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lung_CD4CD8_202110_newTCR.R
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# lung CD4 and CD8 project
library(Seurat)
library(ggplot2)
library(purrr)
library(dplyr)
library(Matrix)
library(tibble)
library(cowplot)
library(ggrastr)
library(Cairo)
library(ggrepel)
library(openxlsx)
library(stringr)
library(magrittr)
# themes and functions ------------------------------------------------------------------
source('/home/big/tanlikai/script/rscripts/funcs.r')
# file.edit('//home/big/tanlikai/script/rscripts/funcs.r')
multicores()
#paralell computation
# work dir ----------------------------------------------------------------
setwd('/home/big/tanlikai/Lung/abt')
CD4CD8 <- readRDS('CD4CD8_integrated_2021_0716.rds')
Feature_rast(CD4CD8)
# read_rawdata ---------------------------------------------------------------
# directories to raw gene expression and CITEseq data
dirs <- list.dirs(path = 'raw') %>% str_subset('surface.+outs$' )
dirs
proj <- c('p32', 'p45','p25_CD4', 'p25_CD8','p27','p31','p71')
pt <- c('p32', 'p45','p25', 'p25','p27','p31','p71')
# read raw GEX and CITEseq data
rawdata <- dirs %>% map(~ Read10X_h5(paste0(.x, '/filtered_feature_bc_matrix.h5')) ) %>% setNames(proj)
# The quality of p31 is very poor, so we need remove it.
rawdata$p31 <- NULL
proj <- c('p32', 'p45','p25_CD4', 'p25_CD8','p27','p71')
pt <- str_extract(proj, 'p\\d\\d')
# creat seurat project of gene expression datasepeately into a big list
lungTcell <- map2(rawdata,proj, ~
CreateSeuratObject(counts = .x$`Gene Expression`,project = .y,
min.features = 20) %>%
AddMetaData(str_extract(.y, 'p\\d\\d'),col.name = 'patient')
)
map(lungTcell, ~ [email protected] %>% colnames )
map(lungTcell, ~ [email protected] %>% ncol )
map(lungTcell, ~ dim(.))
# the data quality of p31 is very poor, thus need to be removed
lungTcell$p31 <- NULL
# demultiplexing ----------------------------------------------------------
# citeseq and hashtaq data
hash <- unlist(map(rawdata, ~ .$`Antibody Capture` %>% rownames )) %>% str_subset('^\\d_')
hash
cite <- setdiff(unlist(map(rawdata, ~ .$`Antibody Capture` %>% rownames )), hash)
cite
# integrate HTO and CITESEQdata into seurat projects
for (x in proj) {
lungTcell[[x]][['HTO']] <- CreateAssayObject(rawdata[[x]]$`Antibody Capture`[intersect(hash, rownames(rawdata[[x]]$`Antibody Capture`)),])
lungTcell[[x]][['CITE']] <- CreateAssayObject(rawdata[[x]]$`Antibody Capture`[intersect(cite, rownames(rawdata[[x]]$`Antibody Capture`)),])
rownames(lungTcell[[x]][['HTO']]@counts) <- paste0('X',rownames(lungTcell[[x]][['HTO']]@counts) )
rownames(lungTcell[[x]][['HTO']]@data) <- paste0('X',rownames(lungTcell[[x]][['HTO']]@data) )
}
# change names of HTO to more recognizable names
for (x in proj) {
rownames(lungTcell[[x]][['HTO']]@counts) %<>% str_extract('(?<=\\w-).+') %>% str_extract('(?<=\\w-).+') %>% paste0('S',.)
rownames(lungTcell[[x]][['HTO']]@data)%<>% str_extract('(?<=\\w-).+') %>% str_extract('(?<=\\w-).+')%>% paste0('S',.)
}
hash_sample <- lungTcell %>% map(~ .[['HTO']] %>% rownames)
hash_sample
lungTcell$p32@assays$HTO@counts
# normalization
for (i in proj) {
lungTcell[[i]] %<>% NormalizeData( assay = "HTO", normalization.method = "CLR") %>%
ScaleData(assay='HTO', features = rownames( .[['HTO']])) %>%
HTODemux( assay = "HTO", positive.quantile = 0.9, nstarts = 20)
}
for (i in proj) {
lungTcell[[i]] %<>% NormalizeData( assay = "CITE", normalization.method = "CLR")
}
map(lungTcell, ~ [email protected] %>% colnames )
map(lungTcell, ~ [email protected] %>% ncol )
for (x in proj) {
DefaultAssay(lungTcell[[x]] ) <- 'HTO'
}
saveRDS(lungTcell,'lungTcell_pre_inte.rds')
lungTcell <- readRDS('lungTcell_pre_inte.rds')
lungTcell$p32$hash.ID
map2(lungTcell,hash_sample, ~ Feature_rast(.x, d1 =.y[[1]], d2= .y[[2]], assay = 'HTO',
g = 'hash.ID',noaxis =F, axis.number=T)+
xlim(0,5)+ylim(0,5)+
geom_hline(yintercept = 0.5)+
geom_vline(xintercept = 0.8)) %>% PG(ncol =6) %T>%
figsave('hash.id.automatic.pdf', 400, 100)
Feature_rast(lungTcell$p32, 'hash.ID')
# the automiatic demultiplexing by seurat is not very accurate
# add HTO to metadata
for (x in proj) {
lungTcell[[x]] %<>% AddMetaData(t(lungTcell[[x]]@assays$HTO@data), col.name = rownames(lungTcell[[x]]@assays$HTO@data))
}
for (x in proj) {
lungTcell[[x]] %<>% AddMetaData( rownames(lungTcell[[x]]@meta.data),
col.name = 'bc_backup')
}
# demultiplexing manually
map(lungTcell,~ ncol([email protected])
)
syms(hash_sample)
newmeta <- list()
# marker tissues based on HTO
newmeta <- map(lungTcell, ~
[email protected] %<>%
mutate(tissue = case_when(.[[15]] >= 0.8 & .[[16]] < 0.5 ~ 'lung',
.[[15]] < 0.8 & .[[16]] >= 0.5 ~ 'luLN',
.[[15]] >= 0.8 & .[[16]] >= 0.5 ~ 'DP'))%>%
`rownames<-`(.x$bc_backup)
)
newmeta$p32 %<>%
mutate(tissue = case_when(.[[15]] >= 0.8 & .[[16]] < 0.5 ~ 'luLN',
.[[15]] < 0.8 & .[[16]] >= 0.5 ~ 'lung',
.[[15]] >= 0.8 & .[[16]] >= 0.5 ~ 'DP'))%>%
`rownames<-`(.$bc_backup)
for (i in proj) {
lungTcell[[i]]@meta.data <- newmeta[[i]]
}
map2(lungTcell,hash_sample, ~ Feature_rast(.x, d1 =.y[[1]], d2= .y[[2]],
g = 'tissue',noaxis =F, axis.number=T)+
ggtitle(unique(.x$orig.ident))+
xlim(0,5)+ylim(0,5)+
geom_hline(yintercept = 0.5)+
geom_vline(xintercept = 0.8)) %>% PG(ncol =6) %T>%
figsave('hash.id.mannual.pdf', 400, 100)
for (x in proj) {
DefaultAssay(lungTcell[[x]] ) <- 'RNA'
}
# QC ----------------------------------------------------------------------
s.genes <- cc.genes$s.genes
g2m.genes <- cc.genes$g2m.genes
lungTcell$p32$percent.ribo
# calculate percent of mitochondrial ribosam and hspa genes
lungTcell <- map(lungTcell, ~ PercentageFeatureSet(.x, '^MT', col.name = 'percent.mito') %>%
PercentageFeatureSet('^RP', col.name = 'percent.ribo') %>%
PercentageFeatureSet('^HSPA', col.name = 'percent.hspa') )
# QC plot
QCvio <- map(lungTcell, ~ViolinPlot(.x,c("nFeature_RNA", "nCount_RNA",'percent.mito'),group.by = 'tissue',
colors =umap.colors ,box = T, jitter = T , ncol = 3 )) %>%
PG(labels = proj, ncol = 1) %T>%
figsave('beforeQC_violin.pdf', 150, 270)
QCvio
figsave(QCvio,'beforeQC_violin_CD4CD8.pdf', 200, 200)
QC_scatter <- map(lungTcell, ~ Feature_rast(.x, g = 'percent.mito', d1 ="nCount_RNA",d2 ='nFeature_RNA',
noaxis = F, axis.number = T)+grd+
geom_smooth(method = "lm")+
scale_x_continuous(breaks = seq(0, 10000, 1000), limits = c(0,10000))+
scale_y_continuous(breaks = seq(0, 5000, 500), limits = c(0,5000))+
geom_hline(yintercept = c(400,2000))+geom_vline(xintercept = c(1200,7000))) %>%
PG(labels = proj)%T>%
figsave('beforeQC_scatter.pdf',600,400)
QC_scatter
figsave(QC_scatter,'beforeQC_scatter.pdf',600,300)
saveRDS(lungTcell, 'lungTcell_pre_inte.rds')
# data cleaning
for (x in proj) {
lungTcell[[x]] <- subset(lungTcell[[x]], subset = nCount_RNA %in% 1200:7000 &
nFeature_RNA %in% 400:2000 & percent.mito <15 &
tissue %in% c('lung','luLN'))
}
QCvio_clean <- map(lungTcell, ~ViolinPlot(.x,c("nFeature_RNA", "nCount_RNA",'percent.mito'),group.by = 'tissue',
colors =umap.colors ,box = T, jitter = T , ncol = 3 )) %>%
PG(labels = proj, ncol = 2) %T>%
figsave('afterQC_violin_CD4CD8.pdf',200,270)
QCvio_clean
map(lungTcell,~ dim(.x))
# $CD4
# [1] 33538 2398
#
# $CD8
# [1] 33538 3626
# normalization and scaling -----------------------------------------------
# scale and find high var genes
lungTcell %<>% map(~ NormalizeData(.x, normalization.method = 'LogNormalize',
scale.factor = 10000, assay = 'RNA')%>%
CellCycleScoring( s.features = s.genes, g2m.features = g2m.genes, set.ident = F) %>%
ScaleData( assay = 'RNA',
vars.to.regress = c("percent.mito",
"S.Score", 'G2M.Score',
"percent.ribo",
'nCount_RNA','nFeature_RNA' )) %>%
FindVariableFeatures(assay = 'RNA',nfeatures = 3000, selection.method = 'vst') )
lungTcell$p32@[email protected]
for (x in proj) {
lungTcell[[x]]@[email protected] <- lungTcell[[x]]@[email protected]%>%
str_subset('^RP|^MT|^HIST', negate = T)
}
saveRDS(lungTcell, 'lungTcell_pre_inte.rds')
map(lungTcell, ~ .x@[email protected] %>% length() )
# integration -------------------------------------------------------------
anchors <- FindIntegrationAnchors(lungTcell, dims = 1:60)
CD4CD8 <- IntegrateData(anchors, dims = 1:60)
# mark CD4 and CD8 based on CITEseq ---------------------------------------
CD4CD8@assays$CITE@data
# change name of cite seq antibodies
CD4CD8 %<>% ScaleData( assay = 'CITE',vars.to.regress = c('patient'))
rownames(CD4CD8@assays$CITE@data) %<>% str_replace('-TotalSeqC', '.protein')
rownames(CD4CD8@assays$CITE@counts) %<>% str_replace('-TotalSeqC', '.protein')
rownames(CD4CD8@[email protected]) %<>% str_replace('-TotalSeqC', '.protein')
Feature_rast(CD4CD8, d1='CD4.protein', d2='CD8.protein', assay = 'CITE', color_grd = 'grd', noaxis = F)
Feature_rast(CD4CD8, d1='CD4', d2='CD8B', color_grd = 'grd', noaxis = F)
Feature_rast(CD4CD8, g='patient', d1='CD4.protein', d2='CD8.protein', assay = 'CITE', color_grd = 'grd', noaxis = F, axis.number = T) +facet_grid(~ patient)+geom_hline(yintercept = 0.6)+geom_vline(xintercept = 0.8)
dim(CD4CD8)
Feature_rast(CD4CD8, c('CD4', 'CD8A', 'CD8B'))
CD4CD8 %<>% AddMetaData(FetchData(CD4CD8, c('CD4.protein', 'CD8.protein'), slot = 'data'))
# CD4 CD8 demultiplexing
[email protected] %<>% mutate(CD4CD8= case_when(orig.ident == 'p25_CD4'~ 'CD4', orig.ident == 'p25_CD8'~ 'CD8', cite_CD4.protein >= 0.8 & cite_CD8.protein >= 0.6 ~ 'DP',cite_CD8.protein >= 0.6 ~ 'CD8',cite_CD4.protein >= 0.8 ~'CD4',cite_CD4.protein < 0.8 & cite_CD8.protein < 0.6 ~ 'DN'))
(Feature_rast(CD4CD8 %>% subset(patient != 'p25'), g='CD4CD8', 'patient', d1='CD4.protein', d2='CD8.protein', assay = 'CITE', color_grd = 'grd', noaxis = F, axis.number = T) +facet_wrap(~ patient,ncol = 2)+ggtitle('CITEseq on CD4 and CD8')+geom_hline(yintercept = 0.6)+geom_vline(xintercept = 0.8) )%T>% figsave('CD4andCD8staining_cite.pdf', 200, 200)
table(CD4CD8$CD4CD8)
Feature_rast(CD4CD8, 'CD4CD8','patient', colorset = 'gg')
table(CD4CD8$CD4CD8)
ViolinPlot(CD4CD8, 'nCount_RNA', group.by = 'CD4CD8')
saveRDS(CD4CD8, 'CD4CD8_integrated_2021_0716.rds')
# DP cells are significantly larger than other cells, thus we remove it as doublets
# CD4CD8 %<>% subset(subset = CD4CD8 %in% c('CD4', 'CD8', 'DN'))
Feature_rast(CD4CD8,c('CD103.protein', 'CD49a.protein', 'CD69.protein'), assay = 'CITE', color_grd = 'grd')
# dimensional reduction by PCA and UMAP -----------------------------------
DefaultAssay(CD4CD8) <- "integrated"
# scale data and run PCA
CD4CD8 %<>% ScaleData( vars.to.regress = c('patient',
"percent.mito",
"S.Score",
'G2M.Score',
"percent.ribo",
'nCount_RNA',
'nFeature_RNA' )) %>%
RunPCA(npcs = 100, verbose = T,nfeatures.print = 40)
ElbowPlot(CD4CD8, ndims = 50)
# jackstraw is a way to choose significant PCs
CD4CD8 %<>% JackStraw( num.replicate = 100, dims = 80)%>%
ScoreJackStraw(dims = 1:80)
CD4CD8 %>% JackStrawPlot( dims = 1:50 ) %T>%
figsave('CD4CD8_lung_5p.jackstraw.pdf' , w = 400, h = 400)
CD4CD8 <- RunUMAP(object = CD4CD8, dims = 1:42,
reduction = 'pca', min.dist = 0.2) %>%
FindNeighbors(dims = c(1:42))
for (i in seq(0.6,1.5,0.1) %>% rev()) {
CD4CD8 <- FindClusters(CD4CD8, resolution = i)
}
CD4CD8$integrated_snn_res.0.6
Feature_rast(CD4CD8, paste0('integrated_snn_res.',seq(0.6,1.3,0.1)), ncol =4) %T>% figsave('CD4CD8_different_reso.pdf', 400,200)
Feature_rast(CD4CD8,c('patient','integrated_snn_res.1.4','tissue', 'CD4CD8','CCR6', 'RORC', 'FOXP3', 'DPP4', 'IL23R'), sz = 1)
Feature_rast(CD4CD8,'tissue',facet = 'patient')+facet_grid(~patient)
cite
CD4CD8@assays$CITE@counts
saveRDS(CD4CD8, 'CD4CD8_integrated_2021_0716.rds')
Feature_rast(CD4CD8, c('CCR6', 'RORC', 'FOXP3', 'DPP4', 'IL23R', 'IL17A', 'IL17F', 'AREG', 'IFNG', 'IL22'), assay = 'RNA')
# clean and 2nd clustering ------------------------------------------------
# remove cells that not T cells
Feature_rast(CD4CD8, noaxis = F, axis.number = T)
CD4CD8 %<>% subset(UMAP_1 >= -6)
DefaultAssay(CD4CD8) <- "RNA"
# CD4CD8<- PercentageFeatureSet(CD4CD8, '^HSPA', col.name = 'percent.hspa')
DefaultAssay(CD4CD8) <- "integrated"
CD4CD8$percent.hspa
CD4CD8@[email protected]
# scaling and 2nd round of clustering
CD4CD8 %<>% ScaleData( vars.to.regress = c(
"percent.mito",
'percent.hspa',
"S.Score",
'G2M.Score',
"percent.ribo",
'nCount_RNA',
'nFeature_RNA' )) %>%
RunPCA(npcs = 100, verbose = T,nfeatures.print = 40)
CD4CD8 %<>% JackStraw( num.replicate = 100, dims = 80)%>%
ScoreJackStraw(dims = 1:80)
CD4CD8 %>% JackStrawPlot( dims = 1:50 ) %T>%
figsave('CD4CD8_lung_5p.jackstraw.pdf' , w = 400, h = 400)
saveRDS(CD4CD8, 'CD4CD8_integrated_2021_0716.rds')
# set.seed(123)
CD4CD8 <- RunUMAP(object = CD4CD8, dims = 1:45, seed.use = 1007,
reduction = 'pca', min.dist = 0.1) %>%
FindNeighbors(dims = c(1:45))
for (i in seq(0.6,1.8,0.1) %>% rev()) {
CD4CD8 <- FindClusters(CD4CD8, resolution = i, random.seed = 123)
}
Feature_rast(CD4CD8)
table([email protected])
ClusterCompare(CD4CD8, '17', '2')
Feature_rast(CD4CD8)
# decide to use resolution 1.4 for clustering
Idents(CD4CD8)<- CD4CD8$integrated_snn_res.1.4
CD4CD8$Cell_cluster<- Idents(CD4CD8)
ClusterCompare(CD4CD8, '10', '7', group.by = 'integrated_snn_res.1.5')
ViFeature_rast(CD4CD8, c('CD4.protein', 'CD8.protein'), assay = 'CITE', color_grd = 'grd')
Feature_rast(CD4CD8, c('ident','CD4CD8'))
Feature_rast(CD4CD8)+facet_wrap('ident')
# # distribution between tissue and CD4CD9 --------------------------------
library(ggalluvial)
library(RColorBrewer)
ID_cl <- c(brewer.pal(9,'Blues')[5:9], brewer.pal(9,'Reds')[5:9])
# tissue
CD4CD8$ID <- paste0(CD4CD8$tissue,'_',CD4CD8$patient)
#calculate tissue composition table
comp_tissue <[email protected] %>% group_by(tissue, ID, Cell_cluster) %>%
summarise(n = n() ) %>% mutate(n = case_when(tissue == 'luLN'~ -n,
tissue == 'lung' ~ n)) %>% group_by(ID) %>%
mutate(percent = case_when(tissue == 'luLN'~ -(n/sum(n)*100),
tissue == 'lung' ~ n/sum(n)*100 )) %>% as.data.frame()
comp_tissue
cl_comp_flow <- ggplot(comp_tissue, aes(y = n, x = Cell_cluster, fill = ID, color = ID,
stratum = ID )) +
scale_x_discrete(expand = c(.1, .1)) +
geom_stratum(alpha = .6)+
scale_fill_manual(values = ID_cl )+
scale_color_manual(values = ID_cl)+
theme_minimal() +
ylab('Cell number of cluster per donor')+
xlab('cluster')+
xlab(NULL)+
scale_y_continuous(labels = abs)+
geom_hline(yintercept = 0,size = 0.5)+
guides(fill = guide_legend(nrow = 2, title = NULL, byrow = T, label.position = 'bottom'),
color = F )+
mytheme+
theme(legend.position = 'bottom', legend.key.height = unit(2, 'mm'), legend.key.width = unit(15,'mm'),
axis.line = element_blank())+
NULL
cl_comp_flow
# CD4CD8
CD4CD8$ID_CD <- paste0(CD4CD8$CD4CD8,'_',CD4CD8$patient)
comp_CD <[email protected] %>% filter(CD4CD8 %in% c('CD4', 'CD8') ) %>%
group_by(CD4CD8, patient, Cell_cluster) %>%
summarise(n = n() ) %>%group_by(patient, Cell_cluster) %>%
mutate(percent = case_when(CD4CD8 == 'CD8'~ -(n/sum(n)*100),
CD4CD8 == 'CD4' ~ n/sum(n)*100 )) %>%
mutate(n = case_when(CD4CD8 == 'CD8'~ -n,CD4CD8 == 'CD4' ~ n) ) %>% ungroup() %>% tidyr::complete(Cell_cluster, patient,CD4CD8,fill = list(n = 0, percent = 0))%>%group_by(patient, Cell_cluster) %>% mutate(ID_CD = paste0(CD4CD8, '_', patient), total = sum(abs(n))) %>% filter(total>0)
comp_CD %>% filter(Cell_cluster == 17) %>% as.data.frame()
comp_CD
library('tidyr')
library('broom')
comp_CD %>% group_by(Cell_cluster, patient) %>% t_test(formula = abs(percent) ~ CD4CD8,
order = c("CD4", "CD8"),
alternative = "two-sided")
cl_comp_flow_CD <- ggplot(comp_CD, aes(y = percent, x = Cell_cluster, fill = ID_CD, color = ID_CD,
stratum = ID_CD )) +
scale_x_discrete(expand = c(.1, .1)) +
geom_stratum(alpha = .6)+
scale_fill_manual(values = ID_cl )+
scale_color_manual(values = ID_cl)+
theme_minimal() +
ylab('% of cluster per donor')+
xlab(NULL)+
scale_y_continuous(labels = abs)+
geom_hline(yintercept = 0,size = 0.5)+
guides(fill = guide_legend(nrow = 2, title = NULL, byrow = T, label.position = 'bottom'),
color = F )+
mytheme+
theme(legend.position = 'bottom', legend.key.height = unit(2, 'mm'), legend.key.width = unit(15,'mm'),
axis.line = element_blank())+
NULL
cl_comp_flow_CD
meant_comp_CD<- comp_CD %>% group_by(Cell_cluster, CD4CD8) %>% summarise( SD = sd(percent),percent = mean(percent)) %>% as.data.frame()
meant_comp_CD
comp_CD%>%ungroup %>% group_by(Cell_cluster, patient) %>%
summarise_each(pv=funs(t.test(.[CD4CD8 == "CD4"], .[CD4CD8 == "CD8"])$p.value), vars=abs(percent))
library(ggpubr)
ggplot(meant_comp_CD, aes(y = percent, x = Cell_cluster ))+
geom_bar(stat = 'identity', aes(fill = CD4CD8), color='black', size=0.3, width = 0.95)+
# geom_errorbar(color = 'black', size = 0.2,aes(ymax = percent+SD, ymin=percent-SD))+
theme_bw(base_line_size = 0)+
geom_point(data=comp_CD, aes(color=ID_CD, y = percent, x = Cell_cluster, group = patient), position = position_dodge(width = 0.8) )+
scale_color_manual(values = c(ID_cl) )+
scale_y_continuous(labels = abs)+
scale_fill_manual(values = alpha(c('blue','red' ), 0.2))+
stat_compare_means(paired = T,method = 't.test')
comp_CD
ggplot(comp_CD, aes(y = percent, x = Cell_cluster , group = patient))+
geom_point(data=comp_CD, aes(color=ID_CD, y = percent, x = Cell_cluster, group = patient), position = position_dodge(width = 0.8) )+
scale_color_manual(values = c(ID_cl) )+
scale_y_continuous(labels = abs)+
scale_fill_manual(values = alpha(c('blue','red' ), 0.2))+
stat_compare_means(paired = T,method = 't.test')
comp_CD
comp_CD %>% group_by(Cell_cluster) %>% t.test(percent ~ CD4CD8, data = .)
comp_CD2<- comp_CD %>% mutate(percent = abs(percent) ) %>% ungroup() %>% group_by(Cell_cluster) %>% mutate(nrows = NROW(CD4CD8)) %>% filter(nrows >4 )
pvs <- comp_CD2 %>% ungroup() %>% group_by(Cell_cluster) %>% summarise(pv = t.test(abs(percent) ~ CD4CD8, paired = T)$p.value)
pvs$p.adjust <- p.adjust(pvs$pv, method = 'fdr', n = length(pvs$pv))
pvs
comp_CD2 %<>% ungroup %>% group_split(Cell_cluster)
map(comp_CD2, ~ nrow(.x))
comp_CD2
comp_CD %>% mutate(percent = abs(percent) ) %>% ungroup() %>% group_by(Cell_cluster) %>% mutate(nrows = NROW(CD4CD8))
comp_CD %>% group_by(Cell_cluster) %>% mutate(nrow = NROW(CD4CD8))
comp_CD2%>% map(~ t.test(percent ~ CD4CD8, data = .x, paired = TRUE)$p.value)
t.test(percent ~ CD4CD8, data = comp_CD)$p.value
pv$p.value
# DEGs --------------------------------------------------------------------
DefaultAssay(CD4CD8) <- 'RNA'
CD4CD8_DEGs <-
FindAllMarkers(CD4CD8, test.use = 'bimod', min.pct = 0.10, only.pos = FALSE )%>%
filter(p_val_adj < 0.05) %>%
mutate(pct.dff = pct.1 - pct.2) %>% arrange(cluster, desc(avg_log2FC))
CD4CD8<- FindVariableFeatures(CD4CD8,assay = 'RNA',nfeatures = 2500, selection.method = 'vst')
# to reduce the site of suerat object, not every gene will be scaled, only the potential DEGs
gene_to_scale <- unique(c(CD4CD8_DEGs$gene, CD4CD8@[email protected]))
CD4CD8 %<>% ScaleData( vars.to.regress = c('patient',
'percent.hspa',
"percent.mito",
"S.Score",
'G2M.Score',
"percent.ribo",
'nCount_RNA',
'nFeature_RNA' ), assay = 'RNA', feature = gene_to_scale)
CD4CD8_DEGs
ClusterCompare(CD4CD8, '1', '8')
DoHeatmap(CD4CD8, c('CD4', 'CD8A', 'HLA-C','AL133415.1') )
top10deg <- CD4CD8_DEGs %>% filter(!grepl('^RP|^MT', gene)) %>%
arrange(cluster, desc(avg_log2FC))%>%
group_by(cluster) %>% top_n(10, avg_log2FC )
top10DEG_heat <-
DoHeatmap(subset(CD4CD8, downsample=500),features = top10deg$gene,raster = T,
group.colors = umap.colors,size = gs(8))%>%heat_theme() %T>% figsave('top10DEG.pdf', 190, 370)
top10DEG_heat
# Trm makrers
TrmMarker <- FindAllMarkers(subset(CD4CD8, Cell_cluster %in% c(3,9,11,17)),min.pct = 0.1, logfc.threshold = 0.5,assay = 'RNA')
TrmMarker
top15_TrmMarker <- TrmMarker %>% filter(!grepl('^RP|^MT', gene)) %>%
arrange(cluster, desc(avg_log2FC))%>%
group_by(cluster) %>% top_n(15, avg_log2FC ) %>% as.data.frame()
top10_TrmMarker
DoHeatmap(subset(CD4CD8, Cell_cluster %in% c(3,9,11,17)), features = top15_TrmMarker$gene) %>% heat_theme()
# Tnv markers
Tnvmarkers <-FindAllMarkers(subset(CD4CD8, Cell_cluster %in% c(7,8,10)),min.pct = 0.1, logfc.threshold = 0.5,assay = 'RNA')
top15_Tnvmarkers <- Tnvmarkers %>% filter(!grepl('^RP|^MT', gene)) %>%
arrange(cluster, desc(avg_log2FC))%>%
group_by(cluster) %>% top_n(15, avg_log2FC ) %>% as.data.frame()
DoHeatmap(subset(CD4CD8, Cell_cluster %in% c(7,8,10)), features = top15_Tnvmarkers$gene) %>% heat_theme()
# DEG CD4CD8 --------------------------------------------------------------
CD4CD8$CD_pheno <- paste0(CD4CD8$CD4CD8, '_',CD4CD8$Cell_cluster)
# gene signatrue scors ----------------------------------------------------
[email protected] <- [email protected][,1:75]
colnames([email protected])
sigtable <- read.xlsx('abd5778_Table_S3.xlsx',sheet = 1)
colnames(sigtable) %<>% str_remove('.\\(.+\\)')
sigtable %<>% as.list() %>% map(~ na.exclude(.x) %>% as.vector)
names(sigtable)
sigtable$Tissue.resident
#caculate scores
for (i in names(sigtable)) {
CD4CD8 <- AddModuleScore(CD4CD8, features = list(sigtable[[i]]), name = i, assay = 'RNA')
}
colnames([email protected])[76:91] <- names(sigtable)
Feature_rast(CD4CD8,names(sigtable), color_grd = 'grd')
ViolinPlot(CD4CD8, names(sigtable), colors = umap.colors, box = T)
CD4CD8@assays$GM <- NULL
GMS <- [email protected][,c(names(sigtable))] %>% as.data.frame() %>% t()
scaleGM <- scale(t([email protected][,c(names(sigtable))]))
scaleGMassay <- CreateAssayObject(data = scaleGM)
CD4CD8@assays$GM <- scaleGMassay
DoHeatmap(subset(CD4CD8, downsample = 700),
raster =T, draw.lines = T, angle = 45,
lines.width = 10,group.colors = umap.colors,
assay = 'GM', features = names(sigtable), slot = 'data', size = gs(8)) +hmp2 + mytheme+
theme(legend.position = 'bottom',
legend.key.height = unit(2,'mm'))+
guides(color = FALSE, fill = guide_colourbar(title = 'Scaled modula score', title.position = 'top'))
# GSEA --------------------------------------------------------------------
library(clusterProfiler)
ent2_7 <- entrezlist_generator(CD4CD8, '2','7')
library(msigdbr)
Mc7 <- msigdbr::msigdbr(species = "Homo sapiens", category = "C7") %>%
dplyr::select(gs_name, entrez_gene)
c2_c7_Msigdb_GSEA <- GSEA(geneList = ent2_7, TERM2GENE=Mc7, nPerm = 100000,
minGSSize = 15,
pvalueCutoff = 0.05, pAdjustMethod = "BH") %>% setReadable(OrgDb = org.Hs.eg.db, keyType="ENTREZID")
c2_c7_Msigdb_GSEA@result <- c2_c7_Msigdb_GSEA@result %>% arrange(desc(NES))
view(c2_c7_Msigdb_GSEA@result)
Feature_rast(CD4CD8)
ClusterCompare(CD4CD8, 'C1', 'C2')
ClusterCompare(CD4CD8, 'C13', 'C17')
# CITEseq -----------------------------------------------------------------
Feature_rast(CD4CD8, g='CD4CD8', 'patient', d1='CD4.protein', d2='CD8.protein', assay = 'CITE', color_grd = 'grd', noaxis = F, axis.number = T,slot = 'scale.data') +facet_wrap(~ patient,ncol = 2)+geom_hline(yintercept = 0.6)+geom_vline(xintercept = 0.8)
Feature_rast(subset(CD4CD8, patient %in% c('p27', 'p71')), g='ident', 'tissue', d1='CD103.protein', d2='CD49a.protein', assay = 'CITE', color_grd = 'grd', noaxis = F, axis.number = T, slot = 'scale.data', sz = 4)
+-Feature_rast(CD4CD8, c('CD103.protein', 'CD49a.protein'),assay = 'CITE', color_grd = 'grd',slot = 'scale.data')
# RNA vs CITE
surfacemakers <- c('CD4','CD8A','CD8B', 'CXCR3','CCR6', 'ITGAE', 'CD69','CCR7','KLRB1','CD27','KLRG1', 'IL7R',
'DPP4', 'ITGA1', 'KLRD1', 'PTPRC', 'PDCD1')
surface_RNA <- Feature_rast(subset(CD4CD8, patient %in% c('p27', 'p71')), surfacemakers, sz = 0.3)
surface_CITE <- Feature_rast(subset(CD4CD8, patient %in% c('p27', 'p71')),sz = 0.3, CD4CD8@assays$CITE@data %>% rownames(), assay = 'CITE', color_grd = 'grd', slot = 'scale.data')
RNAvsCITE <- PG(list(surface_RNA, surface_CITE), labels = c('RNA', 'CITE'), ncol = 1,vjust = -10) %T>% figsave('RNAvsCITE.pdf',200, 290)
PG(list(surface_RNA, surface_CITE), labels = c('RNA', 'CITE'), ncol = 1,label_y = -2)
map(lungTcell, ~ .x@assays$CITE@counts %>% rownames )
# Cluster adjustment ------------------------------------------------------
Feature_rast(CD4CD8)
integrated_snn_res.1.4 <- unique(CD4CD8$integrated_snn_res.1.4) %>% as.numeric() %>% sort() %>% as.character()
integrated_snn_res.1.4
Cell_pheno <- c(
'Tcm_CD4/8_L_LMNA.hi', #0
'Trm_CD4/8_M_type1.hi',#1
'Tcm_CD4_L_CD69.hi',#2
'Trm_CD8_T_LMNA.hi',#3
'Tmem_CD8_M_MHCII.hi',#4
'Trm_CD4/8_T_type3',#5
'Th1_CD4/8_M',#6
'Tnv_CD4/8_L_CD69.hi' ,#7
'Tnv_CD4/8_L_CCR7.hi', #8
'Trm_CD8_T_TXNIP.hi', #9
'Tnv_CD4/8_L_HSPA.hi',#10
'Trm_CD8_T_CD29.hi', #11
'Th17_CD4_M',#12
'Th2_CD4/8_M',#13
'Treg_CD4_L',#14
'Trm_CD4/8_T_IFN.induced',#15
'Tfh_CD4_L',#16
'Trm_CD8_T_KIRs.hi',#17
'unclear_1',#18
'unclear_1',#19
'Trm_CD8_T_LMNA.hi',#20
'unclear_2'#21
)
phenotable <- data.frame(integrated_snn_res.1.4, Cell_pheno) %>% arrange(Cell_pheno)
phenotable$Cell_cluster <- factor(paste0('C',c(1:17, 17, 18,19,19,20)),
levels = paste0('C', 1:20))
phenotable$Cluster_pheno <- paste0(phenotable$Cell_cluster,': ', phenotable$Cell_pheno)
phenotable %<>% mutate(Cluster_pheno = factor(Cluster_pheno, levels = unique(Cluster_pheno)))
CD4CD8$Cell_cluster <- NULL
# [email protected] %<>% left_join(phenotable, by = 'integrated_snn_res.1.4') %>% `rownames<-`(CD4CD8$bc_backup)
[email protected] %<>% left_join(phenotable, by = 'integrated_snn_res.1.4', suffix = c('','')) %>% `rownames<-`(CD4CD8$bc_backup)
Feature_rast(CD4CD8,'Cluster_pheno', colorset = 'gg' )
Idents(CD4CD8) <- CD4CD8$Cell_cluster
UMAP_CD4CD8 <-(Feature_rast(CD4CD8, noaxis = F,sz = 0.3) +
ggtitle('T cells from lung tissue and lymph nodes')+
scale_color_manual(labels = levels(CD4CD8$Cluster_pheno), values = umap.colors)+
guides(color = guide_legend(ncol = 2, title = 'phenotypes', override.aes = list(size = 1.5)))) %T>% figsave('umap_CD4CD8_with_phenotypes.pdf', 180, 100 )
saveRDS(CD4CD8, 'CD4CD8_integrated_2021_0716.rds')
# !!!!!!!!!!!since here we adjusted and renamed the clustering, some of scripts above from line 432 to 711 need to be run again
# # distribution between tissue and CD4CD9 --------------------------------
library(ggalluvial)
library(RColorBrewer)
RColorBrewer::display.brewer.all()
ID_cl <- c(brewer.pal(9,'Blues')[5:9], brewer.pal(9,'Reds')[5:9])
CD_cl <- c(brewer.pal(9,'RdPu')[4:8], brewer.pal(9,'Greens')[4:8])
# tissue
CD4CD8$ID <- paste0(CD4CD8$tissue,'_',CD4CD8$patient)
Feature_rast(CD4CD8, 'ID', colorset = CD_cl, do.label = F, noaxis = F)
Umap_donor_tissue <- Feature_rast(CD4CD8, 'ID', colorset = ID_cl, do.label = F, noaxis = F)+ ggtitle(NULL)+NoLegend()
#calculate tissue composition table
comp_tissue <[email protected] %>% group_by(tissue, ID, Cell_cluster) %>%
summarise(n = n() ) %>% mutate(n = case_when(tissue == 'luLN'~ n,
tissue == 'lung' ~ -n)) %>% group_by(ID) %>%
mutate(percent = case_when(tissue == 'luLN'~ (n/sum(n)*100),
tissue == 'lung' ~ -n/sum(n)*100 )) %>% as.data.frame()
comp_tissue
cl_comp_flow <- ggplot(comp_tissue, aes(y = n, x = Cell_cluster, fill = ID, color = ID,
stratum = ID )) +
scale_x_discrete(expand = c(.1, .1)) +
geom_stratum(alpha = .6)+
scale_fill_manual(values = ID_cl )+
scale_color_manual(values = ID_cl)+
theme_bw() +
ylab('Cell number of cluster per donor')+
xlab('cluster')+
xlab(NULL)+
scale_y_continuous(labels = abs)+
geom_hline(yintercept = 0,size = 0.5)+
guides(fill = guide_legend(nrow = 2, title = NULL, byrow = T, label.position = 'bottom'),
color = F )+
mytheme+
theme(legend.position = 'bottom', legend.key.height = unit(2, 'mm'), legend.key.width = unit(15,'mm'),
axis.line = element_blank())+
NULL
cl_comp_flow
figtissue <- PG(list(Umap_donor_tissue, cl_comp_flow), rw = c(1, 1.8))
# CD4CD8
CD4CD8$ID_CD <- paste0(CD4CD8$CD4CD8,'_',CD4CD8$patient)
Umap_donor_CD <- Feature_rast(subset(CD4CD8, CD4CD8 %in% c('CD4', 'CD8')), 'ID_CD', colorset = CD_cl, do.label = F, noaxis = F)+ ggtitle(NULL)+NoLegend()
Umap_donor_CD
comp_CD <[email protected] %>% filter(CD4CD8 %in% c('CD4', 'CD8') ) %>%
group_by(CD4CD8, patient, Cell_cluster) %>%
summarise(n = n() ) %>%group_by(patient, Cell_cluster) %>%
mutate(percent = case_when(CD4CD8 == 'CD8'~ -(n/sum(n)*100),
CD4CD8 == 'CD4' ~ n/sum(n)*100 )) %>%
mutate(n = case_when(CD4CD8 == 'CD8'~ -n,CD4CD8 == 'CD4' ~ n) ) %>% ungroup() %>% tidyr::complete(Cell_cluster, patient,CD4CD8,fill = list(n = 0, percent = 0))%>%group_by(patient, Cell_cluster) %>% mutate(ID_CD = paste0(CD4CD8, '_', patient), total = sum(abs(n))) %>% filter(total>0)
comp_CD
library('tidyr')
library('broom')
comp_CD %>% group_by(Cell_cluster, patient) %>% t_test(formula = abs(percent) ~ CD4CD8,
order = c("CD4", "CD8"),
alternative = "two-sided")
meant_comp_CD<- comp_CD %>% group_by(Cell_cluster, CD4CD8) %>% summarise( SD = sd(percent),percent = mean(percent)) %>% as.data.frame()
meant_comp_CD
comp_CD%>%ungroup %>% group_by(Cell_cluster, patient) %>%
summarise_each(pv=funs(t.test(.[CD4CD8 == "CD4"], .[CD4CD8 == "CD8"])$p.value), vars=abs(percent))
library(ggpubr)
cl_comp_flow_CD<-ggplot(meant_comp_CD, aes(y = percent, x = Cell_cluster ))+
geom_bar(stat = 'identity', aes(fill = CD4CD8), color='black', size=0.3, width = 0.95)+
# geom_errorbar(color = 'black', size = 0.2,aes(ymax = percent+SD, ymin=percent-SD))+
theme_bw(base_line_size = 0)+
geom_point(data=comp_CD, aes(color=ID_CD, y = percent, x = Cell_cluster, group = patient), position = position_dodge(width = 0.8) , size = 0.8 )+
scale_color_manual(values = c(CD_cl), labels = rep(sort(unique(CD4CD8$patient)),2) )+
scale_y_continuous(labels = abs)+
scale_fill_manual(values = alpha(c('purple','green' ), 0.2))+
theme(legend.position = 'bottom')+
guides( fill = guide_legend(nrow = 2, title = NULL),
color = guide_legend(nrow = 2, byrow = T, label.position = 'right', title = 'patient',override.aes = list(size = 2))
)
cl_comp_flow_CD
figCD <- PG(list(Umap_donor_CD, cl_comp_flow_CD), rw = c(1, 1.8))
compositions <- PG(list(figtissue, figCD), ncol = 1) %T>% figsave('tissue_donor_CD4_8.pdf',200,220)
comp_CD2<- comp_CD %>% mutate(percent = abs(percent) ) %>% ungroup() %>% group_by(Cell_cluster) %>% mutate(nrows = NROW(CD4CD8)) %>% filter(nrows >4 )
pvs <- comp_CD2 %>% ungroup() %>% group_by(Cell_cluster) %>% summarise(pv = t.test(abs(percent) ~ CD4CD8, paired = T)$p.value)
pvs$fdr <- p.adjust(pvs$pv, method = 'fdr', n = length(pvs$pv))
pvs$p.adjust <- p.adjust(pvs$pv, method = 'bonferroni', n = length(pvs$pv))
pvs
# DEGs --------------------------------------------------------------------
DefaultAssay(CD4CD8) <- 'RNA'
CD4CD8_DEGs <-
FindAllMarkers(CD4CD8, test.use = 'bimod', min.pct = 0.10, only.pos = FALSE )%>%
filter(p_val_adj < 0.05 | abs(log2FC) >0.5) %>%
mutate(pct.dff = pct.1 - pct.2) %>% arrange(cluster, desc(avg_log2FC))
CD4CD8_DEGs %>% filter(avg_log2FC >0) %>% count(cluster)
CD4CD8_DEGs %<>% filter(p_val_adj < 0.05 | abs(avg_log2FC) >0.5)
write.xlsx(CD4CD8_DEGs, 'lungabT_DEGs.xls')
top10deg <- CD4CD8_DEGs %>%
# filter(!grepl('^RP|^MT', gene)) %>%
arrange(cluster, desc(avg_log2FC))%>%
group_by(cluster) %>% top_n(10, avg_log2FC )
top10DEG_heat <-
DoHeatmap(subset(CD4CD8, downsample=500),features = top10deg$gene,raster = T,
group.colors = umap.colors,size = gs(8))%>%heat_theme() %T>% figsave('top10DEG.pdf', 190, 370)
top10DEG_heat
# Trm makrers
TrmMarker <- FindAllMarkers(subset(CD4CD8, Cell_cluster %in% paste0('C',13:18)),min.pct = 0.1, logfc.threshold = 0.5,assay = 'RNA')
TrmMarker
top15_TrmMarker <- TrmMarker %>% filter(!grepl('^RP|^MT', gene)) %>%
arrange(cluster, desc(avg_log2FC))%>%
group_by(cluster) %>% top_n(15, avg_log2FC ) %>% as.data.frame()
DoHeatmap(subset(CD4CD8, Cell_cluster %in% paste0('C',13:18)), features = top15_TrmMarker$gene) %>% heat_theme()
# Tnv markers
Tnvmarkers <-FindAllMarkers(subset(CD4CD8, Cell_cluster %in% c(7,8,10)),min.pct = 0.1, logfc.threshold = 0.5,assay = 'RNA')
top15_Tnvmarkers <- Tnvmarkers %>% filter(!grepl('^RP|^MT', gene)) %>%
arrange(cluster, desc(avg_log2FC))%>%
group_by(cluster) %>% top_n(15, avg_log2FC ) %>% as.data.frame()
DoHeatmap(subset(CD4CD8, Cell_cluster %in% c(7,8,10)), features = top15_Tnvmarkers$gene) %>% heat_theme()