Spindle neurons in human cortex possess distinctive firing properties and transcriptomic signatures.

Code ↔ Paper

16 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 16 matches

[1] § Results › SPNs transcriptomically belong to IT/ET clusters ↔ mapping.ipynb, lines 363–374 · score 0.91 · LINC00507 FREM3, THEMIS C1QL3, Exc L2, FEZF2 SCN4B, Exc L4, Exc L5
[2] § Methods › Differential gene expression analysis ↔ VEN submission.R, lines 1127–1214 · score 0.79 · FindAllMarkers, FindMarkers, logfc.threshold, min.pct, COSG, subtypes
[3] § Methods › Statistics and reproducibility ↔ Figure 4/Code_subthresWave_new.m, lines 111–200 · score 0.71 · Wilcoxon rank sum, Shapiro Wilk, MATLAB, error
[4] § Methods › Quality control of the patch-seq data ↔ VEN submission.R, lines 1–83 · score 0.70 · GABAergic, human MTG, gene expression, exon, glutamatergic, matrix
[5] § Results › SPNs transcriptomically belong to IT/ET clusters ↔ VEN submission.R, lines 1360–1430 · score 0.67 · cellular component, biological process, molecular function, GO, enriched, enrichment
[6] § Results › Molecular fingerprint of SPNs ↔ VEN submission.R, lines 1127–1214 · score 0.66 · POU3F1, CACNA1H, ADRA1A, Spearman, subtypes, SULF2
[7] § Methods › Feature selection for SPNs ↔ feature_select_step1.ipynb, lines 65–76 · score 0.66 · random forest classifier, feature selection, Boruta
[8] § Methods › Feature selection for SPNs ↔ feature_select_step2.ipynb, lines 94–107 · score 0.66 · random forest classifier, feature selection, Boruta
[9] § Results › SPNs transcriptomically belong to IT/ET clusters ↔ VEN submission.R, lines 761–847 · score 0.64 · CACNA1H, min.pct, ontology, KEGG, GO, DEGs
[10] § Results › SPNs are transcriptomically distinct from nearby TRI/PCs ↔ VEN submission.R, lines 761–847 · score 0.63 · POU3F1, SCN4B, GRIK2, SYT2, NPTX1, ridge
[11] § Methods › Patch-seq sample mapping ↔ mapping.ipynb, lines 240–264 · score 0.60 · mapping probability, bootstrapped, iterations, root, tree, correlation
[12] § Methods › GO and KEGG enrichment analyses ↔ VEN submission.R, lines 1037–1124 · score 0.57 · clusterProfiler, simplified, KEGG, cutoff, GO, enriched
[13] § Methods › Clustering patch-seq data ↔ VEN submission.R, lines 672–758 · score 0.53 · Seurat, dimensional, PCA, HVGs, log2, UMAP
[14] § Results › Molecular fingerprint of SPNs ↔ VEN submission.R, lines 1270–1308 · score 0.52 · hdWGCNA, co expression, modules, network, gene
[15] § Methods › RNA-seq ↔ src/knownadapters.h, lines 71–130 · score 0.52 · Kit V2, primer, mix, PCR, Illumina, RNA
[16] § Results › SPNs show overall ET-like electrophysiology but differ in morphology ↔ Figure 3/Code_FIcurve_statistic_Etype.m, lines 356–439 · score 0.52 · Kruskal Wallis, AP waveform parameters, SEM, Figure 3, cell

Paper

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The authors' code

R · 1,434 lines · 75 KB · no license · 9 matches

suppressPackageStartupMessages({
library(Seurat)
library(cowplot)
library(ggplot2)
library(ggsci)
library(dplyr)
library(psych)
library(pheatmap)
library(clusterProfiler)
library(AnnotationDbi)
library(org.Hs.eg.db)
library(DOSE)
library(GOSemSim)
library(enrichplot)
library(patchwork)
library(gridExtra)
library(data.table)
library(tidyverse)
library(AnnoProbe)
library(VENcelltypes)
library(feather)
library(matrixStats)
library(edgeR)
library(GenomicFeatures)
library(parallel)
library(COSG)
library(WGCNA)
library(hdWGCNA)
library(scPred)
library(magrittr)
library(plot1cell)
library(EnhancedVolcano)
library(viridis)
library(msigdbr)
library(GSVA)
library(SCENIC)
library(report)
library(enrichR)
library(igraph)
library(JASPAR2020)
library(JASPAR2024)
library(motifmatchr)
library(TFBSTools)
library(EnsDb.Hsapiens.v86)
library(BSgenome.Hsapiens.UCSC.hg38)
library(GenomicRanges)
library(xgboost)
})
rm(list = ls())
options(stringsAsFactors = FALSE)
# loading reference (2018 nature MTG)
MTG18_exon <- fread("reference/Human MTG 2018/human_MTG_gene_expression_matrices_2018-06-14/human_MTG_2018-06-14_exon-matrix.csv", header = T, data.table = F)
MTG18_exon <- column_to_rownames(MTG18_exon, var = "V1")
MTG18_gene_info <- read.csv(file = "reference/Human MTG 2018/human_MTG_gene_expression_matrices_2018-06-14/human_MTG_2018-06-14_genes-rows.csv", header = TRUE)
table(duplicated(MTG18_gene_info$gene))
table(duplicated(MTG18_gene_info$entrez_id))
MTG18_meta.data <- read.csv(file = "reference/Human MTG 2018/human_MTG_gene_expression_matrices_2018-06-14/human_MTG_2018-06-14_samples-columns.csv", header = TRUE)
table(MTG18_meta.data$class)
## GABAergic Glutamatergic no class Non-neuronal
## 4164 10525 325 914
txdb <- makeTxDbFromGFF(file = "reference/Human MTG 2018/rsem_GRCh38.p2.gtf/rsem_GRCh38.p2.gtf", format = "gtf")
exons.list.per.gene <- exonsBy(txdb, by = "gene")
gene_len_from_exon <- sum(width(exons.list.per.gene))
gene_len_from_exon <- data.frame(ENTREZID = names(gene_len_from_exon), length = as.numeric(gene_len_from_exon))
MTG18_exons.counts <- subset(MTG18_exon, rownames(MTG18_exon) %in% gene_len_from_exon$ENTREZID)
dim(MTG18_exons.counts)
## [1] 50267 15928
gene_len_sub = gene_len_from_exon[match(rownames(MTG18_exons.counts), gene_len_from_exon$ENTREZID), ]
gene_len_sub = data.frame(length = gene_len_sub$length, row.names = gene_len_sub$ENTREZID)
head(gene_len_sub)
identical(rownames(MTG18_exons.counts), rownames(gene_len_sub))
counts_to_TPM <- function(counts, geneLength) {
rpk <- counts / geneLength
scaling_factor <- colSums(rpk, na.rm = TRUE)
tpm <- sweep(rpk, 2, scaling_factor, "/") * 1e6
tpm[, scaling_factor == 0] <- NA
return(tpm)
}
MTG18_TPM <- counts_to_TPM(MTG18_exons.counts, gene_len_sub$length)
colSums(MTG18_TPM)
MTG18_TPM[4:8,4:8]
rownames(MTG18_TPM) <- MTG18_gene_info[match(rownames(MTG18_TPM), MTG18_gene_info$entrez_id), 1]
identical(colnames(MTG18_TPM), MTG18_meta.data$sample_name)
## prepare MTG18GluN ref data for celltype mapping
MTG18GluN_meta.data <- subset(MTG18_meta.data, class == "Glutamatergic")
MTG18GluN_TPM <- MTG18_TPM[,MTG18GluN_meta.data$sample_name]
dim(MTG18GluN_TPM)
#[1] 50267 10525
identical(colnames(MTG18GluN_TPM), MTG18GluN_meta.data$sample_name)
MTG18GluN <- CreateSeuratObject(counts = log2(MTG18GluN_TPM + 1), project = "MTG18GluN", min.cells = 1)
MTG18GluN_TPM4mapping <- MTG18GluN@assays$RNA@counts
write.table(MTG18GluN_TPM4mapping, file = "MTG18GluN_TPM4mapping.txt", col.names = TRUE, sep = "\t", quote = FALSE)
## Identify ON and OFF markers
MTG18ref_meta.data <- subset(MTG18_meta.data, !class %in% "no class")
MTG18ref_meta.data <- subset(MTG18ref_meta.data, !(brain_subregion %in% c("L1", "L2", "L3") & class == "Glutamatergic"))
table(MTG18ref_meta.data$class)
## GABAergic Glutamatergic Non-neuronal
## 4164 6861 914
table(MTG18ref_meta.data$cluster)
MTG18ref_meta.data$cell_type <- MTG18ref_meta.data$cluster
MTG18ref_meta.data$cell_type <- sub("\\s.*", "", MTG18ref_meta.data$cell_type)
table(MTG18ref_meta.data$cell_type)
## Astro Endo Exc Inh Micro Oligo OPC
## 291 9 6861 4164 63 313 238
MTG18.refdata <- MTG18_TPM[,MTG18ref_meta.data$sample_name]
MTG18_ref <- CreateSeuratObject(counts = log2(MTG18.refdata + 1), project = "MTG18_ref", min.cells = 1)
MTG18_ref$cell_type <- MTG18ref_meta.data[match(colnames(MTG18.refdata), MTG18ref_meta.data$sample_name), 35]
MTG18_ref
## An object of class Seurat
## 47991 features across 11939 samples within 1 assay
## Active assay: RNA (47991 features, 0 variable features)
Idents(MTG18_ref) <- "cell_type"
MTG18_Allmarkers <- FindAllMarkers(MTG18_ref, only.pos = TRUE, min.pct = 0.9, logfc.threshold = 3.2)
ON_proportion <- rowSums(MTG18.refdata > 10) / ncol(MTG18.refdata)
ON_matrix <- MTG18.refdata[ON_proportion > 0.75, ]
cat("Ori_gene:", nrow(MTG18.refdata), "\n")
cat("filtered_gene:", nrow(ON_matrix), "\n")
cat("filtered_gene_example:", head(rownames(ON_matrix)), "\n")
MTG_deepPC_ONmarkers <- subset(MTG18_Allmarkers, !rownames(MTG18_Allmarkers) %in% rownames(ON_matrix))
MTG_deepPC_ONmarkers <- subset(MTG_deepPC_ONmarkers, cluster == "Exc")
table(duplicated(MTG_deepPC_ONmarkers$gene))
MTG_deepPC_ONmarkers_AverExp <- MTG18.refdata[MTG_deepPC_ONmarkers$gene,]
MTG18deepPC_ID <- subset(MTG18ref_meta.data, cell_type == "Exc")
MTG_deepPC_ONmarkers_AverExp <- MTG_deepPC_ONmarkers_AverExp[,MTG18deepPC_ID$sample_name]
MTG_deepPC_ONmarkers_AverExp <- as.data.frame(rowMeans(MTG_deepPC_ONmarkers_AverExp, na.rm = TRUE))
MTG_deepPC_ONmarkers_AverExp <- subset(MTG_deepPC_ONmarkers_AverExp, MTG_deepPC_ONmarkers_AverExp > 100)
MTG_deepPC_ONmarkers <- subset(MTG_deepPC_ONmarkers, rownames(MTG_deepPC_ONmarkers) %in% rownames(MTG_deepPC_ONmarkers_AverExp))
Astro_OFF_markers <- FindMarkers(MTG18_ref, ident.1 = "Astro", ident.2 = "Exc", only.pos = TRUE, logfc.threshold = 3.2)
Endo_OFF_markers <- FindMarkers(MTG18_ref, ident.1 = "Endo", ident.2 = "Exc", only.pos = TRUE, logfc.threshold = 3.2)
Micro_OFF_markers <- FindMarkers(MTG18_ref, ident.1 = "Micro", ident.2 = "Exc", only.pos = TRUE, logfc.threshold = 3.2)
Oligo_OFF_markers <- FindMarkers(MTG18_ref, ident.1 = "Oligo", ident.2 = "Exc", only.pos = TRUE, logfc.threshold = 3.2)
OPC_OFF_markers <- FindMarkers(MTG18_ref, ident.1 = "OPC", ident.2 = "Exc", only.pos = TRUE, logfc.threshold = 3.2)
Inh_OFF_markers <- FindMarkers(MTG18_ref, ident.1 = "Inh", ident.2 = "Exc", only.pos = TRUE, logfc.threshold = 3.2)
Astro_OFF_markers <- subset(Astro_OFF_markers, pct.2 < 0.1)
Endo_OFF_markers <- subset(Endo_OFF_markers, pct.2 < 0.1)
Micro_OFF_markers <- subset(Micro_OFF_markers, pct.2 < 0.1)
Oligo_OFF_markers <- subset(Oligo_OFF_markers, pct.2 < 0.1)
OPC_OFF_markers <- subset(OPC_OFF_markers, pct.2 < 0.1)
Inh_OFF_markers <- subset(Inh_OFF_markers, pct.2 < 0.1)
Astro_OFF_matrix <- subset(MTG18ref_meta.data, cell_type == "Astro")
Astro_OFF_matrix <- MTG18.refdata[,Astro_OFF_matrix$sample_name]
Astro_OFF_proportion <- rowSums(Astro_OFF_matrix < 10) / ncol(Astro_OFF_matrix)
Astro_OFF_matrix <- Astro_OFF_matrix[Astro_OFF_proportion > 0.5, ]
Astro_OFF_markers <- subset(Astro_OFF_markers, !rownames(Astro_OFF_markers) %in% rownames(Astro_OFF_matrix))
Endo_OFF_matrix <- subset(MTG18ref_meta.data, cell_type == "Endo")
Endo_OFF_matrix <- MTG18.refdata[,Endo_OFF_matrix$sample_name]
Endo_OFF_proportion <- rowSums(Endo_OFF_matrix < 10) / ncol(Endo_OFF_matrix)
Endo_OFF_matrix <- Endo_OFF_matrix[Endo_OFF_proportion > 0.5, ]
Endo_OFF_markers <- subset(Endo_OFF_markers, !rownames(Endo_OFF_markers) %in% rownames(Endo_OFF_matrix))
Micro_OFF_matrix <- subset(MTG18ref_meta.data, cell_type == "Micro")
Micro_OFF_matrix <- MTG18.refdata[,Micro_OFF_matrix$sample_name]
Micro_OFF_proportion <- rowSums(Micro_OFF_matrix < 10) / ncol(Micro_OFF_matrix)
Micro_OFF_matrix <- Micro_OFF_matrix[Micro_OFF_proportion > 0.5, ]
Micro_OFF_markers <- subset(Micro_OFF_markers, !rownames(Micro_OFF_markers) %in% rownames(Micro_OFF_matrix))
Oligo_OFF_matrix <- subset(MTG18ref_meta.data, cell_type == "Oligo")
Oligo_OFF_matrix <- MTG18.refdata[,Oligo_OFF_matrix$sample_name]
Oligo_OFF_proportion <- rowSums(Oligo_OFF_matrix < 10) / ncol(Oligo_OFF_matrix)
Oligo_OFF_matrix <- Oligo_OFF_matrix[Oligo_OFF_proportion > 0.5, ]
Oligo_OFF_markers <- subset(Oligo_OFF_markers, !rownames(Oligo_OFF_markers) %in% rownames(Oligo_OFF_matrix))
OPC_OFF_matrix <- subset(MTG18ref_meta.data, cell_type == "OPC")
OPC_OFF_matrix <- MTG18.refdata[,OPC_OFF_matrix$sample_name]
OPC_OFF_proportion <- rowSums(OPC_OFF_matrix < 10) / ncol(OPC_OFF_matrix)
OPC_OFF_matrix <- OPC_OFF_matrix[OPC_OFF_proportion > 0.5, ]
OPC_OFF_markers <- subset(OPC_OFF_markers, !rownames(OPC_OFF_markers) %in% rownames(OPC_OFF_matrix))
Inh_OFF_matrix <- subset(MTG18ref_meta.data, cell_type == "Inh")
Inh_OFF_matrix <- MTG18.refdata[,Inh_OFF_matrix$sample_name]
Inh_OFF_proportion <- rowSums(Inh_OFF_matrix < 10) / ncol(Inh_OFF_matrix)
Inh_OFF_matrix <- Inh_OFF_matrix[Inh_OFF_proportion > 0.5, ]
Inh_OFF_markers <- subset(Inh_OFF_markers, !rownames(Inh_OFF_markers) %in% rownames(Inh_OFF_matrix))
Glu_OFF_matrix <- subset(MTG18ref_meta.data, cell_type == "Exc")
Glu_OFF_matrix <- MTG18.refdata[,Glu_OFF_matrix$sample_name]
Glu_OFF_proportion <- rowSums(Glu_OFF_matrix > 10) / ncol(Glu_OFF_matrix)
Glu_OFF_matrix <- Glu_OFF_matrix[Glu_OFF_proportion > 0.33, ]
Astro_OFF_markers <- subset(Astro_OFF_markers, !rownames(Astro_OFF_markers) %in% rownames(Glu_OFF_matrix))
Endo_OFF_markers <- subset(Endo_OFF_markers, !rownames(Endo_OFF_markers) %in% rownames(Glu_OFF_matrix))
Micro_OFF_markers <- subset(Micro_OFF_markers, !rownames(Micro_OFF_markers) %in% rownames(Glu_OFF_matrix))
Oligo_OFF_markers <- subset(Oligo_OFF_markers, !rownames(Oligo_OFF_markers) %in% rownames(Glu_OFF_matrix))
OPC_OFF_markers <- subset(OPC_OFF_markers, !rownames(OPC_OFF_markers) %in% rownames(Glu_OFF_matrix))
Inh_OFF_markers <- subset(Inh_OFF_markers, !rownames(Inh_OFF_markers) %in% rownames(Glu_OFF_matrix))
MTG_deepPC_ONmarkers$cluster <- "Exc on"
Astro_OFF_markers$cluster <- "Ast"
Astro_OFF_markers$gene <- rownames(Astro_OFF_markers)
Endo_OFF_markers$cluster <- "End"
Endo_OFF_markers$gene <- rownames(Endo_OFF_markers)
Micro_OFF_markers$cluster <- "Mic"
Micro_OFF_markers$gene <- rownames(Micro_OFF_markers)
Oligo_OFF_markers$cluster <- "Oli"
Oligo_OFF_markers$gene <- rownames(Oligo_OFF_markers)
OPC_OFF_markers$cluster <- "OPC"
OPC_OFF_markers$gene <- rownames(OPC_OFF_markers)
Inh_OFF_markers$cluster <- "Inh"
Inh_OFF_markers$gene <- rownames(Inh_OFF_markers)
MTG18deepPC_ONOFF_markers <- rbind(MTG_deepPC_ONmarkers, Astro_OFF_markers, Endo_OFF_markers, Micro_OFF_markers, Oligo_OFF_markers, OPC_OFF_markers, Inh_OFF_markers)
table(duplicated(MTG18deepPC_ONOFF_markers$gene))
## FALSE TRUE
## 182 22
duplicate_rows <- MTG18deepPC_ONOFF_markers[duplicated(MTG18deepPC_ONOFF_markers$gene), ]
dup_markers <- unique(duplicate_rows$gene)
MTG18_dup_markers_Exp <- as.data.frame(AverageExpression(MTG18_ref, features = dup_markers, assays = "RNA", slot = "counts"))
Astro_OFF_markers <- subset(Astro_OFF_markers, !gene %in% c("HTRA1", "FGFR2", "PLEKHB1"))
Endo_OFF_markers <- subset(Endo_OFF_markers, !gene %in% c("MT2A", "SLC7A11"))
OPC_OFF_markers <- subset(OPC_OFF_markers, !gene %in% c("S100B", "COL9A2", "PLLP", "PON2", "DOCK10"))
Micro_OFF_markers <- subset(Micro_OFF_markers, !gene %in% c("TLR4", "SLC1A3", "HTRA1", "SPP1"))
Oligo_OFF_markers <- subset(Oligo_OFF_markers, !gene %in% c("HTRA1", "NDRG2", "S100B", "TSC22D4", "BCAS1", "OLIG1"))
Inh_OFF_markers <- subset(Inh_OFF_markers, !gene %in% c("DOCK10", "ZNF536"))
MTG18deepPC_ONOFF_markers_unique <- rbind(MTG_deepPC_ONmarkers, Astro_OFF_markers, Endo_OFF_markers, Micro_OFF_markers, Oligo_OFF_markers, OPC_OFF_markers, Inh_OFF_markers)
# loading data for cell type mapping
patchseq.data.1 <- fread("data/NCBI ID feature_counts 1.csv", header = T, data.table = F)
patchseq.data.2 <- fread("data/NCBI ID feature_counts 2.csv", header = T, data.table = F)
patchseq.data <- merge(patchseq.data.1[,1:116], patchseq.data.2[,1:16], by = "gene_id", all = TRUE)
table(duplicated(patchseq.data$gene_id))
## FALSE
## 48354
table(duplicated(patchseq.data.1$gene_name))
## FALSE TRUE
## 44091 4263
patchseq.count <- column_to_rownames(patchseq.data, var = "gene_id")
geneid_efflen <- subset(patchseq.data.1, select = c("gene_id", "gene_length"))
colnames(geneid_efflen) <- c("geneid", "efflen")
efflen <- geneid_efflen[match(rownames(patchseq.count), geneid_efflen$geneid), "efflen"]
counts2TPM <- function(count = patchseq.count, efflength = efflen){
RPK <- count/(efflength/1000)
PMSC_rpk <- sum(RPK)/1e6
RPK/PMSC_rpk
}
patchseq.TPM <- as.data.frame(apply(patchseq.count, 2, counts2TPM))
colSums(patchseq.TPM)
NCBI_gene_symbol <- patchseq.data.1[match(rownames(patchseq.count), patchseq.data.1$gene_id), "gene_name"]
table(duplicated(NCBI_gene_symbol))
patchseq.count <- aggregate(patchseq.count, by = list(NCBI_gene_symbol), FUN = sum)
patchseq.count$Group.1 <- gsub("\t", "", patchseq.count$Group.1)
patchseq.count <- column_to_rownames(patchseq.count,'Group.1')
patchseq.TPM <- aggregate(patchseq.TPM, by = list(NCBI_gene_symbol), FUN = sum)
patchseq.TPM$Group.1 <- gsub("\t", "", patchseq.TPM$Group.1)
patchseq.TPM <- column_to_rownames(patchseq.TPM,'Group.1')
head(patchseq.TPM)
## QC for patch-seq mapping data (calculate contamination score)
MTG18deepPC4score <- subset(MTG18_ref, ident = "Exc")
MTG18deepPC4score <- MTG18deepPC4score@assays$RNA@counts
PC_Ast.mtx <- MTG18deepPC4score[rownames(MTG18deepPC4score) %in% rownames(Astro_OFF_markers), ]
PC_Ast.sums <- colSums(PC_Ast.mtx, na.rm = TRUE)
d_PC_Ast <- median(PC_Ast.sums, na.rm = TRUE)
PC_End.mtx <- MTG18deepPC4score[rownames(MTG18deepPC4score) %in% rownames(Endo_OFF_markers), ]
PC_End.sums <- colSums(PC_End.mtx, na.rm = TRUE)
d_PC_End <- median(PC_End.sums, na.rm = TRUE)
PC_Mic.mtx <- MTG18deepPC4score[rownames(MTG18deepPC4score) %in% rownames(Micro_OFF_markers), ]
PC_Mic.sums <- colSums(PC_Mic.mtx, na.rm = TRUE)
d_PC_Mic <- median(PC_Mic.sums, na.rm = TRUE)
PC_Oli.mtx <- MTG18deepPC4score[rownames(MTG18deepPC4score) %in% rownames(Oligo_OFF_markers), ]
PC_Oli.sums <- colSums(PC_Oli.mtx, na.rm = TRUE)
d_PC_Oli <- median(PC_Oli.sums, na.rm = TRUE)
PC_OPC.mtx <- MTG18deepPC4score[rownames(MTG18deepPC4score) %in% rownames(OPC_OFF_markers), ]
PC_OPC.sums <- colSums(PC_OPC.mtx, na.rm = TRUE)
d_PC_OPC <- median(PC_OPC.sums, na.rm = TRUE)
PC_Inh.mtx <- MTG18deepPC4score[rownames(MTG18deepPC4score) %in% rownames(Inh_OFF_markers), ]
PC_Inh.sums <- colSums(PC_Inh.mtx, na.rm = TRUE)
d_PC_Inh <- median(PC_Inh.sums, na.rm = TRUE)
table([email hidden])
## Inh Exc Oligo OPC Astro Micro Endo
## 4164 6861 313 238 291 63 9
Ast_Ast.mtx <- subset(MTG18_ref, ident = "Astro")
Ast_Ast.mtx <- Ast_Ast.mtx@assays$RNA@counts
Ast_Ast.mtx <- Ast_Ast.mtx[rownames(Ast_Ast.mtx) %in% rownames(Astro_OFF_markers), ]
Ast_Ast.sums <- colSums(Ast_Ast.mtx, na.rm = TRUE)
d_Ast_Ast <- median(Ast_Ast.sums, na.rm = TRUE)
End_End.mtx <- subset(MTG18_ref, ident = "Endo")
End_End.mtx <- End_End.mtx@assays$RNA@counts
End_End.mtx <- End_End.mtx[rownames(End_End.mtx) %in% rownames(Endo_OFF_markers), ]
End_End.sums <- colSums(End_End.mtx, na.rm = TRUE)
d_End_End <- median(End_End.sums, na.rm = TRUE)
Mic_Mic.mtx <- subset(MTG18_ref, ident = "Micro")
Mic_Mic.mtx <- Mic_Mic.mtx@assays$RNA@counts
Mic_Mic.mtx <- Mic_Mic.mtx[rownames(Mic_Mic.mtx) %in% rownames(Micro_OFF_markers), ]
Mic_Mic.sums <- colSums(Mic_Mic.mtx, na.rm = TRUE)
d_Mic_Mic <- median(Mic_Mic.sums, na.rm = TRUE)
Oli_Oli.mtx <- subset(MTG18_ref, ident = "Oligo")
Oli_Oli.mtx <- Oli_Oli.mtx@assays$RNA@counts
Oli_Oli.mtx <- Oli_Oli.mtx[rownames(Oli_Oli.mtx) %in% rownames(Oligo_OFF_markers), ]
Oli_Oli.sums <- colSums(Oli_Oli.mtx, na.rm = TRUE)
d_Oli_Oli <- median(Oli_Oli.sums, na.rm = TRUE)
OPC_OPC.mtx <- subset(MTG18_ref, ident = "OPC")
OPC_OPC.mtx <- OPC_OPC.mtx@assays$RNA@counts
OPC_OPC.mtx <- OPC_OPC.mtx[rownames(OPC_OPC.mtx) %in% rownames(OPC_OFF_markers), ]
OPC_OPC.sums <- colSums(OPC_OPC.mtx, na.rm = TRUE)
d_OPC_OPC <- median(OPC_OPC.sums, na.rm = TRUE)
Inh_Inh.mtx <- subset(MTG18_ref, ident = "Inh")
Inh_Inh.mtx <- Inh_Inh.mtx@assays$RNA@counts
Inh_Inh.mtx <- Inh_Inh.mtx[rownames(Inh_Inh.mtx) %in% rownames(Inh_OFF_markers), ]
Inh_Inh.sums <- colSums(Inh_Inh.mtx, na.rm = TRUE)
d_Inh_Inh <- median(Inh_Inh.sums, na.rm = TRUE)
## calculating contamination score for patchseq data
meta.data <- read.csv(file = "data/meta.data.csv", header = TRUE)
patchseq.data4QC <- patchseq.TPM
colnames(patchseq.data4QC) <- meta.data[match(colnames(patchseq.data4QC), meta.data$Sample.ID), 5]
patchseq4QC <- CreateSeuratObject(counts = log2(patchseq.data4QC + 1), project = "VENQC_project", min.cells = 1)
[email hidden]$sample_ID <- meta.data[match(colnames(patchseq.data4QC), meta.data$Cell.name), 1]
[email hidden]$cell_name <- meta.data[match(colnames(patchseq.data4QC), meta.data$Cell.name), 5]
[email hidden]$M_type <- meta.data[match(colnames(patchseq.data4QC), meta.data$Cell.name), 6]
[email hidden]$brain_region <- meta.data[match(colnames(patchseq.data4QC), meta.data$Cell.name), 10]
patchseq_TPM4QC <- patchseq4QC@assays$RNA@counts
patch_Ast.mtx <- patchseq_TPM4QC[rownames(patchseq_TPM4QC) %in% rownames(Astro_OFF_markers), ]
patch_Ast.sums <- colSums(patch_Ast.mtx, na.rm = TRUE)
CS.patch_Ast <- as.matrix((patch_Ast.sums - d_PC_Ast)/(d_Ast_Ast - d_PC_Ast))
[email hidden]$Ast_contam <- CS.patch_Ast[match(colnames(patchseq.data4QC), rownames(CS.patch_Ast)), 1]
patch_End.mtx <- patchseq_TPM4QC[rownames(patchseq_TPM4QC) %in% rownames(Endo_OFF_markers), ]
patch_End.sums <- colSums(patch_End.mtx, na.rm = TRUE)
CS.patch_End <- as.matrix((patch_End.sums - d_PC_End)/(d_End_End - d_PC_End))
[email hidden]$End_contam <- CS.patch_End[match(colnames(patchseq.data4QC), rownames(CS.patch_End)), 1]
patch_Mic.mtx <- patchseq_TPM4QC[rownames(patchseq_TPM4QC) %in% rownames(Micro_OFF_markers), ]
patch_Mic.sums <- colSums(patch_Mic.mtx, na.rm = TRUE)
CS.patch_Mic <- as.matrix((patch_Mic.sums - d_PC_Mic)/(d_Mic_Mic - d_PC_Mic))
[email hidden]$Mic_contam <- CS.patch_Mic[match(colnames(patchseq.data4QC), rownames(CS.patch_Mic)), 1]
patch_Oli.mtx <- patchseq_TPM4QC[rownames(patchseq_TPM4QC) %in% rownames(Oligo_OFF_markers), ]
patch_Oli.sums <- colSums(patch_Oli.mtx, na.rm = TRUE)
CS.patch_Oli <- as.matrix((patch_Oli.sums - d_PC_Oli)/(d_Oli_Oli - d_PC_Oli))
[email hidden]$Oli_contam <- CS.patch_Oli[match(colnames(patchseq.data4QC), rownames(CS.patch_Oli)), 1]
patch_OPC.mtx <- patchseq_TPM4QC[rownames(patchseq_TPM4QC) %in% rownames(OPC_OFF_markers), ]
patch_OPC.sums <- colSums(patch_OPC.mtx, na.rm = TRUE)
CS.patch_OPC <- as.matrix((patch_OPC.sums - d_PC_OPC)/(d_OPC_OPC - d_PC_OPC))
[email hidden]$OPC_contam <- CS.patch_OPC[match(colnames(patchseq.data4QC), rownames(CS.patch_OPC)), 1]
patch_Inh.mtx <- patchseq_TPM4QC[rownames(patchseq_TPM4QC) %in% rownames(Inh_OFF_markers), ]
patch_Inh.sums <- colSums(patch_Inh.mtx, na.rm = TRUE)
CS.patch_Inh <- as.matrix((patch_Inh.sums - d_PC_Inh)/(d_Inh_Inh - d_PC_Inh))
[email hidden]$Inh_contam <- CS.patch_Inh[match(colnames(patchseq.data4QC), rownames(CS.patch_Inh)), 1]
[email hidden] <- [email hidden] %>%
mutate(contamination_score = rowSums(across(all_of(c("Ast_contam", "End_contam", "Mic_contam", "Oli_contam", "OPC_contam", "Inh_contam"))), na.rm = TRUE))
MTG18deepPC <- subset(MTG18_ref, ident = "Exc")
MTG18deepPC_ONOFF_Exp <- AverageExpression(MTG18deepPC, features = MTG18deepPC_ONOFF_markers_unique$gene, assays = "RNA", slot = "counts")
typeof(MTG18deepPC_ONOFF_Exp)
head(MTG18deepPC_ONOFF_Exp$RNA)
MTG18deepPC_ONOFF_Exp <- as.data.frame(MTG18deepPC_ONOFF_Exp)
colnames(MTG18deepPC_ONOFF_Exp) <- "MTG18deepPC"
MTG18deepPC_ONOFF_Exp$gene <- rownames(MTG18deepPC_ONOFF_Exp)
patchseq_ONOFF_Exp <- AverageExpression(patchseq4QC, features = MTG18deepPC_ONOFF_markers_unique$gene, assays = "RNA", slot = "counts")
patchseq_ONOFF_Exp <- as.data.frame(patchseq_ONOFF_Exp)
colnames(patchseq_ONOFF_Exp) <- "patchseq"
patchseq_ONOFF_Exp$gene <- rownames(patchseq_ONOFF_Exp)
ONOFFgene_cor_mtx <- merge(MTG18deepPC_ONOFF_Exp, patchseq_ONOFF_Exp, by = "gene", all = TRUE)
ONOFFgene_cor_mtx <- column_to_rownames(ONOFFgene_cor_mtx, var = "gene")
ONOFFgene_cor_result <- shapiro.test(ONOFFgene_cor_mtx$patchseq)
print(ONOFFgene_cor_result)
ONOFFgene_cor <- cor.test(ONOFFgene_cor_mtx$MTG18deepPC, ONOFFgene_cor_mtx$patchseq, method = "spearman")
cor_value <- round(ONOFFgene_cor$estimate, 2)
cor_p_value <- format.pval(ONOFFgene_cor$p.value, digits = 2)
[email hidden] <- [email hidden] %>% mutate(scaled_score = contamination_score * 0.3105976)
ONOFFgene_cor_mtx$cluster <- MTG18deepPC_ONOFF_markers_unique[match(rownames(ONOFFgene_cor_mtx), MTG18deepPC_ONOFF_markers_unique$gene), 6]
ggplot(ONOFFgene_cor_mtx, aes(x = MTG18deepPC, y = patchseq, color = cluster)) + geom_point(size = 3) +
annotate("text", x = min(ONOFFgene_cor_mtx$MTG18deepPC), y = max(ONOFFgene_cor_mtx$patchseq),
label = paste0("spearman r = ", cor_value, "\nP = ", cor_p_value), hjust = 0, vjust = 1, size = 4, color = "black") +
geom_smooth(method = "lm", formula = y ~ x, se = FALSE, color = "black") +
theme(axis.ticks.length = unit(.25, "cm"), axis.line = element_line(color = "black", size = 0.5), panel.background = element_rect(fill = "white")) +
scale_y_continuous(breaks = seq(0,10, by = 2), limits = c(0,10)) + scale_x_continuous(breaks = seq(0,10, by = 2), limits = c(0,10))
ggplot(ONOFFgene_cor_mtx, aes(x = MTG18deepPC, y = patchseq, color = cluster)) + geom_point(size = 3) + facet_wrap(~ cluster, scales = "free") +
theme(axis.ticks.length = unit(.25, "cm"), axis.line = element_line(color = "black", size = 0.5), panel.background = element_rect(fill = "white"))
## cell type mapping data preparation (filter patchseq data)
OFF_markers4QC <- subset(MTG18deepPC_ONOFF_markers_unique, cluster != "Exc on")
OFF_markers4QC_ID <- subset(patchseq.data.1, gene_name %in% OFF_markers4QC$gene)
table(duplicated(OFF_markers4QC_ID$gene_name))
## FALSE TRUE
## 176 47
table(duplicated(OFF_markers4QC_ID$gene_id))
patchseq.count <- column_to_rownames(patchseq.data, var = "gene_id")
patchseq.count_QC <- subset(patchseq.count, !rownames(patchseq.count) %in% OFF_markers4QC_ID$gene_id)
efflen <- geneid_efflen[match(rownames(patchseq.count_QC), geneid_efflen$geneid), "efflen"]
counts2TPM <- function(count = patchseq.count_QC, efflength = efflen){
RPK <- count/(efflength/1000)
PMSC_rpk <- sum(RPK)/1e6
RPK/PMSC_rpk
}
patchseq.TPM_QC <- as.data.frame(apply(patchseq.count_QC, 2, counts2TPM))
colSums(patchseq.TPM_QC)
NCBI_gene_symbol <- patchseq.data.1[match(rownames(patchseq.count_QC), patchseq.data.1$gene_id), "gene_name"]
table(duplicated(NCBI_gene_symbol))
patchseq.count_QC <- aggregate(patchseq.count_QC, by = list(NCBI_gene_symbol), FUN = sum)
patchseq.count_QC$Group.1 <- gsub("\t", "", patchseq.count_QC$Group.1)
patchseq.count_QC <- column_to_rownames(patchseq.count_QC,'Group.1')
patchseq.TPM_QC <- aggregate(patchseq.TPM_QC, by = list(NCBI_gene_symbol), FUN = sum)
patchseq.TPM_QC$Group.1 <- gsub("\t", "", patchseq.TPM_QC$Group.1)
patchseq.TPM_QC <- column_to_rownames(patchseq.TPM_QC,'Group.1')
head(patchseq.TPM_QC)
colnames(patchseq.TPM_QC) <- meta.data[match(colnames(patchseq.TPM_QC), meta.data$Sample.ID), 5]
patchseq.TPM_QC <- subset(patchseq.TPM_QC, select = -c(PC43, PC64, TRI33, TRI32, TRI20, PC52))
patchseq4mapping <- CreateSeuratObject(counts = log2(patchseq.TPM_QC + 1), project = "patchseq2mapping", min.cells = 1)
[email hidden]$sample_ID <- meta.data[match(colnames(patchseq.TPM_QC), meta.data$Cell.name), 1]
[email hidden]$cell_name <- meta.data[match(colnames(patchseq.TPM_QC), meta.data$Cell.name), 5]
[email hidden]$M_type <- meta.data[match(colnames(patchseq.TPM_QC), meta.data$Cell.name), 6]
[email hidden]$brain_region <- meta.data[match(colnames(patchseq.TPM_QC), meta.data$Cell.name), 10]
patchseq_TPM4mapping <- patchseq4mapping@assays$RNA@counts
write.table(patchseq_TPM4mapping, file = "VEN4mapping.txt", col.names = TRUE, sep = "\t", quote = FALSE)
write.table([email hidden], file = "VEN4mapping_meta_data.txt", col.names = TRUE, sep = "\t", quote = FALSE)
patchseq4mapping <- FindVariableFeatures(patchseq4mapping, selection.method = "vst", nfeatures = 3000)
VariableFeaturePlot(patchseq4mapping, log = TRUE)
patchseq4mapping <- ScaleData(patchseq4mapping, features = rownames(patchseq4mapping), verbose = FALSE)
VEN4mapping_mat <- as.data.frame(patchseq4mapping@assays$[email hidden])
ETIT_markers <- c("POU3F1", "FAM84B", "BCL11B", "NRP1", "DSCAML1", "NFIB", "NTNG1", "SYT2", "SEMA6A", "DAB1", "SEZ6",
"MYO16", "ASAP1", "SEMA3D", "GFRA1", "PTPRM", "CRTAC1", "IGSF21", "SERPINE2", "ABAT", "GRIK2", "SLC1A1",
"ETV5", "SEZ6L", "AKAP12", "PTPRF", "SORCS2", "ADRA1A", "SPARC", "SLC6A1", "CDHR3", "GRIK1", "CACNA1H",
"CACNA2D2", "SCN4B", "TRPC4", "SCN9A", "GRM4", "SLC5A8", "PCSK6", "LOC101929728", "PDE9A", "LOC105378657",
"ATP6V1C2", "RNF152", "MEIS2", "DGKD", "ALCAM", "LOC105378653", "FEZF2", "EYA4", "ADCY8", "RXFP1", "NPTX1",
"PDZRN3", "DACH1", "LY86-AS1", "SPATS2L", "FIGN", "LINC01202", "MGAT5B", "THEMIS")
ETIT_markers <- c("POU3F1", "FAM84B", "BCL11B", "NRP1", "DSCAML1", "NFIB", "NTNG1", "SYT2", "SEMA6A", "DAB1", "SEZ6",
"MYO16", "ASAP1", "SEMA3D", "GFRA1", "PTPRM", "CRTAC1", "IGSF21", "SERPINE2", "ABAT", "GRIK2", "SLC1A1",
"ETV5", "SEZ6L", "AKAP12", "PTPRF", "SORCS2", "ADRA1A", "SPARC", "SLC6A1", "CDHR3", "CACNA1H",
"CACNA2D2", "SCN4B", "TRPC4", "SCN9A", "GRM4", "SLC5A8", "PCSK6", "LOC101929728", "PDE9A", "LOC105378657",
"ATP6V1C2", "RNF152", "MEIS2", "DGKD", "ALCAM", "LOC105378653", "FEZF2", "EYA4", "ADCY8", "RXFP1", "NPTX1",
"PDZRN3", "DACH1", "LY86-AS1", "SPATS2L", "FIGN", "LINC01202", "MGAT5B", "THEMIS")
ETIT_markers_mat <- VEN4mapping_mat[ETIT_markers,]
pheatmap(ETIT_markers_mat, fontsize_row = 5, clustering_method = "ward.D", fontsize_col = 5, cluster_rows = F,
color = c("blue", "lightgrey", "firebrick3"))
# loading and QC VEN patchseq data for clustering and other downstream analysis
## remove OFF markers
VEN_data_1 <- fread("data/feature_counts 1.csv", header = T, data.table = F)
VEN_data_2 <- fread("data/feature_counts 2.csv", header = T, data.table = F)
VEN_data_3 <- fread("data/feature_counts 3.csv", header = T, data.table = F)
VEN_data_4 <- fread("data/feature_counts 4.csv", header = T, data.table = F)
VEN_data_5 <- fread("data/feature_counts 5.csv", header = T, data.table = F)
VEN_counts1 <- VEN_data_1[,1:84]
VEN_counts2 <- VEN_data_2[,1:24]
VEN_counts3 <- VEN_data_3[,1:7]
VEN_counts4 <- VEN_data_4[,1:19]
VEN_counts5 <- VEN_data_5[,1:16]
VEN_counts <- merge(VEN_counts1, VEN_counts2, by = "gene_id", all = TRUE)
VEN_counts <- merge(VEN_counts, VEN_counts3, by = "gene_id", all = TRUE)
VEN_counts <- merge(VEN_counts, VEN_counts4, by = "gene_id", all = TRUE)
VEN_counts <- merge(VEN_counts, VEN_counts5, by = "gene_id", all = TRUE)
VEN_counts <- column_to_rownames(VEN_counts, var = "gene_id")
OFF_markers_miss <- subset(OFF_markers4QC, !rownames(OFF_markers4QC) %in% VEN_data_1$gene_name)
OFF_markers_miss$gene
## "LOC101930275", "PPAP2B", "LOC105376917", "LOC105369345", "FYB", "LOC101929249", "LOC105379054", "LPPR1"
OFF_markers_miss_NCBI_ID <- patchseq.data.1[match(OFF_markers_miss$gene, patchseq.data.1$gene_name), "gene_id"]
OFF_markers_miss_NCBI_ID
## "101930275", "8613", "105376917", "105369345", "2533", "101929249", "105379054", "54886_1"
## "ENSG00000253944", "ENSG00000162407", "ENSG00000082074", "ENSG00000249835", "ENSG00000148123"
OFF_markers_overlap <- subset(VEN_data_1, gene_name %in% OFF_markers4QC$gene)
table(duplicated(OFF_markers_overlap$gene_name))
## FALSE
## 168
table(duplicated(OFF_markers_overlap$gene_id))
table(duplicated(OFF_markers4QC$gene))
## FALSE
## 176
VEN_counts_QC <- subset(VEN_counts, !rownames(VEN_counts) %in% c("ENSG00000253944", "ENSG00000162407", "ENSG00000082074", "ENSG00000249835", "ENSG00000148123"))
VEN_counts_QC <- subset(VEN_counts_QC, !rownames(VEN_counts_QC) %in% OFF_markers_overlap$gene_id)
geneid_efflen <- subset(VEN_data_1, select = c("gene_id", "gene_length"))
colnames(geneid_efflen) <- c("geneid", "efflen")
efflen <- geneid_efflen[match(rownames(VEN_counts_QC), geneid_efflen$geneid), "efflen"]
counts2TPM <- function(count = VEN_counts_QC, efflength = efflen){
RPK <- count/(efflength/1000)
PMSC_rpk <- sum(RPK)/1e6
RPK/PMSC_rpk
}
VEN.TPM_QC <- as.data.frame(apply(VEN_counts_QC, 2, counts2TPM))
colSums(VEN.TPM_QC)
gene_symbol <- VEN_data_1[match(rownames(VEN_counts_QC), VEN_data_1$gene_id), "gene_name"]
table(duplicated(gene_symbol))
## FALSE TRUE
## 56996 1566
VEN_counts_QC <- aggregate(VEN_counts_QC, by = list(gene_symbol), FUN = sum)
VEN_counts_QC$Group.1 <- gsub("\t", "", VEN_counts_QC$Group.1)
VEN_counts_QC <- column_to_rownames(VEN_counts_QC,'Group.1')
VEN.TPM_QC <- aggregate(VEN.TPM_QC, by = list(gene_symbol), FUN = sum)
VEN.TPM_QC$Group.1 <- gsub("\t", "", VEN.TPM_QC$Group.1)
VEN.TPM_QC <- column_to_rownames(VEN.TPM_QC,'Group.1')
head(VEN.TPM_QC)
colnames(VEN.TPM_QC) <- meta.data[match(colnames(VEN.TPM_QC), meta.data$Sample.ID), 5]
## Sample filter
VEN.TPM4clust <- VEN.TPM_QC[,colnames(VEN.TPM_QC) %in% colnames(patchseq4mapping)]
all_genes <- subset(VEN_data_1, select = c("gene_id", "gene_name", "gene_chr", "gene_biotype"))
table(duplicated(all_genes$gene_name))
all_genes <- subset(all_genes, all_genes$gene_name %in% rownames(VEN.TPM4clust))
table(duplicated(all_genes$gene_name))
all_genes <- all_genes[!duplicated(all_genes[c("gene_name")]), ]
all_genes$gene_name <- gsub("\t", "", all_genes$gene_name)
allgenes_biotype <- as.data.frame(table(all_genes$gene_biotype))
colnames(allgenes_biotype) <- c("biotype", "count")
ggplot() + geom_bar(data = allgenes_biotype, aes(x = biotype, y = count), position = position_dodge2(padding = 0.3), stat = "identity") +
theme(axis.title = element_blank(), axis.text.x = element_text(size = 6, angle = 45, vjust = 1, hjust = 1),
axis.ticks.x = element_blank(), axis.ticks.length.y = unit(.25, "cm"), axis.line = element_line(color = "black", size = 0.4),
axis.text.y = element_text(size = 10), panel.background = element_rect(fill = "white")) +
scale_y_continuous(expand = c(0, 0), breaks = seq(0,20000, by = 4000), limits = c(0,20000))
all_genes4clustering <- subset(all_genes, !gene_chr == "X")
all_genes4clustering <- subset(all_genes4clustering, !gene_chr == "Y")
all_genes4clustering <- subset(all_genes4clustering, !gene_chr == "MT")
Human_Mito <- fread("data/Human.MitoCarta3.0.csv", header = T, data.table = F)
table(duplicated(Human_Mito$Symbol))
all_genes4clustering <- subset(all_genes4clustering, !gene_name %in% Human_Mito$Symbol)
VEN.data4clust <- VEN.TPM4clust[all_genes4clustering$gene_name,]
write.table(VEN.data4clust, file = "VEN.data4clustTPM.txt", col.names = TRUE, sep = "\t", quote = FALSE)
VEN4FS <- CreateSeuratObject(counts = log2(VEN.TPM4clust + 1), project = "VEN4FS", min.cells = 1)
[email hidden]$sample_ID <- meta.data[match(colnames(VEN.TPM4clust), meta.data$Cell.name), 1]
[email hidden]$batch <- meta.data[match(colnames(VEN.TPM4clust), meta.data$Cell.name), 2]
[email hidden]$donor <- meta.data[match(colnames(VEN.TPM4clust), meta.data$Cell.name), 3]
[email hidden]$cell_name <- meta.data[match(colnames(VEN.TPM4clust), meta.data$Cell.name), 5]
[email hidden]$M_type <- meta.data[match(colnames(VEN.TPM4clust), meta.data$Cell.name), 6]
[email hidden]$PM_type <- meta.data[match(colnames(VEN.TPM4clust), meta.data$Cell.name), 8]
[email hidden]$E_type <- meta.data[match(colnames(VEN.TPM4clust), meta.data$Cell.name), 9]
[email hidden]$brain_region <- meta.data[match(colnames(VEN.TPM4clust), meta.data$Cell.name), 10]
[email hidden]$contamination_score <- [email hidden][match(colnames(VEN.TPM4clust), colnames(patchseq.data4QC)), "scaled_score"]
VEN4FS <- ScaleData(VEN4FS, features = rownames(VEN4FS), verbose = FALSE)
write.table(VEN4FS@assays$[email hidden], file = "VEN4FS_scaled.data.txt", col.names = TRUE, sep = "\t", quote = FALSE)
# downstream analysis for clustering
VEN4clust <- CreateSeuratObject(counts = log2(VEN.data4clust + 1), project = "VEN_project", min.cells = 2)
write.table(VEN4clust@assays$RNA@data, file = "VEN_datalog2TPM.txt", col.names = TRUE, sep = "\t", quote = FALSE)
[email hidden]$sample_ID <- meta.data[match(colnames(VEN.data4clust), meta.data$Cell.name), 1]
[email hidden]$batch <- meta.data[match(colnames(VEN.data4clust), meta.data$Cell.name), 2]
[email hidden]$donor <- meta.data[match(colnames(VEN.data4clust), meta.data$Cell.name), 3]
[email hidden]$cell_name <- meta.data[match(colnames(VEN.data4clust), meta.data$Cell.name), 5]
[email hidden]$M_type <- meta.data[match(colnames(VEN.data4clust), meta.data$Cell.name), 6]
[email hidden]$PM_type <- meta.data[match(colnames(VEN.data4clust), meta.data$Cell.name), 8]
[email hidden]$E_type <- meta.data[match(colnames(VEN.data4clust), meta.data$Cell.name), 9]
[email hidden]$brain_region <- meta.data[match(colnames(VEN.data4clust), meta.data$Cell.name), 10]
[email hidden]$contamination_score <- [email hidden][match(colnames(VEN.data4clust), colnames(patchseq.data4QC)), "scaled_score"]
VEN4clust[["percent.mt"]] <- PercentageFeatureSet(VEN4clust, pattern = "^MT-")
write.table([email hidden], file = "VEN4clust_meta.data.txt", col.names = TRUE, sep = "\t", quote = FALSE)
Idents(VEN4clust) <- "M_type"
QCP1 <- VlnPlot(VEN4clust, features = "nFeature_RNA", pt.size = 1) + scale_y_continuous(breaks = seq(0,50000, by = 10000), limits = c(0,50000)) + NoLegend()
QCP2 <- VlnPlot(VEN4clust, features = "nCount_RNA", pt.size = 1) + scale_y_continuous(breaks = seq(0,200000, by = 40000), limits = c(0,200000)) + NoLegend()
wrap_plots(plots = list(QCP1, QCP2), ncol = 2)
VEN4clust
## An object of class Seurat
## 50890 features across 124 samples within 1 assay
## Active assay: RNA (50890 features, 0 variable features)
table(VEN4clust$PM_type)
## ET_PC ET_VEN IT_PC IT_VEN
## 31 13 71 9
table(VEN4clust$E_type)
## 1 2 3
## 38 12 6
table(VEN4clust$brain_region)
## Frontal Parietal Temporal
## 41 18 65
VEN4clust <- FindVariableFeatures(VEN4clust, selection.method = "vst", nfeatures = 2500)
VariableFeaturePlot(VEN4clust, log = TRUE)
HVG_2500 <- subset(all_genes, all_genes$gene_name %in% VEN4clust@assays$[email hidden])
HVG_2500_biotype <- as.data.frame(table(HVG_2500$gene_biotype))
colnames(HVG_2500_biotype) <- c("biotype", "count")
ggplot() + geom_bar(data = HVG_2500_biotype, aes(x = biotype, y = count), position = position_dodge2(padding = 0.3), stat = "identity") +
theme(axis.title.x = element_blank(), axis.text.x = element_text(size = 6, angle = 45, vjust = 1, hjust = 1),
axis.ticks.x = element_blank(), axis.ticks.length.y = unit(.25, "cm"), axis.line = element_line(color = "black", size = 0.4),
axis.text.y = element_text(size = 10), panel.background = element_rect(fill = "white")) +
scale_y_continuous(expand = c(0, 0), breaks = seq(0,500, by = 100), limits = c(0,500))
VEN4clust <- ScaleData(VEN4clust, features = rownames(VEN4clust), verbose = FALSE)
VEN4clust <- RunPCA(VEN4clust, npcs = 50, verbose = FALSE)
VEN4clust
## An object of class Seurat
## 50890 features across 124 samples within 1 assay
## Active assay: RNA (50890 features, 2500 variable features)
## 1 dimensional reduction calculated: pca
VizDimLoadings(VEN4clust, dims = 1:9, reduction = "pca")
DimPlot(object = VEN4clust, reduction = "pca", pt.size = 2)
FeatureScatter(VEN4clust, feature1 = "nCount_RNA", feature2 = "nFeature_RNA", pt.size = 2)
VEN4clust <- JackStraw(VEN4clust, num.replicate = 100)
VEN4clust <- ScoreJackStraw(VEN4clust, dims = 1:20)
JackStrawPlot(VEN4clust, dims = 1:20)
ElbowPlot(VEN4clust)
VEN4clust$PM_type <- factor(x = VEN4clust$PM_type, levels = c("ET_VEN", "ET_PC", "IT_VEN", "IT_PC"))
VEN4clust <- FindNeighbors(VEN4clust, reduction = "pca", dims = c(1,2,3,4,5,6,9))
VEN4clust <- FindClusters(VEN4clust, resolution = 0.7)
VEN4clust <- RunUMAP(VEN4clust, reduction = "pca", dims = c(1,2,3,4,5,6,9))
DimPlot(VEN4clust, reduction = "umap", label = FALSE, pt.size = 3, group.by = "seurat_clusters")
DimPlot(VEN4clust, reduction = "umap", label = FALSE, pt.size = 3, group.by = "M_type")
DimPlot(VEN4clust, reduction = "umap", label = FALSE, pt.size = 3, group.by = "PM_type")
DimPlot(VEN4clust, reduction = "umap", label = FALSE, pt.size = 3, group.by = "E_type")
DimPlot(VEN4clust, reduction = "umap", label = FALSE, pt.size = 3, group.by = "brain_region")
DimPlot(VEN4clust, reduction = "umap", label = TRUE, pt.size = 3, group.by = "sample_ID") + NoLegend()
VlnPlot(VEN4clust, features = c("SLC17A6", "SLC17A7", "POU3F1", "COL22A1", "PVALB", "SLC32A1", "GAD1", "GAD2", "TH",
"SLC6A3", "SLC18A2", "AQP4", "CX3CR1", "MBP"), group.by = "M_type", same.y.lims = TRUE, add.noise = FALSE, slot = "counts")
write.table(VEN4clust@assays$[email hidden], file = "VEN4clust_scaled.data.txt", col.names = TRUE, sep = "\t", quote = FALSE)
## DEG among clusters
All_cluster_markers <- FindAllMarkers(VEN4clust, logfc.threshold = 1, only.pos = TRUE, min.pct = 1)
All_cluster_markers <- subset(All_cluster_markers, p_val_adj < 0.05)
table(All_cluster_markers$cluster)
cluster1_markers <- subset(All_cluster_markers, cluster == 0)
cluster2_markers <- subset(All_cluster_markers, cluster == 1)
cluster1_markers_biotype <- all_genes[match(cluster1_markers$gene, all_genes$gene_name), 4]
cluster2_markers_biotype <- all_genes[match(cluster2_markers$gene, all_genes$gene_name), 4]
table(cluster1_markers_biotype)
## antisense lincRNA protein_coding sense_intronic sense_overlapping
## 3 4 176 1 1
table(cluster2_markers_biotype)
## antisense lincRNA processed_pseudogene
## 3 9 1
## processed_transcript protein_coding sense_intronic
## 2 261 3
## sense_overlapping snoRNA transcribed_unprocessed_pseudogene
## 1 1 1
write.table(All_cluster_markers, file = "All_cluster_markers.txt", col.names = TRUE, sep = "\t", quote = FALSE)
All_cluster_markers_cosg <- cosg(VEN4clust, groups = "all", assay = "RNA", slot = "data", mu = 1, remove_lowly_expressed = T,
expressed_pct = 1, n_genes_user = 300)
clust_marker4plot <- All_cluster_markers_cosg$names[1:5,]
clust_marker4plot <- c(clust_marker4plot[,1], clust_marker4plot[,2])
DoHeatmap(VEN4clust, features = All_cluster_markers$gene, draw.lines = FALSE, hjust = 0) + scale_fill_gradientn(colors = c("#2f58a7", "lightgrey", "#FF6347"))
VlnPlot(VEN4clust, features = clust_marker4plot, same.y.lims = TRUE, stack = TRUE, flip = TRUE)
RidgePlot(VEN4clust, features = clust_marker4plot, fill.by = "ident", ncol = 5)
RidgePlot(VEN4clust, features = c("SULF2", "FAM84B", "ADRA1A", "VAT1L", "POU3F1", "FEZF2", "SCN4B", "BCL11B"), stack = TRUE, fill.by = "ident")
RidgePlot(VEN4clust, features = c("NPTX1", "RXFP1", "PDZRN3", "LY86-AS1", "SPATS2L", "MGAT5B","SCN3B", "THEMIS"), stack = TRUE, fill.by = "ident")
All_cluster_markers.df <- bitr(All_cluster_markers$gene, fromType = "SYMBOL", toType = c("ENTREZID", "ENSEMBL"), OrgDb = org.Hs.eg.db)
## 5.57% of input gene IDs are fail to map...
All_cluster_markers_ID <- unique(All_cluster_markers.df$ENTREZID)
All_cluster_markers_ego <- enrichGO(gene = All_cluster_markers_ID, OrgDb = org.Hs.eg.db, ont = "BP", readable = TRUE)
d <- godata('org.Hs.eg.db', ont = "BP")
goCls <- pairwise_termsim(All_cluster_markers_ego, method = "Resnik", semData = d)
treeplot(goCls)
All_cluster_markers_ego <- as.data.frame(All_cluster_markers_ego)
All_cluster_markers_ego_top5 <- All_cluster_markers_ego %>% group_by(ONTOLOGY) %>% arrange(p.adjust) %>% slice_head(n = 5)
All_cluster_markers_ego_top5$ONTOLOGY <- factor(x = All_cluster_markers_ego_top5$ONTOLOGY, levels = c("MF", "BP", "CC"))
All_cluster_markers_ego_top5$Description <- factor(All_cluster_markers_ego_top5$Description, levels = rev(All_cluster_markers_ego_top5$Description))
mycol3 <- c('#FF7F50', '#6B8E23', '#6BA5CE')
p <- ggplot(data = All_cluster_markers_ego_top5, aes(x = Count, y = Description, fill = ONTOLOGY)) +
geom_bar(width = 0.5, stat = "identity") + theme_classic() +
scale_x_continuous(expand = c(0,0.5), breaks = seq(0,70, by = 14), limits = c(0,70)) +
scale_fill_manual(values = alpha(mycol3, 0.8))
p <- p + theme(axis.text.y = element_blank()) +
geom_text(data = All_cluster_markers_ego_top5, aes(x = 0.1, y = Description, label = Description), size = 5, hjust = 0)
p <- p + geom_text(data = All_cluster_markers_ego_top5, aes(x = 0.1, y = Description, label = geneID, color = -log10(pvalue)), size = 3,
fontface = 'italic', hjust = 0, vjust = 2.7) + scale_colour_viridis(option = "G", direction = -1, end = 0.8)
p <- p + labs(title = 'Enriched top 5 GO terms') +
theme(plot.title = element_text(size = 14, face = 'bold'), axis.title = element_text(size = 14),
axis.line = element_line(colour = "black"), axis.text = element_text(size = 12, colour = "black"),
axis.ticks.y = element_blank())
p
All_cluster_markers_kegg <- enrichKEGG(gene = All_cluster_markers_ID, keyType = "kegg", organism = "hsa", pAdjustMethod = "BH", pvalueCutoff = 0.05,
qvalueCutoff = 0.2)
clusterProfiler::dotplot(All_cluster_markers_kegg, title = "All_cluster_markers_KEGG")
ETIT_markers <- c("POU3F1", "FAM84B", "BCL11B", "NRP1", "DSCAML1", "NFIB", "NTNG1", "SYT2", "SEMA6A", "DAB1", "SEZ6",
"MYO16", "ASAP1", "SEMA3D", "GFRA1", "PTPRM", "CRTAC1", "IGSF21", "SERPINE2", "ABAT", "GRIK2", "SLC1A1",
"ETV5", "SEZ6L", "AKAP12", "PTPRF", "SORCS2", "ADRA1A", "SPARC", "SLC6A1", "CDHR3", "GRIK1", "CACNA1H",
"CACNA2D2", "SCN4B", "TRPC4", "SCN9A", "GRM4", "SLC5A8", "PCSK6", "LOC101929728", "PDE9A", "LOC105378657",
"ATP6V1C2", "RNF152", "MEIS2", "DGKD", "ALCAM", "AL139158.2", "FEZF2", "EYA4", "ADCY8", "RXFP1", "NPTX1",
"PDZRN3", "DACH1", "LY86-AS1", "SPATS2L", "FIGN", "LINC01202", "MGAT5B", "THEMIS")
DoHeatmap(VEN4clust, features = ETIT_markers, draw.lines = FALSE, hjust = 0) + scale_fill_gradientn(colors = c("#2f58a7", "lightgrey", "#FF6347"))
## channels and receptors
Idents(VEN4clust) <- "E_type"
VEN4Etype <- subset(VEN4clust, ident = c(1, 2, 3))
VEN4Etype
## An object of class Seurat
## 50890 features across 56 samples within 1 assay
## Active assay: RNA (50890 features, 2500 variable features)
## 2 dimensional reductions calculated: pca, umap
DimPlot(VEN4Etype, reduction = "umap", pt.size = 3, group.by = "E_type", shape.by = "seurat_clusters")
VlnPlot(VEN4Etype, features = c("CACNA1A", "CACNA1B", "CACNA1C", "CACNA1D", "CACNA1E", "CACNA1F", "CACNA1G", "CACNA1H", "CACNA1I", "CACNA1S",
"CACNA2D1", "CACNA2D2", "CACNA2D3", "CACNA2D4", "CACNB1", "CACNB2", "CACNB3", "CACNB4", "CACNG1", "CACNG2",
"CACNG3", "CACNG4", "CACNG5", "CACNG6", "CACNG7", "CACNG8"), fill.by = "ident", adjust = 1, stack = TRUE,
same.y.lims = TRUE, flip = TRUE) + NoLegend()
VlnPlot(VEN4Etype, features = c("CLCC1", "CLCF1", "CLCN1", "CLCN2", "CLCN3", "CLCN4", "CLCN5", "CLCN6", "CLCN7", "CLCNKA", "CLCNKB"),
fill.by = "ident", stack = TRUE, same.y.lims = TRUE, flip = TRUE) + NoLegend()
VlnPlot(VEN4Etype, features = c("SCN1A", "SCN1B", "SCN2A", "SCN2B", "SCN3A", "SCN3B", "SCN4A", "SCN4B", "SCN5A", "SCN7A", "SCN8A",
"SCN9A", "SCN10A", "SCN11A", "SCNM1", "SCNN1A", "SCNN1B", "SCNN1G"), fill.by = "ident", stack = TRUE,
same.y.lims = TRUE, flip = TRUE) + NoLegend()
VlnPlot(VEN4Etype, features = c("HCN1", "HCN2", "HCN3", "HCN4", "KCNA1", "KCNA2", "KCNA3", "KCNA4", "KCNA5", "KCNA7", "KCNA10", "KCNAB1",
"KCNAB2", "KCNAB3", "KCNB1", "KCNB2", "KCNC1", "KCNC2", "KCNC3", "KCNC4", "KCND1", "KCND2", "KCND3"),
fill.by = "ident", stack = TRUE, same.y.lims = TRUE, flip = TRUE) + NoLegend()
VlnPlot(VEN4Etype, features = c("KCNE1", "KCNE2", "KCNE3", "KCNE4", "KCNF1", "KCNG1", "KCNG3", "KCNG4", "KCNH1", "KCNH2", "KCNH3", "KCNH5",
"KCNH6", "KCNH7", "KCNH8", "KCNIP1", "KCNIP2", "KCNIP3", "KCNIP4", "KCNJ1", "KCNJ2", "KCNJ3", "KCNJ4"),
fill.by = "ident", stack = TRUE, same.y.lims = TRUE, flip = TRUE) + NoLegend()
VlnPlot(VEN4Etype, features = c("KCNJ5", "KCNJ6", "KCNJ8", "KCNJ9", "KCNJ10", "KCNJ11", "KCNJ12", "KCNJ13", "KCNJ14", "KCNJ15", "KCNJ16",
"KCNK1", "KCNK2", "KCNK3", "KCNK4", "KCNK5", "KCNK6", "KCNK7", "KCNK9", "KCNK10", "KCNK12", "KCNK13",
"KCNK15"), fill.by = "ident", stack = TRUE, same.y.lims = TRUE, flip = TRUE) + NoLegend()
VlnPlot(VEN4Etype, features = c("KCNK18", "KCNMA1", "KCNMB1", "KCNMB2", "KCNMB4", "KCNN1", "KCNN2", "KCNN3", "KCNN4", "KCNQ1", "KCNQ2",
"KCNQ3", "KCNQ5", "KCNRG", "KCNS1", "KCNS2", "KCNS3", "KCNT1", "KCNT2", "KCNU1", "KCNV1", "KCNV2"),
fill.by = "ident", stack = TRUE, same.y.lims = TRUE, flip = TRUE) + NoLegend()
VlnPlot(VEN4Etype, features = c("GABBR1", "GABBR2", "GABRA1", "GABRA2", "GABRA3", "GABRA4", "GABRA5", "GABRA6", "GABRB1", "GABRB2", "GABRB3",
"GABRD", "GABRE", "GABRG1", "GABRG2", "GABRG3", "GABRP", "GABRQ", "GABRR1", "GABRR2", "GABRR3"),
fill.by = "ident", stack = TRUE, same.y.lims = TRUE, flip = TRUE) + NoLegend()
VlnPlot(VEN4Etype, features = c("GRM1", "GRM2", "GRM3", "GRM4", "GRM5", "GRM6", "GRM7", "GRM8", "GRIA1", "GRIA2", "GRIA3", "GRIA4", "GRID1",
"GRID2", "GRIK1", "GRIK2", "GRIK3", "GRIK4", "GRIK5", "GRIN1", "GRIN2A", "GRIN2B", "GRIN2D", "GRIN3A",
"GRIN3B", "GRINA"), fill.by = "ident", stack = TRUE, same.y.lims = TRUE, flip = TRUE) + NoLegend()
VlnPlot(VEN4Etype, features = c("DRD1", "DRD2", "DRD3", "DRD4", "DRD5", "HTR1A", "HTR1B", "HTR1D", "HTR1F", "HTR2A", "HTR2B", "HTR2C",
"HTR3A", "HTR3B", "HTR4", "HTR5A", "HTR6", "HTR7"),
fill.by = "ident", stack = TRUE, same.y.lims = TRUE, flip = TRUE) + NoLegend()
VlnPlot(VEN4Etype, features = c('CHRM1', 'CHRM2', 'CHRM3', 'CHRM4', 'CHRM5', 'CHRNA1', 'CHRNA10', 'CHRNA2', 'CHRNA3', 'CHRNA4', 'CHRNA5',
'CHRNA6', 'CHRNA7', 'CHRNA9', 'CHRNB1', 'CHRNB2', 'CHRNB3', 'CHRNB4', 'CHRND', 'CHRNE', 'CHRNG'),
fill.by = "ident", stack = TRUE, same.y.lims = TRUE, flip = TRUE) + NoLegend()
## VEN markers
Idents(VEN4clust) <- "M_type"
VEN4clust <- RenameIdents(VEN4clust, `cVEN` = "VEN", `PC` = "non_VEN", `TRI` = "non_VEN")
VEN4clust$VEN_type <- [email hidden]
Idents(VEN4clust) <- "VEN_type"
VEN_markers.cad_1 <- FindMarkers(VEN4clust, ident.1 = "VEN", ident.2 = "non_VEN", only.pos = TRUE, min.pct = 1, logfc.threshold = 0.5)
VEN_markers.cad_2 <- cosg(VEN4clust, groups = "all", assay = "RNA", slot = "data", mu = 1, remove_lowly_expressed = T,
expressed_pct = 1, n_genes_user = 1939)
VEN_markers.cad_1 <- subset(VEN_markers.cad_1, rownames(VEN_markers.cad_1) %in% VEN_markers.cad_2$names$VEN)
VEN_Negmarkers.cad_1 <- FindMarkers(VEN4clust, ident.1 = "VEN", ident.2 = "non_VEN", only.pos = FALSE, min.pct = 0.8, logfc.threshold = 0.5)
VEN_Negmarkers.cad_1 <- subset(VEN_Negmarkers.cad_1, avg_log2FC < 0)
VEN_Negmarkers.cad_1 <- subset(VEN_Negmarkers.cad_1, pct.2 == 1)
VEN_Negmarkers.cad_2 <- cosg(VEN4clust, groups = "all", assay = "RNA", slot = "data", mu = 1, remove_lowly_expressed = T,
expressed_pct = 1, n_genes_user = 1279)
VEN_Negmarkers.cad_1 <- subset(VEN_Negmarkers.cad_1, rownames(VEN_Negmarkers.cad_1) %in% VEN_Negmarkers.cad_2$names$non_VEN)
VEN_markers.cad <- rbind(VEN_markers.cad_1, VEN_Negmarkers.cad_1)
EnhancedVolcano(VEN_markers.cad, lab = rownames(VEN_markers.cad), x = 'avg_log2FC', y = 'p_val', pCutoff = 0.05, shape = c(4, 7, 2, 1),
colAlpha = 1, selectLab = c("COL5A2", "EPHA6", "SLC35F2", "LYPD1", "SLC5A8", "PCDH9", "SLC7A14", "NPTXR", "DNAJA4", "HSPA1A"),
labSize = 3, labFace = "italic", boxedLabels = TRUE, title = "VEN markers", subtitle = "vs. non-VEN",
cutoffLineCol = "black", gridlines.minor = F, gridlines.major = F, FCcutoff = 1) +
scale_x_continuous(expand = c(0, 0), breaks = seq(-4,3, by = 1), limits = c(-4,3)) +
scale_y_continuous(expand = c(0, 0), breaks = seq(0,9, by = 3), limits = c(0,9)) +
theme(axis.text.x = element_text(size = 16, colour = "black"), axis.text.y = element_text(size = 16, colour = "black"),
axis.line = element_line(color = "black", size = 1))
VEN_markers.cad_flit <- subset(VEN_markers.cad, p_val < 0.05)
VEN_markers.cad_flit <- subset(VEN_markers.cad_flit, abs(avg_log2FC) > 1)
write.table(VEN_markers.cad_flit, file = "VENvsNonVEN_markers.cad_flit.txt", col.names = TRUE, sep = "\t", quote = FALSE)
VEN_markers.cad_ETIT <- subset(VEN_markers.cad_flit, rownames(VEN_markers.cad_flit) %in% ETIT_markers)
VEN_known_markers <- c("POU3F1", "ITGA4", "BMP3", "VAT1L", "SULF2", "LYPD1", "CHST8", "ADRA1A", "GABRQ", "SLC18A2", "DRD3",
"HTR2B", "NMB", "DISC1", "ATF3", "IL4R", "AVPR1A")
VEN_markers.cad_known <- subset(VEN_markers.cad_flit, rownames(VEN_markers.cad_flit) %in% VEN_known_markers)
Idents(VEN4clust) <- "M_type"
VEN4clust$M_type <- factor(x = VEN4clust$M_type, levels = c("cVEN", "PC", "TRI"))
RidgePlot(VEN4clust, features = rownames(VEN_markers.cad_ETIT), stack = TRUE, fill.by = "ident")
RidgePlot(VEN4clust, features = c("LYPD1", "SULF2", "ATF3", "ADRA1A", "CHST8"), stack = TRUE, fill.by = "ident")
VEN_markers_biotype <- subset(all_genes, all_genes$gene_name %in% rownames(VEN_markers.cad_flit))
VEN_markers_biotype <- as.data.frame(table(VEN_markers_biotype$gene_biotype))
colnames(VEN_markers_biotype) <- c("biotype", "count")
ggplot() + geom_bar(data = VEN_markers_biotype, aes(x = biotype, y = count), position = position_dodge2(padding = 0.3), stat = "identity") +
theme(axis.title = element_blank(), axis.text.x = element_text(size = 6, angle = 45, vjust = 1, hjust = 1),
axis.ticks.x = element_blank(), axis.ticks.length.y = unit(.25, "cm"), axis.line = element_line(color = "black", size = 0.4),
axis.text.y = element_text(size = 10), panel.background = element_rect(fill = "white")) +
scale_y_continuous(expand = c(0, 0), breaks = seq(0,400, by = 80), limits = c(0,400))
write.table(VEN_markers_biotype, file = "VEN_markers_biotype.txt", col.names = TRUE, sep = "\t", quote = FALSE)
## VEN vs IT-PC
Idents(VEN4clust) <- "seurat_clusters"
VEN4clust$clust_PM <- paste(Idents(VEN4clust), VEN4clust$PM_type, sep = " ")
Idents(VEN4clust) <- "clust_PM"
DimPlot(VEN4clust, reduction = "umap", label = FALSE, pt.size = 3, group.by = "clust_PM")
VENIT <- subset(VEN4clust, ident = c("0 IT_PC", "0 ET_VEN", "0 IT_VEN", "1 ET_VEN", "1 IT_VEN"))
DimPlot(VENIT, reduction = "umap", label = FALSE, pt.size = 3, group.by = "clust_PM")
Idents(VENIT) <- "VEN_type"
DimPlot(VENIT, reduction = "umap", label = FALSE, pt.size = 3, group.by = "VEN_type")
VENvsITPC.cad_1 <- FindMarkers(VENIT, ident.1 = "VEN", ident.2 = "non_VEN", only.pos = TRUE, min.pct = 1, logfc.threshold = 0.5)
VENvsITPC.cad_2 <- cosg(VENIT, groups = "all", assay = "RNA", slot = "data", mu = 1, remove_lowly_expressed = T,
expressed_pct = 1, n_genes_user = 1597)
VENvsITPC.cad_1 <- subset(VENvsITPC.cad_1, rownames(VENvsITPC.cad_1) %in% VENvsITPC.cad_2$names$VEN)
ITPCvsVEN.cad_1 <- FindMarkers(VENIT, ident.1 = "VEN", ident.2 = "non_VEN", only.pos = FALSE, min.pct = 0.8, logfc.threshold = 0.5)
ITPCvsVEN.cad_1 <- subset(ITPCvsVEN.cad_1, avg_log2FC < 0)
ITPCvsVEN.cad_1 <- subset(ITPCvsVEN.cad_1, pct.2 == 1)
ITPCvsVEN.cad_2 <- cosg(VENIT, groups = "all", assay = "RNA", slot = "data", mu = 1, remove_lowly_expressed = T, expressed_pct = 1,
n_genes_user = 1644)
ITPCvsVEN.cad_1 <- subset(ITPCvsVEN.cad_1, rownames(ITPCvsVEN.cad_1) %in% ITPCvsVEN.cad_2$names$non_VEN)
VENvsITPC_markers.cad <- rbind(VENvsITPC.cad_1, ITPCvsVEN.cad_1)
EnhancedVolcano(VENvsITPC_markers.cad, lab = rownames(VENvsITPC_markers.cad), x = 'avg_log2FC', y = 'p_val', pCutoff = 0.05,
shape = c(4, 7, 2, 1), colAlpha = 1, labSize = 3, labFace = "italic", boxedLabels = TRUE,
selectLab = c("CCK", "NPTXR", "NPTX1", "NCALD", "CCL4L2", "CCL3L1", "KCTD16", "POU3F1", "ADRA1A", "SULF2", "PCP4L1",
"VAT1L", "SYT2", "COL5A2"), title = "VEN markers", subtitle = "vs. IT_PC", cutoffLineCol = "black",
gridlines.minor = F, gridlines.major = F, FCcutoff = 1) +
scale_x_continuous(expand = c(0, 0), breaks = seq(-8,6, by = 2), limits = c(-8,6)) +
scale_y_continuous(expand = c(0, 0), breaks = seq(0,12, by = 3), limits = c(0,12)) +
theme(axis.text.x = element_text(size = 16, colour = "black"), axis.text.y = element_text(size = 16, colour = "black"),
axis.line = element_line(color = "black", size = 1))
VENvsITPC_markers.cad_filt <- subset(VENvsITPC_markers.cad, p_val < 0.05)
VENvsITPC_markers.cad_filt <- subset(VENvsITPC_markers.cad_filt, abs(avg_log2FC) > 1)
write.table(VENvsITPC_markers.cad_filt, file = "VENvsITPC_markers.cad_filt.txt", col.names = TRUE, sep = "\t", quote = FALSE)
VENvsITPC_markers_biotype <- subset(all_genes, all_genes$gene_name %in% rownames(VENvsITPC_markers.cad_filt))
VENvsITPC_markers_biotype <- as.data.frame(table(VENvsITPC_markers_biotype$gene_biotype))
colnames(VENvsITPC_markers_biotype) <- c("biotype", "count")
ggplot() + geom_bar(data = VENvsITPC_markers_biotype, aes(x = biotype, y = count), position = position_dodge2(padding = 0.3), stat = "identity") +
theme(axis.title = element_blank(), axis.text.x = element_text(size = 6, angle = 45, vjust = 1, hjust = 1),
axis.ticks.x = element_blank(), axis.ticks.length.y = unit(.25, "cm"), axis.line = element_line(color = "black", size = 0.4),
axis.text.y = element_text(size = 10), panel.background = element_rect(fill = "white")) +
scale_y_continuous(expand = c(0, 0), breaks = seq(0,600, by = 120), limits = c(0,600))
write.table(VENvsITPC_markers_biotype, file = "VENvsITPC_markers_biotype.txt", col.names = TRUE, sep = "\t", quote = FALSE)
VENETIT <- subset(VEN4clust, ident = c("0 IT_PC", "0 ET_VEN", "0 IT_VEN", "1 ET_VEN", "1 IT_VEN", "1 ET_PC"))
Idents(VENETIT) <- "PM_type"
DimPlot(VENETIT, reduction = "umap", label = FALSE, pt.size = 3, group.by = "PM_type")
VENETIT <- RenameIdents(VENETIT, `ET_VEN` = "VEN", `IT_VEN` = "VEN")
[email hidden]$VEN_M_type <- [email hidden]
DimPlot(VENETIT, reduction = "umap", label = FALSE, pt.size = 3)
VENvsIT_markers.cad_ETIT <- subset(VENvsITPC_markers.cad_filt, rownames(VENvsITPC_markers.cad_filt) %in% ETIT_markers)
VENvsIT_markers.cad_known <- subset(VENvsITPC_markers.cad_filt, rownames(VENvsITPC_markers.cad_filt) %in% VEN_known_markers)
RidgePlot(VENETIT, features = c("POU3F1", "SYT2", "SPARC", "FEZF2", "NPTX1", "RNF152"), stack = TRUE, fill.by = "ident")
RidgePlot(VENETIT, features = c("SULF2", "ADRA1A", "LYPD1", "VAT1L", "CHST8", "BMP3"), stack = TRUE, fill.by = "ident")
## VEN vs ET-PC
DimPlot(VEN4clust, reduction = "umap", label = FALSE, pt.size = 3, group.by = "clust_PM")
VENET <- subset(VEN4clust, ident = c("0 ET_VEN", "0 IT_VEN", "1 ET_PC", "1 ET_VEN", "1 IT_VEN"))
DimPlot(VENET, reduction = "umap", label = FALSE, pt.size = 3, group.by = "clust_PM")
Idents(VENET) <- "M_type"
DimPlot(VENET, reduction = "umap", label = FALSE, pt.size = 3, group.by = "M_type")
VENvsET.cad_1 <- FindMarkers(VENET, ident.1 = "cVEN", ident.2 = "PC", only.pos = TRUE, min.pct = 1, logfc.threshold = 0.5)
VENvsET.cad_2 <- cosg(VENET, groups = "all", assay = "RNA", slot = "data", mu = 1, remove_lowly_expressed = T,
expressed_pct = 1, n_genes_user = 5017)
VENvsET.cad_1 <- subset(VENvsET.cad_1, rownames(VENvsET.cad_1) %in% VENvsET.cad_2$names$cVEN)
ETvsVEN.cad_1 <- FindMarkers(VENET, ident.1 = "cVEN", ident.2 = "PC", only.pos = FALSE, min.pct = 0.7, logfc.threshold = 0.5)
ETvsVEN.cad_1 <- subset(ETvsVEN.cad_1, avg_log2FC < 0)
ETvsVEN.cad_1 <- subset(ETvsVEN.cad_1, pct.2 == 1)
ETvsVEN.cad_2 <- cosg(VENET, groups = "all", assay = "RNA", slot = "data", mu = 1, remove_lowly_expressed = T,
expressed_pct = 1, n_genes_user = 1747)
ETvsVEN.cad_1 <- subset(ETvsVEN.cad_1, rownames(ETvsVEN.cad_1) %in% ETvsVEN.cad_2$names$PC)
VENvsET_markers.cad <- rbind(VENvsET.cad_1, ETvsVEN.cad_1)
EnhancedVolcano(VENvsET_markers.cad, lab = rownames(VENvsET_markers.cad), x = 'avg_log2FC', y = 'p_val', pCutoff = 0.05,
shape = c(4, 7, 2, 1), colAlpha = 1, labSize = 3, labFace = "italic", boxedLabels = TRUE,
selectLab = c("SYNJ1", "DIRAS1", "PEG10", "SYT2", "SCN4B", "LINC02002", "AL512590.1", "CCK", "CARD16", "CLLU1"),
title = "VEN markers", subtitle = "vs. ET_PC", cutoffLineCol = "black",
gridlines.minor = F, gridlines.major = F, FCcutoff = 1) +
scale_x_continuous(expand = c(0, 0), breaks = seq(-3,3, by = 1), limits = c(-3,3)) +
scale_y_continuous(expand = c(0, 0), breaks = seq(0,8, by = 2), limits = c(0,8)) +
theme(axis.text.x = element_text(size = 16, colour = "black"), axis.text.y = element_text(size = 16, colour = "black"),
axis.line = element_line(color = "black", size = 1))
VENvsET_markers.cad_filt <- subset(VENvsET_markers.cad, p_val < 0.05)
VENvsET_markers.cad_filt <- subset(VENvsET_markers.cad_filt, abs(avg_log2FC) > 1)
write.table(VENvsET_markers.cad_filt, file = "VENvsET_markers.cad_filt.txt", col.names = TRUE, sep = "\t", quote = FALSE)
VENvsET_markers_biotype <- subset(all_genes, all_genes$gene_name %in% rownames(VENvsET_markers.cad_filt))
VENvsET_markers_biotype <- as.data.frame(table(VENvsET_markers_biotype$gene_biotype))
colnames(VENvsET_markers_biotype) <- c("biotype", "count")
ggplot() + geom_bar(data = VENvsET_markers_biotype, aes(x = biotype, y = count), position = position_dodge2(padding = 0.3), stat = "identity") +
theme(axis.title = element_blank(), axis.text.x = element_text(size = 6, angle = 45, vjust = 1, hjust = 1),
axis.ticks.x = element_blank(), axis.ticks.length.y = unit(.25, "cm"), axis.line = element_line(color = "black", size = 0.4),
axis.text.y = element_text(size = 10), panel.background = element_rect(fill = "white")) +
scale_y_continuous(expand = c(0, 0), breaks = seq(0,1000, by = 200), limits = c(0,1000))
write.table(VENvsET_markers_biotype, file = "VENvsET_markers_biotype.txt", col.names = TRUE, sep = "\t", quote = FALSE)
VENvsET_markers.cad_ETIT <- subset(VENvsET_markers.cad_filt, rownames(VENvsET_markers.cad_filt) %in% ETIT_markers)
VENvsET_markers.cad_known <- subset(VENvsET_markers.cad_filt, rownames(VENvsET_markers.cad_filt) %in% VEN_known_markers)
RidgePlot(VENETIT, features = c("AVPR1A", "CCK", "LINC02002", "AL512590.1", "CARD16", "RXFP2"), stack = TRUE, fill.by = "ident")
RidgePlot(VENETIT, features = c("SCN4B", "ETV5", "AKAP12", "SYNJ1", "DIRAS1", "LONRF2"), stack = TRUE, fill.by = "ident")
VENvsET.cad_1_chr <- subset(VENvsET.cad_1, p_val < 0.05)
VENvsET.cad_1_chr <- subset(VENvsET.cad_1_chr, avg_log2FC > 1)
VENvsET.cad_1_chr <- rownames(VENvsET.cad_1_chr)
VENvsET.cad_1_chr.df <- bitr(VENvsET.cad_1_chr, fromType = "SYMBOL", toType = c("ENTREZID", "ENSEMBL"), OrgDb = org.Hs.eg.db)
## 21.97% of input gene IDs are fail to map...
VENvsET.cad_1_ID <- unique(VENvsET.cad_1_chr.df$ENTREZID)
VENvsET.cad_1_ego <- enrichGO(gene = VENvsET.cad_1_ID, OrgDb = org.Hs.eg.db, ont = "BP", readable = TRUE)
VENvsET.cad_1_kegg <- enrichKEGG(gene = VENvsET.cad_1_ID, keyType = "kegg", organism = 'hsa', pAdjustMethod = "BH", pvalueCutoff = 0.05,
qvalueCutoff = 0.2)
ETvsVEN.cad_1_chr <- subset(ETvsVEN.cad_1, p_val < 0.05)
ETvsVEN.cad_1_chr <- subset(ETvsVEN.cad_1_chr, avg_log2FC < -1)
ETvsVEN.cad_1_chr <- rownames(ETvsVEN.cad_1_chr)
ETvsVEN.cad_1_chr.df <- bitr(ETvsVEN.cad_1_chr, fromType = "SYMBOL", toType = c("ENTREZID", "ENSEMBL"), OrgDb = org.Hs.eg.db)
## 3.01% of input gene IDs are fail to map...
ETvsVEN.cad_1_ID <- unique(ETvsVEN.cad_1_chr.df$ENTREZID)
ETvsVEN.cad_1_ego <- enrichGO(gene = ETvsVEN.cad_1_ID, OrgDb = org.Hs.eg.db, ont = "BP", readable = TRUE)
ETvsVEN.cad_1_ego <- clusterProfiler::simplify(ETvsVEN.cad_1_ego, cutoff = 0.7, by = "p.adjust", select_fun = min)
goCls <- pairwise_termsim(ETvsVEN.cad_1_ego, method = "Resnik", semData = d)
treeplot(goCls)
ETvsVEN.cad_1_kegg <- enrichKEGG(gene = ETvsVEN.cad_1_ID, keyType = "kegg", organism = 'hsa', pAdjustMethod = "BH", pvalueCutoff = 0.05,
qvalueCutoff = 0.2)
clusterProfiler::dotplot(ETvsVEN.cad_1_kegg, title = "ETvsVEN.cad_1_kegg")
## VEN subtypes
Idents(VEN4clust) <- "M_type"
VEN <- subset(VEN4clust, ident = "cVEN")
Idents(VEN) <- "seurat_clusters"
VEN <- RenameIdents(VEN, `0` = "subtype_1", `1` = "subtype_2")
VEN$subtype <- [email hidden]
Idents(VEN) <- "subtype"
VEN$subtype <- factor(x = VEN$subtype, levels = c("subtype_1", "subtype_2"))
DimPlot(VEN, reduction = "umap", label = TRUE, pt.size = 4) + NoLegend()
Idents(VEN) <- "sample_ID"
DimPlot(VEN, reduction = "umap", label = TRUE, pt.size = 4, shape.by = "subtype") + NoLegend()
Idents(VEN) <- "subtype"
VEN_submarkers <- FindAllMarkers(VEN, only.pos = TRUE, min.pct = 1, logfc.threshold = 0.5)
VEN_submarkers_1 <- subset(VEN_submarkers, cluster == "subtype_1")
VEN_submarkers_2 <- subset(VEN_submarkers, cluster == "subtype_2")
VEN_sub1markers <- FindMarkers(VEN, ident.1 = "subtype_1", ident.2 = "subtype_2", only.pos = FALSE, logfc.threshold = 0.5)
VEN_sub1markers <- subset(VEN_sub1markers, avg_log2FC < 0)
VEN_sub1markers <- subset(VEN_sub1markers, rownames(VEN_sub1markers) %in% VEN_submarkers_2$gene)
VEN_sub1markers$cluster <- "subtype_2"
VEN_sub1markers$gene <- rownames(VEN_sub1markers)
VEN_submarkers_cosg <- cosg(VEN, groups = "all", assay = "RNA", slot = "data", mu = 1, remove_lowly_expressed = T,
expressed_pct = 1, n_genes_user = 268)
VEN_submarkers_1 <- subset(VEN_submarkers_1, rownames(VEN_submarkers_1) %in% VEN_submarkers_cosg$names$subtype_1)
VEN_submarkers_cosg <- cosg(VEN, groups = "all", assay = "RNA", slot = "data", mu = 1, remove_lowly_expressed = T,
expressed_pct = 1, n_genes_user = 1187)
VEN_sub1markers <- subset(VEN_sub1markers, rownames(VEN_sub1markers) %in% VEN_submarkers_cosg$names$subtype_2)
VEN_submarkers_1_biotype <- subset(all_genes, all_genes$gene_name %in% rownames(VEN_submarkers_1))
VEN_submarkers_1_biotype <- as.data.frame(table(VEN_submarkers_1_biotype$gene_biotype))
colnames(VEN_submarkers_1_biotype) <- c("biotype", "count")
VEN_submarkers_2_biotype <- subset(all_genes, all_genes$gene_name %in% rownames(VEN_sub1markers))
VEN_submarkers_2_biotype <- as.data.frame(table(VEN_submarkers_2_biotype$gene_biotype))
colnames(VEN_submarkers_2_biotype) <- c("biotype", "count")
write.table(VEN_submarkers_1_biotype, file = "VEN_submarkers_1_biotype.txt", col.names = TRUE, sep = "\t", quote = FALSE)
write.table(VEN_submarkers_2_biotype, file = "VEN_submarkers_2_biotype.txt", col.names = TRUE, sep = "\t", quote = FALSE)
VEN_submarkers_filt <- rbind(VEN_submarkers_1, VEN_sub1markers)
write.table(VEN_submarkers_filt, file = "VEN_submarkers_filt.txt", col.names = TRUE, sep = "\t", quote = FALSE)
VEN_submarkers_mean <- AverageExpression(VEN, features = VEN_submarkers_filt$gene, assays = "RNA", slot = "counts")
VEN_submarkers_mean <- as.data.frame(VEN_submarkers_mean)
VEN_submarkers_mean$group <- VEN_submarkers_filt[match(rownames(VEN_submarkers_mean), VEN_submarkers_filt$gene), 6]
VEN_submarkers_mean$L2FC <- VEN_submarkers_filt[match(rownames(VEN_submarkers_mean), VEN_submarkers_filt$gene), 2]
ggplot(VEN_submarkers_mean, aes(x = RNA.subtype_1, y = RNA.subtype_2, color = L2FC)) + geom_point(size = 3) +
scale_color_gradient2(low = "blue", mid = "lightgrey", high = "#d7191c", midpoint = 0, name = "L2FC") +
geom_abline(intercept = -1, slope = 1, col = 'black', linetype = 'dashed', size = 0.8) +
geom_abline(intercept = 1, slope = 1, col = 'black', linetype = 'dashed', size = 0.8) +
geom_abline(intercept = 0, slope = 1, col = 'black', linetype = 'dashed', size = 0.8) +
scale_x_continuous(expand = c(0, 0), breaks = seq(0,12, by = 3), limits = c(0,12)) +
scale_y_continuous(expand = c(0, 0), breaks = seq(0,12, by = 3), limits = c(0,12)) +
theme(axis.ticks.length = unit(.25, "cm"), axis.line = element_line(color = "black", size = 0.5), panel.background = element_rect(fill = "white"))
RidgePlot(VEN, features = c("NEUROD6", "CACNA1H", "MIAT", "SNURF", "KCNQ5-AS1", "GRIK2"), stack = TRUE, fill.by = "ident")
RidgePlot(VEN, features = c("OR7E21P", "HLX-AS1", "MIR4791", "RN7SL826P", "MIR4276", "PEX12P1"), stack = TRUE, fill.by = "ident")
DoHeatmap(VEN, features = ETIT_markers, draw.lines = FALSE, hjust = 0) + scale_fill_gradientn(colors = c("#2f58a7", "lightgrey", "#FF6347"))
Idents(VENETIT) <- "clust_PM"
DimPlot(VENETIT, reduction = "umap", label = FALSE, pt.size = 3)
VENETIT <- RenameIdents(VENETIT, `0 IT_PC` = "ITPC", `1 ET_PC` = "ETPC", `1 ET_VEN` = "S2VEN", `1 IT_VEN` = "S2VEN",
`0 IT_VEN` = "S1VEN", `0 ET_VEN` = "S1VEN")
[email hidden]$CM_type <- [email hidden]
VENETIT$CM_type <- factor(x = VENETIT$CM_type , levels = c("ITPC", "ETPC", "S1VEN", "S2VEN"))
Idents(VENETIT) <- "CM_type"
DimPlot(VENETIT, reduction = "umap", pt.size = 3)
RidgePlot(VENETIT, features = c("POU3F1", "SULF2", "ADRA1A", "VAT1L", "LYPD1", "AVPR1A", "CHST8", "ITGA4"), stack = TRUE, fill.by = "ident")
RidgePlot(VENETIT, features = c("BMP3", "SLC18A2", "DRD3", "HTR2B", "NMB", "DISC1", "ATF3", "IL4R"), stack = TRUE, fill.by = "ident")
AverageExp <- AverageExpression(VENETIT, features = VENETIT@assays$[email hidden], assays = "RNA", slot = "counts")
typeof(AverageExp)
head(AverageExp$RNA)
coorda <- corr.test(AverageExp$RNA, AverageExp$RNA, method = "spearman")
pheatmap(coorda$r, color = c("#2f58a7", "lightgrey", "#FF6347"))
## extract data for M-type feature selecting
Idents(VEN4FS) <- "cell_name"
M_level2_62cells <- c('PC17', 'TRI31', 'PC25', 'PC26', 'PC27', 'PC05', 'TRI09', 'TRI23', 'cVEN01', 'cVEN02', 'cVEN03', 'cVEN04',
'cVEN05', 'cVEN06', 'cVEN07', 'cVEN08', 'cVEN14', 'PC37', 'PC38', 'PC39', 'PC40', 'PC41', 'PC42', 'cVEN09',
'cVEN10', 'cVEN11', 'PC33', 'PC34', 'cVEN12', 'cVEN13', 'PC36', 'PC44', 'PC45', 'PC46', 'PC47', 'PC48',
'PC49', 'PC50', 'PC51', 'PC53', 'PC54', 'PC55', 'PC56', 'PC57', 'PC58', 'PC59', 'PC60', 'PC61', 'PC62',
'PC63', 'cVEN15', 'cVEN16', 'cVEN17', 'cVEN18', 'cVEN19', 'cVEN20', 'PC65', 'PC66', 'cVEN21', 'PC67',
'PC68', 'cVEN22')
VEN4Mtype_L2 <- subset(VEN4FS, idents = M_level2_62cells)
Idents(VEN4Mtype_L2) <- "M_type"
VEN4Mtype_L2 <- RenameIdents(VEN4Mtype_L2, `cVEN` = "VEN", `PC` = "non_VEN", `TRI` = "non_VEN")
VEN4Mtype_L2$VEN_type <- [email hidden]
Idents(VEN4Mtype_L2) <- "VEN_type"
Mlevel2_markers.cad_1 <- FindMarkers(VEN4Mtype_L2, ident.1 = "VEN", ident.2 = "non_VEN", only.pos = TRUE, min.pct = 1, logfc.threshold = 0.5)
Mlevel2_markers.cad_1_chr <- rownames(Mlevel2_markers.cad_1)
Mlevel2_markers.cad_2 <- cosg(VEN4Mtype_L2, groups = "all", assay = "RNA", slot = "data", mu = 1, remove_lowly_expressed = T,
expressed_pct = 1, n_genes_user = 4200)
Mlevel2_markers.cad_2_chr <- Mlevel2_markers.cad_2$names[,1]
Mlevel2_markers.cad <- generics ::intersect(Mlevel2_markers.cad_1_chr, Mlevel2_markers.cad_2_chr)
Mlevel2_markers <- subset(all_genes, all_genes$gene_name %in% Mlevel2_markers.cad)
write.table(Mlevel2_markers, file = "62cells_Mlevel2_markers.txt", col.names = TRUE, sep = "\t", quote = FALSE)
Mlevel2_markers_biotype <- as.data.frame(table(Mlevel2_markers$gene_biotype))
colnames(Mlevel2_markers_biotype) <- c("biotype", "count")
ggplot() + geom_bar(data = Mlevel2_markers_biotype, aes(x = biotype, y = count), position = position_dodge2(padding = 0.3), stat = "identity") +
theme(axis.title = element_blank(), axis.text.x = element_text(size = 6, angle = 45, vjust = 1, hjust = 1),
axis.ticks.x = element_blank(), axis.ticks.length.y = unit(.25, "cm"), axis.line = element_line(color = "black", size = 0.4),
axis.text.y = element_text(size = 10), panel.background = element_rect(fill = "white")) +
scale_y_continuous(expand = c(0, 0), breaks = seq(0,2500, by = 500), limits = c(0,2500))
VEN4Mtype_L2_mat <- as.data.frame(VEN4Mtype_L2@assays$[email hidden])
VEN4Mtype_L2_mat <- VEN4Mtype_L2_mat[Mlevel2_markers.cad,]
pheatmap(VEN4Mtype_L2_mat, fontsize_row = 1, clustering_method = "ward.D2", fontsize_col = 5)
VEN4clust_mat <- as.data.frame(VEN4clust@assays$[email hidden])
M_typeFS_38genes <- c("GRIA4", "SYTL2", "NPTX1", "RXFP1", "MMD", "SPOCK1", "AC025287.3", "DNAJA4", "FOS", "SLITRK1", "ATP8A1",
"MAP2K1", "NUDT4", "SLC7A14", "PCDH9", "CAMK1D", "OPTN", "RNF41", "INPP5A", "FAM126B", "PLEKHB2", "MEIS2",
"POU3F1", "ADRA1A", "SULF2", "COL21A1", "WNT5A", "C6orf163", "SETBP1", "FAM111A-DT", "AL512590.1",
"HCG17", "PLA2G12B", "SLC27A5", "CARD16", "LINC01845", "AC092634.5", "SLC7A2")
M_typeFS_38genes_mat <- VEN4clust_mat[M_typeFS_38genes,]
pheatmap(M_typeFS_38genes_mat, fontsize_row = 5, clustering_method = "ward.D", fontsize_col = 5, color = c("#2f58a7", "lightgrey", "#FF6347"))
# hdWGCNA and TF
theme_set(theme_cowplot())
VENnetwork <- SetupForWGCNA(VENETIT, gene_select = "variable", wgcna_name = "VENnetwork")
length(VENnetwork@misc$VENnetwork$wgcna_genes)
VENnetwork <- MetacellsByGroups(seurat_obj = VENnetwork, group.by = "VEN_M_type", reduction = "pca", ident.group = "VEN_M_type",
min_cells = 22, k = 15)
VENnetwork <- NormalizeMetacells(VENnetwork)
VENnetwork <- SetDatExpr(VENnetwork, group_name = "VEN", group.by = "VEN_M_type", assay = "RNA", slot = "data")
VENnetwork <- TestSoftPowers(VENnetwork, networkType = "signed")
plot_list <- PlotSoftPowers(VENnetwork)
wrap_plots(plot_list, ncol = 2)
power_table <- GetPowerTable(VENnetwork)
head(power_table)
## construct co-expression network
VENnetwork <- ConstructNetwork(VENnetwork, soft_power = 12, setDatExpr = FALSE, tom_name = "VEN")
PlotDendrogram(VENnetwork, main = "VEN hdWGCNA Dendrogram")
VENnetwork@misc$VENnetwork$wgcna_modules %>% head
write.table(VENnetwork@misc$VENnetwork$wgcna_modules, file = "VEN_modules.txt", col.names = TRUE, sep = "\t", quote = FALSE)
table(VENnetwork@misc$VENnetwork$wgcna_modules$module)
## green turquoise purple brown grey midnightblue black red salmon cyan
## 113 369 69 292 494 57 92 99 58 58
## grey60 blue pink yellow greenyellow lightcyan tan magenta
## 51 292 89 115 67 54 61 70
Tom <- GetTOM(VENnetwork)
## Compute harmonized module eigengenes
VENnetwork <- ScaleData(VENnetwork, features = VariableFeatures(VENnetwork))
VENnetwork <- ModuleEigengenes(VENnetwork, reduction.use = "pca")
## harmonized module eigengenes
hMEs <- GetMEs(VENnetwork)
head(hMEs)
MEs <- GetMEs(VENnetwork, harmonized = FALSE)
head(MEs)
## Compute module connectivity
VENnetwork <- ModuleConnectivity(VENnetwork, group.by = "VEN_M_type", group_name = "VEN")
VENnetwork <- ResetModuleNames(VENnetwork, new_name = "VEN-M")
PlotKMEs(VENnetwork, ncol = 5)
modules <- GetModules(VENnetwork)
hub_df <- GetHubGenes(VENnetwork, n_hubs = 10)
head(hub_df)
write.table(hub_df, file = "hub_genes.txt", col.names = TRUE, sep = "\t", quote = FALSE)
VENnetwork <- ModuleExprScore(VENnetwork, n_genes = 25, method = "Seurat")
## Visualization
plot_list <- ModuleFeaturePlot(VENnetwork, features = "hMEs", order = TRUE, point_size = 1)
wrap_plots(plot_list, ncol = 5)
plot_list <- ModuleFeaturePlot(VENnetwork, features = "scores", order = "shuffle", ucell = TRUE, point_size = 1)
wrap_plots(plot_list, ncol = 5)
MEs <- GetMEs(VENnetwork, harmonized = TRUE)
mods <- colnames(MEs); mods <- mods[mods != "grey"]
[email hidden] <- cbind([email hidden], MEs)
P <- DotPlot(VENnetwork, features = mods, group.by = "VEN_M_type")
P <- P + coord_flip() + RotatedAxis() + scale_color_gradient2(high = "red", mid = "grey95", low = "blue")
P
theme_set(theme_cowplot())
ModuleNetworkPlot(VENnetwork)
HubGeneNetworkPlot(VENnetwork, n_hubs = 3, n_other = 5, edge_prop = 0.75, mods = 'all')
hubFS_genes <- intersect(hub_df$gene_name, M_typeFS_38genes)
VENnetwork <- RunModuleUMAP(VENnetwork, n_hubs = 10, n_neighbors = 15, min_dist = 0.1)
ModuleUMAPPlot(VENnetwork, edge.alpha = 0.5, sample_edges = TRUE, edge_prop = 0.1, label_hubs = 1,
keep_grey_edges = FALSE, vertex.label.cex = 0.1, label_genes = c("ADRA1A", "SULF2"))
## Enrichment analysis
set.seed(12345)
dbs <- c('GO_Biological_Process_2023','GO_Cellular_Component_2023','GO_Molecular_Function_2023')
VENnetwork <- RunEnrichr(VENnetwork, dbs = dbs, max_genes = Inf)
enrich_df <- GetEnrichrTable(VENnetwork)
EnrichrDotPlot(VENnetwork, mods = "all", database = "GO_Biological_Process_2023", n_terms = 2, term_size = 8, p_adj = TRUE) +
scale_color_stepsn(colors = rev(viridis::magma(256)))
EnrichrBarPlot(VENnetwork, outdir = "enrichr_plots", n_terms = 10, plot_size = c(5,7), logscale = TRUE)
## TF
theme_set(theme_cowplot())
set.seed(12345)
pfm_core <- TFBSTools::getMatrixSet(x = JASPAR2020,
opts = list(collection = "CORE", tax_group = 'vertebrates', all_versions = FALSE))
VENnetwork <- MotifScan(VENnetwork, species_genome = 'hg38', pfm = pfm_core, EnsDb = EnsDb.Hsapiens.v86)
motif_df <- GetMotifs(VENnetwork)
tf_genes <- unique(motif_df$gene_name)
modules <- GetModules(VENnetwork)
nongrey_genes <- subset(modules, module != 'grey') %>% .$gene_name
genes_use <- c(tf_genes, nongrey_genes)
VENnetwork <- SetWGCNAGenes(VENnetwork, genes_use)
VENnetwork <- SetDatExpr(VENnetwork, group.by = 'VEN_M_type', group_name = "VEN", assay = "RNA")
model_params <- list(objective = 'reg:squarederror', max_depth = 1, eta = 0.1, nthread = 16, alpha = 0.5)
VENnetwork <- ConstructTFNetwork(VENnetwork, model_params)
results <- GetTFNetwork(VENnetwork)
head(results)
VENnetwork <- AssignTFRegulons(VENnetwork, strategy = "A", reg_thresh = 0.01, n_tfs = 10)
RegulonBarPlot(VENnetwork, selected_tf = 'TBP')
VENnetwork <- RegulonScores(VENnetwork, target_type = 'positive', ncores = 8)
VENnetwork <- RegulonScores(VENnetwork, target_type = 'negative', cor_thresh = -0.05, ncores = 8)
pos_regulon_scores <- GetRegulonScores(VENnetwork, target_type = 'positive')
neg_regulon_scores <- GetRegulonScores(VENnetwork, target_type = 'negative')
tf_regulons <- GetTFRegulons(VENnetwork)
hub_df <- GetHubGenes(VENnetwork, n_hubs = 25) %>% subset(gene_name %in% tf_regulons$tf)
Idents(VENnetwork) <- "VEN_M_type"
marker_tfs <- FindAllMarkers(VENnetwork, features = unique(tf_regulons$tf))
top_tfs <- marker_tfs %>% subset(cluster == 'VEN') %>% slice_max(n = 25, order_by = avg_log2FC)
intersect(top_tfs$gene, hub_df$gene_name)
cur_tf <- 'FOXF2'
TFNetworkPlot(VENnetwork, selected_tfs = cur_tf)
group1 <- [email hidden] %>% subset(VEN_M_type == "VEN") %>% rownames
group2 <- [email hidden] %>% subset(VEN_M_type == "ET_PC") %>% rownames
dregs <- FindDifferentialRegulons(VENnetwork, barcodes1 = group1, barcodes2 = group2)
head(dregs)
# WD ^ ^ written by Shuxuan Lyu 2025/07/17

VEN submission.R at commit ca03d5c, no license · at the source

Overview

Authors: Wei Ke¹, Shuxuan Lyu¹, Mengmeng Jin², Shaoqing Jiao³, Liang Li¹, Heyuan Zhang¹, Kexin Ling¹, Cuiping Tian⁴, Yiquan Song⁵, Liu Chen⁵, Quansheng He¹, Yujie Xiao¹, Suixin Deng⁶, Liu Fan⁷, Huawei Mu⁸, Hui Yang⁹, Hua-Tai Xu⁷, Hao Wang⁸, Ai-Hui Tang⁵, Jiajie Peng³, Jie He², Hui Guo¹⁰, Yousheng Shu¹

ORCID iDs: Wei Ke, Shuxuan Lyu, Shaoqing Jiao, Hui Yang, Hua-Tai Xu, Hao Wang, Ai-Hui Tang, Jiajie Peng, Jie He, Hui Guo, Yousheng Shu

Institutions: Fudan University (China); Chinese Academy of Sciences (China); Northwestern Polytechnical University (China); ShanghaiTech University (China); University of Science and Technology of China (China); Central South University (China); Qingdao University (China)

Journal: Nature communications, volume 17, issue 1, article 6330

Dates: received 26 July 2025; accepted 24 April 2026; published online 12 May 2026

Type: Research article · Language: English

License: CC BY-NC-ND

Identifiers: DOI 10.1038/s41467-026-72935-2 · PMID 42120382 · PMCID PMC13376633 · OpenAlex W7160938575

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: genetics / omics (modality), intracellular / patch clamp (modality), human (organism), cellular / molecular (subfield)

Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning

Keywords: Cellular neuroscience, Neuronal physiology

MeSH: Action Potentials*, Cerebral Cortex*, Neocortex*, Neurons*, Transcriptome*, Adult, Axons, Dendrites, Female, Gene Expression Profiling, Humans, Male, Patch-Clamp Techniques, Pyramidal Cells (* major topic)

Topic: Neuroscience and Neuropharmacology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex

Funding: National Natural Science Foundation of China (National Science Foundation of China) (32200953, 32100930); Changping Laboratory (Grant Reference Number: 2025B-07-21) Brain Science and Brain-like Intelligence Technology-National Science and Technology Major Project (Grant Reference Number: 2021ZD0202500) Program of Shanghai Academic/Technology Research Leader (Grant Reference Number: 21XD1400100) Program of China Postdoctoral Science Foundation (Grant Reference Number: 2023M740657)

Citations: cited by 2 papers (Europe PMC); 68 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 16 matches between paragraphs and lines of code.

OpenGene/fastp

License: MIT

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 8a2397b6628ae14127efdb7566f67fc05f9aea56, 10 September 2026

Languages: C/C++ (34), C++ (29), Shell (2), Python (1)

Size: 73 files, 66 scripts

Software Heritage: not archived

Found in: the text, “RNA-seq data quantification”

Holds: README, license file, continuous integration

Not found: CITATION.cff, environment file, tests, documentation

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

68 files

parallel.py, Python, 592 lines
scripts/bench_e2e.sh, Shell, 238 lines
scripts/test_issue_697_stdout_merge.sh, Shell, 28 lines
src/adaptertrimmer.cpp, C++, 185 lines
src/adaptertrimmer.h, C/C++, 27 lines
src/basecorrector.cpp, C++, 107 lines
src/basecorrector.h, C/C++, 24 lines
src/bgzf.h, C/C++, 243 lines
src/cmdline.h, C/C++, 809 lines
src/common.h, C/C++, 67 lines
src/duplicate.cpp, C++, 169 lines
src/duplicate.h, C/C++, 42 lines
src/evaluator.cpp, C++, 633 lines
src/evaluator.h, C/C++, 38 lines
src/fastareader.cpp, C++, 120 lines
src/fastareader.h, C/C++, 66 lines
src/fastqreader.cpp, C++, 460 lines
src/fastqreader.h, C/C++, 106 lines
src/filter.cpp, C++, 264 lines
src/filter.h, C/C++, 32 lines
src/filterresult.cpp, C++, 474 lines
src/filterresult.h, C/C++, 82 lines
src/htmlreporter.cpp, C++, 629 lines
src/htmlreporter.h, C/C++, 51 lines
src/jsonreporter.cpp, C++, 172 lines
src/jsonreporter.h, C/C++, 37 lines
src/knownadapters.h, C/C++, 251 lines, 1 match
src/main.cpp, C++, 522 lines
src/matcher.cpp, C++, 101 lines
src/matcher.h, C/C++, 22 lines
src/nucleotidetree.cpp, C++, 104 lines
src/nucleotidetree.h, C/C++, 40 lines
src/options.cpp, C++, 525 lines
src/options.h, C/C++, 389 lines
src/overlapanalysis.cpp, C++, 210 lines
src/overlapanalysis.h, C/C++, 38 lines
src/peprocessor.cpp, C++, 1,090 lines
src/peprocessor.h, C/C++, 75 lines
src/polyx.cpp, C++, 130 lines
src/polyx.h, C/C++, 28 lines
src/processor.cpp, C++, 23 lines
src/processor.h, C/C++, 22 lines
src/read.cpp, C++, 301 lines
src/read.h, C/C++, 73 lines
src/readpool.cpp, C++, 75 lines
src/readpool.h, C/C++, 38 lines
src/seprocessor.cpp, C++, 488 lines
src/seprocessor.h, C/C++, 59 lines
src/sequence.cpp, C++, 54 lines
src/sequence.h, C/C++, 29 lines
src/simd.cpp, C++, 568 lines
src/simd.h, C/C++, 38 lines
src/singleproducersingleconsumerlist.h, C/C++, 178 lines
src/stats.cpp, C++, 982 lines
src/stats.h, C/C++, 109 lines
src/threadconfig.cpp, C++, 161 lines
src/threadconfig.h, C/C++, 71 lines
src/umiprocessor.cpp, C++, 88 lines
src/umiprocessor.h, C/C++, 25 lines
src/unittest.cpp, C++, 39 lines
src/unittest.h, C/C++, 17 lines
src/util.h, C/C++, 293 lines
src/writer.cpp, C++, 154 lines
src/writer.h, C/C++, 73 lines
src/writerthread.cpp, C++, 214 lines
src/writerthread.h, C/C++, 68 lines
LICENSE, License, 21 lines
README.md, Text, 558 lines

At the source: github.com/OpenGene/fastp

Youshengshu/Human_VEN

License: none: the authors keep all their rights

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Commit: ca03d5cef5b0e9e23e8b7b7c2971bd5ba5264723, 28 July 2025

Languages: MATLAB (18), Jupyter (4), R (2)

Size: 28 files, 24 scripts

Software Heritage: not archived

Found in: “Code availability”

Holds: 4 notebooks

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: Statistics and Machine Learning Toolbox (5 files), Curve Fitting Toolbox (4 files), Matplotlib (4 files), NumPy (4 files), pandas (4 files), Scanpy (4 files), scikit-learn (4 files), seaborn (4 files), UMAP (4 files), Violinplot-Matlab (3 files), ggplot2 (2 files), SciPy (2 files), tidyverse (2 files), clusterProfiler (1 file), cowplot (1 file), data.table (1 file), easystats (1 file), edgeR (1 file), igraph (1 file), Image Processing Toolbox (1 file), Signal Processing Toolbox (1 file), patchwork (1 file), pheatmap (1 file), psych (1 file), Seurat (1 file), shadedErrorBar (1 file), WGCNA (1 file), XGBoost (1 file)

Availability: 1 check, the latest on 28 September 2026: the link answers

28 September 2026: the link answers

24 files

Figure 1/Step1_plotCellDensity.m, MATLAB, 103 lines
Figure 1/Step2_LocateVEN.m, MATLAB, 62 lines
Figure 1/Step3_PlotTotalData.m, MATLAB, 144 lines
Figure 3/AP_Statistic_new.m, MATLAB, 122 lines
Figure 3/Clustering_tSNE.R, R, 159 lines
Figure 3/Code_FIcurve_extract_Etype.m, MATLAB, 170 lines
Figure 3/Code_FIcurve_statistic_Etype.m, MATLAB, 509 lines, 1 match
Figure 3/Code_FIcurve_statisticforCollectingdata.m, MATLAB, 170 lines
Figure 3/Code_FiringPattern_forCollectingdata.m, MATLAB, 132 lines
Figure 3/Code_RinTau_extract_HERE.m, MATLAB, 183 lines
Figure 3/Code_RinTau_statistic_HERE.m, MATLAB, 147 lines
Figure 3/eventedgefinder.m, MATLAB, 47 lines
Figure 3/peakfinder.m, MATLAB, 261 lines
Figure 4/AP_Statistic_new.m, MATLAB, 122 lines
Figure 4/Code_FIcurve_AP_Statistic_new.m, MATLAB, 441 lines
Figure 4/Code_FIcurve_extract.m, MATLAB, 168 lines
Figure 4/Code_subthresWave_new.m, MATLAB, 242 lines, 1 match
Figure 4/eventedgefinder.m, MATLAB, 47 lines
Figure 4/peakfinder.m, MATLAB, 261 lines
VEN submission.R, R, 1,434 lines, 9 matches
feature_select_step1.ipynb, Jupyter, 134 lines, 1 match
feature_select_step2.ipynb, Jupyter, 171 lines, 1 match
feature_select_step3.ipynb, Jupyter, 95 lines
mapping.ipynb, Jupyter, 437 lines, 2 matches

At the source: github.com/Youshengshu/Human_VEN

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

it points to the authors' code: Youshengshu/Human_VEN

Read it in the paper: doi.org/10.1038/s41467-026-72935-2.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
90 scripts, each with its path and the digest of its content;
16 matches between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

it says that the data are available on request

Read it in the paper: doi.org/10.1038/s41467-026-72935-2.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 23 authors, 2 keywords, 14 MeSH terms, 2 funders, 67 references.

Cite

This paper

Ke, W., Lyu, S., Jin, M., Jiao, S., Li, L., Zhang, H., Ling, K., Tian, C., Song, Y., Chen, L., He, Q., Xiao, Y., Deng, S., Fan, L., Mu, H., Yang, H., Xu, H.-T., Wang, H., Tang, A.-H., . . . Shu, Y. (2026). Spindle neurons in human cortex possess distinctive firing properties and transcriptomic signatures. Nature communications, 17(1), 6330. https://doi.org/10.1038/s41467-026-72935-2

BibTeX

@article{ke2026spindle, author = {Ke, Wei and Lyu, Shuxuan and Jin, Mengmeng and Jiao, Shaoqing and Li, Liang and Zhang, Heyuan and Ling, Kexin and Tian, Cuiping and Song, Yiquan and Chen, Liu and He, Quansheng and Xiao, Yujie and Deng, Suixin and Fan, Liu and Mu, Huawei and Yang, Hui and Xu, Hua-Tai and Wang, Hao and Tang, Ai-Hui and Peng, Jiajie and He, Jie and Guo, Hui and Shu, Yousheng}, title = {{Spindle neurons in human cortex possess distinctive firing properties and transcriptomic signatures}}, journal = {Nature communications}, year = {2026}, month = may, volume = {17}, number = {1}, pages = {6330}, publisher = {Nature Publishing Group}, issn = {2041-1723}, doi = {10.1038/s41467-026-72935-2}, url = {https://doi.org/10.1038/s41467-026-72935-2}, pmid = {42120382}, pmcid = {PMC13376633} }

RIS

TY - JOUR AU - Ke, Wei AU - Lyu, Shuxuan AU - Jin, Mengmeng AU - Jiao, Shaoqing AU - Li, Liang AU - Zhang, Heyuan AU - Ling, Kexin AU - Tian, Cuiping AU - Song, Yiquan AU - Chen, Liu AU - He, Quansheng AU - Xiao, Yujie AU - Deng, Suixin AU - Fan, Liu AU - Mu, Huawei AU - Yang, Hui AU - Xu, Hua-Tai AU - Wang, Hao AU - Tang, Ai-Hui AU - Peng, Jiajie AU - He, Jie AU - Guo, Hui AU - Shu, Yousheng TI - Spindle neurons in human cortex possess distinctive firing properties and transcriptomic signatures T2 - Nature communications J2 - Nat Commun PY - 2026 DA - 2026/05/12 VL - 17 IS - 1 SP - 6330 SN - 2041-1723 PB - Nature Publishing Group DO - 10.1038/s41467-026-72935-2 UR - https://doi.org/10.1038/s41467-026-72935-2 LA - en ER -

CSL-JSON

{ "id": "10.1038/s41467-026-72935-2", "type": "article-journal", "title": "Spindle neurons in human cortex possess distinctive firing properties and transcriptomic signatures", "container-title": "Nature communications", "author": [ { "family": "Ke", "given": "Wei" }, { "family": "Lyu", "given": "Shuxuan" }, { "family": "Jin", "given": "Mengmeng" }, { "family": "Jiao", "given": "Shaoqing" }, { "family": "Li", "given": "Liang" }, { "family": "Zhang", "given": "Heyuan" }, { "family": "Ling", "given": "Kexin" }, { "family": "Tian", "given": "Cuiping" }, { "family": "Song", "given": "Yiquan" }, { "family": "Chen", "given": "Liu" }, { "family": "He", "given": "Quansheng" }, { "family": "Xiao", "given": "Yujie" }, { "family": "Deng", "given": "Suixin" }, { "family": "Fan", "given": "Liu" }, { "family": "Mu", "given": "Huawei" }, { "family": "Yang", "given": "Hui" }, { "family": "Xu", "given": "Hua-Tai" }, { "family": "Wang", "given": "Hao" }, { "family": "Tang", "given": "Ai-Hui" }, { "family": "Peng", "given": "Jiajie" }, { "family": "He", "given": "Jie" }, { "family": "Guo", "given": "Hui" }, { "family": "Shu", "given": "Yousheng" } ], "container-title-short": "Nat Commun", "volume": "17", "issue": "1", "page": "6330", "DOI": "10.1038/s41467-026-72935-2", "PMID": "42120382", "PMCID": "PMC13376633", "ISSN": "2041-1723", "publisher": "Nature Publishing Group", "URL": "https://doi.org/10.1038/s41467-026-72935-2", "language": "en", "issued": { "date-parts": [ [ 2026, 5, 12 ] ] } }

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