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Application.2.Bcell.analysis.R
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library(SingleCellExperiment);library(tidyverse);library(reticulate);library(tidyverse)
library(ggplot2);library(scmap);library(fmsb);library(ggsci);library(ROGUE)
library(Seurat);library(M3Drop);library(ROCR);library(cluster);library(parallel);library(scran)
# Load data
lung.sce <- readr::read_rds("/data1/pauling/04_SEmodel/01_data/08_lung_Bcells/com.lung.liver.Bcell.rds.gz")
meta <- readr::read_rds("/home/pauling/projects/04_SEmodel/01_data/08_lung_Bcells/meta.lung.liver.Bcell.rds.gz")
expr <- lung.sce@assays$RNA@counts
# OUT path
fig.path <- "/home/pauling/projects/04_SEmodel/07_NC_revision/02.figures/07.Bcell"
out.path <- "/home/pauling/projects/04_SEmodel/07_NC_revision/03.data/07.Bcell"
# Select variable genes using S-E
expr <- as.matrix(expr)
ent.res <- SE_fun(expr)
# Perform clustering with Seurat
lung.se.sce <- RunPCA(lung.sce, verbose = FALSE, features = ent.res$Gene[1:3000])
pca <- lung.se.sce@[email protected]
use_python("/home/heyao/data/tools/basic/anaconda3/bin/python")
anndata = import("anndata",convert=FALSE)
sc = import("scanpy.api",convert=FALSE)
np = import("numpy",convert=FALSE)
bbknn = import("bbknn", convert=FALSE)
adata.com = anndata$AnnData(X=pca, obs=meta$Donor)
sc$tl$pca(adata.com)
adata.com$obsm$X_pca = pca
bbknn$bbknn(adata.com,batch_key=0)
sc$tl$umap(adata.com, random_state = 3316L)
sc$tl$leiden(adata.com, resolution = 0.8)
umap.com = py_to_r(adata.com$obsm$X_umap)
tibble::tibble(
leiden.res.1.bbknn = py_to_r(np$asarray(adata.com$obs$leiden)),
UMAP1 = umap.com[,1],
UMAP2 = umap.com[,2]) %>%
dplyr::mutate(Donor = meta$Donor) %>%
dplyr::mutate(tissue = meta$tissue) %>%
dplyr::mutate(project = meta$project) -> bb.res
umap.plot <- bb.res %>%
ggplot(aes(UMAP1, UMAP2)) +
geom_point(aes(colour = leiden.res.1.bbknn), size = 2) +
theme_bw() +
theme_void() +
theme(legend.position = "none") +
scale_colour_manual(values = c("#FF83FA", "#66CDAA", "#AB82FF", "#00C5CD", "#00B2EE", "#FF6A6A", "#D02090"))
tis.dist.plot <- bb.res %>%
ggplot(aes(UMAP1, UMAP2)) +
geom_point(aes(colour = tissue), size = 2) +
theme_bw() +
theme_void() +
theme(legend.position = "top") +
scale_colour_manual(values = c("#00EEEE", "#00B2EE", "#FF69B4", "#FF3E96", "#48D1CC", "#FF8247", "#90EE90"))
# Confusion heatmap
conf.matr <- table(bb.res$leiden.res.1.bbknn, meta$leiden.res.1.bbknn)
conf.matr <- as.matrix(conf.matr)
conf.matr <- conf.matr/rowSums(conf.matr)
conf.matr %>%
as.data.frame() %>%
dplyr::rename(SE = Var1, SCT = Var2) %>%
ggplot(aes(SCT, SE)) +
geom_tile(aes(fill = Freq)) +
scale_fill_viridis_c() +
theme_minimal() -> conf.plot
#ROGUE calculation
bb.res <- bb.res %>% dplyr::mutate(ID = 1:nrow(.))
clusters <- unique(bb.res$leiden.res.1.bbknn)
patient.rogue <- function(info, cluster){
tmp <- bb.res %>% dplyr::filter(leiden.res.1.bbknn == cluster)
patients <- unique(bb.res$Donor)
rogue <- c()
for (i in 1:length(patients)) {
print(i)
index1 <- tmp %>% dplyr::filter(Donor == patients[i]) %>% dplyr::pull(ID)
if(length(index1) >= 20){
tmp.matr <- matr[,index1]
#tmp.matr <- matr.filter(tmp.matr)
tmp.res <- SE_fun(tmp.matr)
rogue[i] <- CalculateRogue(tmp.res)
}
else{
rogue[i] <- NA
}
}
return(rogue)
}
res <- list()
for (i in 1:length(clusters)) {
res[[i]] <- patient.rogue(bb.res, clusters[i])
}
res.tibble <- Reduce(rbind, res) %>% as.matrix() %>% t() %>% as.tibble()
colnames(res.tibble) <- clusters
res.tibble %>%
tidyr::gather(key = clusters, value = ROGUE) %>%
ggplot(aes(clusters, ROGUE)) +
geom_boxplot(color = "#FF3E96", outlier.shape = NA) +
geom_point(color = "#FF3E96", size = 1.5) +
theme_bw() +
theme(axis.text = element_text(size = 12, colour = "black"),
axis.title = element_text(size = 13, colour = "black")) +
labs(
x = "Clusters",
y = "ROGUE"
) -> rogue.plot
# Marker heatmap
Idents(lung.sce) <- bb.res$leiden.res.1.bbknn
b.markers <- FindAllMarkers(lung.sce, only.pos = TRUE, min.pct = 0.25, test.use = "t")
top10 <- b.markers %>%
dplyr::mutate(cluster = as.character(cluster)) %>%
group_by(cluster) %>% top_n(n = 10, wt = avg_logFC) %>%
dplyr::arrange(cluster)
DoHeatmap(lung.sce, top10$gene)
marker.matr <- function(tmp.sce, gene){
matr <- as.matrix(tmp.sce@[email protected])
gene <- intersect(gene, rownames(matr))
matr <- matr[gene,]
tibble(cluster = as.numeric(unique(Idents(tmp.sce)))-1) %>%
dplyr::mutate(
expr = purrr::map(
.x = cluster,
.f = function(.x){
tmp.matr <- matr[,Idents(tmp.sce) == .x]
rowMeans(tmp.matr)
}
)
) -> tmp.res
matr <- Reduce(rbind, tmp.res$expr)
matr <- as.data.frame(matr)
rownames(matr) <- paste("C", tmp.res$cluster, sep = "")
return(matr)
}
pda <- marker.matr(lung.sce, top10$gene)
pda %>%
tibble::rownames_to_column(var = "Cluster") %>%
dplyr::mutate_if(is.numeric, funs((. - mean(.))/sd(.))) %>%
tidyr::gather(key = "Gene", value = "expr", -Cluster) %>%
ggplot(aes(Cluster, factor(Gene, levels = rev(unique(top10$gene))))) +
geom_tile(aes(fill = expr)) +
#scale_fill_distiller(palette = "Spectral") +
theme(strip.text.x = element_blank(),
axis.title = element_text(size = 15),
axis.text = element_text(size = 9),
legend.title = element_text(size = 13),
legend.text = element_text(size = 13),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black"),
panel.background = element_rect(colour = "black", fill = "white"),
panel.grid = element_line(colour = "grey", linetype = "dashed"),
panel.grid.major = element_line(colour = "grey", linetype = "dashed", size = 0.2)) +
#facet_grid(. ~ group, scales = "free", space = "free") +
scale_fill_distiller(palette = "RdBu") +
labs(x = "", y = "") -> marker.heatmap
# R O/E calculation
meta.sub <- bb.res %>% dplyr::filter(project == "liver")
cluster.table <- table(meta.sub$leiden.res.1.bbknn, meta.sub$tissue)
cluster.table %>%
as.data.frame() %>%
dplyr::mutate(
p.value = purrr::pmap_dbl(
list(
.x = Var1,
.y = Var2,
.z = Freq
),
.f = function(.x, .y, .z){
a <- .z
b <- sum(cluster.table[,.y]) - a
c <- sum(cluster.table[.x,]) - a
d <- sum(cluster.table) - a - b - c
#o <- fisher.test(matrix(c(a, b,c, d), ncol = 2), alternative = "greater")
#o$estimate
o <- chisq.test(matrix(c(a, b, c, d), ncol = 2))
oe <- o$observed/o$expected
oe[1,1]
}
)
) -> enrich.res
#adj.p.value <- p.adjust(enrich.res$p.value, method = "BH")
#enrich.res <- enrich.res %>% dplyr::mutate(adj.p.value = adj.p.value)
enrich.res %>%
dplyr::rename(`Ro/e` = p.value) %>%
#dplyr::mutate(p.value = ifelse(p.value < 1, -1/p.value, p.value)) %>%
#dplyr::mutate(`-log10(adj.P-value)` = -log10(adj.p.value)) %>%
ggplot(aes(Var2, Var1, fill = `Ro/e`)) +
geom_tile(colour = "white", lwd = 0.8) +
theme(axis.title = element_text(size = 12)) +
theme(axis.text = element_text(size = 12)) +
theme(legend.title = element_text(size = 10)) +
theme(legend.text = element_text(size = 10)) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank()) +
theme(axis.text.y = element_text(color="black"),
axis.text.x = element_text(color="black" ,angle = 45, hjust = 1)) +
scale_fill_distiller(palette = "Spectral") -> roe.plot
# Ro/e barplot
tissues <- unique(bb.res$tissue)
a <- bb.res %>% dplyr::filter(leiden.res.1.bbknn == 2) %>% dplyr::filter(project == "liver") # [== 2] # [== "Lung"] #
a$tissue <- as.factor(a$tissue)
sda <- table(a$Donor, a$tissue) %>% as.data.frame()
sda %>%
as.data.frame() %>%
tidyr::spread(key = "Var2", value = "Freq") %>%
as.data.frame() %>%
tibble::column_to_rownames(var = "Var1") -> sda
sda2 <- t(sda) %>% as.data.frame() %>% dplyr::mutate_all(funs((./sum(.))))
rownames(sda2) <- colnames(sda)
tibble(Tissue = tissues[1:5]) %>%
dplyr::mutate(mean = purrr::map_dbl(.x = Tissue, function(.x){mean(unlist(sda2[.x,]))})) %>%
dplyr::mutate(sem = purrr::map_dbl(.x = Tissue, function(.x){sd(unlist(sda2[.x,]))/sqrt(5)})) %>%
dplyr::mutate(ymax = mean+sem) %>%
dplyr::mutate(ymin = mean-sem) %>%
ggplot(aes(factor(Tissue, levels = c("Tumor","Lymphnode","Blood", "Normal", "Ascites")), mean)) +
geom_col(aes(fill = Tissue), colour = "black") +
scale_fill_manual(values = c("#009ACD", "#EE3A8C", "#EE82EE", "#20B2AA", "#FF7F50")) +
geom_errorbar(aes(ymin = ymin, ymax = ymax), width = 0.2) +
theme_minimal() +
theme(legend.position = "none") +
theme(axis.title = element_text(size = 12)) +
theme(axis.text = element_text(size = 12)) +
theme(legend.title = element_text(size = 10)) +
theme(axis.text.y = element_text(color="black"),
axis.text.x = element_text(color="black")) +
labs(
x = "",
y = "Fraction"
) +
ylim(0,0.9) -> liver.c2
rownames(umap.com) <- rownames(lung.sce@[email protected])
colnames(umap.com) <- colnames(lung.sce@[email protected])
lung.sce@[email protected] <- umap.com
FeaturePlot(lung.sce, features = c("MKI67","STMN1"), pt.size = 0.8, cols = c("#EEE9BF", "red"))
# Save
bb.res %>% readr::write_rds(file.path(out.path,"01.clustering.res.rds.gz"), compress = "gz")
ggsave(plot = umap.plot, filename = "01.umap.plot.pdf", path = fig.path, width = 6, height = 4.5)
ggsave(plot = tis.dist.plot, filename = "02.tissue.distribution.plot.pdf", path = fig.path, width = 6, height = 5)
ggsave(plot = rogue.plot, filename = "03.rogue.boxplot.pdf", path = fig.path, width = 6, height = 4.5)
ggsave(plot = conf.plot, filename = "04.confution.heatmap.pdf", path = fig.path, width = 6, height = 4.5)
ggsave(plot = marker.heatmap, filename = "05.marker.heatmap.pdf", path = fig.path, width = 8, height = 8)
ggsave(plot = roe.plot, filename = "06.roe.heatmap.pdf", path = fig.path, width = 4, height = 5)
ggsave(plot = liver.c4, filename = "07.liver.c4.pdf", path = fig.path, width = 5, height = 3.7)
ggsave(plot = liver.c2, filename = "08.liver.c2.pdf", path = fig.path, width = 5, height = 3.7)