Age Modelling: Tonsils
Propeller analysis
Gunjan Dixit
February 04, 2025
Last updated: 2025-02-04
Checks: 6 1
Knit directory: paed-airway-allTissues/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 8f254aa | Gunjan Dixit | 2024-10-09 | Modified age analysis with interaction model |
| html | 0f07f72 | Gunjan Dixit | 2024-10-07 | Added BAL subclustering without DecontX |
Introduction
This RMarkdown performs age modelling analysis for the earlyAIR tissue- Tonsils
suppressPackageStartupMessages({
library(here)
library(glue)
library(patchwork)
library(Seurat)
library(dplyr)
library(tidyverse)
library(gridExtra)
library(paletteer)
library(viridis)
library(ggforce)
library(RColorBrewer)
library(scran)
library(ggridges)
library(speckle)
library(edgeR)
library(kableExtra)
library(dplyr)
library(limma)
library(knitr)
library(openxlsx)
})Load data
tissue <- "Tonsils"
out <- here("output/RDS/AllBatches_Annotation_SEUs_v2/G000231_Neeland_Tonsils.annotated_clusters.SEU.rds")
merged_obj <- readRDS(out)
merged_objAn object of class Seurat
17566 features across 208077 samples within 1 assay
Active assay: RNA (17566 features, 2000 variable features)
10 layers present: data.1, data.2, data.3, counts.1, scale.data.1, counts.2, scale.data.2, counts.3, scale.data.3, scale.data
2 dimensional reductions calculated: pca, umap.mergedPlot sample wise cell-type proportions
metadata_df <- data.frame(
sample = merged_obj$sample_id,
donor = merged_obj$donor_id,
age_years = as.character(merged_obj$age_years),
cell_type = merged_obj$cell_labels_v2
)
color_palette = readRDS(here("output/RDS/color_palette_unique.rds"))
metadata_df$age_years <- as.numeric(metadata_df$age_years)
barplot_data <- metadata_df %>%
group_by(sample, age_years, cell_type) %>%
summarise(n_cells = n()) %>%
ungroup() %>%
group_by(sample, age_years) %>%
mutate(n_cells_total = sum(n_cells)) %>%
ungroup() %>%
mutate(percentage_cells = n_cells / n_cells_total)`summarise()` has grouped output by 'sample', 'age_years'. You can override
using the `.groups` argument.barplot_data <- barplot_data %>%
arrange(age_years)
a <- ggplot(barplot_data, aes(x = reorder(paste(sample, age_years, sep = ":"), age_years),
y = percentage_cells, fill = cell_type)) +
geom_bar(stat = "identity") +
ggtitle(paste0("Age vs Cell Type Proportions: ", tissue)) +
labs(x = "Sample:Age (Years)", y = "Proportion", fill = "Cell Type") +
scale_fill_manual(values = color_palette) +
theme_minimal() +
theme(
plot.title = element_text(size = 13, hjust = 0.5, face = "bold"),
legend.position = "top",
axis.text.x = element_text(angle = 45, hjust = 1)
)
a
| Version | Author | Date |
|---|---|---|
| 0f07f72 | Gunjan Dixit | 2024-10-07 |
Calculate proportions using Propeller
props <- getTransformedProps(clusters = merged_obj$cell_labels_v2,
sample = merged_obj$sample_id, transform = "asin")Performing arcsin square root transformation of proportionscat('### ', tissue, '\n')### Tonsils # Plot Cell Type Mean Variance
p1 <- plotCellTypeMeanVar(props$Counts)Using classic mode.
| Version | Author | Date |
|---|---|---|
| 0f07f72 | Gunjan Dixit | 2024-10-07 |
# Plot Cell Type Proportions Mean Variance
p2 <- plotCellTypePropsMeanVar(props$Counts)
| Version | Author | Date |
|---|---|---|
| 0f07f72 | Gunjan Dixit | 2024-10-07 |
p1 / p2numeric(0)print(knitr::kable(props$Proportions, caption = "Cell-type proportions in samples"))
Table: Cell-type proportions in samples
| | s017| s018| s019| s020| s021| s022| s023| s024| s025| s026| s027| s028| s029| s030| s031| s032| s138| s139| s140| s141| s142| s143| s144| s145| s146| s147| s148| s149| s150| s151| s152| s153|
|:--------------------------------|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
|CD4 effector | 0.0051455| 0.0081839| 0.0075166| 0.0106525| 0.0109062| 0.0127363| 0.0078302| 0.0098335| 0.0226036| 0.0083716| 0.0160352| 0.0078316| 0.0140918| 0.0077519| 0.0131679| 0.0143773| 0.0094178| 0.0085904| 0.0152370| 0.0152130| 0.0137840| 0.0167375| 0.0062964| 0.0336041| 0.0173704| 0.0141020| 0.0136667| 0.0341006| 0.0227383| 0.0095045| 0.0153790| 0.0203961|
|CD4 TCM | 0.0065927| 0.0106216| 0.0154753| 0.0136485| 0.0104250| 0.0084462| 0.0065939| 0.0136358| 0.0179992| 0.0126924| 0.0218533| 0.0076685| 0.0070459| 0.0080965| 0.0086272| 0.0097144| 0.0051370| 0.0058571| 0.0092175| 0.0078600| 0.0155070| 0.0150106| 0.0066742| 0.0408666| 0.0217798| 0.0213854| 0.0181667| 0.0180449| 0.0177529| 0.0126727| 0.0495748| 0.0206332|
|CD4 TFH | 0.0641582| 0.0795751| 0.0624908| 0.0722370| 0.0588613| 0.0666309| 0.0541933| 0.0630654| 0.0615320| 0.0893870| 0.0899674| 0.0566161| 0.0644441| 0.0599483| 0.0466172| 0.0711094| 0.0751712| 0.0681374| 0.0750564| 0.0461460| 0.0540755| 0.0587141| 0.0345045| 0.0521910| 0.0522448| 0.1005734| 0.0756667| 0.0572606| 0.0766051| 0.0841465| 0.0439660| 0.0568007|
|CD4 TN | 0.0443801| 0.0485809| 0.0461312| 0.0684088| 0.0336808| 0.0930420| 0.0304966| 0.0744723| 0.0939724| 0.0315960| 0.0336313| 0.0282265| 0.0163258| 0.0294574| 0.0470713| 0.0190402| 0.0421233| 0.0236236| 0.0477803| 0.0631339| 0.0461233| 0.0468916| 0.0193930| 0.0877646| 0.0407536| 0.0441655| 0.0550000| 0.1206309| 0.0722276| 0.0238246| 0.0756287| 0.0162457|
|CD4 Treg | 0.0041807| 0.0066168| 0.0067797| 0.0069907| 0.0104250| 0.0080440| 0.0135998| 0.0116691| 0.0104646| 0.0055361| 0.0117781| 0.0040790| 0.0065303| 0.0060293| 0.0062055| 0.0054401| 0.0056507| 0.0042952| 0.0056433| 0.0096349| 0.0080848| 0.0103613| 0.0065483| 0.0203102| 0.0148316| 0.0088331| 0.0141667| 0.0143507| 0.0165370| 0.0097580| 0.0209879| 0.0118582|
|CD4 Treg-eff | 0.0073967| 0.0168901| 0.0119381| 0.0169774| 0.0101043| 0.0205121| 0.0088605| 0.0249115| 0.0177899| 0.0102619| 0.0146161| 0.0169685| 0.0118577| 0.0168820| 0.0231573| 0.0118516| 0.0102740| 0.0226474| 0.0103461| 0.0177485| 0.0159046| 0.0115569| 0.0148596| 0.0107090| 0.0163014| 0.0230900| 0.0133333| 0.0194658| 0.0196984| 0.0182486| 0.0101321| 0.0190917|
|CD8 TF | 0.0033767| 0.0074874| 0.0126750| 0.0171438| 0.0070569| 0.0119319| 0.0080363| 0.0326472| 0.0190456| 0.0207940| 0.0420037| 0.0078316| 0.0417598| 0.0113695| 0.0051461| 0.0149602| 0.0217466| 0.0044904| 0.0048909| 0.0212982| 0.0441352| 0.0118225| 0.0258154| 0.0397587| 0.0410208| 0.0120874| 0.0233333| 0.0431941| 0.0172665| 0.0271195| 0.0423376| 0.0419779|
|CD8 TN | 0.0120598| 0.0224621| 0.0219602| 0.0451065| 0.0348035| 0.0305671| 0.0271997| 0.0352694| 0.0362076| 0.0167432| 0.0046828| 0.0110948| 0.0060148| 0.0080965| 0.0145300| 0.0048572| 0.0099315| 0.0132761| 0.0178706| 0.0152130| 0.0125911| 0.0245749| 0.0295933| 0.0256032| 0.0165687| 0.0106927| 0.0218333| 0.0527138| 0.0199416| 0.0111519| 0.0231590| 0.0084193|
|Cycling GCB | 0.0114166| 0.0308201| 0.0212233| 0.0188083| 0.0149158| 0.0182330| 0.0251391| 0.0331716| 0.0259523| 0.0391574| 0.0129133| 0.0345897| 0.0209658| 0.0246339| 0.0199788| 0.0172916| 0.0092466| 0.0343616| 0.0237020| 0.0304260| 0.0165673| 0.0151435| 0.0151114| 0.0059084| 0.0156334| 0.0218503| 0.0120000| 0.0164820| 0.0244407| 0.0204030| 0.0113986| 0.0209890|
|Cycling T | 0.0012864| 0.0024378| 0.0022108| 0.0046605| 0.0006415| 0.0012066| 0.0032969| 0.0051134| 0.0060695| 0.0035107| 0.0024124| 0.0034263| 0.0022341| 0.0034453| 0.0009081| 0.0007772| 0.0008562| 0.0031238| 0.0030098| 0.0012677| 0.0021206| 0.0022582| 0.0018889| 0.0009847| 0.0021379| 0.0049589| 0.0011667| 0.0058255| 0.0034047| 0.0012673| 0.0023521| 0.0026088|
|Double negative T | 0.0012864| 0.0041790| 0.0028003| 0.0083222| 0.0120289| 0.0045583| 0.0129817| 0.0107513| 0.0102553| 0.0024305| 0.0055343| 0.0079948| 0.0053274| 0.0031008| 0.0057515| 0.0033029| 0.0047945| 0.0060523| 0.0039503| 0.0055781| 0.0087475| 0.0015940| 0.0061705| 0.0064008| 0.0050775| 0.0074384| 0.0113333| 0.0046888| 0.0087549| 0.0088709| 0.0023521| 0.0036760|
|DZ early Sphase | 0.0332851| 0.0252481| 0.0380251| 0.0272969| 0.0184443| 0.0419627| 0.0230785| 0.0367117| 0.0186270| 0.0290305| 0.0154676| 0.0484581| 0.0343702| 0.0428941| 0.0455577| 0.0374976| 0.0150685| 0.0517376| 0.0325433| 0.0377789| 0.0227966| 0.0208555| 0.0341267| 0.0057853| 0.0204436| 0.0224702| 0.0130000| 0.0144928| 0.0183609| 0.0299075| 0.0101321| 0.0267995|
|DZ G2Mphase | 0.0752533| 0.0597249| 0.0966839| 0.0823901| 0.0449078| 0.0864727| 0.0624356| 0.0963682| 0.0527417| 0.0800702| 0.0417199| 0.1016479| 0.0929713| 0.0990525| 0.0979264| 0.1002526| 0.0138699| 0.0335806| 0.0289691| 0.0174949| 0.0113983| 0.0211211| 0.0275784| 0.0070162| 0.0134955| 0.0190609| 0.0153333| 0.0082410| 0.0232247| 0.0229375| 0.0066944| 0.0149413|
|DZ GCB | 0.0463097| 0.0332579| 0.0415623| 0.0342876| 0.0378508| 0.0454485| 0.0368844| 0.0506097| 0.0251151| 0.0396975| 0.0292323| 0.0662425| 0.0567108| 0.0737295| 0.0612986| 0.0555663| 0.0226027| 0.0566185| 0.0423251| 0.0233266| 0.0295560| 0.0429065| 0.0402972| 0.0096012| 0.0209781| 0.0373470| 0.0256667| 0.0129298| 0.0373298| 0.0385249| 0.0128460| 0.0383019|
|DZ late Sphase | 0.0006432| 0.0010448| 0.0008843| 0.0026631| 0.0030473| 0.0005363| 0.0014424| 0.0019667| 0.0006279| 0.0009452| 0.0015609| 0.0009790| 0.0003437| 0.0001723| 0.0012108| 0.0003886| 0.0239726| 0.0769231| 0.0507901| 0.0585700| 0.0280981| 0.0366631| 0.0513789| 0.0113245| 0.0233832| 0.0452503| 0.0276667| 0.0194658| 0.0330739| 0.0428336| 0.0151981| 0.0361674|
|DZtoLZ GCB transition | 0.0082007| 0.0261188| 0.0172439| 0.0189747| 0.0328789| 0.0111275| 0.0210179| 0.0094401| 0.0148598| 0.0120173| 0.0093657| 0.0055474| 0.0070459| 0.0111972| 0.0059028| 0.0087430| 0.0595890| 0.1622413| 0.1038375| 0.0550203| 0.0486415| 0.0957758| 0.0525123| 0.0224028| 0.0439604| 0.0887959| 0.0530000| 0.0356635| 0.0671206| 0.0705867| 0.0253302| 0.0761295|
|Early GC-committed NBC | 0.0270140| 0.0236810| 0.0318349| 0.0149800| 0.0203689| 0.0280198| 0.0195755| 0.0266160| 0.0200921| 0.0325412| 0.0154676| 0.0389949| 0.0405568| 0.0478898| 0.0339034| 0.0295318| 0.0167808| 0.0572042| 0.0287810| 0.0210446| 0.0144467| 0.0270988| 0.0210301| 0.0076317| 0.0195083| 0.0429258| 0.0200000| 0.0126456| 0.0218872| 0.0248384| 0.0117604| 0.0235978|
|Early MBC | 0.0736453| 0.0647745| 0.1042004| 0.0437750| 0.0587009| 0.0895562| 0.0502782| 0.0708011| 0.0458351| 0.0704834| 0.0465446| 0.1132322| 0.1135934| 0.1238587| 0.1159376| 0.0940354| 0.0006849| 0.0093713| 0.0022573| 0.0002535| 0.0006627| 0.0039851| 0.0010074| 0.0007386| 0.0002672| 0.0024795| 0.0006667| 0.0007104| 0.0015807| 0.0032949| 0.0001809| 0.0049804|
|Early PC precursor | 0.0064319| 0.0045273| 0.0028003| 0.0049933| 0.0057739| 0.0045583| 0.0074181| 0.0031467| 0.0041859| 0.0141777| 0.0036895| 0.0107685| 0.0219969| 0.0039621| 0.0059028| 0.0108801| 0.0035959| 0.0111285| 0.0052671| 0.0070994| 0.0049039| 0.0047821| 0.0057927| 0.0028311| 0.0058792| 0.0139470| 0.0090000| 0.0019892| 0.0047422| 0.0095045| 0.0056088| 0.0027274|
|Epithelial cells | 0.0003216| 0.0027860| 0.0011791| 0.0004993| 0.0171612| 0.0001341| 0.0043272| 0.0001311| 0.0010465| 0.0079665| 0.0017029| 0.0006526| 0.0010311| 0.0141258| 0.0003027| 0.0003886| 0.0003425| 0.0001952| 0.0003762| 0.0005071| 0.0002651| 0.0009299| 0.0001259| 0.0002462| 0.0010689| 0.0000000| 0.0018333| 0.0002842| 0.0001216| 0.0005069| 0.0009046| 0.0013044|
|Follicular dendritic cells | 0.0017688| 0.0125370| 0.0073692| 0.0069907| 0.0075381| 0.0120660| 0.0032969| 0.0048512| 0.0179992| 0.0048609| 0.0019867| 0.0042421| 0.0271524| 0.0055125| 0.0054488| 0.0048572| 0.0042808| 0.0064428| 0.0026336| 0.0106491| 0.0010603| 0.0027896| 0.0244302| 0.0036928| 0.0097541| 0.0179761| 0.0150000| 0.0208866| 0.0049854| 0.0036751| 0.0027139| 0.0037946|
|Gamma delta T | 0.0065927| 0.0059203| 0.0056006| 0.0093209| 0.0040096| 0.0041561| 0.0049454| 0.0120624| 0.0142319| 0.0157980| 0.0024124| 0.0011421| 0.0030933| 0.0065461| 0.0069623| 0.0134059| 0.0032534| 0.0054666| 0.0031979| 0.0043103| 0.0063618| 0.0066419| 0.0180078| 0.0046775| 0.0113576| 0.0044940| 0.0085000| 0.0127877| 0.0227383| 0.0124192| 0.0043423| 0.0014230|
|GC-commited metabolic activation | 0.0061103| 0.0158454| 0.0250553| 0.0166445| 0.0049719| 0.0156857| 0.0092726| 0.0152091| 0.0106739| 0.0056711| 0.0052505| 0.0177843| 0.0173569| 0.0206718| 0.0128651| 0.0087430| 0.0018836| 0.0048809| 0.0043266| 0.0081136| 0.0030484| 0.0025239| 0.0050371| 0.0012309| 0.0037413| 0.0057338| 0.0028333| 0.0026996| 0.0029183| 0.0035483| 0.0030758| 0.0049804|
|GCB-IFN | 0.0000000| 0.0000000| 0.0000000| 0.0000000| 0.0000000| 0.0001341| 0.0000000| 0.0000000| 0.0000000| 0.0000000| 0.0000000| 0.0000000| 0.0003437| 0.0000000| 0.0003027| 0.0000000| 0.0000000| 0.0005857| 0.0000000| 0.0002535| 0.0001325| 0.0029224| 0.0350082| 0.0000000| 0.0000000| 0.0000000| 0.0008333| 0.0000000| 0.0004864| 0.0000000| 0.0000000| 0.0005929|
|MAIT cells | 0.0003216| 0.0012189| 0.0023581| 0.0013316| 0.0016038| 0.0004022| 0.0022666| 0.0018356| 0.0025115| 0.0012152| 0.0034057| 0.0017947| 0.0015467| 0.0012059| 0.0013622| 0.0029143| 0.0010274| 0.0015619| 0.0003762| 0.0058316| 0.0050364| 0.0019926| 0.0031482| 0.0030773| 0.0037413| 0.0018596| 0.0056667| 0.0062518| 0.0025535| 0.0024078| 0.0036186| 0.0028460|
|Mast cells | 0.0003216| 0.0000000| 0.0004422| 0.0003329| 0.0004812| 0.0002681| 0.0000000| 0.0000000| 0.0008372| 0.0001350| 0.0000000| 0.0003263| 0.0003437| 0.0003445| 0.0007568| 0.0000000| 0.0001712| 0.0005857| 0.0003762| 0.0025355| 0.0001325| 0.0002657| 0.0005037| 0.0000000| 0.0000000| 0.0001550| 0.0001667| 0.0002842| 0.0002432| 0.0000000| 0.0001809| 0.0009487|
|Memory B cells | 0.0566007| 0.1366881| 0.0899042| 0.0850533| 0.1198075| 0.0647540| 0.1405316| 0.1160351| 0.1092507| 0.0870915| 0.1228892| 0.0618372| 0.0933150| 0.0911283| 0.1085213| 0.1169613| 0.1313356| 0.0368996| 0.0594432| 0.1653144| 0.1070908| 0.1544899| 0.0431936| 0.1453717| 0.1265366| 0.1063071| 0.1576667| 0.0740267| 0.1026265| 0.1013813| 0.0796092| 0.1504803|
|Monocytes/macrophages | 0.0040199| 0.0141041| 0.0079587| 0.0228029| 0.0118685| 0.0136748| 0.0090666| 0.0083912| 0.0414399| 0.0199838| 0.0183057| 0.0407897| 0.0073896| 0.0089578| 0.0140760| 0.0110744| 0.0070205| 0.0091761| 0.0126035| 0.0159736| 0.0095427| 0.0134166| 0.0241783| 0.0136632| 0.0106895| 0.0151867| 0.0118333| 0.0211708| 0.0141051| 0.0110252| 0.0123032| 0.0106724|
|Naïve B cell-IFN | 0.0099694| 0.0076615| 0.0131172| 0.0943742| 0.0091419| 0.0868749| 0.0451267| 0.0175692| 0.0950188| 0.0091817| 0.0167447| 0.1226954| 0.0231999| 0.0210164| 0.0208869| 0.0091315| 0.0250000| 0.0499805| 0.0165538| 0.0126775| 0.0131213| 0.0820935| 0.1644629| 0.0073855| 0.0144308| 0.0089881| 0.0191667| 0.0439045| 0.0265078| 0.0054492| 0.0162837| 0.0148227|
|Naïve B cells | 0.3619553| 0.1958906| 0.1921887| 0.1448069| 0.2633520| 0.1366135| 0.2239852| 0.1190507| 0.0772290| 0.2019984| 0.2287498| 0.0930005| 0.0982987| 0.1459087| 0.1574088| 0.2082767| 0.3261986| 0.1050371| 0.2535741| 0.1795132| 0.2770046| 0.0843518| 0.0560383| 0.2149188| 0.2632282| 0.1403998| 0.1676667| 0.1656721| 0.1538181| 0.2087188| 0.3200651| 0.2084667|
|Naïve B cells activated | 0.0480785| 0.0301236| 0.0338983| 0.0274634| 0.0330393| 0.0265451| 0.0407995| 0.0154714| 0.0253244| 0.0384823| 0.0502341| 0.0207212| 0.0178725| 0.0273902| 0.0254276| 0.0338061| 0.0657534| 0.0150332| 0.0284048| 0.0319473| 0.0421471| 0.0127524| 0.0081854| 0.0638848| 0.0375468| 0.0314582| 0.0226667| 0.0261438| 0.0128891| 0.0328222| 0.0698390| 0.0310684|
|Neutrophils | 0.0016080| 0.0017413| 0.0042741| 0.0019973| 0.0025662| 0.0042901| 0.0012363| 0.0003933| 0.0037673| 0.0016203| 0.0107847| 0.0026105| 0.0135762| 0.0053402| 0.0007568| 0.0013600| 0.0005137| 0.0001952| 0.0000000| 0.0015213| 0.0103380| 0.0001328| 0.0006296| 0.0002462| 0.0012026| 0.0004649| 0.0006667| 0.0018471| 0.0000000| 0.0025345| 0.0037995| 0.0028460|
|NK cells | 0.0062711| 0.0029601| 0.0020634| 0.0033289| 0.0035285| 0.0014747| 0.0041212| 0.0017045| 0.0085810| 0.0055361| 0.0046828| 0.0037527| 0.0041244| 0.0010336| 0.0013622| 0.0017486| 0.0025685| 0.0035143| 0.0020692| 0.0065923| 0.0030484| 0.0108927| 0.0071779| 0.0082472| 0.0018707| 0.0030993| 0.0075000| 0.0045467| 0.0060798| 0.0011405| 0.0081418| 0.0023716|
|Plasma B cells | 0.0442193| 0.0419641| 0.0390567| 0.0316245| 0.0473136| 0.0258748| 0.0422419| 0.0306805| 0.0397656| 0.0425331| 0.0517951| 0.0324686| 0.0692559| 0.0428941| 0.0522173| 0.0411890| 0.0383562| 0.0591566| 0.0395034| 0.0428499| 0.0405567| 0.0498140| 0.0516308| 0.0344658| 0.0546499| 0.0475748| 0.0648333| 0.0329639| 0.0351411| 0.0601952| 0.0334720| 0.0452982|
|Plasmacytoid DCs | 0.0025728| 0.0052238| 0.0041268| 0.0038282| 0.0043304| 0.0001341| 0.0006182| 0.0001311| 0.0046044| 0.0006751| 0.0001419| 0.0004895| 0.0000000| 0.0025840| 0.0001514| 0.0000000| 0.0005137| 0.0011714| 0.0030098| 0.0032961| 0.0011928| 0.0043836| 0.0021408| 0.0049237| 0.0013362| 0.0012397| 0.0015000| 0.0058255| 0.0024319| 0.0013940| 0.0014474| 0.0010672|
|Pre-BCRi II | 0.0036983| 0.0095769| 0.0072218| 0.0046605| 0.0076985| 0.0072396| 0.0086544| 0.0085224| 0.0087903| 0.0145828| 0.0051086| 0.0086474| 0.0130607| 0.0101637| 0.0099894| 0.0116573| 0.0085616| 0.0173760| 0.0208804| 0.0063387| 0.0117959| 0.0185972| 0.0133484| 0.0126785| 0.0189738| 0.0241748| 0.0183333| 0.0184712| 0.0240759| 0.0200228| 0.0115795| 0.0246650|
|Pre-T cells | 0.0001608| 0.0000000| 0.0010317| 0.0016644| 0.0003208| 0.0005363| 0.0000000| 0.0026223| 0.0004186| 0.0000000| 0.0000000| 0.0001632| 0.0022341| 0.0000000| 0.0003027| 0.0003886| 0.0003425| 0.0001952| 0.0041384| 0.0000000| 0.0000000| 0.0000000| 0.0001259| 0.0000000| 0.0000000| 0.0000000| 0.0001667| 0.0001421| 0.0001216| 0.0000000| 0.0000000| 0.0000000|
|T-IFN | 0.0014472| 0.0010448| 0.0051584| 0.0091545| 0.0016038| 0.0034857| 0.0212240| 0.0032778| 0.0035580| 0.0010802| 0.0032638| 0.0019579| 0.0177006| 0.0017227| 0.0127138| 0.0005829| 0.0017123| 0.0056619| 0.0020692| 0.0030426| 0.0035785| 0.0212540| 0.1147211| 0.0024618| 0.0010689| 0.0021695| 0.0015000| 0.0065359| 0.0116732| 0.0012673| 0.0028949| 0.0010672|
|TFH-LZ-GC | 0.0118990| 0.0384816| 0.0135593| 0.0218043| 0.0338412| 0.0120660| 0.0179271| 0.0318605| 0.0309753| 0.0221442| 0.0540656| 0.0127264| 0.0249184| 0.0118863| 0.0093840| 0.0238974| 0.0306507| 0.0236236| 0.0349887| 0.0446247| 0.0664016| 0.0619022| 0.0273265| 0.0873954| 0.0471673| 0.0472648| 0.0676667| 0.0626598| 0.0697957| 0.0620961| 0.0407092| 0.0449425|Set model and design matrix
merged_obj@meta.data <- merged_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 <- merged_obj@meta.data %>%
dplyr::filter(donor_id %in% unique(merged_obj@meta.data$donor_id)) %>%
dplyr::group_by(donor_id) %>%
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$batch)
design <- model.matrix(~age + sex + batch)
design (Intercept) age sexM batchG000231_batch9
1 1 1.09 1 0
2 1 6.79 0 0
3 1 5.82 0 0
4 1 1.64 1 0
5 1 3.73 1 0
6 1 2.31 1 0
7 1 3.82 1 0
8 1 6.67 0 0
9 1 2.73 1 0
10 1 3.93 1 0
11 1 11.86 0 0
12 1 2.60 0 0
13 1 3.76 1 0
14 1 4.45 1 0
15 1 5.28 0 0
16 1 4.69 0 0
17 1 8.90 1 1
18 1 2.41 1 1
19 1 1.58 0 1
20 1 8.01 1 1
21 1 11.16 1 1
22 1 6.78 1 1
23 1 6.42 1 1
24 1 16.27 0 1
25 1 12.75 1 1
26 1 7.77 0 1
27 1 6.27 1 1
28 1 6.50 1 1
29 1 4.83 0 1
30 1 8.53 0 1
31 1 13.22 0 1
32 1 12.21 0 1
attr(,"assign")
[1] 0 1 2 3
attr(,"contrasts")
attr(,"contrasts")$sex
[1] "contr.treatment"
attr(,"contrasts")$batch
[1] "contr.treatment"Linear model fitting with Limma
fit <- lmFit(props$TransformedProps, design)
fit <- eBayes(fit, robust=TRUE)
coef = "age"
toptable.transformedProps <- topTable(fit, coef = coef, number = Inf)
fit.prop <- lmFit(props$Proportions, design)
fit.prop <- eBayes(fit.prop, robust=TRUE)
toptable.props <- topTable(fit.prop, sort.by = "p", coef = coef, number = Inf)
cat(paste('### ', tissue, '\n', sep = ""))### Tonsilsprint(knitr::kable(toptable.transformedProps, caption = paste0("Transformed proportions Toptable results: ", tissue)))
Table: Transformed proportions Toptable results: Tonsils
| | logFC| AveExpr| t| P.Value| adj.P.Val| B|
|:--------------------------------|----------:|---------:|----------:|---------:|---------:|---------:|
|CD8 TF | 0.0107896| 0.1387817| 5.5868472| 0.0000038| 0.0001479| 2.366465|
|DZ G2Mphase | -0.0070465| 0.2068927| -4.1757603| 0.0002183| 0.0042576| -1.662299|
|CD4 TCM | 0.0058001| 0.1160822| 3.9742555| 0.0003843| 0.0049964| -2.216781|
|Naïve B cells activated | 0.0076874| 0.1756144| 3.7933026| 0.0006350| 0.0050975| -2.706862|
|DZ GCB | -0.0066358| 0.1927723| -3.7096080| 0.0007993| 0.0050975| -2.930685|
|DZtoLZ GCB transition | -0.0091466| 0.1833999| -3.6667199| 0.0008988| 0.0050975| -3.044625|
|DZ late Sphase | -0.0060822| 0.1093271| -3.6601968| 0.0009149| 0.0050975| -3.061908|
|Memory B cells | 0.0100154| 0.3247181| 3.4752039| 0.0015101| 0.0068979| -3.546666|
|Early GC-committed NBC | -0.0057234| 0.1576590| -3.4555649| 0.0015918| 0.0068979| -3.597485|
|DZ early Sphase | -0.0059026| 0.1639000| -3.3968967| 0.0018621| 0.0072623| -3.748520|
|CD4 Treg | 0.0034475| 0.0955855| 3.1616516| 0.0034590| 0.0122638| -4.341559|
|Neutrophils | 0.0040962| 0.0435832| 3.0043175| 0.0051841| 0.0168483| -4.725852|
|Pre-T cells | -0.0026976| 0.0137338| -2.8337354| 0.0079611| 0.0238832| -5.130040|
|Cycling GCB | -0.0039900| 0.1417592| -2.6980635| 0.0111103| 0.0308847| -5.441434|
|TFH-LZ-GC | 0.0055243| 0.1866960| 2.6704736| 0.0118787| 0.0308847| -5.503602|
|MAIT cells | 0.0021362| 0.0470655| 2.5024231| 0.0177273| 0.0432103| -5.873341|
|Naïve B cell-IFN | -0.0088163| 0.1681446| -1.8971504| 0.0671931| 0.1497048| -7.062114|
|Naïve B cells | 0.0102301| 0.4390636| 1.8833901| 0.0690945| 0.1497048| -7.086176|
|Early MBC | -0.0038929| 0.1611794| -1.7756866| 0.0854488| 0.1753949| -7.269182|
|Pre-BCRi II | -0.0019335| 0.1102476| -1.6887202| 0.1011518| 0.1972460| -7.411049|
|Early PC precursor | -0.0021121| 0.0787276| -1.5647673| 0.1276311| 0.2270313| -7.602902|
|Mast cells | -0.0011950| 0.0149422| -1.5629030| 0.1280689| 0.2270313| -7.605692|
|CD4 effector | 0.0021801| 0.1149409| 1.5104365| 0.1409009| 0.2389190| -7.683055|
|CD4 TFH | -0.0023000| 0.2557934| -1.3700468| 0.1803605| 0.2930859| -7.878708|
|GC-commited metabolic activation | -0.0016397| 0.0856524| -1.2750290| 0.2116249| 0.3301349| -8.001506|
|Epithelial cells | 0.0018176| 0.0332756| 1.1757657| 0.2485001| 0.3727502| -8.121258|
|Cycling T | -0.0010478| 0.0488681| -1.0992811| 0.2799777| 0.4044123| -8.207459|
|Gamma delta T | -0.0015607| 0.0833453| -0.9359301| 0.3564307| 0.4964570| -8.373435|
|T-IFN | -0.0025723| 0.0717145| -0.7642408| 0.4504119| 0.5885878| -8.520577|
|Follicular dendritic cells | -0.0014541| 0.0842402| -0.7420019| 0.4635841| 0.5885878| -8.537548|
|CD4 Treg-eff | -0.0008690| 0.1231964| -0.7348762| 0.4678518| 0.5885878| -8.542883|
|Monocytes/macrophages | -0.0012461| 0.1173527| -0.6887567| 0.4960192| 0.6045234| -8.576210|
|GCB-IFN | -0.0012052| 0.0130231| -0.6340624| 0.5306187| 0.6270949| -8.613014|
|CD8 TN | -0.0013989| 0.1367512| -0.6002867| 0.5526105| 0.6338767| -8.634259|
|NK cells | 0.0005537| 0.0621524| 0.4798955| 0.6346178| 0.7071456| -8.700708|
|Plasmacytoid DCs | -0.0005567| 0.0401559| -0.4257621| 0.6731767| 0.7292748| -8.725830|
|CD4 TN | 0.0008197| 0.2160560| 0.2436118| 0.8091114| 0.8358856| -8.788476|
|Double negative T | -0.0003020| 0.0757670| -0.2366606| 0.8144526| 0.8358856| -8.790195|
|Plasma B cells | 0.0001799| 0.2098409| 0.1230429| 0.9028550| 0.9028550| -8.811250|print(knitr::kable(toptable.props, caption = paste0("Proportions Toptable results: ", tissue)))
Table: Proportions Toptable results: Tonsils
| | logFC| AveExpr| t| P.Value| adj.P.Val| B|
|:--------------------------------|----------:|---------:|----------:|---------:|---------:|-----------:|
|CD8 TF | 0.0030651| 0.0214238| 5.6478383| 0.0000042| 0.0001644| 0.5306465|
|CD4 TCM | 0.0016242| 0.0145399| 4.0684358| 0.0003326| 0.0052001| -3.8228233|
|DZtoLZ GCB transition | -0.0047174| 0.0400090| -3.9155205| 0.0005036| 0.0052001| -4.2302099|
|Naïve B cells activated | 0.0028203| 0.0321006| 3.8942547| 0.0005333| 0.0052001| -4.2864944|
|DZ late Sphase | -0.0022490| 0.0187255| -3.7658203| 0.0007534| 0.0058767| -4.6243142|
|DZ GCB | -0.0022663| 0.0382800| -3.3378958| 0.0023290| 0.0136106| -5.7190068|
|Memory B cells | 0.0056963| 0.1044420| 3.2678057| 0.0027909| 0.0136106| -5.8929838|
|CD4 Treg | 0.0006880| 0.0095694| 3.2667131| 0.0027919| 0.0136106| -5.8940738|
|DZ early Sphase | -0.0017826| 0.0278829| -3.1651730| 0.0036289| 0.0157252| -6.1446227|
|Early GC-committed NBC | -0.0017401| 0.0256953| -3.1156667| 0.0041145| 0.0160467| -6.2646132|
|DZ G2Mphase | -0.0023475| 0.0486118| -2.8666583| 0.0076515| 0.0271280| -6.8531757|
|TFH-LZ-GC | 0.0021202| 0.0372079| 2.6621254| 0.0125366| 0.0407438| -7.3159238|
|Cycling GCB | -0.0010359| 0.0207148| -2.4540497| 0.0203749| 0.0611246| -7.7650120|
|Neutrophils | 0.0004037| 0.0026519| 2.2791900| 0.0301765| 0.0840632| -8.1232910|
|MAIT cells | 0.0001842| 0.0024466| 2.1084123| 0.0437268| 0.1136897| -8.4557398|
|Pre-BCRi II | -0.0004842| 0.0127858| -2.0075806| 0.0540752| 0.1318083| -8.6431827|
|Pre-T cells | -0.0001261| 0.0004711| -1.8317305| 0.0772524| 0.1772261| -8.9529333|
|Naïve B cells | 0.0076760| 0.1866683| 1.7916025| 0.0836480| 0.1812374| -9.0206071|
|Naïve B cell-IFN | -0.0035492| 0.0350793| -1.6596487| 0.1077722| 0.2212167| -9.2341883|
|CD4 effector | 0.0005504| 0.0138492| 1.5058243| 0.1429377| 0.2787286| -9.4659709|
|Early PC precursor | -0.0003303| 0.0066879| -1.3309782| 0.1935544| 0.3492992| -9.7058263|
|CD4 TFH | -0.0010926| 0.0647529| -1.3203910| 0.1970406| 0.3492992| -9.7195077|
|Cycling T | -0.0001094| 0.0025897| -1.1010116| 0.2799211| 0.4624313| -9.9809782|
|Gamma delta T | -0.0003339| 0.0076931| -1.0902749| 0.2845731| 0.4624313| -9.9926367|
|GC-commited metabolic activation | -0.0002367| 0.0083871| -0.9089520| 0.3708596| 0.5680665| -10.1738588|
|Early MBC | -0.0010256| 0.0410117| -0.8939569| 0.3787110| 0.5680665| -10.1874445|
|CD4 Treg-eff | -0.0002383| 0.0154495| -0.8592976| 0.3972187| 0.5737603| -10.2180769|
|Monocytes/macrophages | -0.0003997| 0.0145770| -0.8081509| 0.4255896| 0.5927856| -10.2611430|
|Epithelial cells | 0.0001440| 0.0019615| 0.6715890| 0.5071485| 0.6820273| -10.3637325|
|Follicular dendritic cells | -0.0002400| 0.0082263| -0.6100168| 0.5466058| 0.7105875| -10.4039592|
|NK cells | 0.0000811| 0.0042230| 0.5367298| 0.5955368| 0.7364920| -10.4469562|
|T-IFN | -0.0005888| 0.0084892| -0.5008073| 0.6202901| 0.7364920| -10.4660412|
|CD8 TN | -0.0003375| 0.0201497| -0.4966468| 0.6231855| 0.7364920| -10.4681695|
|Mast cells | -0.0000196| 0.0003481| -0.3554214| 0.7248389| 0.8108051| -10.5300222|
|GCB-IFN | -0.0001271| 0.0012999| -0.3516497| 0.7276456| 0.8108051| -10.5313881|
|Double negative T | -0.0000576| 0.0061277| -0.2985177| 0.7674317| 0.8313843| -10.5491959|
|Plasmacytoid DCs | -0.0000271| 0.0020777| -0.2347857| 0.8160203| 0.8393466| -10.5667316|
|CD4 TN | 0.0003578| 0.0488695| 0.2324472| 0.8178249| 0.8393466| -10.5672923|
|Plasma B cells | 0.0000511| 0.0439236| 0.0852233| 0.9326700| 0.9326700| -10.5914580|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
}
}
age_group_colors <- sapply(age, get_age_group_color)
par(mfrow=c(1,1))
for (i in rownames(toptable.transformedProps)) {
plot(age, props$TransformedProps[i,],
pch=16, cex=3, ylab="Transformed Proportions", cex.lab=1.5, cex.axis=1.5,cex.main=2, col=age_group_colors)
abline(a=fit$coefficients[i, 1], b=fit$coefficients[i, 2], col=4, lwd=2)
title(paste0(tissue, "-", i, " : Age as Continuous"), cex.main = 1.2, adj = 0)
}
| Version | Author | Date |
|---|---|---|
| 0f07f72 | Gunjan Dixit | 2024-10-07 |
| Version | Author | Date |
|---|---|---|
| 0f07f72 | Gunjan Dixit | 2024-10-07 |
| Version | Author | Date |
|---|---|---|
| 0f07f72 | Gunjan Dixit | 2024-10-07 |
| Version | Author | Date |
|---|---|---|
| 0f07f72 | Gunjan Dixit | 2024-10-07 |
| Version | Author | Date |
|---|---|---|
| 0f07f72 | Gunjan Dixit | 2024-10-07 |
| Version | Author | Date |
|---|---|---|
| 0f07f72 | Gunjan Dixit | 2024-10-07 |
| Version | Author | Date |
|---|---|---|
| 0f07f72 | Gunjan Dixit | 2024-10-07 |
| Version | Author | Date |
|---|---|---|
| 0f07f72 | Gunjan Dixit | 2024-10-07 |
| Version | Author | Date |
|---|---|---|
| 0f07f72 | Gunjan Dixit | 2024-10-07 |
| Version | Author | Date |
|---|---|---|
| 0f07f72 | Gunjan Dixit | 2024-10-07 |
Plot results for Sex
coef = "sexM"
toptable.transformedProps.sex <- topTable(fit, coef = coef)
# Plot Sex
cat(paste0("#### Sex"," {.tabset}\n\n"))#### Sex {.tabset} par(mfrow=c(1,2))
for (i in rownames(toptable.transformedProps.sex)) {
plot(sex, props$TransformedProps[i,],
pch=16, cex=3, ylab="Transformed Proportions", xlab="Sex", cex.lab=1.5, cex.axis=1.5,
cex.main=2, col=c("hotpink", "darkblue"))
abline(a=fit$coefficients[i, 1], b=fit$coefficients[i, 3], col=4,
lwd=2)
title(paste0(tissue, "-", i), cex.main = 1.2, adj = 0)
}
| Version | Author | Date |
|---|---|---|
| 0f07f72 | Gunjan Dixit | 2024-10-07 |
| Version | Author | Date |
|---|---|---|
| 0f07f72 | Gunjan Dixit | 2024-10-07 |
| Version | Author | Date |
|---|---|---|
| 0f07f72 | Gunjan Dixit | 2024-10-07 |
| Version | Author | Date |
|---|---|---|
| 0f07f72 | Gunjan Dixit | 2024-10-07 |
| Version | Author | Date |
|---|---|---|
| 0f07f72 | Gunjan Dixit | 2024-10-07 |
Save results
out <- here("output",
"CSV_v2",
paste0("G000231_Neeland_",tissue,".propeller.xlsx"))
if (!file.exists(out)) {
write.xlsx(toptable.transformedProps, file = out, rowNames= T)
}Interaction model for Age and Sex
design <- model.matrix(~age*sex)
design (Intercept) age sexM age:sexM
1 1 1.09 1 1.09
2 1 6.79 0 0.00
3 1 5.82 0 0.00
4 1 1.64 1 1.64
5 1 3.73 1 3.73
6 1 2.31 1 2.31
7 1 3.82 1 3.82
8 1 6.67 0 0.00
9 1 2.73 1 2.73
10 1 3.93 1 3.93
11 1 11.86 0 0.00
12 1 2.60 0 0.00
13 1 3.76 1 3.76
14 1 4.45 1 4.45
15 1 5.28 0 0.00
16 1 4.69 0 0.00
17 1 8.90 1 8.90
18 1 2.41 1 2.41
19 1 1.58 0 0.00
20 1 8.01 1 8.01
21 1 11.16 1 11.16
22 1 6.78 1 6.78
23 1 6.42 1 6.42
24 1 16.27 0 0.00
25 1 12.75 1 12.75
26 1 7.77 0 0.00
27 1 6.27 1 6.27
28 1 6.50 1 6.50
29 1 4.83 0 0.00
30 1 8.53 0 0.00
31 1 13.22 0 0.00
32 1 12.21 0 0.00
attr(,"assign")
[1] 0 1 2 3
attr(,"contrasts")
attr(,"contrasts")$sex
[1] "contr.treatment"fit <- lmFit(props$TransformedProps, design)
cont.matrix <- cbind(
AgeInFemales = c(0, 1, 0, 0),
AgeInMales = c(0, 1, 0, 1),
Diff = c(0, 0, 0, 1)
)
cont.matrix AgeInFemales AgeInMales Diff
[1,] 0 0 0
[2,] 1 1 0
[3,] 0 0 0
[4,] 0 1 1fit2 <- contrasts.fit(fit, cont.matrix)
fit2 <- eBayes(fit2)
coef = "Diff"
toptable.transformedProps <- topTable(fit2, coef = coef)
toptable.transformedProps logFC AveExpr t P.Value adj.P.Val
CD4 TCM -0.005360869 0.11608221 -2.344966 0.02572326 0.9124107
NK cells -0.003102576 0.06215240 -1.635101 0.11231126 0.9124107
DZ late Sphase 0.012672407 0.10932712 1.628402 0.11372996 0.9124107
Naïve B cells activated -0.005169514 0.17561445 -1.456520 0.15547005 0.9124107
Pre-BCRi II 0.003702976 0.11024757 1.423696 0.16468766 0.9124107
Early MBC -0.013666180 0.16117942 -1.350792 0.18670200 0.9124107
DZ early Sphase 0.003875947 0.16389995 1.339302 0.19037095 0.9124107
CD4 Treg -0.002380929 0.09558554 -1.327557 0.19417870 0.9124107
MAIT cells 0.001793287 0.04706547 1.278969 0.21055631 0.9124107
DZtoLZ GCB transition 0.010100824 0.18339989 1.200482 0.23919600 0.9262141
B
CD4 TCM -5.636851
NK cells -6.923152
DZ late Sphase -6.933528
Naïve B cells activated -7.187288
Pre-BCRi II -7.232967
Early MBC -7.331144
DZ early Sphase -7.346201
CD4 Treg -7.361473
MAIT cells -7.423377
DZtoLZ GCB transition -7.518976Age modelling across GC cell subsets (population)
idx <- which(merged_obj$cell_labels %in% "Germinal centre B cells")
merged_obj <- merged_obj[,idx]
merged_objAn object of class Seurat
17566 features across 56859 samples within 1 assay
Active assay: RNA (17566 features, 2000 variable features)
4 layers present: data.2, counts.2, scale.data.2, scale.data
2 dimensional reductions calculated: pca, umap.mergedRepeat modeling steps
metadata_df <- data.frame(
sample = merged_obj$sample_id,
#donor = merged_obj$donor_id,
age_years = as.character(merged_obj$age_years),
cell_type = merged_obj$cell_labels_v2
)
metadata_df$age_years <- as.numeric(metadata_df$age_years)
barplot_data <- metadata_df %>%
group_by(sample, age_years, cell_type) %>%
summarise(n_cells = n()) %>%
ungroup() %>%
group_by(sample, age_years) %>%
mutate(n_cells_total = sum(n_cells)) %>%
ungroup() %>%
mutate(percentage_cells = n_cells / n_cells_total)`summarise()` has grouped output by 'sample', 'age_years'. You can override
using the `.groups` argument.barplot_data <- barplot_data %>%
arrange(age_years)
b <- ggplot(barplot_data, aes(x = reorder(paste(sample, age_years, sep = ":"), age_years),
y = percentage_cells, fill = cell_type)) +
geom_bar(stat = "identity") +
ggtitle(paste0("Age vs Cell Type Proportions (GC cell population): ", tissue)) +
labs(x = "Sample:Age (Years)", y = "Proportion", fill = "Cell Type") +
scale_fill_manual(values = color_palette) +
theme_minimal() +
theme(
plot.title = element_text(size = 13, hjust = 0.5, face = "bold"),
legend.position = "top",
axis.text.x = element_text(angle = 45, hjust = 1)
)
b
props <- getTransformedProps(clusters = merged_obj$cell_labels_v2,
sample = merged_obj$sample_id, transform = "asin")Performing arcsin square root transformation of proportions# Plot Cell Type Mean Variance
p1 <- plotCellTypeMeanVar(props$Counts)Using classic mode.
# Plot Cell Type Proportions Mean Variance
p2 <- plotCellTypePropsMeanVar(props$Counts)
p1 / p2numeric(0)print(knitr::kable(props$Proportions, caption = "Cell-type proportions (GC cell population) in samples"))
Table: Cell-type proportions (GC cell population) in samples
| | s017| s018| s019| s020| s021| s022| s023| s024| s025| s026| s027| s028| s029| s030| s031| s032| s138| s139| s140| s141| s142| s143| s144| s145| s146| s147| s148| s149| s150| s151| s152| s153|
|:--------------------------------|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
|Cycling GCB | 0.0395984| 0.1081246| 0.0559223| 0.0710245| 0.0616711| 0.0533543| 0.0979920| 0.0964177| 0.1186603| 0.1209341| 0.0712608| 0.0789278| 0.0516074| 0.0549789| 0.0498489| 0.0476445| 0.0552712| 0.0689115| 0.0734266| 0.1173021| 0.0919118| 0.0553130| 0.0523104| 0.0793388| 0.0934505| 0.0728682| 0.0666667| 0.1315193| 0.1039297| 0.0765937| 0.1115044| 0.0838863|
|DZ early Sphase | 0.1154490| 0.0885767| 0.1001942| 0.1030798| 0.0762599| 0.1227933| 0.0899598| 0.1067073| 0.0851675| 0.0896580| 0.0853563| 0.1105733| 0.0846024| 0.0957324| 0.1136707| 0.1033191| 0.0900716| 0.1037588| 0.1008159| 0.1456500| 0.1264706| 0.0761766| 0.1181343| 0.0776860| 0.1222045| 0.0749354| 0.0722222| 0.1156463| 0.0780765| 0.1122740| 0.0991150| 0.1071090|
|DZ G2Mphase | 0.2610151| 0.2095296| 0.2547573| 0.3111251| 0.1856764| 0.2530404| 0.2433735| 0.2801067| 0.2411483| 0.2472894| 0.2302271| 0.2319434| 0.2288494| 0.2210688| 0.2443353| 0.2762313| 0.0829069| 0.0673453| 0.0897436| 0.0674487| 0.0632353| 0.0771470| 0.0954664| 0.0942149| 0.0806709| 0.0635659| 0.0851852| 0.0657596| 0.0987590| 0.0861085| 0.0654867| 0.0597156|
|DZ GCB | 0.1606247| 0.1166768| 0.1095146| 0.1294783| 0.1564987| 0.1329933| 0.1437751| 0.1471037| 0.1148325| 0.1226022| 0.1613156| 0.1511541| 0.1395939| 0.1645521| 0.1529456| 0.1531049| 0.1351075| 0.1135474| 0.1311189| 0.0899316| 0.1639706| 0.1567200| 0.1394943| 0.1289256| 0.1253994| 0.1245478| 0.1425926| 0.1031746| 0.1587384| 0.1446242| 0.1256637| 0.1530806|
|DZ late Sphase | 0.0022309| 0.0036652| 0.0023301| 0.0100566| 0.0125995| 0.0015692| 0.0056225| 0.0057165| 0.0028708| 0.0029191| 0.0086139| 0.0022338| 0.0008460| 0.0003845| 0.0030211| 0.0010707| 0.1432958| 0.1542678| 0.1573427| 0.2258065| 0.1558824| 0.1339156| 0.1778553| 0.1520661| 0.1397764| 0.1509044| 0.1537037| 0.1553288| 0.1406412| 0.1607992| 0.1486726| 0.1445498|
|DZtoLZ GCB transition | 0.0284439| 0.0916310| 0.0454369| 0.0716530| 0.1359416| 0.0325618| 0.0819277| 0.0274390| 0.0679426| 0.0371143| 0.0516836| 0.0126582| 0.0173435| 0.0249904| 0.0147281| 0.0240899| 0.3561924| 0.3253720| 0.3216783| 0.2121212| 0.2698529| 0.3498302| 0.1817786| 0.3008264| 0.2627796| 0.2961240| 0.2944444| 0.2845805| 0.2854188| 0.2649857| 0.2477876| 0.3042654|
|Early GC-committed NBC | 0.0936977| 0.0830788| 0.0838835| 0.0565682| 0.0842175| 0.0819929| 0.0763052| 0.0773628| 0.0918660| 0.1005004| 0.0853563| 0.0889799| 0.0998308| 0.1068820| 0.0845921| 0.0813704| 0.1003071| 0.1147220| 0.0891608| 0.0811339| 0.0801471| 0.0989811| 0.0727986| 0.1024793| 0.1166134| 0.1431525| 0.1111111| 0.1009070| 0.0930714| 0.0932445| 0.1150442| 0.0943128|
|Early MBC | 0.2554378| 0.2272450| 0.2745631| 0.1653048| 0.2427056| 0.2620636| 0.1959839| 0.2057927| 0.2095694| 0.2176814| 0.2568520| 0.2583768| 0.2796108| 0.2764321| 0.2892749| 0.2591006| 0.0040942| 0.0187940| 0.0069930| 0.0009775| 0.0036765| 0.0145560| 0.0034874| 0.0099174| 0.0015974| 0.0082687| 0.0037037| 0.0056689| 0.0067218| 0.0123692| 0.0017699| 0.0199052|
|Early PC precursor | 0.0223090| 0.0158827| 0.0073786| 0.0188561| 0.0238727| 0.0133386| 0.0289157| 0.0091463| 0.0191388| 0.0437865| 0.0203602| 0.0245719| 0.0541455| 0.0088428| 0.0147281| 0.0299786| 0.0214944| 0.0223179| 0.0163170| 0.0273705| 0.0272059| 0.0174672| 0.0200523| 0.0380165| 0.0351438| 0.0465116| 0.0500000| 0.0158730| 0.0201655| 0.0356803| 0.0548673| 0.0109005|
|GC-commited metabolic activation | 0.0211935| 0.0555895| 0.0660194| 0.0628536| 0.0205570| 0.0459004| 0.0361446| 0.0442073| 0.0488038| 0.0175146| 0.0289742| 0.0405808| 0.0427242| 0.0461361| 0.0320997| 0.0240899| 0.0112590| 0.0097886| 0.0134033| 0.0312805| 0.0169118| 0.0092188| 0.0174368| 0.0165289| 0.0223642| 0.0191214| 0.0157407| 0.0215420| 0.0124095| 0.0133206| 0.0300885| 0.0199052|
|GCB-IFN | 0.0000000| 0.0000000| 0.0000000| 0.0000000| 0.0000000| 0.0003923| 0.0000000| 0.0000000| 0.0000000| 0.0000000| 0.0000000| 0.0000000| 0.0008460| 0.0000000| 0.0007553| 0.0000000| 0.0000000| 0.0011746| 0.0000000| 0.0009775| 0.0007353| 0.0106744| 0.1211857| 0.0000000| 0.0000000| 0.0000000| 0.0046296| 0.0000000| 0.0020683| 0.0000000| 0.0000000| 0.0023697|fit <- lmFit(props$TransformedProps, design)
fit <- eBayes(fit, robust=TRUE)
coef = "age"
toptable.transformedProps <- topTable(fit, coef = coef)
cat(paste('### ', tissue, '\n', sep = ""))### Tonsilsprint(knitr::kable(toptable.transformedProps, caption = paste0("Transformed proportions (Subclustering) Toptable results: ", tissue)))
Table: Transformed proportions (Subclustering) Toptable results: Tonsils
| | logFC| AveExpr| t| P.Value| adj.P.Val| B|
|:--------------------------------|----------:|---------:|----------:|---------:|---------:|---------:|
|DZ G2Mphase | -0.0120666| 0.3993427| -1.8553226| 0.0736045| 0.2733602| -6.377029|
|DZtoLZ GCB transition | 0.0183839| 0.3854512| 1.7106599| 0.0976804| 0.2733602| -6.617411|
|DZ late Sphase | 0.0160687| 0.2321274| 1.6697205| 0.1055925| 0.2733602| -6.682551|
|Early MBC | -0.0191510| 0.2974614| -1.6265012| 0.1145181| 0.2733602| -6.749901|
|Early PC precursor | 0.0042236| 0.1553675| 1.5819079| 0.1242547| 0.2733602| -6.817748|
|Early GC-committed NBC | 0.0023276| 0.3092810| 1.2191277| 0.2323827| 0.4260350| -7.309426|
|DZ early Sphase | -0.0018434| 0.3201743| -0.9180318| 0.3659920| 0.5751303| -7.629142|
|Cycling GCB | 0.0024629| 0.2807144| 0.8014057| 0.4292573| 0.5902287| -7.730154|
|GC-commited metabolic activation | -0.0018096| 0.1638320| -0.6044196| 0.5501510| 0.6724068| -7.870751|
|DZ GCB | -0.0004815| 0.3787401| -0.2371690| 0.8141528| 0.8955680| -8.029207|Save results for GCcell population
if (!file.exists(out)) {
write.xlsx(toptable.transformedProps, file = out, rowNames = TRUE, sheetName = "GCcell_subclustering")
} else {
write.xlsx(toptable.transformedProps, file = out, rowNames = TRUE, sheetName = "GCcell_subclustering", append = TRUE)
}Session Info
sessionInfo()R version 4.3.2 (2023-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 15.3
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] 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 RColorBrewer_1.1-3
[21] ggforce_0.4.2 viridis_0.6.5
[23] viridisLite_0.4.2 paletteer_1.6.0
[25] gridExtra_2.3 lubridate_1.9.3
[27] forcats_1.0.0 stringr_1.5.1
[29] purrr_1.0.2 readr_2.1.5
[31] tidyr_1.3.1 tibble_3.2.1
[33] ggplot2_3.5.0 tidyverse_2.0.0
[35] dplyr_1.1.4 Seurat_5.0.1.9009
[37] SeuratObject_5.0.1 sp_2.1-3
[39] patchwork_1.2.0 glue_1.7.0
[41] here_1.0.1 workflowr_1.7.1
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 processx_3.8.3
[11] lattice_0.22-5 MASS_7.3-60.0.1
[13] magrittr_2.0.3 plotly_4.10.4
[15] sass_0.4.8 rmarkdown_2.25
[17] jquerylib_0.1.4 yaml_2.3.8
[19] metapod_1.10.1 httpuv_1.6.14
[21] sctransform_0.4.1 zip_2.3.1
[23] spam_2.10-0 spatstat.sparse_3.0-3
[25] reticulate_1.35.0 cowplot_1.1.3
[27] pbapply_1.7-2 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] DelayedArray_0.28.0 leiden_0.4.3.1
[49] codetools_0.2-19 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] callr_3.7.5 digest_0.6.34
[73] timechange_0.3.0 R6_2.5.1
[75] mime_0.12 colorspace_2.1-0
[77] scattermore_1.2 tensor_1.5
[79] spatstat.data_3.0-4 utf8_1.2.4
[81] generics_0.1.3 data.table_1.15.0
[83] httr_1.4.7 htmlwidgets_1.6.4
[85] S4Arrays_1.2.0 whisker_0.4.1
[87] uwot_0.1.16 pkgconfig_2.0.3
[89] gtable_0.3.4 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] ps_1.7.6 getPass_0.2-4
[125] plyr_1.8.9 fs_1.6.3
[127] stringi_1.8.3 BiocParallel_1.36.0
[129] deldir_2.0-2 munsell_0.5.0
[131] lazyeval_0.2.2 spatstat.geom_3.2-8
[133] Matrix_1.6-5 RcppHNSW_0.6.0
[135] hms_1.1.3 sparseMatrixStats_1.14.0
[137] future_1.33.1 statmod_1.5.0
[139] shiny_1.8.0 highr_0.10
[141] ROCR_1.0-11 igraph_2.0.2
[143] bslib_0.6.1
sessionInfo()R version 4.3.2 (2023-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 15.3
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] 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 RColorBrewer_1.1-3
[21] ggforce_0.4.2 viridis_0.6.5
[23] viridisLite_0.4.2 paletteer_1.6.0
[25] gridExtra_2.3 lubridate_1.9.3
[27] forcats_1.0.0 stringr_1.5.1
[29] purrr_1.0.2 readr_2.1.5
[31] tidyr_1.3.1 tibble_3.2.1
[33] ggplot2_3.5.0 tidyverse_2.0.0
[35] dplyr_1.1.4 Seurat_5.0.1.9009
[37] SeuratObject_5.0.1 sp_2.1-3
[39] patchwork_1.2.0 glue_1.7.0
[41] here_1.0.1 workflowr_1.7.1
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 processx_3.8.3
[11] lattice_0.22-5 MASS_7.3-60.0.1
[13] magrittr_2.0.3 plotly_4.10.4
[15] sass_0.4.8 rmarkdown_2.25
[17] jquerylib_0.1.4 yaml_2.3.8
[19] metapod_1.10.1 httpuv_1.6.14
[21] sctransform_0.4.1 zip_2.3.1
[23] spam_2.10-0 spatstat.sparse_3.0-3
[25] reticulate_1.35.0 cowplot_1.1.3
[27] pbapply_1.7-2 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] DelayedArray_0.28.0 leiden_0.4.3.1
[49] codetools_0.2-19 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] callr_3.7.5 digest_0.6.34
[73] timechange_0.3.0 R6_2.5.1
[75] mime_0.12 colorspace_2.1-0
[77] scattermore_1.2 tensor_1.5
[79] spatstat.data_3.0-4 utf8_1.2.4
[81] generics_0.1.3 data.table_1.15.0
[83] httr_1.4.7 htmlwidgets_1.6.4
[85] S4Arrays_1.2.0 whisker_0.4.1
[87] uwot_0.1.16 pkgconfig_2.0.3
[89] gtable_0.3.4 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] ps_1.7.6 getPass_0.2-4
[125] plyr_1.8.9 fs_1.6.3
[127] stringi_1.8.3 BiocParallel_1.36.0
[129] deldir_2.0-2 munsell_0.5.0
[131] lazyeval_0.2.2 spatstat.geom_3.2-8
[133] Matrix_1.6-5 RcppHNSW_0.6.0
[135] hms_1.1.3 sparseMatrixStats_1.14.0
[137] future_1.33.1 statmod_1.5.0
[139] shiny_1.8.0 highr_0.10
[141] ROCR_1.0-11 igraph_2.0.2
[143] bslib_0.6.1