meta.data %>%
  mutate(age_group = case_when(
    age_years >= 1 & age_years < 6  ~ "Preschool_1to5_years",
    age_years >= 6 & age_years < 12 ~ "Kids_6to11_years",
    age_years >= 12 ~ "Adolescent_12to17_years",
    TRUE ~ "Other"  
  ))
  
  samples_metadata <- tissue_obj@meta.data %>%
    dplyr::filter(Sample %in% unique(tissue_obj@meta.data$Sample)) %>%
    dplyr::group_by(Sample) %>%
    dplyr::summarise(
      age = dplyr::first(age_years),
      sex = dplyr::first(sex),
      batch = dplyr::first(batch_name),
      age_group = dplyr::first(age_group),
      .groups = 'drop'  
    )
  
  age <- samples_metadata$age
  sex <- as.factor(samples_metadata$sex)
  batch <- as.factor(samples_metadata$batch)
  design <- model.matrix(~age + sex + batch)
  design
  
  fit <- lmFit(props$TransformedProps, design)
  fit <- eBayes(fit, robust=TRUE)
  toptable.transformedProps <- topTable(fit) 
  
  fit.prop <- lmFit(props$Proportions, design)
  fit.prop <- eBayes(fit.prop, robust=TRUE)
  toptable.props <- topTable(fit.prop, sort.by = "F")
  

  cat(paste('### ', tissue_name, '\n', sep = ""))
  print(knitr::kable(toptable.transformedProps, caption = paste0("Transformed proportions Toptable results: ", tissue_name)))
  print(knitr::kable(toptable.props, caption = paste0("Proportions Toptable results: ", tissue_name)))
}

Adenoids

Transformed proportions Toptable results: Adenoids

	age	sexM	batchG000231_batch8	AveExpr	F	P.Value	adj.P.Val
T-follicular helper and/or T-follicular memory and/or CD4 Treg	-0.0117037	-0.0120926	0.0517669	0.2141541	17.985088	0.0000007	0.0000154
NK cells and/or NK- T cells and/or gamma delta T cells	0.0080113	0.0150085	-0.0007604	0.1987685	9.471398	0.0001451	0.0015960
DZ B cells (early or late phase)	-0.0067509	0.0203084	-0.0027247	0.1855346	5.843507	0.0028550	0.0170125
memory B cells	0.0142844	-0.0360757	-0.0601674	0.3792752	5.771221	0.0030932	0.0170125
Cycling GM B cells	-0.0118589	0.0406248	0.0047769	0.2228255	5.033119	0.0061036	0.0268557
pre-T cells	-0.0089586	-0.0108462	0.0118156	0.0413268	3.953251	0.0173828	0.0637369
Germinal centre B cell: DZ-LZ transition and/or LZ and/or LZ-DZ	-0.0112149	0.0200866	0.0137227	0.3355456	3.483934	0.0279487	0.0878388
activated DC3 (aDC3)?	-0.0016382	-0.0050395	-0.0198613	0.0795866	3.187446	0.0377645	0.1038523
monocytes/macrophages	-0.0015626	0.0110010	-0.0126048	0.1003342	2.954046	0.0482559	0.1179590
plasma cells	-0.0012890	0.0024777	-0.0144053	0.0810940	2.397421	0.0875522	0.1815055

Proportions Toptable results: Adenoids

	age	sexM	batchG000231_batch8	AveExpr	F	P.Value	adj.P.Val
T-follicular helper and/or T-follicular memory and/or CD4 Treg	-0.0050606	-0.0057930	0.0241311	0.0470619	21.585749	0.0000002	0.0000035
NK cells and/or NK- T cells and/or gamma delta T cells	0.0032333	0.0072623	-0.0005919	0.0407381	9.738957	0.0001348	0.0014823
memory B cells	0.0093029	-0.0264214	-0.0380241	0.1420777	5.321678	0.0048456	0.0271595
DZ B cells (early or late phase)	-0.0022529	0.0089447	-0.0021345	0.0361300	5.300340	0.0049381	0.0271595
Germinal centre B cell: DZ-LZ transition and/or LZ and/or LZ-DZ	-0.0061409	0.0149197	0.0044698	0.1136184	2.922459	0.0507906	0.2046084
activated DC3 (aDC3)?	-0.0002176	-0.0011815	-0.0032367	0.0071207	2.772420	0.0593692	0.2046084
Cycling GM B cells	-0.0049410	0.0227681	0.0068758	0.0551574	2.584207	0.0725826	0.2046084
neutrophils	0.0007822	-0.0025766	-0.0057039	0.0024897	2.457390	0.0830003	0.2046084
CM CD4 T cells and/or pre TFH cells	0.0044829	-0.0315803	0.0079408	0.1383713	2.401684	0.0882148	0.2046084
monocytes/macrophages	-0.0003022	0.0023282	-0.0022167	0.0106225	2.351417	0.0930038	0.2046084

Tonsils

Transformed proportions Toptable results: Tonsils

	age	sexM	batchG000231_batch9	AveExpr	F	P.Value	adj.P.Val
Germinal centre B cell: DZ-LZ transition and/or LZ and/or LZ-DZ	-0.0141598	-0.0343478	-0.0451938	0.3441087	12.120142	0.0000205	0.0004913
DZ B cells (early or late phase)	-0.0083404	-0.0178757	-0.0458115	0.2588213	9.766035	0.0001087	0.0013045
CD8 T cells	0.0099773	0.0176558	-0.0010949	0.1993472	7.068491	0.0009379	0.0058935
Regulatory CD4 T cells	0.0036381	-0.0087860	0.0229636	0.2002894	6.994355	0.0009823	0.0058935
CM CD4 T cells and/or pre TFH cells	0.0021446	-0.0306858	0.0463883	0.3897797	6.370373	0.0017215	0.0082632
activated DC3 (aDC3)?	-0.0023906	-0.0236181	0.0405279	0.1252740	6.082416	0.0021956	0.0087825
memory B cells	0.0089230	0.0235752	0.0258019	0.2944656	5.637572	0.0033380	0.0114447
Cycling GM B cells	-0.0096661	-0.0175264	0.0359624	0.2839906	5.292177	0.0046029	0.0138087
neutrophils	0.0044743	0.0196783	-0.0460594	0.0524873	4.903825	0.0065897	0.0175726
Naive B cell activated	0.0065440	0.0076935	-0.0302492	0.1777379	2.765422	0.0584426	0.1402623

Proportions Toptable results: Tonsils

	age	sexM	batchG000231_batch9	AveExpr	F	P.Value	adj.P.Val
Germinal centre B cell: DZ-LZ transition and/or LZ and/or LZ-DZ	-0.0084452	-0.0224598	-0.0283193	0.1199224	9.463885	0.0001627	0.0039053
DZ B cells (early or late phase)	-0.0037181	-0.0097905	-0.0240633	0.0692911	8.251433	0.0004067	0.0048801
Regulatory CD4 T cells	0.0014747	-0.0030909	0.0088465	0.0407203	7.303567	0.0008671	0.0057532
activated DC3 (aDC3)?	-0.0006465	-0.0060291	0.0103591	0.0164936	6.948272	0.0011537	0.0057532
CD8 T cells	0.0037783	0.0064033	0.0001585	0.0420241	6.912413	0.0011986	0.0057532
CM CD4 T cells and/or pre TFH cells	0.0015077	-0.0229258	0.0326892	0.1463575	6.371939	0.0018967	0.0075868
memory B cells	0.0047209	0.0145889	0.0155014	0.0880232	5.176634	0.0055069	0.0188806
Cycling GM B cells	-0.0049036	-0.0099528	0.0188909	0.0805841	4.778497	0.0079846	0.0239539
Naive B cell activated	0.0023247	0.0034793	-0.0093027	0.0329960	2.761341	0.0600604	0.1601612
Pre-T follicular helper / CD4 Treg	-0.0002605	-0.0003124	0.0016358	0.0024761	2.504281	0.0788196	0.1834559

Nasal_brushings

Transformed proportions Toptable results: Nasal_brushings

	age	sexM	batchG000231_batch5	AveExpr	F	P.Value	adj.P.Val
NK-T cells	-0.0147996	0.0735284	0.0328663	0.1611396	3.488244	0.0276287	0.3000845
monocyte and neutrophil-like	-0.0010021	0.0525727	-0.0879388	0.1682310	3.146680	0.0393800	0.3000845
mast cells	0.0081814	0.0109839	-0.0419161	0.0782283	2.548362	0.0743479	0.3000845
plasmacytoid DCs	0.0018378	0.0319181	-0.0359758	0.0512956	2.252954	0.1023900	0.3000845
neutrophils	-0.0040300	-0.0628129	-0.0339068	0.0791660	2.046915	0.1282605	0.3000845
B cells	-0.0075329	-0.1448491	0.0807056	0.2791431	2.031871	0.1306262	0.3000845
CD8 T cells	-0.0136321	-0.0472306	0.1269937	0.4502654	2.027266	0.1312870	0.3000845
proliferating T/NK	-0.0051705	0.0299025	-0.0211687	0.0884831	1.898459	0.1509935	0.3019871
plasma B cells	-0.0064534	0.0015147	0.0246574	0.0620145	1.123979	0.3548865	0.5193914
viral-activated cells	-0.0065133	0.0865647	-0.0068470	0.0836597	1.101964	0.3636798	0.5193914

Proportions Toptable results: Nasal_brushings

	age	sexM	batchG000231_batch5	AveExpr	F	P.Value	adj.P.Val
plasmacytoid DCs	0.0005436	0.0041524	-0.0078962	0.0053672	2.481066	0.0808514	0.4688430
NK-T cells	-0.0051875	0.0368998	0.0168949	0.0390653	2.417714	0.0866305	0.4688430
monocyte and neutrophil-like	0.0002761	0.0244582	-0.0336835	0.0364423	2.404075	0.0879081	0.4688430
mast cells	0.0022573	0.0040851	-0.0163894	0.0123677	2.008948	0.1347357	0.5135964
B cells	0.0004128	-0.1106854	0.0626827	0.1071052	1.849244	0.1604989	0.5135964
CD8 T cells	-0.0089496	-0.0107879	0.0931520	0.2130470	1.534815	0.2266501	0.5208806
proliferating T/NK	-0.0009965	0.0083334	-0.0072645	0.0109445	1.352027	0.2769961	0.5208806
neutrophils	-0.0008920	-0.0240376	-0.0172784	0.0159943	1.331904	0.2832750	0.5208806
goblet/club/basal cells	0.0084183	0.0076736	-0.1100610	0.3124801	1.301163	0.2929953	0.5208806
ionocytes	0.0000201	-0.0003328	-0.0035608	0.0055732	1.020224	0.3980570	0.6342899

Model as continuous age- Bronchial brushings and BAL (single batch)

for (tissue_name in c("Bronchial_brushings", "BAL")) {
  tissue_obj <- seurat_objects[[tissue_name]]
tissue_obj <- seurat_objects[[tissue_name]]

props <- getTransformedProps(clusters = tissue_obj$cell_labels,
                             sample = tissue_obj$Sample, transform = "asin")
  
tissue_obj@meta.data <- tissue_obj@meta.data %>%
  mutate(age_group = case_when(
    age_years >= 1 & age_years < 6  ~ "Preschool_1to5_years",
    age_years >= 6 & age_years < 12 ~ "Kids_6to11_years",
    age_years >= 12 ~ "Adolescent_12to17_years",
    TRUE ~ "Other"  
  ))
  
samples_metadata <- tissue_obj@meta.data %>%
    dplyr::filter(Sample %in% unique(tissue_obj@meta.data$Sample)) %>%
    dplyr::group_by(Sample) %>%
    dplyr::summarise(
      age = dplyr::first(age_years),
      sex = dplyr::first(sex),
      batch = dplyr::first(batch_name),
      age_group = dplyr::first(age_group),
      .groups = 'drop'  
    )
  
  age <- samples_metadata$age
  sex <- as.factor(samples_metadata$sex)
  batch <- as.factor(samples_metadata$batch)
  design <- model.matrix(~age + sex)
  design
  
  fit <- lmFit(props$TransformedProps, design)
  fit <- eBayes(fit, robust=TRUE)
  toptable.transformedProps <- topTable(fit) 
  
  fit.prop <- lmFit(props$Proportions, design)
  fit.prop <- eBayes(fit.prop, robust=TRUE)
  toptable.props <- topTable(fit.prop, sort.by = "F")
  

  cat(paste('### ', tissue_name, '\n', sep = ""))
  print(knitr::kable(toptable.transformedProps, caption = paste0("Transformed proportions Toptable results: ", tissue_name)))
  print(knitr::kable(toptable.props, caption = paste0("Proportions Toptable results: ", tissue_name)))
}

Bronchial_brushings

Transformed proportions Toptable results: Bronchial_brushings

	age	sexM	AveExpr	F	P.Value	adj.P.Val
mast cells	0.0116466	-0.0009733	0.1331685	2.9266155	0.0847851	0.6701451
macrophages	0.0234778	0.0419536	0.4072729	2.3302894	0.1321037	0.6701451
B cells	-0.0141048	-0.0058262	0.2334501	1.8926890	0.1856016	0.6701451
CD8 T cells	-0.0198651	0.1297779	0.3950899	1.6714969	0.2217707	0.6701451
plasmacytoid DCs	-0.0056202	0.0055107	0.0791936	1.4637702	0.2625753	0.6701451
ionocytes	0.0007057	0.0284909	0.0517758	1.4147059	0.2736107	0.6701451
mesothelial cells	0.0043385	-0.0052527	0.0357439	1.0080158	0.3883600	0.6701451
neutrophils	0.0008549	-0.0884807	0.1205195	0.9991736	0.3918576	0.6701451
monocyte and neutrophil-like	0.0081777	-0.0699999	0.3273618	0.9425358	0.4119612	0.6701451
monocytes	-0.0074533	-0.0586614	0.0730773	0.7869357	0.4735021	0.6701451

Proportions Toptable results: Bronchial_brushings

	age	sexM	AveExpr	F	P.Value	adj.P.Val
mast cells	0.0040779	-0.0023539	0.0228456	5.3363097	0.0192908	0.2893622
B cells	-0.0062234	-0.0062298	0.0643829	2.2769239	0.1401883	0.6266495
macrophages	0.0158633	0.0435442	0.1787851	2.0869250	0.1618952	0.6266495
plasmacytoid DCs	-0.0010796	0.0018541	0.0079512	1.5407623	0.2489559	0.6266495
CD8 T cells	-0.0125703	0.0839463	0.1710747	1.4176603	0.2758104	0.6266495
neutrophils	0.0006563	-0.0351866	0.0279557	1.3343963	0.2955988	0.6266495
mesothelial cells	0.0005451	0.0007252	0.0025693	1.2702643	0.3116726	0.6266495
monocytes	-0.0044430	-0.0408158	0.0225210	1.1034170	0.3595967	0.6266495
ionocytes	0.0001560	0.0025292	0.0035135	0.9933136	0.3953788	0.6266495
monocyte and neutrophil-like	0.0050388	-0.0367671	0.1126906	0.8308037	0.4565844	0.6266495

BAL

Transformed proportions Toptable results: BAL

	age	sexM	AveExpr	F	P.Value	adj.P.Val
macro-CCL	0.0284364	-0.1956226	0.1189898	8.227825	0.0040601	0.0547699
cycling T cells	-0.0046891	-0.0377098	0.0782002	7.026875	0.0073027	0.0547699
basal epithelial cells	0.0053279	0.0547655	0.0634272	6.223033	0.0111270	0.0556348
secretory epithelial cells	0.0082822	0.0872567	0.1600431	4.058494	0.0397134	0.1365328
B cells	-0.0070608	-0.0848273	0.1874117	3.616306	0.0530569	0.1365328
CD4 T cells	0.0117651	-0.0751215	0.2069717	3.573132	0.0546131	0.1365328
CD8 T cells	0.0035335	-0.0709605	0.2523926	3.062164	0.0775899	0.1662641
unknown	0.0066367	-0.0779742	0.1387083	1.995790	0.1713692	0.3072872
macro-proliferating	-0.0087665	0.0081691	0.1419812	1.903085	0.1843723	0.3072872
plasma B cells	-0.0035736	-0.0001937	0.0327661	1.473782	0.2612708	0.3919061

Proportions Toptable results: BAL

	age	sexM	AveExpr	F	P.Value	adj.P.Val
cycling T cells	-0.0007932	-0.0065097	0.0074173	7.862979	0.0052799	0.0547638
macro-CCL	0.0130786	-0.0892528	0.0335833	7.184407	0.0073018	0.0547638
secretory epithelial cells	0.0044322	0.0336076	0.0341704	5.692053	0.0157986	0.0669654
basal epithelial cells	0.0015079	0.0073824	0.0067140	5.470208	0.0178574	0.0669654
B cells	-0.0035809	-0.0354194	0.0427189	3.427660	0.0619486	0.1858459
CD8 T cells	0.0013578	-0.0334899	0.0652950	2.642141	0.1068786	0.2553534
CD4 T cells	0.0051477	-0.0245968	0.0469998	2.492730	0.1191649	0.2553534
unknown	0.0010737	-0.0251501	0.0248860	1.668217	0.2245401	0.4210128
macro-lipid	-0.0322758	0.1594412	0.2734180	1.379164	0.2846821	0.4744701
macro-proliferating	-0.0020999	0.0018987	0.0232386	1.228027	0.3230471	0.4845706

Age modelling with updated cell-labels

#rm(merged_list)
gc()

         used    (Mb) gc trigger    (Mb) limit (Mb)   max used    (Mb)

Ncells 10720940 572.6 18111504 967.3 NA 18111504 967.3 Vcells 3401094522 25948.3 4906635422 37434.7 143360 3441797318 26258.9

get_age_group_color <- function(age) {
  if (age >= 1 && age <= 5) {
    return("orange")  # Preschool (1-5 years)
  } else if (age > 5 && age <= 12) {
    return("purple")  # Kids (6-11 years)
  } else if (age > 12 && age <= 17) {
    return("darkgreen")  # Adolescent (12-17 years)
  } else {
    return("black")  # Default color for other cases
  }
}

for (tissue_name in c("Adenoids", "Tonsils","Nasal_brushings")) {
  tissue_obj <- seurat_objects[[tissue_name]]
  props <- getTransformedProps(clusters = tissue_obj$cell_labels,
                               sample = tissue_obj$Sample, transform = "asin")
  
  tissue_obj@meta.data <- tissue_obj@meta.data %>%
  mutate(age_group = case_when(
    age_years >= 1 & age_years < 6  ~ "Preschool_1to5_years",
    age_years >= 6 & age_years < 12 ~ "Kids_6to11_years",
    age_years >= 12 ~ "Adolescent_12to17_years",
    TRUE ~ "Other"  
  ))
  
  samples_metadata <- tissue_obj@meta.data %>%
    dplyr::filter(Sample %in% unique(tissue_obj@meta.data$Sample)) %>%
    dplyr::group_by(Sample) %>%
    dplyr::summarise(
      age = dplyr::first(age_years),
      sex = dplyr::first(sex),
      batch = dplyr::first(batch_name),
      age_group = dplyr::first(age_group),
      .groups = 'drop'  
    )
  
  age <- samples_metadata$age
  sex <- as.factor(samples_metadata$sex)
  batch <- as.factor(samples_metadata$batch)
  design <- model.matrix(~age + sex + batch)
  design
  
  fit <- lmFit(props$TransformedProps, design)
  fit <- eBayes(fit, robust=TRUE)
  toptable.transformedProps <- topTable(fit) 
  
  fit.prop <- lmFit(props$Proportions, design)
  fit.prop <- eBayes(fit.prop, robust=TRUE)
  toptable.props <- topTable(fit.prop, sort.by = "F")
  
 age_group_colors <- sapply(age, get_age_group_color)
  
  # Add tabset for each tissue
  cat(paste0("### ", tissue_name, " {.tabset}\n\n"))
  
  sorted_indices <- match(rownames(toptable.transformedProps), rownames(props$Proportions))
  
  # Plot Age as Continuous
  cat(paste0("#### Age as Continuous","\n", sep = ""))
  #pdf(file = here("output/plots/", paste0(tissue_name, "_proportions_Age.pdf")), width = 15)
  par(mfrow=c(1,1))
  for (i in sorted_indices) {
    plot(age, props$Proportions[i,], 
         pch=16, cex=3, ylab="Proportions", cex.lab=1.5, cex.axis=1.5,
         cex.main=2, col=age_group_colors)
    abline(a=fit.prop$coefficients[i, 1], b=fit.prop$coefficients[i, 2], col=4, 
           lwd=2)
    title(paste0(tissue_name, "-", rownames(props$Proportions)[i], " : Age as Continuous"), cex.main = 1.2, adj = 0)
  }
  #dev.off()
  
  # Plot Sex
  cat(paste0("#### Sex"," {.tabset}\n\n"))
  #pdf(file = here("output/plots/", paste0(tissue_name, "_proportions_Sex.pdf")), width = 15)
  par(mfrow=c(1,2))
  for (i in sorted_indices) {
    plot(sex, props$Proportions[i,], 
         pch=16, cex=3, ylab="Proportions", xlab="Sex", cex.lab=1.5, cex.axis=1.5,
         cex.main=2, col=c("hotpink", "darkblue"))
    abline(a=fit.prop$coefficients[i, 1], b=fit.prop$coefficients[i, 3], col=4, 
           lwd=2)
    title(paste0(tissue_name, "-", rownames(props$Proportions)[i]), cex.main = 1.2, adj = 0)
  }
  #dev.off()
  
  # Print the tables
  cat(paste0("#### Transformed proportions Toptable results: ", tissue_name, "\n", sep = ""))
  print(knitr::kable(toptable.transformedProps, caption = paste0("Transformed proportions Toptable results: ", tissue_name)))
  
  cat(paste0("#### Proportions Toptable results: ", tissue_name, "\n", sep = ""))
  print(knitr::kable(toptable.props, caption = paste0("Proportions Toptable results: ", tissue_name)))
}

Adenoids

Age as Continuous

#### Sex {.tabset}

#### Transformed proportions Toptable results: Adenoids

Transformed proportions Toptable results: Adenoids

	age	sexM	batchG000231_batch8	AveExpr	F	P.Value	adj.P.Val
T-follicular helper and/or T-follicular memory and/or CD4 Treg	-0.0117037	-0.0120926	0.0517669	0.2141541	17.985088	0.0000007	0.0000154
NK cells and/or NK- T cells and/or gamma delta T cells	0.0080113	0.0150085	-0.0007604	0.1987685	9.471398	0.0001451	0.0015960
DZ B cells (early or late phase)	-0.0067509	0.0203084	-0.0027247	0.1855346	5.843507	0.0028550	0.0170125
memory B cells	0.0142844	-0.0360757	-0.0601674	0.3792752	5.771221	0.0030932	0.0170125
Cycling GM B cells	-0.0118589	0.0406248	0.0047769	0.2228255	5.033119	0.0061036	0.0268557
pre-T cells	-0.0089586	-0.0108462	0.0118156	0.0413268	3.953251	0.0173828	0.0637369
Germinal centre B cell: DZ-LZ transition and/or LZ and/or LZ-DZ	-0.0112149	0.0200866	0.0137227	0.3355456	3.483934	0.0279487	0.0878388
activated DC3 (aDC3)?	-0.0016382	-0.0050395	-0.0198613	0.0795866	3.187446	0.0377645	0.1038523
monocytes/macrophages	-0.0015626	0.0110010	-0.0126048	0.1003342	2.954046	0.0482559	0.1179590
plasma cells	-0.0012890	0.0024777	-0.0144053	0.0810940	2.397421	0.0875522	0.1815055

Proportions Toptable results: Adenoids

Proportions Toptable results: Adenoids

	age	sexM	batchG000231_batch8	AveExpr	F	P.Value	adj.P.Val
T-follicular helper and/or T-follicular memory and/or CD4 Treg	-0.0050606	-0.0057930	0.0241311	0.0470619	21.585749	0.0000002	0.0000035
NK cells and/or NK- T cells and/or gamma delta T cells	0.0032333	0.0072623	-0.0005919	0.0407381	9.738957	0.0001348	0.0014823
memory B cells	0.0093029	-0.0264214	-0.0380241	0.1420777	5.321678	0.0048456	0.0271595
DZ B cells (early or late phase)	-0.0022529	0.0089447	-0.0021345	0.0361300	5.300340	0.0049381	0.0271595
Germinal centre B cell: DZ-LZ transition and/or LZ and/or LZ-DZ	-0.0061409	0.0149197	0.0044698	0.1136184	2.922459	0.0507906	0.2046084
activated DC3 (aDC3)?	-0.0002176	-0.0011815	-0.0032367	0.0071207	2.772420	0.0593692	0.2046084
Cycling GM B cells	-0.0049410	0.0227681	0.0068758	0.0551574	2.584207	0.0725826	0.2046084
neutrophils	0.0007822	-0.0025766	-0.0057039	0.0024897	2.457390	0.0830003	0.2046084
CM CD4 T cells and/or pre TFH cells	0.0044829	-0.0315803	0.0079408	0.1383713	2.401684	0.0882148	0.2046084
monocytes/macrophages	-0.0003022	0.0023282	-0.0022167	0.0106225	2.351417	0.0930038	0.2046084

Tonsils

Age as Continuous

#### Sex {.tabset}

#### Transformed proportions Toptable results: Tonsils

Transformed proportions Toptable results: Tonsils

	age	sexM	batchG000231_batch9	AveExpr	F	P.Value	adj.P.Val
Germinal centre B cell: DZ-LZ transition and/or LZ and/or LZ-DZ	-0.0141598	-0.0343478	-0.0451938	0.3441087	12.120142	0.0000205	0.0004913
DZ B cells (early or late phase)	-0.0083404	-0.0178757	-0.0458115	0.2588213	9.766035	0.0001087	0.0013045
CD8 T cells	0.0099773	0.0176558	-0.0010949	0.1993472	7.068491	0.0009379	0.0058935
Regulatory CD4 T cells	0.0036381	-0.0087860	0.0229636	0.2002894	6.994355	0.0009823	0.0058935
CM CD4 T cells and/or pre TFH cells	0.0021446	-0.0306858	0.0463883	0.3897797	6.370373	0.0017215	0.0082632
activated DC3 (aDC3)?	-0.0023906	-0.0236181	0.0405279	0.1252740	6.082416	0.0021956	0.0087825
memory B cells	0.0089230	0.0235752	0.0258019	0.2944656	5.637572	0.0033380	0.0114447
Cycling GM B cells	-0.0096661	-0.0175264	0.0359624	0.2839906	5.292177	0.0046029	0.0138087
neutrophils	0.0044743	0.0196783	-0.0460594	0.0524873	4.903825	0.0065897	0.0175726
Naive B cell activated	0.0065440	0.0076935	-0.0302492	0.1777379	2.765422	0.0584426	0.1402623

Proportions Toptable results: Tonsils

Proportions Toptable results: Tonsils

	age	sexM	batchG000231_batch9	AveExpr	F	P.Value	adj.P.Val
Germinal centre B cell: DZ-LZ transition and/or LZ and/or LZ-DZ	-0.0084452	-0.0224598	-0.0283193	0.1199224	9.463885	0.0001627	0.0039053
DZ B cells (early or late phase)	-0.0037181	-0.0097905	-0.0240633	0.0692911	8.251433	0.0004067	0.0048801
Regulatory CD4 T cells	0.0014747	-0.0030909	0.0088465	0.0407203	7.303567	0.0008671	0.0057532
activated DC3 (aDC3)?	-0.0006465	-0.0060291	0.0103591	0.0164936	6.948272	0.0011537	0.0057532
CD8 T cells	0.0037783	0.0064033	0.0001585	0.0420241	6.912413	0.0011986	0.0057532
CM CD4 T cells and/or pre TFH cells	0.0015077	-0.0229258	0.0326892	0.1463575	6.371939	0.0018967	0.0075868
memory B cells	0.0047209	0.0145889	0.0155014	0.0880232	5.176634	0.0055069	0.0188806
Cycling GM B cells	-0.0049036	-0.0099528	0.0188909	0.0805841	4.778497	0.0079846	0.0239539
Naive B cell activated	0.0023247	0.0034793	-0.0093027	0.0329960	2.761341	0.0600604	0.1601612
Pre-T follicular helper / CD4 Treg	-0.0002605	-0.0003124	0.0016358	0.0024761	2.504281	0.0788196	0.1834559

Nasal_brushings

Age as Continuous

#### Sex {.tabset}

#### Transformed proportions Toptable results: Nasal_brushings

Transformed proportions Toptable results: Nasal_brushings

	age	sexM	batchG000231_batch5	AveExpr	F	P.Value	adj.P.Val
NK-T cells	-0.0147996	0.0735284	0.0328663	0.1611396	3.488244	0.0276287	0.3000845
monocyte and neutrophil-like	-0.0010021	0.0525727	-0.0879388	0.1682310	3.146680	0.0393800	0.3000845
mast cells	0.0081814	0.0109839	-0.0419161	0.0782283	2.548362	0.0743479	0.3000845
plasmacytoid DCs	0.0018378	0.0319181	-0.0359758	0.0512956	2.252954	0.1023900	0.3000845
neutrophils	-0.0040300	-0.0628129	-0.0339068	0.0791660	2.046915	0.1282605	0.3000845
B cells	-0.0075329	-0.1448491	0.0807056	0.2791431	2.031871	0.1306262	0.3000845
CD8 T cells	-0.0136321	-0.0472306	0.1269937	0.4502654	2.027266	0.1312870	0.3000845
proliferating T/NK	-0.0051705	0.0299025	-0.0211687	0.0884831	1.898459	0.1509935	0.3019871
plasma B cells	-0.0064534	0.0015147	0.0246574	0.0620145	1.123979	0.3548865	0.5193914
viral-activated cells	-0.0065133	0.0865647	-0.0068470	0.0836597	1.101964	0.3636798	0.5193914

Proportions Toptable results: Nasal_brushings

Proportions Toptable results: Nasal_brushings

	age	sexM	batchG000231_batch5	AveExpr	F	P.Value	adj.P.Val
plasmacytoid DCs	0.0005436	0.0041524	-0.0078962	0.0053672	2.481066	0.0808514	0.4688430
NK-T cells	-0.0051875	0.0368998	0.0168949	0.0390653	2.417714	0.0866305	0.4688430
monocyte and neutrophil-like	0.0002761	0.0244582	-0.0336835	0.0364423	2.404075	0.0879081	0.4688430
mast cells	0.0022573	0.0040851	-0.0163894	0.0123677	2.008948	0.1347357	0.5135964
B cells	0.0004128	-0.1106854	0.0626827	0.1071052	1.849244	0.1604989	0.5135964
CD8 T cells	-0.0089496	-0.0107879	0.0931520	0.2130470	1.534815	0.2266501	0.5208806
proliferating T/NK	-0.0009965	0.0083334	-0.0072645	0.0109445	1.352027	0.2769961	0.5208806
neutrophils	-0.0008920	-0.0240376	-0.0172784	0.0159943	1.331904	0.2832750	0.5208806
goblet/club/basal cells	0.0084183	0.0076736	-0.1100610	0.3124801	1.301163	0.2929953	0.5208806
ionocytes	0.0000201	-0.0003328	-0.0035608	0.0055732	1.020224	0.3980570	0.6342899

Bronchial and BAL

tissue_list <- c("Bronchial_brushings", "BAL")

seurat_objects[["BAL"]]@meta.data$Sample <- sub("_\\d+$", "", seurat_objects[["BAL"]]@meta.data$Sample)

# Loop over each tissue
for (tissue_name in tissue_list) {
  tissue_obj <- seurat_objects[[tissue_name]]
  
  props <- getTransformedProps(clusters = tissue_obj$cell_labels,
                               sample = tissue_obj$Sample, transform = "asin")
  
  tissue_obj@meta.data <- tissue_obj@meta.data %>%
    mutate(age_group = case_when(
      age_years >= 1 & age_years < 6  ~ "Preschool_1to5_years",
      age_years >= 6 & age_years < 12 ~ "Kids_6to11_years",
      age_years >= 12 ~ "Adolescent_12to17_years",
      TRUE ~ "Other"
    ))
  
  samples_metadata <- tissue_obj@meta.data %>%
    dplyr::filter(Sample %in% unique(tissue_obj@meta.data$Sample)) %>%
    dplyr::group_by(Sample) %>%
    dplyr::summarise(
      age = dplyr::first(age_years),
      sex = dplyr::first(sex),
      batch = dplyr::first(batch_name),
      age_group = dplyr::first(age_group),
      .groups = 'drop'
    )
  
  age <- samples_metadata$age
  sex <- as.factor(samples_metadata$sex)
  batch <- as.factor(samples_metadata$batch)
  design <- model.matrix(~age + sex)
  
  fit <- lmFit(props$TransformedProps, design)
  fit <- eBayes(fit, robust=TRUE)
  toptable.transformedProps <- topTable(fit)
  
  fit.prop <- lmFit(props$Proportions, design)
  fit.prop <- eBayes(fit.prop, robust=TRUE)
  toptable.props <- topTable(fit.prop, sort.by = "F")
  
  age_group_colors <- sapply(age, get_age_group_color)
  
  # Add tabset for each tissue
  cat(paste0("### ", tissue_name, " {.tabset}\n\n"))
  
  sorted_indices <- match(rownames(toptable.transformedProps), rownames(props$Proportions))
  
  # Plot Age as Continuous
  cat(paste0("#### Age as Continuous\n"))
  #pdf(file = here("output/plots/", paste0(tissue_name, "_proportions_Age.pdf")), width = 15)
  par(mfrow=c(1,1))
  for (i in sorted_indices) {
    plot(age, props$Proportions[i,], 
         pch=16, cex=3, ylab="Proportions", cex.lab=1.5, cex.axis=1.5,
         cex.main=2, col=age_group_colors)
    abline(a=fit.prop$coefficients[i, 1], b=fit.prop$coefficients[i, 2], col=4, 
           lwd=2)
    title(paste0(tissue_name, "-", rownames(props$Proportions)[i], " : Age as Continuous"), cex.main = 1.8, adj = 0)
  }
  #dev.off()
  
  # Plot Sex
  cat(paste0("##Sex\n"))
  #pdf(file = here("output/plots/", paste0(tissue_name, "_proportions_Sex.pdf")), width = 15)
  par(mfrow=c(1,2))
  for (i in sorted_indices) {
    plot(sex, props$Proportions[i,], 
         pch=16, cex=3, ylab="Proportions", xlab="Sex", cex.lab=1.5, cex.axis=1.5,
         cex.main=2, col=c("hotpink", "darkblue"))
    abline(a=fit.prop$coefficients[i, 1], b=fit.prop$coefficients[i, 3], col=4, 
           lwd=2)
    title(paste0(tissue_name, "-", rownames(props$Proportions)[i]), cex.main = 2, adj = 0)
  }
  #dev.off()
  
  # Print the tables
  cat(paste0("## Transformed proportions Toptable results: ", tissue_name, "\n"))
  print(knitr::kable(toptable.transformedProps, caption = paste0("Transformed proportions Toptable results: ", tissue_name)))
  
  cat(paste0("## Proportions Toptable results: ", tissue_name, "\n"))
  print(knitr::kable(toptable.props, caption = paste0("Proportions Toptable results: ", tissue_name)))
}

Bronchial_brushings

Age as Continuous

##Sex ## Transformed proportions Toptable results: Bronchial_brushings

Transformed proportions Toptable results: Bronchial_brushings

	age	sexM	AveExpr	F	P.Value	adj.P.Val
mast cells	0.0116466	-0.0009733	0.1331685	2.9266155	0.0847851	0.6701451
macrophages	0.0234778	0.0419536	0.4072729	2.3302894	0.1321037	0.6701451
B cells	-0.0141048	-0.0058262	0.2334501	1.8926890	0.1856016	0.6701451
CD8 T cells	-0.0198651	0.1297779	0.3950899	1.6714969	0.2217707	0.6701451
plasmacytoid DCs	-0.0056202	0.0055107	0.0791936	1.4637702	0.2625753	0.6701451
ionocytes	0.0007057	0.0284909	0.0517758	1.4147059	0.2736107	0.6701451
mesothelial cells	0.0043385	-0.0052527	0.0357439	1.0080158	0.3883600	0.6701451
neutrophils	0.0008549	-0.0884807	0.1205195	0.9991736	0.3918576	0.6701451
monocyte and neutrophil-like	0.0081777	-0.0699999	0.3273618	0.9425358	0.4119612	0.6701451
monocytes	-0.0074533	-0.0586614	0.0730773	0.7869357	0.4735021	0.6701451

Proportions Toptable results: Bronchial_brushings

Proportions Toptable results: Bronchial_brushings

	age	sexM	AveExpr	F	P.Value	adj.P.Val
mast cells	0.0040779	-0.0023539	0.0228456	5.3363097	0.0192908	0.2893622
B cells	-0.0062234	-0.0062298	0.0643829	2.2769239	0.1401883	0.6266495
macrophages	0.0158633	0.0435442	0.1787851	2.0869250	0.1618952	0.6266495
plasmacytoid DCs	-0.0010796	0.0018541	0.0079512	1.5407623	0.2489559	0.6266495
CD8 T cells	-0.0125703	0.0839463	0.1710747	1.4176603	0.2758104	0.6266495
neutrophils	0.0006563	-0.0351866	0.0279557	1.3343963	0.2955988	0.6266495
mesothelial cells	0.0005451	0.0007252	0.0025693	1.2702643	0.3116726	0.6266495
monocytes	-0.0044430	-0.0408158	0.0225210	1.1034170	0.3595967	0.6266495
ionocytes	0.0001560	0.0025292	0.0035135	0.9933136	0.3953788	0.6266495
monocyte and neutrophil-like	0.0050388	-0.0367671	0.1126906	0.8308037	0.4565844	0.6266495

BAL

Age as Continuous

##Sex ## Transformed proportions Toptable results: BAL

Transformed proportions Toptable results: BAL

	age	sexM	AveExpr	F	P.Value	adj.P.Val
macro-CCL	0.0282015	-0.1912789	0.1206070	3.4736278	0.0931035	0.6709055
basal epithelial cells	0.0050435	0.0541093	0.0631748	2.1624884	0.1894468	0.6709055
cycling T cells	-0.0045579	-0.0374785	0.0785126	1.8367443	0.2320882	0.6709055
secretory epithelial cells	0.0085370	0.0870557	0.1608856	1.7301923	0.2487191	0.6709055
CD4 T cells	0.0108564	-0.0743384	0.2062713	1.6454887	0.2630628	0.6709055
B cells	-0.0073902	-0.0842602	0.1873697	1.6048406	0.2703294	0.6709055
CD8 T cells	0.0027817	-0.0707270	0.2513273	1.3914570	0.3130892	0.6709055
unknown	0.0062338	-0.0767854	0.1386431	0.8027803	0.4874082	0.8796330
macro-proliferating	-0.0084210	0.0100245	0.1434875	0.7050535	0.5277798	0.8796330
plasma B cells	-0.0038377	0.0020657	0.0338709	0.4444986	0.6589938	0.8925562

Proportions Toptable results: BAL

Proportions Toptable results: BAL

	age	sexM	AveExpr	F	P.Value	adj.P.Val
macro-CCL	0.0130255	-0.0885283	0.0335122	3.0456288	0.1251774	0.7250622
secretory epithelial cells	0.0045279	0.0337934	0.0344494	2.4225157	0.1723875	0.7250622
basal epithelial cells	0.0013901	0.0071018	0.0064462	2.0024213	0.2186517	0.7250622
cycling T cells	-0.0007815	-0.0064928	0.0074466	1.9427667	0.2265324	0.7250622
B cells	-0.0037076	-0.0353703	0.0424672	1.5167024	0.2956827	0.7250622
CD8 T cells	0.0008343	-0.0337060	0.0643089	1.5054975	0.2978638	0.7250622
CD4 T cells	0.0044880	-0.0251548	0.0457755	1.3156218	0.3383623	0.7250622
unknown	0.0009799	-0.0250699	0.0246700	0.6935011	0.5371984	0.9603758
macro-lipid	-0.0309909	0.1608072	0.2757205	0.5416019	0.6091592	0.9603758
macro-proliferating	-0.0020571	0.0019812	0.0233568	0.4822589	0.6402505	0.9603758

Session Info

sessionInfo()

R version 4.3.2 (2023-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.5

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] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] openxlsx_4.2.5.2            knitr_1.45                 
 [3] kableExtra_1.4.0            edgeR_4.0.16               
 [5] limma_3.58.1                speckle_1.2.0              
 [7] ggridges_0.5.6              scran_1.30.2               
 [9] scuttle_1.12.0              SingleCellExperiment_1.24.0
[11] SummarizedExperiment_1.32.0 Biobase_2.62.0             
[13] GenomicRanges_1.54.1        GenomeInfoDb_1.38.6        
[15] IRanges_2.36.0              S4Vectors_0.40.2           
[17] BiocGenerics_0.48.1         MatrixGenerics_1.14.0      
[19] matrixStats_1.2.0           ggforce_0.4.2              
[21] viridis_0.6.5               viridisLite_0.4.2          
[23] paletteer_1.6.0             gridExtra_2.3              
[25] lubridate_1.9.3             forcats_1.0.0              
[27] stringr_1.5.1               purrr_1.0.2                
[29] readr_2.1.5                 tidyr_1.3.1                
[31] tibble_3.2.1                ggplot2_3.5.0              
[33] tidyverse_2.0.0             dplyr_1.1.4                
[35] Seurat_5.0.1.9009           SeuratObject_5.0.1         
[37] sp_2.1-3                    patchwork_1.2.0            
[39] glue_1.7.0                  here_1.0.1                 

loaded via a namespace (and not attached):
  [1] RcppAnnoy_0.0.22          splines_4.3.2            
  [3] later_1.3.2               bitops_1.0-7             
  [5] polyclip_1.10-6           fastDummies_1.7.3        
  [7] lifecycle_1.0.4           rprojroot_2.0.4          
  [9] globals_0.16.2            lattice_0.22-5           
 [11] MASS_7.3-60.0.1           magrittr_2.0.3           
 [13] plotly_4.10.4             sass_0.4.8               
 [15] rmarkdown_2.25            jquerylib_0.1.4          
 [17] yaml_2.3.8                metapod_1.10.1           
 [19] httpuv_1.6.14             sctransform_0.4.1        
 [21] zip_2.3.1                 spam_2.10-0              
 [23] spatstat.sparse_3.0-3     reticulate_1.35.0        
 [25] cowplot_1.1.3             pbapply_1.7-2            
 [27] RColorBrewer_1.1-3        abind_1.4-5              
 [29] zlibbioc_1.48.0           Rtsne_0.17               
 [31] RCurl_1.98-1.14           tweenr_2.0.3             
 [33] git2r_0.33.0              GenomeInfoDbData_1.2.11  
 [35] ggrepel_0.9.5             irlba_2.3.5.1            
 [37] listenv_0.9.1             spatstat.utils_3.0-4     
 [39] goftest_1.2-3             RSpectra_0.16-1          
 [41] dqrng_0.3.2               spatstat.random_3.2-2    
 [43] fitdistrplus_1.1-11       parallelly_1.37.0        
 [45] svglite_2.1.3             DelayedMatrixStats_1.24.0
 [47] leiden_0.4.3.1            codetools_0.2-19         
 [49] DelayedArray_0.28.0       xml2_1.3.6               
 [51] tidyselect_1.2.0          farver_2.1.1             
 [53] ScaledMatrix_1.10.0       spatstat.explore_3.2-6   
 [55] jsonlite_1.8.8            BiocNeighbors_1.20.2     
 [57] ellipsis_0.3.2            progressr_0.14.0         
 [59] survival_3.5-8            systemfonts_1.0.5        
 [61] tools_4.3.2               ica_1.0-3                
 [63] Rcpp_1.0.12               SparseArray_1.2.4        
 [65] xfun_0.42                 withr_3.0.0              
 [67] fastmap_1.1.1             bluster_1.12.0           
 [69] fansi_1.0.6               rsvd_1.0.5               
 [71] digest_0.6.34             timechange_0.3.0         
 [73] R6_2.5.1                  mime_0.12                
 [75] colorspace_2.1-0          scattermore_1.2          
 [77] tensor_1.5                spatstat.data_3.0-4      
 [79] utf8_1.2.4                generics_0.1.3           
 [81] data.table_1.15.0         httr_1.4.7               
 [83] htmlwidgets_1.6.4         S4Arrays_1.2.0           
 [85] whisker_0.4.1             uwot_0.1.16              
 [87] pkgconfig_2.0.3           gtable_0.3.4             
 [89] workflowr_1.7.1           lmtest_0.9-40            
 [91] XVector_0.42.0            htmltools_0.5.7          
 [93] dotCall64_1.1-1           scales_1.3.0             
 [95] png_0.1-8                 rstudioapi_0.15.0        
 [97] tzdb_0.4.0                reshape2_1.4.4           
 [99] nlme_3.1-164              cachem_1.0.8             
[101] zoo_1.8-12                KernSmooth_2.23-22       
[103] parallel_4.3.2            miniUI_0.1.1.1           
[105] pillar_1.9.0              grid_4.3.2               
[107] vctrs_0.6.5               RANN_2.6.1               
[109] promises_1.2.1            BiocSingular_1.18.0      
[111] beachmat_2.18.1           xtable_1.8-4             
[113] cluster_2.1.6             evaluate_0.23            
[115] locfit_1.5-9.8            cli_3.6.2                
[117] compiler_4.3.2            rlang_1.1.3              
[119] crayon_1.5.2              future.apply_1.11.1      
[121] labeling_0.4.3            rematch2_2.1.2           
[123] plyr_1.8.9                fs_1.6.3                 
[125] stringi_1.8.3             BiocParallel_1.36.0      
[127] deldir_2.0-2              munsell_0.5.0            
[129] lazyeval_0.2.2            spatstat.geom_3.2-8      
[131] Matrix_1.6-5              RcppHNSW_0.6.0           
[133] hms_1.1.3                 sparseMatrixStats_1.14.0 
[135] future_1.33.1             statmod_1.5.0            
[137] shiny_1.8.0               highr_0.10               
[139] ROCR_1.0-11               igraph_2.0.2             
[141] bslib_0.6.1              

Session information

sessionInfo()

R version 4.3.2 (2023-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.5

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] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] openxlsx_4.2.5.2            knitr_1.45                 
 [3] kableExtra_1.4.0            edgeR_4.0.16               
 [5] limma_3.58.1                speckle_1.2.0              
 [7] ggridges_0.5.6              scran_1.30.2               
 [9] scuttle_1.12.0              SingleCellExperiment_1.24.0
[11] SummarizedExperiment_1.32.0 Biobase_2.62.0             
[13] GenomicRanges_1.54.1        GenomeInfoDb_1.38.6        
[15] IRanges_2.36.0              S4Vectors_0.40.2           
[17] BiocGenerics_0.48.1         MatrixGenerics_1.14.0      
[19] matrixStats_1.2.0           ggforce_0.4.2              
[21] viridis_0.6.5               viridisLite_0.4.2          
[23] paletteer_1.6.0             gridExtra_2.3              
[25] lubridate_1.9.3             forcats_1.0.0              
[27] stringr_1.5.1               purrr_1.0.2                
[29] readr_2.1.5                 tidyr_1.3.1                
[31] tibble_3.2.1                ggplot2_3.5.0              
[33] tidyverse_2.0.0             dplyr_1.1.4                
[35] Seurat_5.0.1.9009           SeuratObject_5.0.1         
[37] sp_2.1-3                    patchwork_1.2.0            
[39] glue_1.7.0                  here_1.0.1                 

loaded via a namespace (and not attached):
  [1] RcppAnnoy_0.0.22          splines_4.3.2            
  [3] later_1.3.2               bitops_1.0-7             
  [5] polyclip_1.10-6           fastDummies_1.7.3        
  [7] lifecycle_1.0.4           rprojroot_2.0.4          
  [9] globals_0.16.2            lattice_0.22-5           
 [11] MASS_7.3-60.0.1           magrittr_2.0.3           
 [13] plotly_4.10.4             sass_0.4.8               
 [15] rmarkdown_2.25            jquerylib_0.1.4          
 [17] yaml_2.3.8                metapod_1.10.1           
 [19] httpuv_1.6.14             sctransform_0.4.1        
 [21] zip_2.3.1                 spam_2.10-0              
 [23] spatstat.sparse_3.0-3     reticulate_1.35.0        
 [25] cowplot_1.1.3             pbapply_1.7-2            
 [27] RColorBrewer_1.1-3        abind_1.4-5              
 [29] zlibbioc_1.48.0           Rtsne_0.17               
 [31] RCurl_1.98-1.14           tweenr_2.0.3             
 [33] git2r_0.33.0              GenomeInfoDbData_1.2.11  
 [35] ggrepel_0.9.5             irlba_2.3.5.1            
 [37] listenv_0.9.1             spatstat.utils_3.0-4     
 [39] goftest_1.2-3             RSpectra_0.16-1          
 [41] dqrng_0.3.2               spatstat.random_3.2-2    
 [43] fitdistrplus_1.1-11       parallelly_1.37.0        
 [45] svglite_2.1.3             DelayedMatrixStats_1.24.0
 [47] leiden_0.4.3.1            codetools_0.2-19         
 [49] DelayedArray_0.28.0       xml2_1.3.6               
 [51] tidyselect_1.2.0          farver_2.1.1             
 [53] ScaledMatrix_1.10.0       spatstat.explore_3.2-6   
 [55] jsonlite_1.8.8            BiocNeighbors_1.20.2     
 [57] ellipsis_0.3.2            progressr_0.14.0         
 [59] survival_3.5-8            systemfonts_1.0.5        
 [61] tools_4.3.2               ica_1.0-3                
 [63] Rcpp_1.0.12               SparseArray_1.2.4        
 [65] xfun_0.42                 withr_3.0.0              
 [67] fastmap_1.1.1             bluster_1.12.0           
 [69] fansi_1.0.6               rsvd_1.0.5               
 [71] digest_0.6.34             timechange_0.3.0         
 [73] R6_2.5.1                  mime_0.12                
 [75] colorspace_2.1-0          scattermore_1.2          
 [77] tensor_1.5                spatstat.data_3.0-4      
 [79] utf8_1.2.4                generics_0.1.3           
 [81] data.table_1.15.0         httr_1.4.7               
 [83] htmlwidgets_1.6.4         S4Arrays_1.2.0           
 [85] whisker_0.4.1             uwot_0.1.16              
 [87] pkgconfig_2.0.3           gtable_0.3.4             
 [89] workflowr_1.7.1           lmtest_0.9-40            
 [91] XVector_0.42.0            htmltools_0.5.7          
 [93] dotCall64_1.1-1           scales_1.3.0             
 [95] png_0.1-8                 rstudioapi_0.15.0        
 [97] tzdb_0.4.0                reshape2_1.4.4           
 [99] nlme_3.1-164              cachem_1.0.8             
[101] zoo_1.8-12                KernSmooth_2.23-22       
[103] parallel_4.3.2            miniUI_0.1.1.1           
[105] pillar_1.9.0              grid_4.3.2               
[107] vctrs_0.6.5               RANN_2.6.1               
[109] promises_1.2.1            BiocSingular_1.18.0      
[111] beachmat_2.18.1           xtable_1.8-4             
[113] cluster_2.1.6             evaluate_0.23            
[115] locfit_1.5-9.8            cli_3.6.2                
[117] compiler_4.3.2            rlang_1.1.3              
[119] crayon_1.5.2              future.apply_1.11.1      
[121] labeling_0.4.3            rematch2_2.1.2           
[123] plyr_1.8.9                fs_1.6.3                 
[125] stringi_1.8.3             BiocParallel_1.36.0      
[127] deldir_2.0-2              munsell_0.5.0            
[129] lazyeval_0.2.2            spatstat.geom_3.2-8      
[131] Matrix_1.6-5              RcppHNSW_0.6.0           
[133] hms_1.1.3                 sparseMatrixStats_1.14.0 
[135] future_1.33.1             statmod_1.5.0            
[137] shiny_1.8.0               highr_0.10               
[139] ROCR_1.0-11               igraph_2.0.2             
[141] bslib_0.6.1