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lung_gdt_20210602.R
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# lung project 6 patients -------------------------------------------------
library(Seurat)
library(ggplot2)
library(purrr)
library(dplyr)
library(Matrix)
library(ggplot2)
library(tibble)
library(cowplot)
library(ggrastr)
library(ggrepel)
library(xlsx)
# library(rstatix)
library(stringr)
library(magrittr)
library(openxlsx)
library(harmony)
# themes and functions ------------------------------------------------------------------
source('/home/big/tanlikai/script/rscripts/funcs.r')
file.edit('//home/big/tanlikai/script/rscripts/funcs.r')
#paralell computation
options(future.fork.enable = TRUE)
options(future.globals.maxSize= 400*1024^3)
future::plan(strategy = "multicore", workers = 40)
# future::plan(strategy = "sequential")
# ?parallelly::supportsMulticore
#single core
setwd("/home/big/tanlikai/Lung")
GDTlung_s <- readRDS('GDTlung_6p_Seurat_07062021.rds')
# #set the working directory --------------------------------------------
orig_cl <-c("#33cc33", #CB1
"#339933", #CB2
"#660066", #PB1
"#cc00cc" #PB2
)
Feature_rast(GDTlung_s)
# work dir ----------------------------------------------------------------
# GDTlung.rds <- '/home/big/tanlikai/Lung/GDTlung_3p_20210213.RDS'
# GDTlung <- readRDS(GDTlung.rds)
# GDT_2020AUG.rds <- '/home/big/tanlikai/Human_GDT_2019/Integrated/GDT_2020AUG_woCOV.rds'
# GDT_2020AUG <- readRDS(GDT_2020AUG.rds)
# creat 10x ---------------------------------------------------------------
raw_data_1 <- Read10X_h5('raw/surface_Falk1_gd/outs/filtered_feature_bc_matrix.h5')
raw_data_1$`Antibody Capture`
plot(x = raw_data_1$`Antibody Capture`[3,], y = raw_data_1$`Antibody Capture`[4,])
plot(x = raw_data_1$`Antibody Capture`[1,], y = raw_data_1$`Antibody Capture`[2,])
GDTlung_1 <- CreateSeuratObject(counts = raw_data_1$`Gene Expression`,project = 'p25',
meta.data = t(raw_data_1$`Antibody Capture`), min.features = 20)
[email protected] %>% dim()
GDTlung_1@assays$RNA %>% dim()
GDTlung_1$bc_backup <- rownames([email protected])
colnames([email protected])
rwa_data_2_3 <- Read10X_h5('raw/surf_322_325_453_456_gd/outs/filtered_feature_bc_matrix.h5')
plot(x = rwa_data_2_3$`Antibody Capture`[4,], y = rwa_data_2_3$`Antibody Capture`[5,])
rwa_data_2_3$`Antibody Capture`
GDTlung_2_3 <- CreateSeuratObject(counts = rwa_data_2_3$`Gene Expression`,project = 'p32&p45',
meta.data = t(rwa_data_2_3$`Antibody Capture`), min.features = 20)
GDTlung_2_3[['HTO']] <- CreateAssayObject(rwa_data_2_3$`Antibody Capture`[3:6,])
GDTlung_2_3$bc_backup <- rownames([email protected])
rawdata_4 <- Read10X_h5('raw/surface_falk3_gd/outs/filtered_feature_bc_matrix.h5')
rawdata_4$`Antibody Capture` %>% glimpse()
rownames(rawdata_4$`Antibody Capture`)
rawdata_4$`Antibody Capture`[1:6, ]
GDTlung_4_6 <- CreateSeuratObject(rawdata_4$`Gene Expression`, project = 'p71&p31&p27',
meta.data = t(rawdata_4$`Antibody Capture`))
GDTlung_4_6[['HTO']] <- CreateAssayObject(rawdata_4$`Antibody Capture`[1:6,])
GDTlung_4_6[['CITE']] <- CreateAssayObject(rawdata_4$`Antibody Capture`[7:23,])
GDTlung_4_6$bc_backup <- rownames([email protected])
# assign tissue
# demultiplexing ----------------------------------------------------------
Feature_rast(GDTlung_1, g = 'nCount_RNA', d1 = "X1_TotalSeqC_LuPa" , d2 = "X9_TotalSeqC_LN",
noaxis = F, axis.number = T)+xlim(0,5000)+ylim(0, 10000)+
geom_vline(xintercept = 100)+geom_hline(yintercept = 180)+grd
GDTlung_1$X9_TotalSeqC_LN
mutate(tissue = case_when(X1_TotalSeqC_LuPa >= 100 & X9_TotalSeqC_LN < 180 ~ 'lung',
X1_TotalSeqC_LuPa < 100 & X9_TotalSeqC_LN >= 180 ~ 'luLN',
X1_TotalSeqC_LuPa >= 100 & X9_TotalSeqC_LN >= 180 ~ 'Doublet')) %>%
`rownames<-`(GDTlung_1$bc_backup)
GDTlung_1$patient <- 'p25'
dim(GDTlung_1)
GDTlung_1 <-GDTlung_1 %>% subset( subset = tissue %in% c('lung', 'luLN'))
table(GDTlung_1$tissue,useNA = 'always')
table(GDTlung_1$tissue,useNA = 'always')
tissuehashtag <- Feature_rast(GDTlung_1, g = 'tissue', d1 = "X1_TotalSeqC_LuPa" , d2 = "X9_TotalSeqC_LN",
noaxis = F, axis.number = T)+xlim(0,5000)+ylim(0, 10000)+
geom_vline(xintercept = 100)+geom_hline(yintercept = 180)
figsave(tissuehashtag,'tissuehashtag.pdf', 150,150)
GDTlung_2_3 <- NormalizeData(GDTlung_2_3, assay = "HTO", normalization.method = "CLR") %>%
ScaleData(assay='HTO', features = rownames(GDTlung_2_3[['HTO']]))
GDTlung_2_3 <- HTODemux(GDTlung_2_3, assay = "HTO", positive.quantile = 0.99)
GDTlung_2_3$HTO_classification.global %>% table
GDTlung_2_3$HTO_maxID %>% table
GDTlung_2_3$HTO_classification.global
table(GDTlung_2_3$hash.ID ,useNA = 'always')
Hash_hist_2_3 <- RidgePlot(GDTlung_2_3, assay = "HTO", slot = 'scale.data', features = rownames(GDTlung_2_3[["HTO"]]), ncol = 2)
Hash_hist_2_3
figsave(Hash_hist_2_3, 'Hash_hist_2_3.pdf', 200, 150)
VlnPlot(GDTlung_2_3, 'nFeature_RNA',g = 'hash.ID')
GDTlung_2_3 %<>% RunUMAP(assay= 'HTO', features = rownames(GDTlung_2_3[['HTO']]))
DimPlot(GDTlung_2_3)
# add tissue and patien ID
[email protected] %<>%
mutate(tissue = case_when(grepl('Lu', hash.ID) ~ 'lung',
grepl('LN', hash.ID)~ 'luLN'),
patient = case_when(grepl('32', hash.ID) ~ 'p32',
grepl('45', hash.ID)~ 'p45'))%>%
`rownames<-`(GDTlung_2_3$bc_backup)
GDTlung_2_3 <-GDTlung_2_3 %>% subset( subset = tissue %in% c('lung', 'luLN'))
# Feature_rast(GDTlung_2_3, 'hash.ID', d1 = 'X3_TotalSeqC_Lu_453',d2 = 'X9_TotalSeqC_Lu_322', do.label = F,
# noaxis = F, axis.number = T )+geom_hline(yintercept = 200)
#
# Feature_rast(GDTlung_2_3, 'hash.ID',d2 = 'X1_TotalSeqC_LN_325', d1 = 'X4_TotalSeqC_LN_456', do.label = F,
# noaxis = F, axis.number = T )
#
#
# # adjust
# GDTlung_2_3$hash.ID <- as.character(GDTlung_2_3$hash.ID)
# [email protected] <- [email protected] %>% mutate(hash.ID = case_when(
# hash.ID == 'Negative' & X9_TotalSeqC_Lu_322 > 200 ~ '9-TotalSeqC-Lu-322',
# is.character(hash.ID) ~ hash.ID
# )) %>%
# `rownames<-`(GDTlung_2_3$bc_backup)
# table(GDTlung_2_3$hash.ID)
GDTlung_4_6 %<>% NormalizeData( assay = "HTO", normalization.method = "CLR") %>%
ScaleData(assay='HTO', features = rownames(GDTlung_4_6[['HTO']])) %>%
RunUMAP(assay= 'HTO', features = rownames(GDTlung_4_6[['HTO']])) %>%
RunPCA(assay= 'HTO', features = rownames(GDTlung_4_6[['HTO']])) %>%
HTODemux( assay = "HTO", positive.quantile = 0.99)
DimPlot(GDTlung_4_6,reduction = 'pca')
table(GDTlung_4_6$hash.ID, useNA = 'always')
Hash_hist_4_6<- RidgePlot(GDTlung_4_6, assay = "HTO", features = rownames(GDTlung_4_6[["HTO"]]), ncol = 2)
figsave(Hash_hist_4_6, 'Hash_hist_4_6.pdf', 200, 250)
[email protected] %<>%
mutate(tissue = case_when(grepl('P', hash.ID) ~ 'lung',
grepl('L', hash.ID)~ 'luLN'),
patient = case_when(grepl('71', hash.ID) ~ 'p71',
grepl('31', hash.ID)~ 'p31',
grepl('27', hash.ID)~ 'p27'))%>%
`rownames<-`(GDTlung_4_6$bc_backup)
GDTlung_4_6 %<>% subset( subset = tissue %in% c('lung', 'luLN'))
# QC ----------------------------------------------------------------------
s.genes <- cc.genes$s.genes
g2m.genes <- cc.genes$g2m.genes
GDTlung <- list(GDTlung_1, GDTlung_2_3, GDTlung_4_6) %>% setNames(c('p1', 'p23', 'p456'))
GDTlung %<>% map(., ~ PercentageFeatureSet(.x, '^MT', col.name = 'percent.mito') %>%
PercentageFeatureSet('^RP', col.name = 'percent.ribo') #%>%
# CellCycleScoring( s.features = s.genes, g2m.features = g2m.genes, set.ident = F)
)
GDTlung$p1$percent.mito
GDTlung$p23$Phase
QCvio_T <- GDTlung %>% map(.,~ ViolinPlot(.x,c("nFeature_RNA", "nCount_RNA",'percent.mito'),group.by = 'tissue',
colors =umap.colors ,box = T, jitter = T , ncol = 1 ) ) %>%
PG(ncol = 3)
QCvio_T
QCvio_P <- GDTlung %>% map(.,~ ViolinPlot(.x,c("nFeature_RNA", "nCount_RNA",'percent.mito'),group.by = 'patient',
colors =umap.colors ,box = T, jitter = T , ncol = 1 ) ) %>% PG(ncol = 3)
QCvio_P
figsave(QCvio_T,'beforeQC_violin_3patient.pdf', 200, 200)
QC_scatter <- GDTlung %>% map( ., ~Feature_rast(.x, g = 'percent.mito', d1 ="nCount_RNA",d2 ='nFeature_RNA', facet = 'tissue',
noaxis = F, axis.number = T)+grd+facet_wrap(~tissue)+
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(1000,7000))) %>%
PG(ncol = 3)
QC_scatter
figsave(QC_scatter,'beforeQC_scatter.pdf',600,250)
# clean
GDTlung %<>% map(.,~ subset(.x, subset = nCount_RNA %in% 800:7000 &
nFeature_RNA %in% 400:2000 & percent.mito <25&
tissue %in% c('lung','luLN'))
)
dim(GDTlung$p1)
dim(GDTlung$p23)
# cell number
map(GDTlung, ~ dim(.x))
#mean counts
map(GDTlung,~ .x$nCount_RNA %>% mean)
QCvio_T_clean <- GDTlung %>% map(.,~ ViolinPlot(.x,c("nFeature_RNA", "nCount_RNA",'percent.mito'),group.by = 'tissue',
colors =umap.colors ,box = T, jitter = T , ncol = 1 ) ) %>%
PG(ncol = 3) %T>% figsave('afterQC_violin_6patient.pdf',150,150)
QCvio_T_clean
# figsave(QCvio_T_clean,'afterQC_violin_6patient.pdf',150,150)
saveRDS(GDTlung,'GDTlung_6p_beforeintegration.rds')
L_GDTlung <- readRDS('GDTlung_6p_beforeintegration.rds')
# integration by harmony --------------------------------------------------
GDTlung<- L_GDTlung %>% reduce(.f = merge, project = 'lungGDT')
test <- list(GDTlung, GDTlung_s, GDT_2020AUG)%>% reduce(.f = merge, project = 'lungGDT')
L_GDTlung$p23@assays$RNA@key
GDTlung%<>% NormalizeData(verbose = FALSE, assay = 'RNA') %>%
FindVariableFeatures(selection.method = 'vst', nfeatures = 3500)
GDTlung@[email protected] %<>%str_subset('^TRG|^TRD|^MT|^IG|^TRA|^TRB^|^HIST|^MIR|^HSP', negate = T) #%T>% print(length())
GDTlung@[email protected]
#%>%
GDTlung%<>% ScaleData(verbose = FALSE, assay = 'RNA',
vars.to.regress = c('percent.mito', 'nCount_RNA','nFeature_RNA' )
) %>%
RunPCA(npcs = 100, verbose=T)
GDTlung%<>% RunHarmony(c("patient", 'orig.ident', 'percent.mito', 'nCount_RNA','nFeature_RNA'), plot_convergence = TRUE)
ElbowPlot(GDTlung, ndims = 50,reduction = 'harmony')
#chose 35 PCs
# dim reduce via harmony --------------------------------------------------------------
GDTlung %<>% RunUMAP(reduction = 'harmony', dims = 1:30) %>% FindNeighbors(redcution = 'harmony', dims = 1:30)
for (i in seq(0.6,1.5,0.1) %>% rev()) {
GDTlung <- FindClusters(GDTlung, resolution = i)
}
GDTlung %<>% CellCycleScoring( s.features = s.genes, g2m.features = g2m.genes, set.ident = F)
Feature_rast(GDTlung, c('ident', 'tissue', 'patient'))
ViolinPlot(GDTlung, c('nCount_RNA','percent.mito', 'percent.ribo'), colors = umap.colors)
ClusterCompare(GDTlung, '3', '7')
# Since by harmoony we failed to achieve clear UMAP, we decide to stay with Seurat protocol
saveRDS(GDTlung, 'GDTlung.harmony:integration.rds')
rm(GDTlung )
# integration by Seurat ---------------------------------------------------
# normalization -----------------------------------------------------------
L_GDTlung%<>% map(.,~ NormalizeData(.x, normalization.method = 'LogNormalize',
scale.factor = 10000, assay = 'RNA') %>%
ScaleData( assay = 'RNA',
vars.to.regress = c("percent.mito",
# "S.Score", 'G2M.Score',
"percent.ribo",
'nCount_RNA','nFeature_RNA' )) %>%
FindVariableFeatures(assay = 'RNA',nfeatures = 2500, selection.method = 'vst')
)
L_GDTlung$p456
for (i in c('p1','p23', 'p456')) {
L_GDTlung[[i]]@[email protected]%<>%
str_subset('^TRG|^TRD|^MT|^IG|^TRA|^TRB^|^HIST|^MIR|^HSP', negate = T)
}
# data integration --------------------------------------------------------
anchors <- FindIntegrationAnchors(L_GDTlung, dims = 1:60)
GDTlung_s <- IntegrateData(anchors, dims = 1:60)
DefaultAssay(GDTlung_s) <- "integrated"
GDTlung_s %<>% ScaleData(vars.to.regress = c("percent.mito",
'patient',
# "S.Score", 'G2M.Score',
"percent.ribo",
'nCount_RNA','nFeature_RNA' ))
GDTlung_s %<>% ScaleData(assay = 'RNA', features = rownames(GDTlung_s),
vars.to.regress = c("percent.mito", 'patient',
# "S.Score", 'G2M.Score',
"percent.ribo",
'nCount_RNA','nFeature_RNA' )
)
s.genes <- cc.genes$s.genes
g2m.genes <- cc.genes$g2m.genes
GDTlung_s %<>% CellCycleScoring( s.features = s.genes, g2m.features = g2m.genes, set.ident = F)
# PCA --------------------------------------------------------------------
dim(GDTlung_s)
GDTlung_s %<>% RunPCA( npcs = 100, verbose =
T, nfeatures.print = 40) %>%
JackStraw( num.replicate = 100, dims = 80)%>%
ScoreJackStraw(dims = 1:80)
JackStrawPlot(GDTlung_s, dims = 1:50 ) %T>%
figsave('GDTlung_6p.jackstraw.pdf' , w = 400, h = 400)
# saveRDS(GDTlung,GDTlung.rds)
# dimensional reduction & clustering --------------------------------------
GDTlung_s %<>% RunUMAP( dims = 1:31,
reduction = 'pca', min.dist = 0.2) %>%
FindNeighbors(dims = c(1:31))
for (i in seq(0.5,1.2,0.1) %>% rev()) {
GDTlung_s %<>% FindClusters( resolution = i)
}
GDTlung_s$RNA_snn_res.1.2
GDTlung_s$ID <- paste0(GDTlung_s$tissue, GDTlung_s$patient)
Feature_rast(GDTlung_s,c('ident','tissue','patient', 'ID'), ncol = 2, noaxis = F)
ViolinPlot(GDTlung_s,'nFeature_RNA', group.by = 'integrated_snn_res.0.9')
DefaultAssay(GDTlung_s) <- "RNA"
Feature_rast(GDTlung_s,c('TRDV2','TRDV1','TRDV3','TRDC','TRGC1','TRGV9', "CD3D",'CD3E'), ncol =4)
Feature_rast(GDTlung_s, axis.number = T, noaxis = F)+geom_vline(xintercept = c(-5.3,7))
# trim non-T cells
GDTlung_s %<>% subset( UMAP_1 > -5.3 )
Feature_rast(GDTlung_s,c('ident','tissue','patient', 'ID'), ncol = 2, noaxis = F)
# 2nd dim-reduc &clustering -----------------------------------------------
DefaultAssay(GDTlung_s) <- "integrated"
GDTlung_s %<>% ScaleData(vars.to.regress = c("percent.mito", 'patient',
# "S.Score", 'G2M.Score',
"percent.ribo",
'nCount_RNA','nFeature_RNA' ))
GDTlung_s %<>% ScaleData(assay = 'RNA', features = rownames(GDTlung),
vars.to.regress = c("percent.mito", 'patient',
# "S.Score", 'G2M.Score',
"percent.ribo",
'nCount_RNA','nFeature_RNA' )
)
GDTlung_s %<>%RunPCA( npcs = 100, verbose =
T, nfeatures.print = 40)
ElbowPlot(GDTlung_s, ndims = 100)
GDTlung_s %<>% JackStraw(num.replicate = 100, dims = 50)%>%
ScoreJackStraw(dims = 1:50)
JackStrawPlot(GDTlung_s, dims = 1:50 )
JackStrawPlot(GDTlung_s, dims = 1:50 ) %T>%
figsave('GDTlung_6p.jackstraw.pdf' , w = 400, h = 400)
GDTlung_s %<>% RunUMAP( dims = 1:36,
reduction = 'pca', min.dist = 0.15) %>%
FindNeighbors(dims = c(1:36))
for (i in seq(0.5,1.2,0.1) %>% rev()) {
GDTlung_s <- FindClusters(GDTlung_s, resolution = i)
}
Feature_rast(GDTlung_s)
allres <- Feature_rast(GDTlung_s, paste0("integrated_snn_res.", seq(0.5,1.2,0.1)), ncol =4) %T>%
figsave('lunggdt_6p_allres.pdf', 400,200)
allres
table(GDTlung_s$seurat_clusters)
DefaultAssay(GDTlung_s) <- "RNA"
Feature_rast(GDTlung_s, 'Phase')
Feature_rast(GDTlung_s,c('TRDV2','TRDV1','TRDV3','TRDC','TRGC1','TRGV9', "CD3D",'CD3E'), ncol =4)
ClusterCompare(GDTlung_s,'4','6', group.by = 'integrated_snn_res.0.7' , log2fc = 0.5)
ClusterCompare(GDTlung_s,'1','6',group.by = 'integrated_snn_res.0.9' , log2fc = 0.75)
Idents(GDTlung_s) <- paste0('c',(as.numeric(as.vector(GDTlung_s$integrated_snn_res.0.7))+1)) %>%
factor(levels = paste0('c',1:12))
Feature_rast(GDTlung_s, c('ident', 'tissue', 'patient')) %T>% figsave('gdt_lung_6p_UMAP_aftertrimming.pdf', 200, 80)
GDTlung_s$Cell_cluster <- Idents(GDTlung_s)
GDTlung_s$ID <- paste0(GDTlung_s$tissue,'_',GDTlung_s$patient)
Feature_rast(GDTlung_s,c('TRAC','TRBC1','TRBC2'))
cl_comp <- [email protected] %>% group_by(tissue, ID, Cell_cluster) %>%
summarise(n = n() ) %>% group_by(ID) %>%
mutate(percent = n/sum(n)*100) %>% as.data.frame()
# cl_comp$ID <- factor(cl_comp$ID, levels = c(paste0('luLN#',1:),paste0('lung#',1:3) ))
# cl_comp$group <- factor(cl_comp$group, levels = c(, "NCB"))
cl_comp[cl_comp$tissue =='lung',]$n <- -cl_comp[cl_comp$tissue =='lung',]$n
cl_comp[cl_comp$tissue =='lung',]$percent <- -cl_comp[cl_comp$tissue =='lung',]$percent
cl_comp$percent %>% sum
cl_comp
library(ggalluvial)
library(RColorBrewer)
ID_cl <- c(brewer.pal(9,'Blues')[4:9], brewer.pal(9,'Reds')[4:9])
cl_comp_flow <- ggplot(cl_comp, 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#,
# labels = c('luLN #1: 1135','#2: 203','#3: 822',
# 'lung #1: 1756','#2: 632','#3: 964')
)+
scale_color_manual(values = ID_cl)+
theme_minimal() +
ylab('Cell number')+
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
cl_comp_flow +theme(axis.text = element_text(size = 12), legend.text = element_text(size = 12))
Umap_GDTlung<- Feature_rast(GDTlung_s,c('Cell_cluster','tissue'), sz = 0.5,
noaxis = F, labels = c('AUTO')) %>%
list(cl_comp_flow) %>% PG(nrow = 1, rw = c(1.7,1), labels = c(NA,'C'))
Umap_GDTlung
genes <- c('TRDV1','TRDV2','TRDV3','TRGV9','TRBC1',
'ZBTB16','TCF7', 'CCR7','TBX21', 'ITGAE','ITGB2',
'GZMA','FCGR3A','IFNG',"XCL1", "AREG",
'KLRB1', 'KLRC1', 'KLRD1','KLRG1',
"RORC", 'CCR6','DPP4','CXCR3','CXCR6')
gene_UMAP <- Feature_rast(GDTlung_s,genes, sz = 0.4)
gene_UMAP
fig1 <- PG(list(Umap_GDTlung, gene_UMAP), nrow =2, rh = c(1,3), labels = c(NA,'D'))
fig1
# figsave(gene_UMAP2, 'test_geneumap_gdt2020_2.pdf', 400, 400)
figsave(fig1, 'fig1_lunggdt_6p_UMAP.pdf', 210,270)
saveRDS(GDTlung_s, 'GDTlung_6p_Seurat_07062021.rds')
# DEGs --------------------------------------------------------------------
hgdTmarkers <- FindAllMarkers(GDTlung_s, 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))
hgdTmarkers %>% group_by(cluster) %>% filter(avg_log2FC >0) %>% summarise(n = n())
top10_hgdT <- hgdTmarkers %>% filter(!grepl('^RP|^MT|^TRG|^TRD|^AC', gene)) %>%
arrange(cluster, desc(avg_log2FC))%>%
group_by(cluster) %>% top_n(10, avg_log2FC)
Heat_GDTlung <- DoHeatmap(GDTlung_s,features = top10_hgdT$gene,raster = T,
group.colors = umap.colors,size = gs(8))%>%heat_theme() %>%
figsave('heatmap_GDTlung_6p.pdf',150,250)
Heat_GDTlung
surfacemakers <- c('PTPRC','PDCD1','CCR6','ITGAE','CD69','CCR7','KLRB1','CD27','KLRG1','IL7R',
'DPP4','ITGA1','NCR1','NCR3','FCGR3A')
sf <- Feature_rast(GDTlung_s, c('ident','tissue', surfacemakers), ncol = 4,sz = 0.2) %T>%
figsave('sfmarkers_gdt.pdf', 200,200)
sf
library(Nebulosa)
plot_density(GDTlung_s, surfacemakers) %T>%
figsave('density_test.pdf', 400,400)
plot_density(GDTlung_s, 'TRGC2')
Feature_rast(GDTlung_s, 'TRGC2')
Feature_rast(GDTlung_s, c('ident','orig.ident') )
saveRDS(GDTlung_s,'GDTlung_6p_Seurat_07062021.rds')
Cytokines <- c('IL17A','IL22', 'AREG','IFNG','CSF2', 'TNF',
'GZMA', 'GZMB','GZMK', 'PRF1')
Cytokines %>% Feature_rast(GDTlung_s, .)
# Gene modules ------------------------------------------------------------
library(openxlsx)
gene_c_list_ent <-readRDS('/home/big/tanlikai/Human_GDT_2019/Integrated/Genemodule_list_ent_2020AUG.RDS')
gc_name <- gene_c_list_ent$GM %>% unique() %>% sort() %>% as.vector()
gene_c_list_ent$GM
gene_cluster <- map(gc_name, function(x) gene_c_list_ent %>% filter(GM == x) %>% dplyr::select(gene) %>% pull()) %>% setNames(gc_name)
gene_cluster$GM_B
for (i in gc_name) {
GDTlung_s %<>% AddModuleScore( features = list(gene_cluster[[i]]), name = i, assay = 'RNA')
colnames([email protected]) %<>% str_replace("(?<=\\w)1", '')
}
colnames([email protected])
Feature_rast(GDTlung_s, c(gc_name), color_grd = 'grd')
ViolinPlot(GDTlung_s, gc_name, colors = umap.colors)
GMS <- [email protected][,c(gc_name)] %>% as.data.frame() %>% t()
scaleGM <- scale(t([email protected][,c(gc_name)]))
scaleGMassay <- CreateAssayObject(data = scaleGM)
GDTlung_s@assays$GM <- scaleGMassay
gms <- paste0('GM-', LETTERS[1:8])
gcanno <- as.vector(c('GM_A: Naive or immature T cell',
"GM_B: Innate T cell differentiation",
'GM_C: Proliferating',
"GM_D: Type-3 immunity",
"GM_E: Interferon induced",
"GM_F: CTL response (VD2)",
"GM_G: CTL response (VD1)",
"GM_H: Acute activation"))
c(1,2,4,6,7) %>% map(~
Feature_rast(GDTlung_s, gc_name[.x], color_grd = 'grd', mythe = F)+
ggtitle(gcanno[.x])
) %>% PG(ncol = 3) %T>% figsave('GM_score.pdf', 200, 150)
GM_score_heat <- DoHeatmap(subset(GDTlung_s, downsample = 1000),
raster =T, draw.lines = T, angle = 45,
lines.width = 10,group.colors = umap.colors,
assay = 'GM', features = gms, 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'))
GM_score_heat
# TCR --------------------------------------------------------------------
# here we have TCR data from two different datasets: 1 and 2_3
# and we will combine them as one
# for each sample we have two sequence run.
TCRdirs <- list.dirs(path = 'raw') %>% str_subset('VDJ.+outs$' )
lung1TCR <- read.csv('raw/VDJ_Falk1_gd/outs/all_contig_annotations.csv') %>%
filter(productive == 'True' & grepl('GV|DV', v_gene))%>%
dplyr::select(c(1, 5:10, 13,14)) %>% mutate(bc_backup = paste0(barcode, "_1"))
lung2_3TCR <- read.csv('raw/VDJ_322_325_453_456_gd/outs/all_contig_annotations.csv') %>%
filter(productive == 'True' & grepl('GV|DV', v_gene))%>%
dplyr::select(c(1, 5:10, 13,14)) %>% mutate(bc_backup = paste0(barcode, "_2"))
lung4_6TCR <- read.csv('raw/VDJ_falk3_gd/outs/all_contig_annotations.csv') %>%
filter(productive == 'True' & grepl('GV|DV', v_gene))%>%
dplyr::select(c(1, 5:10, 13,14)) %>% mutate(bc_backup = paste0(barcode, "_3"))
lungTCR <- rbind(lung1TCR, lung2_3TCR,lung4_6TCR) %>% select(-barcode)
TRGs <- lungTCR %>% filter(grepl('GV', v_gene))%>% distinct(bc_backup, .keep_all = T) %>%
# mutate(v_gene = str_remove(v_gene,'DV\\d')) %>%
rename_at(vars(-bc_backup), funs(sub('$','_TRG',.)))
TRGs
TRDs <- lungTCR %>% filter(grepl('DV', v_gene))%>% distinct(bc_backup, .keep_all = T) %>%
rename_at(vars(-bc_backup), funs(sub('$','_TRD',.))) %>%
mutate(v_gene_TRD = str_remove(v_gene_TRD, 'AV\\d\\d'))%>%
mutate(v_gene_TRD = str_remove(v_gene_TRD, '-2'))
TRDs
TCRs <- full_join(TRGs,TRDs, by = c('bc_backup'))
TCRs %<>% mutate(paired =
case_when(!is.na(v_gene_TRD) & !is.na(v_gene_TRG) ~
paste0(str_remove(v_gene_TRG,'TR'),' ',str_remove(v_gene_TRD,'TR'))))
TCRs %<>% mutate(cdr3_paired =
case_when(!is.na(v_gene_TRD) & !is.na(v_gene_TRG) ~
paste(str_remove(v_gene_TRG,'TR'),cdr3_TRG,str_remove(v_gene_TRD,'TR'),cdr3_TRD)))
top9paired <- TCRs %>% filter(!is.na(paired)) %>%count(paired) %>%
arrange(desc(n)) %>% top_n(9,n) %>% pull(paired)
unique(TCRs$v_gene_TRD)
TCRs<- TCRs %>%
mutate(paired_sp =
case_when(paired %in% top9paired ~ paired,
!is.na(paired) ~ "Other paired") ) %>%
mutate(paired_sp = factor(paired_sp, levels = c(top9paired, "Other paired")))
# paired TRG and J
TCRs<- TCRs %>% mutate(TRGJP = case_when(!is.na(chain_TRG) ~ paste(v_gene_TRG, j_gene_TRG)))%>%
mutate(TRGJP = str_replace(TRGJP, ' TRG',' '))
TCRs$bc_backup
TCRs
# # joinTCR to seurat --------------------------------------------------------
# GDTlung is the name of seurat object
GDTlung_s$bc_backup <- rownames([email protected])
metabackup <- [email protected]
[email protected] %<>% left_join(TCRs, by = 'bc_backup') %>% `rownames<-`([email protected]$bc_backup )
# this line to remove repeated join
[email protected] %<>% `colnames<-`(str_remove(colnames([email protected]), '.y')) %>%
select(!ends_with('.x'))
# mapped TCR
TRDmapp <- [email protected] %>% count(ID, v_gene_TRD) %>% group_by(ID) %>%
mutate(percent = n/sum(n)*100)
TRGmapp <- [email protected] %>% count(ID,v_gene_TRG) %>% group_by(ID) %>%
mutate(percent = n/sum(n)*100)
pairedmapp <[email protected] %>% count(ID,paired_sp) %>% group_by(ID) %>%
mutate(percent = n/sum(n)*100)
[email protected] %<>% mutate(TCR_summary = case_when(!is.na(paired) ~ 'paired TCR',
!is.na(chain_TRG)~'single TRG',
!is.na(chain_TRD)~ 'single TRD') )
mappedTCR <- map2(list(TRDmapp, TRGmapp, pairedmapp), list('v_gene_TRD','v_gene_TRG', 'paired_sp'),~
ggplot(.x, aes_string('ID', y = "percent", color = .y, fill = .y))+
geom_bar(stat = 'identity', width = 0.5,position = position_stack(reverse = T))+
color_m()+
fill_m()+
xlab(NULL)+
ylab('%')+
ggtitle(.y)+
guides(color = F, fill = guide_legend(nrow =4, title = NULL))+
theme_minimal()+
mytheme+gglp('b')
) %>% PG(nrow =1, labels = 'AUTO')
mappedTCR
ViolinPlot(subset(GDTlung, length_TRD >0 & patient == '#3'),'length_TRD')
TCRmapping <- [email protected] %>% group_by(tissue, patient) %>% count(TCR_summary) %>%
mutate(percent = n/sum(n)*100)
(ggplot(TCRmapping,aes(x = tissue,y= percent, group = TCR_summary, fill = TCR_summary))+geom_bar(stat = 'identity',position = position_stack(reverse = T))+facet_grid(~patient)+fill_m()+theme_bw()+
scale_y_continuous(breaks = c(10,20,30,40,50,60,70,80,90,100))+
mytheme) %T>% figsave('mappingrate_TCR.pdf', 150, 60)
figsave(mappedTCR,'lunggdt_mappedTCR.pdf', 200,100)
# TCR frequency -----------------------------------------------------------
cdr3TRD_freq <- [email protected] %>% filter(c_gene_TRD == 'TRDC' & cdr3_TRD != 'None') %>% group_by(patient) %>%
dplyr::count(cdr3_TRD = cdr3_TRD) %>% arrange(desc(n)) %>% dplyr::rename(cdr3_TRD_freq = 'n') %>%
mutate(cdr3_TRD_perc = cdr3_TRD_freq/sum(cdr3_TRD_freq)*100)
cdr3TRG_freq <- [email protected] %>% filter(grepl('TRGC', c_gene_TRG) & cdr3_TRG != 'None') %>% group_by(patient) %>%
dplyr::count(cdr3_TRG = cdr3_TRG) %>% arrange(desc(n)) %>% dplyr::rename(cdr3_TRG_freq = 'n') %>%
mutate(cdr3_TRG_perc = cdr3_TRG_freq/sum(cdr3_TRG_freq)*100)
cdr3TRG_freq
cdr3TRG_freq$cdr3_TRG_perc %>% sum
cdr3Paired_freq <- [email protected] %>% filter(!is.na(cdr3_paired)) %>% group_by(patient) %>%
dplyr::count(cdr3_paired = cdr3_paired) %>% arrange(desc(n)) %>%
dplyr::rename(cdr3_paired_freq = 'n') %>%
mutate(cdr3_paired_perc = cdr3_paired_freq/sum(cdr3_paired_freq)*100)
cdr3Paired_freq
[email protected] %<>% left_join(cdr3TRD_freq, by = c('cdr3_TRD','patient')) %>%
left_join(cdr3Paired_freq, by = c('cdr3_paired','patient'))%>%
left_join(cdr3TRG_freq, by = c('cdr3_TRG','patient')) %>% `rownames<-`(GDTlung_s$bc_backup)
# rownames([email protected]) <-
##paired_simplified
saveRDS(GDTlung_s,'GDTlung_6p_Seurat_07062021.rds')
patient <- c(GDTlung_s$patient %>% unique()) %>% sort()
TCRD_FREQ <-map(patient,~
Feature_rast(subset(GDTlung_s, patient == .x),'cdr3_TRD_perc',color_grd = 'grd')+
ggtitle(paste('patient',.x,'\nTRD_clone (%)'))
) %>% PG(nrow =2)
TCRD_FREQ
Feature_rast(GDTlung_s, 'cdr3_TRD_perc', color_grd = 'grd', mythe = F)+
ggtitle('TRD_clone (%)')+
theme(plot.title = element_text(size = 12))
# analysis and make figures -----------------------------------------------
# TRD on umap
TCRumap <- Feature_rast(GDTlung_s,c('Cell_cluster','v_gene_TRD','paired_sp'),do.label = F, sz =1, labels =LETTERS[4:6] )
TCRumap
Feature_rast(GDTlung_s,'ident', c('ID'))+facet_grid(~ID)
GDTlung_s$length_TRD
ViolinPlot(GDTlung_s %>% subset(v_gene_TRD == 'TRDV1') , 'length_TRD', box = T)+ ylim(350,700)
Feature_rast(GDTlung, facet = 'patient')+facet_grid(~patient)
# top TRD and paired
Top5TRD_lung <- [email protected] %>% filter( !is.na(cdr3_TRD) & tissue == 'lung') %>%
group_by(patient,v_gene_TRD, cdr3_TRD) %>%
dplyr::count(patient,cdr3_TRD) %>% arrange(desc(n)) %>%
group_by(patient) %>% dplyr::slice(1:5) %>% filter(n>=5) %>%
dplyr::mutate(Top5TRD_lung = paste0(patient, ' ',v_gene_TRD," ",cdr3_TRD,' ', n)) %>%
arrange(patient,desc(n)) %>%
select(patient, cdr3_TRD, Top5TRD_lung) %>%
ungroup()
Top5TRD_luLN <- [email protected] %>% filter( !is.na(cdr3_TRD) & tissue == 'luLN') %>%
group_by(patient,v_gene_TRD, cdr3_TRD) %>%
dplyr::count(patient,cdr3_TRD) %>% arrange(desc(n)) %>%
group_by(patient) %>% dplyr::slice(1:5) %>% filter(n>=5) %>%
dplyr::mutate(Top5TRD_luLN = paste0(patient, ' ',v_gene_TRD," ",cdr3_TRD,' ', n)) %>%
arrange(patient,desc(n)) %>%
select(patient, cdr3_TRD, Top5TRD_luLN) %>%
ungroup()
# top10TRD <- [email protected] %>% filter( !is.na(cdr3_TRD)) %>%
# group_by(tissue,v_gene_TRD, cdr3_TRD) %>%dplyr::count(tissue,patient,cdr3_TRD) %>%
# arrange(desc(n)) %>% group_by(tissue,patient) %>% dplyr::slice(1:10) %>%
# dplyr::mutate(top10_TRD = paste0(tissue, " ",v_gene_TRD," ",cdr3_TRD)) %>%
# arrange(tissue,desc(n)) %>% ungroup()
# top10TRD %>% group_split(patient) %>% map(.,~ .x%>% pull(cdr3_TRD) %>% unique() %>% length)
#
#
#
# top10TRD %>% filter(patient == '#3')
GDTlung_s$Top5TRD_lung <- NULL
GDTlung_s$Top5TRD_luLN <- NULL
[email protected] %<>%
left_join(Top5TRD_lung,by = c('cdr3_TRD','patient') )%>%
left_join(Top5TRD_luLN,by = c('cdr3_TRD','patient') )%>%
mutate(Top5TRD_lung = case_when( !is.na(Top5TRD_lung) ~ Top5TRD_lung,
is.na(Top5TRD_lung) & !is.na(cdr3_TRD) ~ 'mapped TCR')) %>%
mutate(Top5TRD_luLN = case_when( !is.na(Top5TRD_luLN) ~ Top5TRD_luLN,
is.na(Top5TRD_luLN) & !is.na(cdr3_TRD) ~ 'mapped TCR')) %>%
`rownames<-`(GDTlung_s$bc_backup)
GDTlung_s$Top5TRD_lung %<>% factor(levels = GDTlung_s$Top5TRD_lung %>%unique() %>% str_sort())
GDTlung_s$Top5TRD_luLN %<>% factor(levels = GDTlung_s$Top5TRD_luLN %>%unique() %>% str_sort())
Feature_rast(GDTlung_s)
# [email protected] %<>% `colnames<-`(str_remove(colnames([email protected]), '.y')) %>% select(!ends_with('.x'))
Feature_rast(GDTlung_s, c('tissue', 'Top5TRD_lung','Top5TRD_luLN'), do.label = F,
colorset = ggplotColours(28))
top10trdcl <-
c('#993333','#ff3300','#ff0066','#cc0099','#cc3300','#391313','#ff6666','#ffa64d','#ff8080','#660066',
'#000066','#0000ff', '#336699','#006666','#6666ff', '#004d3d','#00b3b3','#003399','#39ac73','#1ab2ff',
'#ffffcc'
)
luLN_expanded<-Feature_rast(GDTlung_s, "Top5TRD_luLN", do.label = F, facet ='patient')+
scale_color_manual(values = c(
top10trdcl[1:(length(unique(GDTlung_s$Top5TRD_luLN))-2)],
'mapped TCR' = alpha('yellow',0.5)),
na.value =alpha('lightgrey',0.5) )+facet_grid(~patient)+
ggtitle("Most Expanded TRD in luLN")
lung_expanded<- Feature_rast(GDTlung, "Top5TRD_lung", do.label = F, facet ='patient')+
scale_color_manual(values = c(
top10trdcl[1:(length(unique(GDTlung$Top5TRD_lung))-2)],
'mapped TCR' = alpha('yellow',0.5)),
na.value =alpha('lightgrey',0.5) )+facet_grid(~patient)+
ggtitle("Most Expanded TRD in lung")
# venny nonVD2 TRD ----------------------------------------------------
Feature_rast(GDTlung_s)
[email protected] %<>% mutate(big_cluster = case_when(Cell_cluster %in% c('c1','c2','c8', 'c9')~ 'nonVD2_lung_1',
Cell_cluster %in% c('c5','c7' ,'12')~ 'nonVD2_lung_2',
Cell_cluster %in% c('c3', 'c6', 'c10')~ 'nonVD2_luLN_1',
Cell_cluster %in% c('c11')~ 'nonVD2_luLN_2',
Cell_cluster %in% c('c4')~ 'VD2')
)%>%
`rownames<-`(GDTlung_s$bc_backup)
GDTlung_s$big_cluster
UMAP_bigcluster <- Feature_rast(GDTlung_s, 'big_cluster')
ClusterCompare(GDTlung_s,'nonVD2_lung_1','nonVD2_lung_2', group.by = 'big_cluster', log2fc = 1)
patient
TRD_uniq <- c()
for (i in patient) {
TRD_uniq[[i]] <- map(c('nonVD2_lung_1','nonVD2_lung_2','nonVD2_luLN_1','nonVD2_luLN_2'), ~
dplyr::filter( big_cluster==.x & v_gene_TRD != 'TRDV2' & !is.na(cdr3_TRD) &patient ==i) %>%
pull(cdr3_TRD) %>% unique ) %>%
setNames(c('nonVD2_lung_1','nonVD2_lung_2','nonVD2_luLN_1','nonVD2_luLN_2'))
}
TRD_uniq$p25
library(eulerr)
library(ggplotify)
set.seed(8)
euler(TRD_uniq$`#3`, shape = 'ellipse')
set.seed(8)
Venny_nonVD2 <- map2(TRD_uniq, list(F,F,list(cex = .66),F,F,list(cex = .66)),~ plot(euler(.x, shape = 'ellipse'),
quantities = list(cex = 0.66), prob = T,
labels = NULL,legend =.y,
fill = alpha(umap.colors,0.8),
) %>% ggplotify::as.ggplot() + ggtitle('nonVD2 unique CDR3')+
theme(plot.title = element_text(size = 8, hjust = 0.5))) %>%
PG(nrow = 2, rw = c(1,1,1.8), labels = patient, label_fontface = 'plain',label_size = 8 )
# venny VD2 TRD ----------------------------------------------------
TRD_VD2_uniq <- c()
for (i in patient) {
TRD_VD2_uniq[[i]] <- map(c('lung','luLN'), function(x) [email protected] %>%
dplyr::filter( Cell_cluster== 'c4' & v_gene_TRD == 'TRDV2' & patient ==i & tissue == x) %>%
pull(cdr3_TRD) %>% unique ) %>%
setNames(c('lung','luLN'))
}
set.seed(8)
Venny_VD2 <- map2(TRD_VD2_uniq, list(F,F,list(cex = .66),F,F,list(cex = .66)),~ plot(euler(.x, shape = 'ellipse'),
quantities = list(cex = 0.66), prob = T,
labels = NULL,legend =.y,
fill = alpha(umap.colors,0.8),
) %>% ggplotify::as.ggplot() + ggtitle('VD2 unique CDR3 in c5')+
theme(plot.title = element_text(size = 8, hjust = 0.5))) %>%
PG(nrow = 2, rw = c(1,1,1.8), labels = patient, label_fontface = 'plain',label_size = 8 )
# TCR_comp <- PG(list(Venn_TRD_uniq, Venn_paired_uniq), labels = c('h','i'))
Fig2test <- PG(list(mappedTCR,TCRumap,TCRD_FREQ),
ncol = 1)
figsave(Fig2test,'fig2_lunggdt_TCRcomp.pdf', 195, 230)