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Application.1.fibroblast.analysis.R
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Application.1.fibroblast.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(loomR)
library(Seurat);library(M3Drop);library(ROCR);library(cluster);library(parallel);library(scran)
# Load data
datapath <- "/home/pauling/projects/04_SEmodel/07_NC_revision/03.data/06.fibroblast"
cell.info <- readr::read_csv(file.path(datapath,"Thienpont_Tumors_52k_v4_R_fixed.cellInfo.txt"))
gene <- readr::read_rds("/home/pauling/projects/02_data/09_Gene/coding_gene.rds.gz")
dloom <- connect(file.path(datapath,"fibroblasts"), mode = "r")
a <- cell.info %>% dplyr::filter(stringr::str_detect(ClusterName,"fibroblasts"))
a <- as.data.frame(a)
rownames(a) <- a$CellID
sce <- as.Seurat(dloom)
expr <- sce@assays$RNA@counts
overlap.gene <- intersect(gene$gene_name, rownames(expr))
filt.expr <- expr[overlap.gene,]
a <- a[colnames(filt.expr),]
#############
pauling.theme <- function(poi = "right", size = 12, title.size = 13){
theme(
legend.position = poi,
axis.title = element_text(size = title.size,color="black"),
axis.text = element_text(size = size,color="black"),
legend.title = element_text(size = size),
legend.text = element_text(size = size),
axis.text.y = element_text(color="black"),
axis.text.x = element_text(color="black"))
}
#############
# OUT path
fig.path <- "/home/pauling/projects/04_SEmodel/07_NC_revision/02.figures/06.fibroblasts"
out.path <- "/home/pauling/projects/04_SEmodel/07_NC_revision/03.data/06.fibroblast"
# Clustering (S-E)
filt.expr <- filt.expr[rownames(sce),]
filt.expr <- as.matrix(filt.expr)
ent.res <- SE_fun(filt.expr)
sce.se <- RunPCA(sce, verbose = FALSE, features = ent.res$Gene[1:3000])
sce.se <- RunUMAP(sce.se, dims = 1:30, verbose = FALSE)
pca <- sce.se@reductions$pca@cell.embeddings
adata.com = anndata$AnnData(X=pca, obs=a$PatientNumber)
sc$tl$pca(adata.com)
adata.com$obsm$X_pca = pca
bbknn$bbknn(adata.com,batch_key=0)
sc$tl$umap(adata.com)
sc$tl$leiden(adata.com, resolution = 1.1)
umap.com = py_to_r(adata.com$obsm$X_umap)
tibble::tibble(
leiden.res.1.bbknn = py_to_r(np$asarray(adata.com$obs$leiden)),
UMAP1 = umap.com[,1],
UMAP2 = umap.com[,2]) -> bb.res2
sce.se <- loadumap(sce.se, umap.com, bb.res2)
new.cluster.ids <- paste0("BF_C",c(2,3,1,4,10,9,0,5,6,7,8))
names(new.cluster.ids) <- levels(sce.se)
sce.se <- RenameIdents(sce.se, new.cluster.ids)
DimPlot(sce.se, reduction = "umap", label = TRUE, pt.size = 0.5) + NoLegend()
# save
readr::write_rds(sce, path = file.path(out.path,"01.sce.11.clusters.rds.gz"), compress = "gz")
# Clustering (SCT)
sce <- CreateSeuratObject(counts = filt.expr)
sce <- SCTransform(sce, verbose = FALSE, do.center = F)
sce <- RunPCA(sce, verbose = FALSE)
sce <- RunUMAP(sce, dims = 1:30, verbose = FALSE)
pca <- sce@reductions$pca@cell.embeddings
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$AnnData(X=pca, obs=a$PatientNumber)
sc$tl$pca(adata.com)
adata.com$obsm$X_pca = pca
bbknn$bbknn(adata.com,batch_key=0)
sc$tl$umap(adata.com)
sc$tl$leiden(adata.com, resolution = 1.1)
umap.com = py_to_r(adata.com$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]) -> bb.res
loadumap <- function(.sce, umap, bb.res){
rownames(umap) <- rownames(.sce@reductions$umap@cell.embeddings)
colnames(umap) <- colnames(.sce@reductions$umap@cell.embeddings)
.sce@reductions$umap@cell.embeddings <- umap
Idents(.sce) <- bb.res$leiden.res.1.bbknn
return(.sce)
}
sce <- loadumap(sce, umap.com, bb.res)
## Confustion matrix
conf.matr <- table(bb.res$leiden.res.1.bbknn, bb.res2$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(factor(SCT, levels = c(2,5,10,1,0,3,6,4,7,8,9)), SE)) +
geom_tile(aes(fill = Freq)) +
scale_fill_viridis_c() +
theme_minimal() +
labs(
x = "SCT",
y = "SE"
) +
pauling.theme() -> conf.plot
## UMAP plot
my.cols <- c("#9F79EE", "#00CDCD", "#008B8B", "#FF6EB4", "#B452CD", "#7EC0EE", "#D02090", "#F5DEB3", "#8B864E", "#3CB371","#9BCD9B")
umap.plot1 <- DimPlot(sce.se, label = T, pt.size = 2.3, cols = my.cols) + theme_void() + NoLegend()
ori.sce <- sce.se
Idents(ori.sce) <- a$ClusterID
umap.plot2 <- DimPlot(ori.sce, label = T, pt.size = 2.3, cols = my.cols) + theme_void() + NoLegend()
## Cell type-specific genes
fib.markers <- FindAllMarkers(sce.se, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25, test.use = "t")
top10 <- fib.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_logFC)
cell.type.genes.plot <- DoHeatmap(sce.se, features = top10$gene) + NoLegend()
## Marker gene expression
mef2c.plot <- FeaturePlot(sce.se, features = "MEF2C", cols = c("#F0E68C","red"), pt.size = 1.5) + theme_void()
myh11.plot <- FeaturePlot(sce.se, features = "MYH11", cols = c("#F0E68C","red"), pt.size = 1.5) + theme_void()
## ROGUE calculation
rogue.res <- rogue(filt.expr,
labels = bb.res2$leiden.res.1.bbknn,
samples = a$PatientNumber,
platform = "UMI",
remove.outlier.n = 5,
filter = T,
min.cell.n = 20)
av.rogue <- c()
for (i in 1:11) {
tmp.r <- rogue.res[,i]
tmp.r <- tmp.r[!is.na(tmp.r)]
av.rogue[i] <- mean(tmp.r)
}
## ROGUE plot
rogue.plot <- rogue.boxplot(rogue.res) + ylim(0.6,0.97)
## pathway analysis
gs <- readr::read_tsv("/data1/pauling/02_data/18_Enrichment/h.all.v6.2.symbols.gmt", col_names = F)
gs <- gs[,-2]
gs <- gs %>% tidyr::gather(key = "pathway", value = "sets", -X1)
gs <- gs %>% dplyr::filter(sets %in% rownames(sce.se))
table(gs$sets) %>%
as.data.frame() %>%
as.tibble() %>%
dplyr::arrange(desc(Freq)) %>%
dplyr::filter(Freq == 1) %>%
dplyr::pull(Var1) -> unique.gene
gs <- gs %>% dplyr::filter(sets %in% unique.gene)
terms <- unique(gs$X1)
geneSets <- list()
for (i in 1:length(terms)) {
genesets <- gs %>% dplyr::filter(X1 == terms[i]) %>% dplyr::pull(sets)
geneSets[[terms[i]]] <- genesets
}
y <- as.matrix(sce.se@assays$SCT@data)
gsva_es <- gsva(y, geneSets, mx.diff=1)
sce.gsva <- CreateSeuratObject(counts = gsva_es)
Idents(sce.gsva) <- Idents(sce.se)
fib.markers <- FindAllMarkers(sce.gsva, only.pos = TRUE, min.pct = 0, logfc.threshold = 0.1, test.use = "t")
top10 <- fib.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_logFC) %>% dplyr::mutate(gene = stringr::str_replace_all(gene, "-", "_"))
sce.gsva@assays$RNA@scale.data <- gsva_es
tibble(cluster = levels(sce.se)) %>%
dplyr::mutate(
expr = purrr::map(
.x = cluster,
.f = function(.x){
tmp.matr <- gsva_es[,Idents(sce.se) == .x]
rowMeans(tmp.matr)
}
)
) -> tmp.res
tmp.res <- Reduce(rbind, tmp.res$expr)
tmp.res <- as.data.frame(tmp.res)
rownames(tmp.res) <- levels(sce.se)
tmp.res <- tmp.res[,top10$gene]
tmp.res %>%
tibble::rownames_to_column(var = "Cluster") %>%
dplyr::mutate_if(is.numeric, funs((. - mean(.))/sd(.))) %>%
tidyr::gather(key = "Gene", value = "expr", -Cluster) %>%
ggplot(aes(factor(Cluster, levels = unique(top10$cluster)), factor(Gene, levels = rev(unique(top10$gene))))) +
geom_tile(aes(fill = expr), color = "white", lwd = 0.8) +
#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",angle = 45, hjust = 1),
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 = "Spectral") +
labs(x = "", y = "") -> pathway.plot
#############
ggsave(plot = conf.plot, filename = "01.confution.heatmap.pdf", path = fig.path, width = 6, height = 4.5)
ggsave(plot = rogue.plot, filename = "02.se.rogue.box.pdf", path = fig.path, width = 5, height = 4)
ggsave(plot = umap.plot1, filename = "03.re.clustering.se.umap.pdf", path = fig.path, width = 6, height = 4.5)
ggsave(plot = umap.plot2, filename = "04.ori.label.umap.pdf", path = fig.path, width = 6, height = 4.5)
ggsave(plot = mef2c.plot, filename = "05.mef2c.gene.pdf", path = fig.path, width = 5, height = 4)
ggsave(plot = myh11.plot, filename = "05.myh11.gene.pdf", path = fig.path, width = 5, height = 4)
ggsave(plot = cell.type.genes.plot, filename = "06.cell.type.gene.heatmap.pdf", path = fig.path, width = 8, height = 11)
ggsave(plot = pathway.plot, filename = "07.pathway.plot.pdf", path = fig.path, width = 8, height = 8)