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R6_nebula_nbcells.R
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R6_nebula_nbcells.R
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pacman::p_load(Seurat, Signac, SingleCellExperiment,
tidyverse, nebula, scater)
Cluster.neunonal <- readRDS(
#file = "/Volumes/GFS_MBIO_AGFORTELNY/people/rohit/projects/neuronal_cluster.rds",
file = "/media/AGFORTELNY/people/rohit/projects/neuronal_cluster.rds")
# add patient group information
groups <- list(c('2', '3', '6', '15', '16'),
c('1', '4', '5', '11'),
c('7','12'),
c('8', '9', '10','13', '14'))
Cluster.neunonal@meta.data$group <- Cluster.neunonal@meta.data %>%
dplyr::mutate(
group = dplyr::case_when(orig.ident %in% groups[[1]] ~"1",
orig.ident %in% groups[[2]] ~"2",
orig.ident %in% groups[[3]] ~"3",
orig.ident %in% groups[[4]] ~"4")) %>%
pull(group)
Cluster.neunonal$group <- Cluster.neunonal@meta.data$group
Cluster.neunonal <- RunTFIDF(Cluster.neunonal, method = 4)
Cluster.neunonal <- FindTopFeatures(Cluster.neunonal, min.cutoff = 'q5')
Cluster.neunonal <- RunSVD(object = Cluster.neunonal)
DepthCor(Cluster.neunonal)
# Non-linear dimension reduction and clustering ---------------------------
Cluster.neunonal <- RunUMAP(
object = Cluster.neunonal,
reduction = 'lsi',
umap.method = 'uwot',
dims = 2:30)
Cluster.neunonal <- FindNeighbors(
object = Cluster.neunonal,
reduction = 'lsi',
nn.method = 'rann',
dims = 2:30)
Cluster.neunonal <- FindClusters(
object = Cluster.neunonal,
algorithm = 3,
resolution = 1,
verbose = FALSE)
umap_seurat_ident <- DimPlot(Cluster.neunonal)
Cluster.neunonal <- RenameIdents(
object = Cluster.neunonal,
'0' = 'clust1', '1' = 'clust1', '2' = 'clust1', '3' = 'clust1',
'4' = 'clust2', '5' = 'clust2', '6' = 'clust2'
)
umap_assign_ident <- DimPlot(Cluster.neunonal)
sce <- SeuratWrappers::as.cell_data_set(Cluster.neunonal)
#sce <- createSingleCellObject(Cluster.neunonal)
sce <- logNormCounts(sce) #, assay.type = "ln_counts")
model_mat <- model.matrix(~group , data = colData(sce))
model_mat <- model_mat[, colSums(model_mat != 0) > 0]
sce_grouped <- group_cell(counts(sce),
id = colData(sce)$orig.ident,
pred = model_mat,
offset = NULL)
result <- nebula(sce_grouped$count,
sce_grouped$id,
pred=sce_grouped$pred)
.get_chipseeker_annot <- function(peaks) {
require(Signac)
require(ChIPseeker)
require(TxDb.Hsapiens.UCSC.hg38.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
granges <- StringToGRanges(peaks)
peakAnno <-
annotatePeak(granges,
tssRegion=c(-3000, 3000),
TxDb=txdb,
annoDb="org.Hs.eg.db")
chipseeker_annot <-
peakAnno@anno %>%
Repitools::annoGR2DF() %>%
dplyr::mutate(gene = paste0(chr,'-' ,start,'-', end))
return(chipseeker_annot)
}
result$summary <- result$summary %>%
as_tibble() %>%
mutate(
algorithm = result$algorithm,
convergence = result$convergence,
overdispersion_subject = result$overdispersion$Subject,
overdispersion_cell = result$overdispersion$cell
)
closest_genes <- .get_chipseeker_annot(result$summary$gene)
closest_genes <- ClosestFeature(Cluster.neunonal,
regions = result$summary$gene) %>%
mutate(gene = query_region)
results_with_closest_gene <- merge(result$summary,
closest_genes, by = 'gene')
results_with_closest_gene <-
results_with_closest_gene %>%
dplyr::mutate(q_group3 = qvalue::qvalue(results_with_closest_gene$p_group3)$qvalues) %>%
dplyr::mutate(q_group4 = qvalue::qvalue(results_with_closest_gene$p_group4)$qvalues) %>%
dplyr::mutate(logFC_group3 = logFC_group3/log(2),
logFC_group4 = logFC_group4/log(2))
myfile = '/media/AGFORTELNY/PROJECTS/Neuroblastoma/analysis/rohit/nbatac/data/nebula/nb.csv'
write.table(results_with_closest_gene, file = myfile)
results_with_closest_gene <- read.table(myfile, header= TRUE, sep = ' ')
results_with_closest_gene %>%
filter(p_group3 < 0.05) %>% dim()
# old_result <- readRDS(file = '/media/AGFORTELNY/people/rohit/projects/nebula_neuroblastoma.rds')
# Enrichment analysis
results <-
read.table('/media/AGFORTELNY/PROJECTS/Neuroblastoma/analysis/rohit/nbatac/data/nebula/nb.csv')
grp3 <-
results %>%
dplyr::filter(q_group3 < 0.5) %>%
dplyr::mutate(logFC = logFC_group3) %>%
dplyr::select(logFC, SYMBOL) %>%
dplyr::arrange(desc(logFC))
grp4 <-
results %>%
dplyr::filter(q_group4 < 0.5) %>%
dplyr::mutate(logFC = logFC_group4) %>%
dplyr::select(logFC, SYMBOL) %>%
dplyr::arrange(desc(logFC))
grp3_genes <- grp3$logFC
names(grp3_genes) <- grp3$SYMBOL
eg <-
clusterProfiler::bitr(
names(grp3_genes),
fromType="SYMBOL",
toType="ENTREZID",
OrgDb="org.Hs.eg.db")
head(eg$ENTREZID)
genes_entrez <- grp3 %>%
left_join(eg, by = "SYMBOL") %>%
dplyr::select(logFC, ENTREZID) %>%
arrange(desc(logFC))
geneList <- genes_entrez$logFC
names(geneList) <- genes_entrez$ENTREZID
library(clusterProfiler)
library(org.Hs.eg.db)
library(enrichplot)
library(DOSE)
ego <- enrichGO(gene = names(grp3_genes),
OrgDb = org.Hs.eg.db,
keyType = 'SYMBOL',
ont = "ALL",
pvalueCutoff = 1,
qvalueCutoff = 1)
ego2 <- gseDO(grp3_genes,
keyType = 'SYMBOL',
ont = "ALL",
pvalueCutoff = 1,
qvalueCutoff = 1)
enp <- enrichPathway(gene=eg$ENTREZID,
pvalueCutoff = 0.5, readable=TRUE)
gsep <- gsePathway(geneList,
pvalueCutoff = 1,
pAdjustMethod = "BH",
verbose = FALSE)
#
barplot(ego, showCategory=20)
dotplot(ego, showCategory=20) + ggtitle("dotplot for GO")
#
barplot(enp)
#
ridgeplot(gsep)
cnetplot(gsep, foldChange=geneList)
gseaplot2(gsep, geneSetID = 1:10, subplots = 1)
gsearank(gsep, 1, title = gsep[1, "Description"])
#plotGOgraph(gsep)