Comparative analysis of the cellular landscape in mammalian striatum.

Code ↔ Paper

23 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 23 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted

[1] § Methods › Single-nucleus RNA sequencing, cell type annotation, and cellular compositional abundance analysis ↔ 05_DGE_analysis/00_prep_pseudobulk_counts.R, lines 484–535 · score 0.98 · k.weight, LogNormalize, FindVariableFeatures, IntegrateData, FindIntegrationAnchors, SelectIntegrationFeatures
[2] § Methods › Single-nucleus RNA sequencing dataset alignment and quality control ↔ 03_SPNs/00_get_orthologs.sh, the whole file · a weak match · score 0.97 · Mustela putorius furo, Callithrix jacchus, Homo Sapiens, Macaca mulatta, Mus musculus, Pan troglodytes
[3] § Methods › Bat interneuron subtype analyses ↔ 02_Bat/04_WGCNA.R, lines 128–166 · score 0.97 · soft threshold power, blockwiseModules, deepSplit, maxBlockSize, minCoreKME, minKMEtoStay
[4] § Methods › Bat interneuron subtype analyses ↔ 02_Human/06_WGCNA.R, lines 133–172 · score 0.96 · soft threshold power, blockwiseModules, deepSplit, maxBlockSize, minCoreKME, minKMEtoStay
[5] § Results › Identification of striatal cell populations of each species ↔ QC_plots.R, lines 353–401 · score 0.85 · Il1rapl2, Ppp1r1b, Csf1r, Apbb1ip, CHRM2, GFAP
[6] § Results › Non-primates have lower eSPN to SPN proportions ↔ 03_SPNs/03_eSPN_D1D2_hybrid_correlation.R, lines 43–82 · score 0.78 · SEMA5B, SV2B, eSPNs, CRYM, KREMEN1, SGK1
[7] § Results › Non-primates have lower eSPN to SPN proportions ↔ 03_SPNs/03_eSPN_D1D2_hybrid_correlation.R, lines 43–82 · score 0.78 · SEMA5B, SV2B, eSPNs, CRYM, KREMEN1, SGK1
[8] § Methods › Human-specific differentially expressed gene (HS DEG) analysis ↔ 02_Human/06_WGCNA.R, lines 235–277 · score 0.70 · HS DEGs, gene enrichment, GO term, cutoffs, WGCNA, modules
[9] § Results › Differential gene expression analysis reveals stronger human specific change in SPNs than glia ↔ 02_Human/06_WGCNA.R, lines 235–277 · score 0.69 · red module genes, blue module genes, GO term, WGCNA, upregulated, enrichment
[10] § Methods › Human-specific differentially expressed gene (HS DEG) analysis ↔ 05_DGE_analysis/01_primates_pseudobulk_DESeq2.R, lines 134–176 · score 0.67 · humanized age, DESeq2, covariates, pseudobulk, sex, log10
[11] § Methods › Single-nucleus RNA sequencing, cell type annotation, and cellular compositional abundance analysis ↔ 05_DGE_analysis/00_prep_pseudobulk_counts.R, lines 94–137 · score 0.65 · FindIntegrationAnchors, SelectIntegrationFeatures, orthologous genes, Seurat, tool, NCBI
[12] § Methods › Single-nucleus RNA sequencing dataset alignment and quality control ↔ 01_Generate_NHP_gtfs/MKREF_LIFTOFF_CHIMP.sh, lines 1–39 · score 0.63 · panTro5, mkref, GTF, liftoff, hg38, genomes
[13] § Methods › Differentially expressed gene (DEG) and correlation analysis ↔ 03_SPNs/03_eSPN_D1D2_hybrid_correlation.R, lines 124–186 · score 0.63 · D1 D2 hybrid, gene expression, heatmap, correlation, SPN, macaque
[14] § Methods › Differentially expressed gene (DEG) and correlation analysis ↔ 03_SPNs/03_eSPN_D1D2_hybrid_correlation.R, lines 124–180 · score 0.63 · D1 D2 hybrid, gene expression, heatmap, correlation, SPN, macaque
[15] § Methods › Single-nucleus RNA sequencing, cell type annotation, and cellular compositional abundance analysis ↔ 03_SPNs/01_INTEGRATION_AND_CLUSTERING.R, lines 210–253 · score 0.61 · FindIntegrationAnchors, SelectIntegrationFeatures, SRR13808461, SRR13808459, NCBI, orthologous
[16] § Results › Identification of interneurons found primarily in the bat ↔ 04_Interneurons/01_Integration&Clustering.R, lines 1076–1143 · score 0.61 · PDGFD PTHLH PVALB, FOXP2 EYA2, CCK VIP, FOXP2 TSHZ2, cluster, SST
[17] § Results › Identification of striatal cell populations of each species ↔ 05_DGE_analysis/00_prep_pseudobulk_counts.R, lines 436–482 · score 0.59 · PPP1R1B, CSF1R, ARHGAP15, PDGFRA, AQP4, PENK
[18] § Results › Identification of interneurons found primarily in the bat ↔ 04_Interneurons/04_Correlation_withinBatPutamen.R, lines 42–104 · score 0.57 · correlation matrix, FOXP2 EYA2, FOXP2 TSHZ2, gene expression, LMO3, interneurons
[19] § Methods › Differentially expressed gene (DEG) and correlation analysis ↔ 04_Interneurons/01_Integration&Clustering.R, lines 1800–1842 · score 0.57 · BuildClusterTree, PlotClusterTree, distances, matrix, clustering
[20] § Results › Identification of interneurons found primarily in the bat ↔ 04_Interneurons/03_DEG_Analysis.R, lines 311–350 · score 0.55 · GO term enrichment, upregulated genes, FOXP2 TSHZ2, enriched, interneurons, bat
[21] § Results › Non-primates have lower eSPN to SPN proportions ↔ 03_SPNs/02_eSPN_markers.R, lines 395–444 · score 0.55 · RUNX1T1, DCLK1, eSPN, TSHZ1, CASZ1, FOXP2
[22] § Methods › Differentially expressed gene (DEG) and correlation analysis ↔ 05_DGE_analysis/02_DEG_figures.R, lines 317–360 · score 0.54 · log2 fold change, downregulated, upregulated, DEGs, putamen, genes
[23] § Methods › Differentially expressed gene (DEG) and correlation analysis ↔ 05_DGE_analysis/01_primates_pseudobulk_DESeq2.R, lines 134–176 · score 0.52 · log2 fold change, volcano, DEGs, sum, genes

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The authors' code

R · 1,667 lines · 69 KB · MIT · 3 matches

# load packages
library(patchwork)
library(Seurat)
library(rhdf5)
library(DropletUtils)
library(DropletQC)
library(dplyr)
library(Matrix)
library(ggplot2)
library(plyr)
library(tidyverse)
library(tidyr)
library(ggpubr)
library(reshape2)
library(rio)
library(data.table)
library(harmony)
library(ggrepel)
library(edgeR)
library(variancePartition)
library(Matrix.utils)
library(SingleCellExperiment)
library(RColorBrewer)
set.seed(1234)
source("/project/Neuroinformatics_Core/Konopka_lab/s422071/SCRIPTS_pr/SCRIPTS/utility_functions.R")
library(curl)
#conda install bioconda::r-wgcna
#BiocManager::install('WGCNA')
library(WGCNA)
library(flashClust)
#remotes::install_url("https://cran.r-project.org/src/contrib/Archive/matrixStats/matrixStats_1.1.0.tar.gz")
library(matrixStats)
library(RColorBrewer)
####
## PREPARE DATASETS
####
# read the data
human_str <- readRDS(paste0(file_dir,"human_integrated_caudate_putamen_ANNOTATED.RDS"))
chimp_str = readRDS(paste0(file_dir,"chimp_integrated_caudate_putamen_ANNOTATED.RDS"))
macaque_str = readRDS(paste0(file_dir,"macaque_integrated_caudate_putamen_ANNOTATED.RDS"))
marmoset_str = readRDS(paste0(file_dir,"marmoset_integrated_caudate_putamen_ANNOTATED.RDS"))
mouse_str = readRDS(paste0(file_dir,"Mouse_Caudate_Annotated_FINAL.RDS"))
mouse_str$Tissue = rep("Caudoputamen", nrow(mouse_str[[]]))
mouse_str$Species = rep("Mouse", nrow(mouse_str[[]]))
mouse_str$id = paste0(mouse_str$orig.ident, mouse_str$Tissue)
bat_str = readRDS(paste0(file_dir,"bat_integrated_caudate_putamen_ANNOTATED.RDS"))
ferret_str = readRDS(paste0(file_dir,"Ferret_Caudate_Krienen_ANNOTATED.RDS"))
ferret_str$newannot_2 = ferret_str$newannot
ferret_str$newannot = ferret_str$broad_annot
# Reshape and combine metadata
human_meta = [email hidden]
human_meta$Species = 'Human'
chimp_meta = [email hidden]
chimp_meta$Species = 'Chimp'
macaque_meta = [email hidden]
macaque_meta$Species = 'Macaque'
marmoset_meta = [email hidden]
marmoset_meta$Species = 'Marmoset'
bat_meta = [email hidden]
bat_meta$Species = 'Bat'
sub_obj_new_list <- list(
Human = human_str,
Chimp = chimp_str,
Macaque = macaque_str,
Marmoset = marmoset_str,
Mouse = mouse_str,
Bat = bat_str,
Ferret = ferret_str
)
saveRDS(sub_obj_new_list, file = "/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/seu_objs/sub_obj_new_list_11_12_2025.RDS")
# make RNA as active assay
a <- lapply(sub_obj_new_list, function(x) {
DefaultAssay(x) <- "RNA"
x@assays$SCT = NULL
x[["RNA"]] <- as(object = x[["RNA"]], Class = "Assay")
x
})
b_new = Reduce(merge, a)
### microglia cleanup
seurM = subset(b_new, subset = newannot_2 == "Microglia")
# Found ortholog genes from human pr coding genes and extracted the genes which are ortholog in all 7 species using ncbi datasets tool
ortho_genes <- read.table("/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/metadata/orthologs_in_7_species_human_pr_codingOnly_2.csv", header = TRUE)$symbol #15055 pr coding ortho genes
### subset the genes and the Microglia from each Species
# subset cells and metadata
# Extract the count matrix from the Seurat object (default is "RNA" assay)
mat = seurM@assays$RNA$counts
new_mat <- mat[rownames(mat) %in% ortho_genes,]
# generate new Seurat object
glia_merged <- CreateSeuratObject(count = new_mat, meta.data = seurM[[]])
####
## SET VARIABLES
####
marksToPlot <- c('NRGN', 'SYT1', 'SNAP25', 'GAD1', 'GAD2', 'DRD1', 'DRD2', 'FOXP2', 'TAC1', 'PENK', 'SST', 'NPY', 'MOG', 'PLP1', 'MBP', 'MOBP', 'SLC1A2', 'SLC1A3', 'APOE', 'GPC5', 'GLI3', 'AQP4', 'CSF1R', 'ARHGAP15', 'PLDL1', 'CASZ1', 'PTPRZ1', 'PCDH15', 'SOX6', 'FYN', 'BCAS1', 'ENPP6', 'GPR17', 'FLT1', 'DUSP1', 'COBLL1', 'CFTR', 'CHAT', 'ADARB2', 'LHX6', 'MEIS2', 'TAC3', 'VIP', 'TH', 'CCK', 'TTC34', 'CROCC', 'FHAD1', 'SOX4', 'PDGFRB', 'PDGFRA', 'SATB2', 'SLC17A7', 'EYA2', 'PDGFD', 'PTHLH', 'PVALB', 'SPARCL1', 'RSPO2', 'RMST', 'LMO3', 'TSHZ2', "CALB1", "CALB2", "NOS1", "TSHZ1", "OPRM1", "CHST9","GRM8", "PPP1R1B", 'GRIK3', 'CXCL14')
pref = 'Microglia_AllSpecies_AllTissues_human_pr_coding_orthologs'
####
## INTEGRATE ACROSS SPECIES
####
# Split data
seurM = glia_merged
seurM[["RNA"]] = as(object = seurM[["RNA"]], Class = "Assay")
seurM$id = paste0(seurM$orig.ident, '_', seurM$Tissue)
seurML = SplitObject(seurM, split.by = "id")
# normalize and identify variable features for each dataset independently
seurML = lapply(X = seurML, FUN = function(x) {
x = NormalizeData(x)
x = FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000)
})
# select features that are repeatedly variable across datasets for integration run PCA on each
# dataset using these features
features = SelectIntegrationFeatures(object.list = seurML)
print(paste0('Number of genes to use for integration: ', length(features)))
seurML = lapply(X = seurML, FUN = function(x) {
x = ScaleData(x, features = features, verbose = FALSE)
x = RunPCA(x, features = features, verbose = FALSE, npcs = 30)
})
anchors = FindIntegrationAnchors(object.list = seurML, anchor.features = features, normalization.method = "LogNormalize", reduction = "rpca")
# save memory prior integration
gc()
# this command creates an 'integrated' data assay
options(future.globals.maxSize = 16000 * 1024^2)
allseur_integrated = IntegrateData(anchorset = anchors, k.weight = 30, normalization.method = 'LogNormalize')
saveRDS(allseur_integrated, '/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/seu_objs/Integrated_rpca_Microglia_AllSpecies_ALLTissues_human_pr_coding_orthologs.RDS')
# Clustering
allseur_integrated = ScaleData(allseur_integrated, verbose = FALSE)
allseur_integrated = RunPCA(allseur_integrated, verbose = FALSE)
allseur_integrated = RunUMAP(allseur_integrated, dims = 1:20, reduction = 'pca')
# Basic Plots
setwd("/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/revision/01_MICROGLIA/CLUSTERING_1")
pdf(paste0(pref, "_INTEGRATED_UMAP.pdf"))
DimPlot(allseur_integrated, group.by = 'Species', raster = T)
dev.off()
DefaultAssay(allseur_integrated) <- "integrated"
allseur_integrated = FindNeighbors(allseur_integrated, dims = 1:20, reduction = 'pca')
allseur_integrated = FindClusters(allseur_integrated, resolution = 1)
pdf(paste0(pref, "_INTEGRATED_CLUSTERS.pdf"))
DimPlot(allseur_integrated, label = T, raster = T) + NoLegend()
dev.off()
pdf(paste0(pref, "_INTEGRATED_PRIMATE_ANNOT.pdf"))
DimPlot(allseur_integrated, label = T, raster = T, group.by = 'newannot') + NoLegend()
dev.off()
stackedbarplot(allseur_integrated[[]], groupx = 'seurat_clusters', groupfill = 'orig.ident', paste0(pref, '_INTEGRATED_STACK_SAMPLES'), wd = 20)
stackedbarplot(allseur_integrated[[]], groupx = 'seurat_clusters', groupfill = 'Species', paste0(pref, '_INTEGRATED_STACK_SPECIES'))
# Plot previously identified markers
DefaultAssay(allseur_integrated) = 'RNA'
allseur_integrated = NormalizeData(allseur_integrated)
pdf(paste0(pref, "_INTEGRATED_DOTPLOT.pdf"), width = 20, height = 10)
DotPlot(allseur_integrated, features = marksToPlot) +
xlab('') +
ylab('') +
theme(axis.text.x=element_text(size=20, face = 'bold'),
axis.text.y=element_text(size=20, face = 'bold'),
legend.text = element_text(size=20, face = 'bold'),
legend.title = element_text(size=20, face = 'bold')) +
rotate_x_text(45)
dev.off()
pdf(paste0(pref, "_INTEGRATED_DEPTH.pdf"))
ggboxplot(allseur_integrated[[]], x = 'seurat_clusters', y = 'nFeature_RNA', color = 'black') + NoLegend() +
rotate_x_text(90)
dev.off()
pdf(paste0(pref, "_INTEGRATED_NUCLEAR_FRACTION.pdf"))
ggboxplot(allseur_integrated[[]], x = 'seurat_clusters', y = 'intronRat') +
rotate_x_text(90) +
theme(text=element_text(size=20, face = 'bold'))
dev.off()
saveRDS(allseur_integrated, paste0('/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/revision/01_MICROGLIA/CLUSTERING_1/', pref, '_integrated_CLUSTERING1.RDS'))
allseur_integrated = readRDS( paste0('/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/revision/01_MICROGLIA/CLUSTERING_1/', pref, '_integrated_CLUSTERING1.RDS'))
################# CLUSTERING_2 ##########################
##Cluster#,CellType
#12*,MOL+micro
#19*,Neu+Micro
#21*,Endo+micro
setwd("/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/revision/01_MICROGLIA/CLUSTERING_2/")
toremove = c(12,19,21)
allseur_integrated = subset(allseur_integrated, subset = seurat_clusters %in% toremove, invert = T)
# Clustering
DefaultAssay(allseur_integrated) = 'integrated'
allseur_integrated = ScaleData(allseur_integrated, verbose = FALSE)
allseur_integrated = RunPCA(allseur_integrated, verbose = FALSE)
allseur_integrated = RunUMAP(allseur_integrated, dims = 1:20, reduction = 'pca')
# Basic Plots
pdf(paste0(pref, "_UMAP.pdf"))
DimPlot(allseur_integrated, group.by = 'Species', raster = T)
dev.off()
allseur_integrated = FindNeighbors(allseur_integrated, dims = 1:20, reduction = 'pca')
allseur_integrated = FindClusters(allseur_integrated, resolution = 1)
pdf(paste0(pref, "_CLUSTERS.pdf"))
DimPlot(allseur_integrated, label = T, raster = T) + NoLegend()
dev.off()
stackedbarplot(allseur_integrated[[]], groupx = 'seurat_clusters', groupfill = 'orig.ident', paste0(pref, '_STACK_SAMPLES'), wd = 20)
stackedbarplot(allseur_integrated[[]], groupx = 'seurat_clusters', groupfill = 'Species', paste0(pref, '_STACK_SPECIES'))
# Plot previously identified markers
DefaultAssay(allseur_integrated) = 'RNA'
allseur_integrated = NormalizeData(allseur_integrated)
pdf(paste0(pref, "INTEGRATED_DOTPLOT.pdf"), width = 20, height = 10)
DotPlot(allseur_integrated, features = marksToPlot) +
xlab('') +
ylab('') +
theme(axis.text.x=element_text(size=20, face = 'bold'),
axis.text.y=element_text(size=20, face = 'bold'),
legend.text = element_text(size=20, face = 'bold'),
legend.title = element_text(size=20, face = 'bold')) +
rotate_x_text(45)
dev.off()
pdf(paste0(pref, "INTEGRATED_DEPTH.pdf"))
ggboxplot(allseur_integrated[[]], x = 'seurat_clusters', y = 'nFeature_RNA', color = 'black') + NoLegend() +
rotate_x_text(90)
dev.off()
pdf(paste0(pref, "INTEGRATED_NUCLEAR_FRACTION.pdf"))
ggboxplot(allseur_integrated[[]], x = 'seurat_clusters', y = 'intronRat') +
rotate_x_text(90) +
theme(text=element_text(size=20, face = 'bold'))
dev.off()
# save
saveRDS(allseur_integrated, paste0(pref,"_integrated_CLUSTERING_2.RDS"))
allseur_integrated = readRDS(paste0(pref,"_integrated_CLUSTERING_2.RDS"))
##################### CLUSTERING_3 #################
#14*, MOL+micro
#19*, MOL+micro
setwd("/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/revision/01_MICROGLIA/CLUSTERING_3/")
toremove = c(14,19)
allseur_integrated = subset(allseur_integrated, subset = seurat_clusters %in% toremove, invert = T)
# Clustering
DefaultAssay(allseur_integrated) = 'integrated'
allseur_integrated = ScaleData(allseur_integrated, verbose = FALSE)
allseur_integrated = RunPCA(allseur_integrated, verbose = FALSE)
allseur_integrated = RunUMAP(allseur_integrated, dims = 1:20, reduction = 'pca')
# Basic Plots
pdf(paste0(pref, "_UMAP.pdf"))
DimPlot(allseur_integrated, group.by = 'Species', raster = T)
dev.off()
allseur_integrated = FindNeighbors(allseur_integrated, dims = 1:20, reduction = 'pca')
allseur_integrated = FindClusters(allseur_integrated, resolution = 1)
pdf(paste0(pref, "_CLUSTERS.pdf"))
DimPlot(allseur_integrated, label = T, raster = T) + NoLegend()
dev.off()
stackedbarplot(allseur_integrated[[]], groupx = 'seurat_clusters', groupfill = 'orig.ident', paste0(pref, '_STACK_SAMPLES'), wd = 20)
stackedbarplot(allseur_integrated[[]], groupx = 'seurat_clusters', groupfill = 'Species', paste0(pref, '_STACK_SPECIES'))
# Plot previously identified markers
DefaultAssay(allseur_integrated) = 'RNA'
allseur_integrated = NormalizeData(allseur_integrated)
pdf(paste0(pref, "INTEGRATED_DOTPLOT.pdf"), width = 20, height = 10)
DotPlot(allseur_integrated, features = marksToPlot) +
xlab('') +
ylab('') +
theme(axis.text.x=element_text(size=20, face = 'bold'),
axis.text.y=element_text(size=20, face = 'bold'),
legend.text = element_text(size=20, face = 'bold'),
legend.title = element_text(size=20, face = 'bold')) +
rotate_x_text(45)
dev.off()
pdf(paste0(pref, "INTEGRATED_DEPTH.pdf"))
ggboxplot(allseur_integrated[[]], x = 'seurat_clusters', y = 'nFeature_RNA', color = 'black') + NoLegend() +
rotate_x_text(90)
dev.off()
pdf(paste0(pref, "INTEGRATED_NUCLEAR_FRACTION.pdf"))
ggboxplot(allseur_integrated[[]], x = 'seurat_clusters', y = 'intronRat') +
rotate_x_text(90) +
theme(text=element_text(size=20, face = 'bold'))
dev.off()
# save
saveRDS(allseur_integrated, paste0(pref,"_integrated_CLUSTERING_3.RDS"))
allseur_integrated = readRDS(paste0(pref,"_integrated_CLUSTERING_3.RDS"))
################### CLUSTERING_4 ###################
#15*,Endo
setwd("/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/revision/01_MICROGLIA/CLUSTERING_4/")
toremove = c(15)
allseur_integrated = subset(allseur_integrated, subset = seurat_clusters %in% toremove, invert = T)
# Clustering
DefaultAssay(allseur_integrated) = 'integrated'
allseur_integrated = ScaleData(allseur_integrated, verbose = FALSE)
allseur_integrated = RunPCA(allseur_integrated, verbose = FALSE)
allseur_integrated = RunUMAP(allseur_integrated, dims = 1:20, reduction = 'pca')
# Basic Plots
pdf(paste0(pref, "_UMAP.pdf"))
DimPlot(allseur_integrated, group.by = 'Species', raster = T)
dev.off()
allseur_integrated = FindNeighbors(allseur_integrated, dims = 1:20, reduction = 'pca')
allseur_integrated = FindClusters(allseur_integrated, resolution = 1)
pdf(paste0(pref, "_CLUSTERS.pdf"))
DimPlot(allseur_integrated, label = T, raster = T) + NoLegend()
dev.off()
stackedbarplot(allseur_integrated[[]], groupx = 'seurat_clusters', groupfill = 'orig.ident', paste0(pref, '_STACK_SAMPLES'), wd = 20)
stackedbarplot(allseur_integrated[[]], groupx = 'seurat_clusters', groupfill = 'Species', paste0(pref, '_STACK_SPECIES'))
# Plot previously identified markers
DefaultAssay(allseur_integrated) = 'RNA'
allseur_integrated = NormalizeData(allseur_integrated)
pdf(paste0(pref, "INTEGRATED_DOTPLOT.pdf"), width = 20, height = 10)
DotPlot(allseur_integrated, features = marksToPlot) +
xlab('') +
ylab('') +
theme(axis.text.x=element_text(size=20, face = 'bold'),
axis.text.y=element_text(size=20, face = 'bold'),
legend.text = element_text(size=20, face = 'bold'),
legend.title = element_text(size=20, face = 'bold')) +
rotate_x_text(45)
dev.off()
pdf(paste0(pref, "INTEGRATED_DEPTH.pdf"))
ggboxplot(allseur_integrated[[]], x = 'seurat_clusters', y = 'nFeature_RNA', color = 'black') + NoLegend() +
rotate_x_text(90)
dev.off()
pdf(paste0(pref, "INTEGRATED_NUCLEAR_FRACTION.pdf"))
ggboxplot(allseur_integrated[[]], x = 'seurat_clusters', y = 'intronRat') +
rotate_x_text(90) +
theme(text=element_text(size=20, face = 'bold'))
dev.off()
# save
saveRDS(allseur_integrated, paste0(pref,"_integrated_CLUSTERING_4.RDS"))
## annotate
(mapnames <-setNames(rep("Microglia", 27),c(0:26)))
# Create the broadannot
allseur_integrated$Micro_annot = unname(mapnames[allseur_integrated[["seurat_clusters"]][,1]]) # to extract the annotations and not the cluster #s
table(allseur_integrated$Micro_annot)
#Microglia
# 32961
setwd("/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/revision/01_MICROGLIA/ANNOTATED")
# QC PLOTS
pdf(paste0(pref, "_Clusters.pdf"))
DimPlot(allseur_integrated, group.by = 'Micro_annot', label = T, raster = T, label.size = 7) +
NoLegend() +
ggtitle('')
dev.off()
pdf(paste0(pref, "_Depth_Gene.pdf"))
ggboxplot(allseur_integrated[[]], x = 'Micro_annot', y = 'nFeature_RNA') +
rotate_x_text(90)
dev.off()
pdf(paste0(pref, "_Depth_UMI.pdf"))
ggboxplot(allseur_integrated[[]], x = 'Micro_annot', y = 'nCount_RNA', ylim = c(0,20000)) +
rotate_x_text(90) +
theme(text=element_text(size=20, face = 'bold'))
dev.off()
pdf(paste0(pref, "_NuclearFraction.pdf"))
ggboxplot(allseur_integrated[[]], x = 'Micro_annot', y = 'intronRat') +
rotate_x_text(90) +
theme(text=element_text(size=20, face = 'bold'))
dev.off()
# MARKER GENE PLOTS
DefaultAssay(allseur_integrated) = 'RNA'
seurM_corr = NormalizeData(allseur_integrated)
pdf(paste0(pref, "_MarkersDotPlot.pdf"), width = 20, height = 10)
DotPlot(allseur_integrated, group.by = "Micro_annot", features = marksToPlot) +
xlab('') +
ylab('') +
theme(axis.text.x=element_text(size=20, face = 'bold'),
axis.text.y=element_text(size=20, face = 'bold'),
legend.text = element_text(size=20, face = 'bold'),
legend.title = element_text(size=20, face = 'bold')) +
rotate_x_text(45)
dev.off()
stackedbarplot(allseur_integrated[[]], groupx = 'Micro_annot', groupfill = 'orig.ident', paste0(pref, '_STACK_SAMPLES'), wd = 20)
stackedbarplot(allseur_integrated[[]], groupx = 'Micro_annot', groupfill = 'Species', paste0(pref, '_STACK_SPECIES'))
# save
saveRDS(allseur_integrated, paste0(pref,"_integrated_ANNOTATED.RDS"))
## add these annotations to the original seu obj
# extract the newest annotations
b$detailed_annot <- as.character(b$newannot_2)
# Match cell names between objects
common_cells <- intersect(rownames([email hidden]), rownames([email hidden]))
# Update annotations for those cells
b$detailed_annot[common_cells] <- allseur_integrated$Micro_annot[common_cells]
b$is_micro = ifelse(rownames([email hidden]) %in% rownames([email hidden]), "yes", "no")
# Identify microglia cells in b that are not in the integrated object
b_keep = subset(b, subset = newannot_2 != "Microglia" | is_micro == "yes")
############################################
####### MOL + OPC
################################################
### subset the genes and the MOL+OPC from each Species
# subset cells and metadata
subset_b = subset(b_keep, subset = newannot_2 %in% c("MOL", "OPC")) # already orthologs only
# Extract the count matrix from the Seurat object (default is "RNA" assay)
mat = subset_b@assays$RNA$counts
new_mat <- mat[rownames(mat) %in% ortho_genes,]
# generate new Seurat object
glia_merged <- CreateSeuratObject(count = new_mat, meta.data = subset_b[[]])
####
## SET VARIABLES
####
# PPP1R1B IS AN SPN MARKER!
# TSHZ1 IS A PATCH MARKER
marksToPlot <- c('NRGN', 'SYT1', 'SNAP25', 'GAD1', 'GAD2', 'DRD1', 'DRD2', 'FOXP2', 'TAC1', 'PENK', 'SST', 'NPY', 'MOG', 'PLP1', 'MBP', 'MOBP', 'SLC1A2', 'SLC1A3', 'AQP4', 'CSF1R', 'ARHGAP15', 'PLD1','CASZ1', 'PTPRZ1', 'PCDH15', 'SOX6', 'FYN', 'BCAS1', 'ENPP6', 'GPR17', 'FLT1', 'DUSP1', 'COBLL1', 'CFTR', 'CHAT', 'ADARB2', 'LHX6', 'MEIS2', 'TAC3', 'VIP', 'TH', 'CCK', 'TTC34', 'CROCC', 'FHAD1', 'SOX4', 'PDGFRB', 'PDGFRA', 'SATB2', 'SLC17A7', 'EYA2', 'PDGFD', 'PTHLH', 'PVALB', 'SPARCL1', 'RSPO2', 'RMST', 'LMO3', 'TSHZ2', "CALB1", "CALB2", "NOS1", "TSHZ1", "OPRM1", "CHST9","GRM8", "PPP1R1B", 'GRIK3', 'CXCL14')
pref = 'MOL_OPC_AllSpecies_AllTissues_human_pr_coding_orthologs'
####
## INTEGRATE ACROSS SPECIES
####
#### use old seurat version
# Split data
seurM = glia_merged
seurM[["RNA"]] = as(object = seurM[["RNA"]], Class = "Assay")
seurM$id = paste0(seurM$orig.ident, '_', seurM$Tissue)
seurML = SplitObject(seurM, split.by = "id")
# normalize and identify variable features for each dataset independently
seurML = lapply(X = seurML, FUN = function(x) {
x = NormalizeData(x)
x = FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000)
})
# select features that are repeatedly variable across datasets for integration run PCA on each
# dataset using these features
features = SelectIntegrationFeatures(object.list = seurML)
print(paste0('Number of genes to use for integration: ', length(features)))
seurML = lapply(X = seurML, FUN = function(x) {
x = ScaleData(x, features = features, verbose = FALSE)
x = RunPCA(x, features = features, verbose = FALSE, npcs = 30)
})
anchors = FindIntegrationAnchors(object.list = seurML, anchor.features = features, normalization.method = "LogNormalize", reduction = "rpca")
# save memory prior integration
gc()
# this command creates an 'integrated' data assay
#(kweg = floor(min(table(seurM$orig.ident))/10)*10) #90
options(future.globals.maxSize = 16000 * 1024^2)
allseur_integrated = IntegrateData(anchorset = anchors, k.weight = 30, normalization.method = 'LogNormalize')
saveRDS(allseur_integrated, '/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/seu_objs/Integrated_rpca_MOL_OPC_AllSpecies_ALLTissues_human_pr_coding_orthologs.RDS')
# Clustering
allseur_integrated = ScaleData(allseur_integrated, verbose = FALSE)
allseur_integrated = RunPCA(allseur_integrated, verbose = FALSE)
allseur_integrated = RunUMAP(allseur_integrated, dims = 1:20, reduction = 'pca')
# Basic Plots
setwd("/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/revision/01_MOL_OPC/CLUSTERING_1")
pdf(paste0(pref, "_INTEGRATED_UMAP.pdf"))
DimPlot(allseur_integrated, group.by = 'Species', raster = T)
dev.off()
DefaultAssay(allseur_integrated) <- "integrated"
allseur_integrated = FindNeighbors(allseur_integrated, dims = 1:20, reduction = 'pca')
allseur_integrated = FindClusters(allseur_integrated, resolution = 1)
pdf(paste0(pref, "_INTEGRATED_CLUSTERS.pdf"))
DimPlot(allseur_integrated, label = T, raster = T) + NoLegend()
dev.off()
pdf(paste0(pref, "_INTEGRATED_PRIMATE_ANNOT.pdf"))
DimPlot(allseur_integrated, label = T, raster = T, group.by = 'newannot') + NoLegend()
dev.off()
stackedbarplot(allseur_integrated[[]], groupx = 'seurat_clusters', groupfill = 'orig.ident', paste0(pref, '_INTEGRATED_STACK_SAMPLES'), wd = 20)
stackedbarplot(allseur_integrated[[]], groupx = 'seurat_clusters', groupfill = 'Species', paste0(pref, '_INTEGRATED_STACK_SPECIES'))
# Plot previously identified markers
DefaultAssay(allseur_integrated) = 'RNA'
allseur_integrated = NormalizeData(allseur_integrated)
pdf(paste0(pref, "_INTEGRATED_DOTPLOT.pdf"), width = 20, height = 10)
DotPlot(allseur_integrated, features = marksToPlot) +
xlab('') +
ylab('') +
theme(axis.text.x=element_text(size=20, face = 'bold'),
axis.text.y=element_text(size=20, face = 'bold'),
legend.text = element_text(size=20, face = 'bold'),
legend.title = element_text(size=20, face = 'bold')) +
rotate_x_text(45)
dev.off()
pdf(paste0(pref, "_INTEGRATED_DEPTH.pdf"))
ggboxplot(allseur_integrated[[]], x = 'seurat_clusters', y = 'nFeature_RNA', color = 'black') + NoLegend() +
rotate_x_text(90)
dev.off()
pdf(paste0(pref, "_INTEGRATED_NUCLEAR_FRACTION.pdf"))
ggboxplot(allseur_integrated[[]], x = 'seurat_clusters', y = 'intronRat') +
rotate_x_text(90) +
theme(text=element_text(size=20, face = 'bold'))
dev.off()
saveRDS(allseur_integrated, paste0('/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/revision/01_MOL_OPC/CLUSTERING_1/', pref, '_integrated_CLUSTERING1.RDS'))
################# CLUSTERING_2 ##########################
##Cluster#,CellType
#17*,Neu+MOL
#19*,MOL+Ast
#25*,Neu+MOL
setwd("/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/revision/01_MOL_OPC/CLUSTERING_2/")
toremove = c(17,19,25)
allseur_integrated = subset(allseur_integrated, subset = seurat_clusters %in% toremove, invert = T)
# Clustering
DefaultAssay(allseur_integrated) = 'integrated'
allseur_integrated = ScaleData(allseur_integrated, verbose = FALSE)
allseur_integrated = RunPCA(allseur_integrated, verbose = FALSE)
allseur_integrated = RunUMAP(allseur_integrated, dims = 1:20, reduction = 'pca')
# Basic Plots
pdf(paste0(pref, "_UMAP.pdf"))
DimPlot(allseur_integrated, group.by = 'Species', raster = T)
dev.off()
allseur_integrated = FindNeighbors(allseur_integrated, dims = 1:20, reduction = 'pca')
allseur_integrated = FindClusters(allseur_integrated, resolution = 1)
pdf(paste0(pref, "_CLUSTERS.pdf"))
DimPlot(allseur_integrated, label = T, raster = T) + NoLegend()
dev.off()
stackedbarplot(allseur_integrated[[]], groupx = 'seurat_clusters', groupfill = 'orig.ident', paste0(pref, '_STACK_SAMPLES'), wd = 20)
stackedbarplot(allseur_integrated[[]], groupx = 'seurat_clusters', groupfill = 'Species', paste0(pref, '_STACK_SPECIES'))
# Plot previously identified markers
DefaultAssay(allseur_integrated) = 'RNA'
allseur_integrated = NormalizeData(allseur_integrated)
pdf(paste0(pref, "INTEGRATED_DOTPLOT.pdf"), width = 20, height = 10)
DotPlot(allseur_integrated, features = marksToPlot) +
xlab('') +
ylab('') +
theme(axis.text.x=element_text(size=20, face = 'bold'),
axis.text.y=element_text(size=20, face = 'bold'),
legend.text = element_text(size=20, face = 'bold'),
legend.title = element_text(size=20, face = 'bold')) +
rotate_x_text(45)
dev.off()
pdf(paste0(pref, "INTEGRATED_DEPTH.pdf"))
ggboxplot(allseur_integrated[[]], x = 'seurat_clusters', y = 'nFeature_RNA', color = 'black') + NoLegend() +
rotate_x_text(90)
dev.off()
pdf(paste0(pref, "INTEGRATED_NUCLEAR_FRACTION.pdf"))
ggboxplot(allseur_integrated[[]], x = 'seurat_clusters', y = 'intronRat') +
rotate_x_text(90) +
theme(text=element_text(size=20, face = 'bold'))
dev.off()
# save
saveRDS(allseur_integrated, paste0(pref,"_integrated_CLUSTERING_2.RDS"))
allseur_integrated= readRDS(paste0(pref,"_integrated_CLUSTERING_2.RDS"))
##################### CLUSTERING_3 #################
#21*, MOL+micro
#24*, neu+MOL
setwd("/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/revision/01_MOL_OPC/CLUSTERING_3/")
toremove = c(21,24)
allseur_integrated = subset(allseur_integrated, subset = seurat_clusters %in% toremove, invert = T)
# Clustering
DefaultAssay(allseur_integrated) = 'integrated'
allseur_integrated = ScaleData(allseur_integrated, verbose = FALSE)
allseur_integrated = RunPCA(allseur_integrated, verbose = FALSE)
allseur_integrated = RunUMAP(allseur_integrated, dims = 1:20, reduction = 'pca')
# Basic Plots
pdf(paste0(pref, "_UMAP.pdf"))
DimPlot(allseur_integrated, group.by = 'Species', raster = T)
dev.off()
allseur_integrated = FindNeighbors(allseur_integrated, dims = 1:20, reduction = 'pca')
allseur_integrated = FindClusters(allseur_integrated, resolution = 1)
pdf(paste0(pref, "_CLUSTERS.pdf"))
DimPlot(allseur_integrated, label = T, raster = T) + NoLegend()
dev.off()
stackedbarplot(allseur_integrated[[]], groupx = 'seurat_clusters', groupfill = 'orig.ident', paste0(pref, '_STACK_SAMPLES'), wd = 20)
stackedbarplot(allseur_integrated[[]], groupx = 'seurat_clusters', groupfill = 'Species', paste0(pref, '_STACK_SPECIES'))
# Plot previously identified markers
DefaultAssay(allseur_integrated) = 'RNA'
allseur_integrated = NormalizeData(allseur_integrated)
pdf(paste0(pref, "INTEGRATED_DOTPLOT.pdf"), width = 20, height = 10)
DotPlot(allseur_integrated, features = marksToPlot) +
xlab('') +
ylab('') +
theme(axis.text.x=element_text(size=20, face = 'bold'),
axis.text.y=element_text(size=20, face = 'bold'),
legend.text = element_text(size=20, face = 'bold'),
legend.title = element_text(size=20, face = 'bold')) +
rotate_x_text(45)
dev.off()
pdf(paste0(pref, "INTEGRATED_DEPTH.pdf"))
ggboxplot(allseur_integrated[[]], x = 'seurat_clusters', y = 'nFeature_RNA', color = 'black') + NoLegend() +
rotate_x_text(90)
dev.off()
pdf(paste0(pref, "INTEGRATED_NUCLEAR_FRACTION.pdf"))
ggboxplot(allseur_integrated[[]], x = 'seurat_clusters', y = 'intronRat') +
rotate_x_text(90) +
theme(text=element_text(size=20, face = 'bold'))
dev.off()
# save
saveRDS(allseur_integrated, paste0(pref,"_integrated_CLUSTERING_3.RDS"))
############### CLUSTERING_4 #################
#21*,MOL+OPC
setwd("/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/revision/01_MOL_OPC/CLUSTERING_4/")
toremove = c(21)
allseur_integrated = subset(allseur_integrated, subset = seurat_clusters %in% toremove, invert = T)
# Clustering
DefaultAssay(allseur_integrated) = 'integrated'
allseur_integrated = ScaleData(allseur_integrated, verbose = FALSE)
allseur_integrated = RunPCA(allseur_integrated, verbose = FALSE)
allseur_integrated = RunUMAP(allseur_integrated, dims = 1:20, reduction = 'pca')
# Basic Plots
pdf(paste0(pref, "_UMAP.pdf"))
DimPlot(allseur_integrated, group.by = 'Species', raster = T)
dev.off()
allseur_integrated = FindNeighbors(allseur_integrated, dims = 1:20, reduction = 'pca')
allseur_integrated = FindClusters(allseur_integrated, resolution = 1)
pdf(paste0(pref, "_CLUSTERS.pdf"))
DimPlot(allseur_integrated, label = T, raster = T) + NoLegend()
dev.off()
stackedbarplot(allseur_integrated[[]], groupx = 'seurat_clusters', groupfill = 'orig.ident', paste0(pref, '_STACK_SAMPLES'), wd = 20)
stackedbarplot(allseur_integrated[[]], groupx = 'seurat_clusters', groupfill = 'Species', paste0(pref, '_STACK_SPECIES'))
# Plot previously identified markers
DefaultAssay(allseur_integrated) = 'RNA'
allseur_integrated = NormalizeData(allseur_integrated)
pdf(paste0(pref, "INTEGRATED_DOTPLOT.pdf"), width = 20, height = 10)
DotPlot(allseur_integrated, features = marksToPlot) +
xlab('') +
ylab('') +
theme(axis.text.x=element_text(size=20, face = 'bold'),
axis.text.y=element_text(size=20, face = 'bold'),
legend.text = element_text(size=20, face = 'bold'),
legend.title = element_text(size=20, face = 'bold')) +
rotate_x_text(45)
dev.off()
pdf(paste0(pref, "INTEGRATED_DEPTH.pdf"))
ggboxplot(allseur_integrated[[]], x = 'seurat_clusters', y = 'nFeature_RNA', color = 'black') + NoLegend() +
rotate_x_text(90)
dev.off()
pdf(paste0(pref, "INTEGRATED_NUCLEAR_FRACTION.pdf"))
ggboxplot(allseur_integrated[[]], x = 'seurat_clusters', y = 'intronRat') +
rotate_x_text(90) +
theme(text=element_text(size=20, face = 'bold'))
dev.off()
# save
saveRDS(allseur_integrated, paste0(pref,"_integrated_CLUSTERING_4.RDS"))
######################### ANNOTATE #################
###Custer#,cell_type_annotation
#0,MOL
#1,MOL
#2,MOL
#3,MOL
#4,MOL
#5,MOL
#6,MOL
#7,MOL
#8,MOL
#9,MOL
#10,OPC
#11,MOL
#12,MOL
#13,MOL
#14,OPC
#15,MOL
#16,MOL
#17,OPC
#18,OPC
#19,MOL
#20,MOL
#21,COP
#22,OPC
#23,OPC
## annotate
(mapnames <-setNames(c("MOL","MOL","MOL","MOL","MOL","MOL","MOL","MOL","MOL",
"MOL","OPC","MOL","MOL","MOL","OPC","MOL","MOL","OPC","OPC","MOL",
"MOL","COP","OPC","OPC"),c(0:23)))
# Create the broadannot
allseur_integrated$MOL_OPC_annot = unname(mapnames[allseur_integrated[["seurat_clusters"]][,1]]) # to extract the annotations and not the cluster #s
table(allseur_integrated$MOL_OPC_annot)
# COP MOL OPC
# 447 263193 28394
setwd("/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/revision/01_MOL_OPC/ANNOTATED")
# QC PLOTS
pdf(paste0(pref, "_Clusters.pdf"))
DimPlot(allseur_integrated, group.by = 'MOL_OPC_annot', label = T, raster = T, label.size = 7) +
NoLegend() +
ggtitle('')
dev.off()
pdf(paste0(pref, "_Depth_Gene.pdf"))
ggboxplot(allseur_integrated[[]], x = 'MOL_OPC_annot', y = 'nFeature_RNA') +
rotate_x_text(90)
dev.off()
pdf(paste0(pref, "_Depth_UMI.pdf"))
ggboxplot(allseur_integrated[[]], x = 'MOL_OPC_annot', y = 'nCount_RNA', ylim = c(0,20000)) +
rotate_x_text(90) +
theme(text=element_text(size=20, face = 'bold'))
dev.off()
pdf(paste0(pref, "_NuclearFraction.pdf"))
ggboxplot(allseur_integrated[[]], x = 'MOL_OPC_annot', y = 'intronRat') +
rotate_x_text(90) +
theme(text=element_text(size=20, face = 'bold'))
dev.off()
# MARKER GENE PLOTS
DefaultAssay(allseur_integrated) = 'RNA'
seurM_corr = NormalizeData(allseur_integrated)
pdf(paste0(pref, "_MarkersDotPlot.pdf"), width = 20, height = 10)
DotPlot(allseur_integrated, group.by = "MOL_OPC_annot", features = marksToPlot) +
xlab('') +
ylab('') +
theme(axis.text.x=element_text(size=20, face = 'bold'),
axis.text.y=element_text(size=20, face = 'bold'),
legend.text = element_text(size=20, face = 'bold'),
legend.title = element_text(size=20, face = 'bold')) +
rotate_x_text(45)
dev.off()
stackedbarplot(allseur_integrated[[]], groupx = 'MOL_OPC_annot', groupfill = 'orig.ident', paste0(pref, '_STACK_SAMPLES'), wd = 20)
stackedbarplot(allseur_integrated[[]], groupx = 'MOL_OPC_annot', groupfill = 'Species', paste0(pref, '_STACK_SPECIES'))
# save
saveRDS(allseur_integrated, paste0(pref,"_integrated_ANNOTATED.RDS"))
### put these annots to the original seurat obj
b = readRDS("/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/seu_objs/merged_clean_AllCellTypes_AllTissues_seu.RDS")
# Match cell names between objects
common_cells <- intersect(rownames([email hidden]), rownames([email hidden]))
# Update annotations for those cells
b$detailed_annot = b$newannot_2
b$detailed_annot[common_cells] <- allseur_integrated$MOL_OPC_annot[common_cells]
b$is_mol = ifelse(rownames([email hidden]) %in% common_cells, "yes", "no")
# Identify cells in b that are not in the integrated object
b_keep <- subset(
b,
subset = !(newannot_2 %in% c("MOL", "OPC", "COP")) | is_mol == "yes")
)
# check
Idents(b_keep) = b_keep$detailed_annot
DotPlot(b_keep, features = marksToPlot) + rotate_x_text(45)
# save
saveRDS(b_keep,"/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/seu_objs/merged_clean_AllCellTypes_AllTissues_seu.RDS")
############################################
####### Astrocyte
################################################
### subset the genes and the MOL+OPC from each Species
# subset cells and metadata
# Subset Seurat object to only include 'Microglia' cells and exclude the specified 'id' values bcs they have <10 microglial cells
seurM = subset(b_keep, subset = newannot_2 %in% c("Astrocyte")) # already orthologs only
# Extract the count matrix from the Seurat object (default is "RNA" assay)
mat = seurM@assays$RNA$counts
new_mat <- mat[rownames(mat) %in% ortho_genes,]
# generate new Seurat object
glia_merged <- CreateSeuratObject(count = new_mat, meta.data = subset_b[[]])
####
## SET VARIABLES
####
marksToPlot <- c('NRGN', 'SYT1', 'SNAP25', 'GAD1', 'GAD2', 'DRD1', 'DRD2', 'FOXP2', 'TAC1', 'PENK', 'SST', 'NPY', 'MOG', 'PLP1', 'MBP', 'MOBP', 'SLC1A2', 'SLC1A3', 'AQP4', 'CSF1R', 'ARHGAP15', 'PLD1','CASZ1', 'PTPRZ1', 'PCDH15', 'SOX6', 'FYN', 'BCAS1', 'ENPP6', 'GPR17', 'FLT1', 'DUSP1', 'COBLL1', 'CFTR', 'CHAT', 'ADARB2', 'LHX6', 'MEIS2', 'TAC3', 'VIP', 'TH', 'CCK', 'TTC34', 'CROCC', 'FHAD1', 'SOX4', 'PDGFRB', 'PDGFRA', 'SATB2', 'SLC17A7', 'EYA2', 'PDGFD', 'PTHLH', 'PVALB', 'SPARCL1', 'RSPO2', 'RMST', 'LMO3', 'TSHZ2', "CALB1", "CALB2", "NOS1", "TSHZ1", "OPRM1", "CHST9","GRM8", "PPP1R1B", 'GRIK3', 'CXCL14')
pref = 'Astrocyte_AllSpecies_AllTissues_human_pr_coding_orthologs'
####
## INTEGRATE ACROSS SPECIES
####
#### use old seurat version
# Split data
seurM = glia_merged
seurM[["RNA"]] = as(object = seurM[["RNA"]], Class = "Assay")
seurM$id = paste0(seurM$orig.ident, '_', seurM$Tissue)
seurML = SplitObject(seurM, split.by = "id")
# normalize and identify variable features for each dataset independently
seurML = lapply(X = seurML, FUN = function(x) {
x = NormalizeData(x)
x = FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000)
})
# select features that are repeatedly variable across datasets for integration run PCA on each
# dataset using these features
features = SelectIntegrationFeatures(object.list = seurML)
print(paste0('Number of genes to use for integration: ', length(features)))
seurML = lapply(X = seurML, FUN = function(x) {
x = ScaleData(x, features = features, verbose = FALSE)
x = RunPCA(x, features = features, verbose = FALSE, npcs = 30)
})
anchors = FindIntegrationAnchors(object.list = seurML, anchor.features = features, normalization.method = "LogNormalize", reduction = "rpca")
# save memory prior integration
gc()
# this command creates an 'integrated' data assay
#(kweg = floor(min(table(seurM$orig.ident))/10)*10) #90
options(future.globals.maxSize = 16000 * 1024^2)
allseur_integrated = IntegrateData(anchorset = anchors, k.weight = 30, normalization.method = 'LogNormalize')
saveRDS(allseur_integrated, '/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/seu_objs/Integrated_rpca_Astrocyte_AllSpecies_ALLTissues_human_pr_coding_orthologs.RDS')
# Clustering
allseur_integrated = ScaleData(allseur_integrated, verbose = FALSE)
allseur_integrated = RunPCA(allseur_integrated, verbose = FALSE)
allseur_integrated = RunUMAP(allseur_integrated, dims = 1:20, reduction = 'pca')
# Basic Plots
setwd("/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/revision/01_ASTROCYTE/CLUSTERING_1")
pdf(paste0(pref, "_INTEGRATED_UMAP.pdf"))
DimPlot(allseur_integrated, group.by = 'Species', raster = T)
dev.off()
DefaultAssay(allseur_integrated) <- "integrated"
allseur_integrated = FindNeighbors(allseur_integrated, dims = 1:20, reduction = 'pca')
allseur_integrated = FindClusters(allseur_integrated, resolution = 1)
pdf(paste0(pref, "_INTEGRATED_CLUSTERS.pdf"))
DimPlot(allseur_integrated, label = T, raster = T) + NoLegend()
dev.off()
pdf(paste0(pref, "_INTEGRATED_PRIMATE_ANNOT.pdf"))
DimPlot(allseur_integrated, label = T, raster = T, group.by = 'newannot') + NoLegend()
dev.off()
stackedbarplot(allseur_integrated[[]], groupx = 'seurat_clusters', groupfill = 'orig.ident', paste0(pref, '_INTEGRATED_STACK_SAMPLES'), wd = 20)
stackedbarplot(allseur_integrated[[]], groupx = 'seurat_clusters', groupfill = 'Species', paste0(pref, '_INTEGRATED_STACK_SPECIES'))
# Plot previously identified markers
DefaultAssay(allseur_integrated) = 'RNA'
allseur_integrated = NormalizeData(allseur_integrated)
pdf(paste0(pref, "_INTEGRATED_DOTPLOT.pdf"), width = 20, height = 10)
DotPlot(allseur_integrated, features = marksToPlot) +
xlab('') +
ylab('') +
theme(axis.text.x=element_text(size=20, face = 'bold'),
axis.text.y=element_text(size=20, face = 'bold'),
legend.text = element_text(size=20, face = 'bold'),
legend.title = element_text(size=20, face = 'bold')) +
rotate_x_text(45)
dev.off()
pdf(paste0(pref, "_INTEGRATED_DEPTH.pdf"))
ggboxplot(allseur_integrated[[]], x = 'seurat_clusters', y = 'nFeature_RNA', color = 'black') + NoLegend() +
rotate_x_text(90)
dev.off()
pdf(paste0(pref, "_INTEGRATED_NUCLEAR_FRACTION.pdf"))
ggboxplot(allseur_integrated[[]], x = 'seurat_clusters', y = 'intronRat') +
rotate_x_text(90) +
theme(text=element_text(size=20, face = 'bold'))
dev.off()
saveRDS(allseur_integrated, paste0('/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/revision/01_MOL_OPC/CLUSTERING_1/', pref, '_integrated_CLUSTERING1.RDS'))
################# CLUSTERING_2 ##########################
##Cluster#,CellType
#10*,MOL+Ast
#12*,MOL+Ast
#13*,Neu+Ast
#15*,MOL+Ast
#21*,MOL+Ast
setwd("/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/revision/01_ASTROCYTE/CLUSTERING_2/")
toremove = c(10,12,13,15,21)
allseur_integrated = subset(allseur_integrated, subset = seurat_clusters %in% toremove, invert = T)
# Clustering
DefaultAssay(allseur_integrated) = 'integrated'
allseur_integrated = ScaleData(allseur_integrated, verbose = FALSE)
allseur_integrated = RunPCA(allseur_integrated, verbose = FALSE)
allseur_integrated = RunUMAP(allseur_integrated, dims = 1:20, reduction = 'pca')
# Basic Plots
pdf(paste0(pref, "_UMAP.pdf"))
DimPlot(allseur_integrated, group.by = 'Species', raster = T)
dev.off()
allseur_integrated = FindNeighbors(allseur_integrated, dims = 1:20, reduction = 'pca')
allseur_integrated = FindClusters(allseur_integrated, resolution = 1)
pdf(paste0(pref, "_CLUSTERS.pdf"))
DimPlot(allseur_integrated, label = T, raster = T) + NoLegend()
dev.off()
stackedbarplot(allseur_integrated[[]], groupx = 'seurat_clusters', groupfill = 'orig.ident', paste0(pref, '_STACK_SAMPLES'), wd = 20)
stackedbarplot(allseur_integrated[[]], groupx = 'seurat_clusters', groupfill = 'Species', paste0(pref, '_STACK_SPECIES'))
# Plot previously identified markers
DefaultAssay(allseur_integrated) = 'RNA'
allseur_integrated = NormalizeData(allseur_integrated)
pdf(paste0(pref, "INTEGRATED_DOTPLOT.pdf"), width = 20, height = 10)
DotPlot(allseur_integrated, features = marksToPlot) +
xlab('') +
ylab('') +
theme(axis.text.x=element_text(size=20, face = 'bold'),
axis.text.y=element_text(size=20, face = 'bold'),
legend.text = element_text(size=20, face = 'bold'),
legend.title = element_text(size=20, face = 'bold')) +
rotate_x_text(45)
dev.off()
pdf(paste0(pref, "INTEGRATED_DEPTH.pdf"))
ggboxplot(allseur_integrated[[]], x = 'seurat_clusters', y = 'nFeature_RNA', color = 'black') + NoLegend() +
rotate_x_text(90)
dev.off()
pdf(paste0(pref, "INTEGRATED_NUCLEAR_FRACTION.pdf"))
ggboxplot(allseur_integrated[[]], x = 'seurat_clusters', y = 'intronRat') +
rotate_x_text(90) +
theme(text=element_text(size=20, face = 'bold'))
dev.off()
# save
saveRDS(allseur_integrated, paste0(pref,"_integrated_CLUSTERING_2.RDS"))
################# CLUSTERING_2 ##########################
##Cluster#,CellType
#14*,Neu+Ast
#15*,MOL+Ast
#17*,Endo+Ast
setwd("/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/revision/01_ASTROCYTE/CLUSTERING_3/")
toremove = c(14,15,17)
allseur_integrated = subset(allseur_integrated, subset = seurat_clusters %in% toremove, invert = T)
# Clustering
DefaultAssay(allseur_integrated) = 'integrated'
allseur_integrated = ScaleData(allseur_integrated, verbose = FALSE)
allseur_integrated = RunPCA(allseur_integrated, verbose = FALSE)
allseur_integrated = RunUMAP(allseur_integrated, dims = 1:20, reduction = 'pca')
# Basic Plots
pdf(paste0(pref, "_UMAP.pdf"))
DimPlot(allseur_integrated, group.by = 'Species', raster = T)
dev.off()
allseur_integrated = FindNeighbors(allseur_integrated, dims = 1:20, reduction = 'pca')
allseur_integrated = FindClusters(allseur_integrated, resolution = 1)
pdf(paste0(pref, "_CLUSTERS.pdf"))
DimPlot(allseur_integrated, label = T, raster = T) + NoLegend()
dev.off()
stackedbarplot(allseur_integrated[[]], groupx = 'seurat_clusters', groupfill = 'orig.ident', paste0(pref, '_STACK_SAMPLES'), wd = 20)
stackedbarplot(allseur_integrated[[]], groupx = 'seurat_clusters', groupfill = 'Species', paste0(pref, '_STACK_SPECIES'))
# Plot previously identified markers
DefaultAssay(allseur_integrated) = 'RNA'
allseur_integrated = NormalizeData(allseur_integrated)
pdf(paste0(pref, "INTEGRATED_DOTPLOT.pdf"), width = 20, height = 10)
DotPlot(allseur_integrated, features = marksToPlot) +
xlab('') +
ylab('') +
theme(axis.text.x=element_text(size=20, face = 'bold'),
axis.text.y=element_text(size=20, face = 'bold'),
legend.text = element_text(size=20, face = 'bold'),
legend.title = element_text(size=20, face = 'bold')) +
rotate_x_text(45)
dev.off()
pdf(paste0(pref, "INTEGRATED_DEPTH.pdf"))
ggboxplot(allseur_integrated[[]], x = 'seurat_clusters', y = 'nFeature_RNA', color = 'black') + NoLegend() +
rotate_x_text(90)
dev.off()
pdf(paste0(pref, "INTEGRATED_NUCLEAR_FRACTION.pdf"))
ggboxplot(allseur_integrated[[]], x = 'seurat_clusters', y = 'intronRat') +
rotate_x_text(90) +
theme(text=element_text(size=20, face = 'bold'))
dev.off()
# save
saveRDS(allseur_integrated, paste0(pref,"_integrated_CLUSTERING_3.RDS"))
############ ANNOTATE #############
(mapnames <-setNames(rep("Astrocyte", 22),c(0:21)))
# Create the broadannot
allseur_integrated$Ast_annot = unname(mapnames[allseur_integrated[["seurat_clusters"]][,1]]) # to extract the annotations and not the cluster #s
table(allseur_integrated$Ast_annot)
#Astrocyte
# 64660
setwd("/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/revision/01_ASTROCYTE/ANNOTATED")
# QC PLOTS
pdf(paste0(pref, "_Clusters.pdf"))
DimPlot(allseur_integrated, group.by = 'Ast_annot', label = T, raster = T, label.size = 7) +
NoLegend() +
ggtitle('')
dev.off()
pdf(paste0(pref, "_Depth_Gene.pdf"))
ggboxplot(allseur_integrated[[]], x = 'Ast_annot', y = 'nFeature_RNA') +
rotate_x_text(90)
dev.off()
pdf(paste0(pref, "_Depth_UMI.pdf"))
ggboxplot(allseur_integrated[[]], x = 'Ast_annot', y = 'nCount_RNA', ylim = c(0,20000)) +
rotate_x_text(90) +
theme(text=element_text(size=20, face = 'bold'))
dev.off()
pdf(paste0(pref, "_NuclearFraction.pdf"))
ggboxplot(allseur_integrated[[]], x = 'Ast_annot', y = 'intronRat') +
rotate_x_text(90) +
theme(text=element_text(size=20, face = 'bold'))
dev.off()
# MARKER GENE PLOTS
DefaultAssay(allseur_integrated) = 'RNA'
seurM_corr = NormalizeData(allseur_integrated)
pdf(paste0(pref, "_MarkersDotPlot.pdf"), width = 20, height = 10)
DotPlot(allseur_integrated, group.by = "Ast_annot", features = marksToPlot) +
xlab('') +
ylab('') +
theme(axis.text.x=element_text(size=20, face = 'bold'),
axis.text.y=element_text(size=20, face = 'bold'),
legend.text = element_text(size=20, face = 'bold'),
legend.title = element_text(size=20, face = 'bold')) +
rotate_x_text(45)
dev.off()
stackedbarplot(allseur_integrated[[]], groupx = 'Ast_annot', groupfill = 'orig.ident', paste0(pref, '_STACK_SAMPLES'), wd = 20)
stackedbarplot(allseur_integrated[[]], groupx = 'Ast_annot', groupfill = 'Species', paste0(pref, '_STACK_SPECIES'))
# save
saveRDS(allseur_integrated, paste0(pref,"_integrated_ANNOTATED.RDS"))
## add these annotations to the original seu obj
# extract the newest annotations
# Match cell names between objects
b_keep$is_ast = ifelse(rownames([email hidden]) %in% rownames([email hidden]), "yes", "no")
# Identify cells in b that are not in the integrated object
b_keep_2 = subset(b_keep, subset = newannot_2 != "Astrocyte" | is_ast == "yes")
# check
Idents(b_keep_2) = b_keep_2$detailed_annot
DotPlot(b_keep_2, features = marksToPlot) + rotate_x_text(45)
# save
saveRDS(b_keep_2,"/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/seu_objs/merged_clean_AllCellTypes_AllTissues_seu.RDS")
####################### POST GLIA CLEANUP ###########################
file_outdir = "/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/seu_objs/"
species_list <- c("human", "chimp", "macaque", "marmoset", "mouse", "bat", "ferret")
primates_list <- c("human", "chimp", "macaque", "marmoset")
#### new obj AFTER GLIA CLEANUP
b = readRDS("/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/seu_objs/merged_clean_AllCellTypes_AllTissues_seu.RDS")
b$CellType = b$detailed_annot
b$CellType = gsub("COP", "OPC", b$CellType)
# put the interneuron subtype annotations to this seu obj
interneurons_seu = readRDS("/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/seu_objs/Interneurons_AllSpecies_AllTissues_ANNOTATED.RDS")
## add these annotations to the original seu obj
# extract the newest annotations
# Match cell names between objects
b$is_interneu = ifelse(rownames([email hidden]) %in% rownames([email hidden]), "yes", "no")
# Identify cells in b that are not in the integrated object
b_keep = subset(b, subset = detailed_annot != "Non_SPN" | is_interneu == "yes")
# order the rownames of smaller seu obj according to the bigger one
ordered_meta_interneurons_seu = [email hidden][match(rownames([email hidden]), rownames([email hidden])),]
b_keep$is_interneu = ordered_meta_interneurons_seu$newannot
b_keep$a = ifelse(is.na(b_keep$is_interneu), as.character(b_keep$detailed_annot), as.character(b_keep$is_interneu))
# check
table(b_keep$detailed_annot, b_keep$a)
# re-assign
b_keep$broadannot_2 = b_keep$detailed_annot
b_keep$detailed_annot = b_keep$a
# orgnize metadata
b_keep$is_micro = NULL
b_keep$is_mol = NULL
b_keep$keep_cell = NULL
b_keep$is_ast = NULL
b_keep$is_interneu = NULL
b_keep$a = NULL
b_keep$broad_annot = NULL
b_keep$tissue = NULL
b_keep$id = paste0(b_keep$orig.ident, "_", b_keep$Tissue)
### remove human caudate sample
b_keep_2 = subset(b_keep, subset = id %in% "Sample_242999_Caudate", invert = T)
#### find the sex and age for all
orig_meta <- [email hidden] # safe copy
### add info to the metadata
a = orig_meta
a[grep("v324", a$orig.ident),]$Sex = "Male"
a[grep("v321", a$orig.ident),]$Sex = "Female"
a$Sex = sub("female", "Female", a$Sex)
a$Sex = sub("male", "Male", a$Sex)
a[grep("Bat", a$Species),]$Sex = "Male"
a[grep("Bat", a$Species),]$Age = "3y"
a[grep("marm027", a$orig.ident),]$Sex = "Male"
a[grep("marm028", a$orig.ident),]$Sex = "Female"
a[grep("marm029", a$orig.ident),]$Sex = "Male"
a[grep("marm027", a$orig.ident),]$Age = "2y4m"
a[grep("marm028", a$orig.ident),]$Age = "3y2m"
a[grep("marm029", a$orig.ident),]$Age = "2y6m"
a[grep("v324", a$orig.ident),]$Age = "2035d"
a[grep("v321", a$orig.ident),]$Age = "2051d"
#a[grep("SRR13808459", a$orig.ident),]$Sex = "Female"
a[grep("SRR13808462", a$orig.ident),]$Sex = "Female"
a[grep("SRR13808463", a$orig.ident),]$Sex = "Female"
#a[grep("SRR13808466", a$orig.ident),]$Sex = "Female"
#a[grep("SRR13808467", a$orig.ident),]$Sex = "Female"
a[grep("SRR11921037", a$orig.ident),]$Sex = "Female"
a[grep("SRR11921038", a$orig.ident),]$Sex = "Female"
a[grep("SRR11921037", a$orig.ident),]$Age = "42d"
a[grep("SRR11921038", a$orig.ident),]$Age = "42d"
patterns <- c(paste0("SRR1192100", 5:9), paste0("SRR119210", 10:12))
pattern_all <- paste(patterns, collapse = "|")
idx <- grep(pattern_all, a$orig.ident)
a[idx, ]$Age <- "70d"
a[idx, ]$Sex = "Male"
a$sex = NULL
a$age = NULL
a$cellbarc = str_split_i(rownames(a), "-", 1)
a$cellID = paste0(a$cellbarc, "_", a$id)
a$Sex = sub("FeMale", "Female", a$Sex)
### add humanized ages
# read species traits taken from https://genomics.senescence.info/species/entry.php?species=Mus_musculus, https://genomics.senescence.info/species/entry.php?species=Mustela_nigripes, https://genomics.senescence.info/species/entry.php?species=Phyllostomus_hastatus, https://animaldiversity.org/accounts/Phyllostomus_hastatus/, https://genomics.senescence.info/species/entry.php?species=Phyllostomus_discolor
outdir = "/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/revision/"
species_traits = read.csv(paste0(outdir, "primate_lifeTraits.csv"))
sample_metadata = read.csv(paste0(outdir, "comparative_striatum_sample_metadata.csv"), sep = "\t")
sample_metadata$id = as.character(paste0(sample_metadata$Barcode.ID, "_", sample_metadata$Brain.Area))
a$id = as.character(a$id)
# add the ages in years to the single cell-level metadata
meta_df <- merge(a, sample_metadata, by = "id", all.x = TRUE)
meta_df$Age <- meta_df$Age.x
meta_df$Sex <- meta_df$Sex.x
meta_df$Species <- meta_df$Species.x
meta_df <- meta_df[, !grepl("\\.x$|\\.y$", names(meta_df))]
meta_df$Brain.Area = NULL
# Add orig.ident only if rownames are not already "cellbarc-orig.ident"
rownames(meta_df) <- ifelse(
grepl("[-_]", meta_df$cellbarc), # check for presence of "-" or "_"
meta_df$cellbarc, # if present, keep as is
paste0(meta_df$cellbarc, "-", meta_df$orig.ident) # else, append orig.ident
)
# restore original cell order (this prevents scrambling)
meta_df <- meta_df[rownames(a), ]
# Check if same set but different order
setequal(rownames(meta_df), rownames(a))
humanize_all_ages <- function(meta_df, species_traits) {
# Initialize vector for humanized ages (same length and order)
humanized_ages <- numeric(nrow(meta_df))
# Get unique species in the metadata
species_list <- unique(meta_df$Species)
for (species in species_list) {
# Convert ages to numerical
species_ages <- meta_df$Age_in_years[meta_df$Species == species]
species_ages_num <- as.numeric(species_ages)
if (species %in% c("Human")) {
# For humans, humanized age = original age
humanized_ages[meta_df$Species == species] <- species_ages_num
} else {
# Check if species exists in lifetraits
if (species %in% colnames(species_traits)) {
# Build linear model: species ~ Human (lifetraits)
model <- lm(species_traits[[species]] ~ species_traits$Human)
# Calculate humanized ages using the model coefficients
humanized <- (species_ages_num - model$coefficients[1]) / model$coefficients[2]
# Assign back to correct rows in humanized_ages vector
humanized_ages[meta_df$Species == species] <- humanized
} else {
warning(paste("Species", species, "not found in lifetraits. Assigning NA"))
humanized_ages[meta_df$Species == species] <- NA
}
}
}
return(humanized_ages)
}
meta_df$Humanized_age = humanize_all_ages(meta_df, species_traits)
# update the metadata of the seu obj
# Join by cell ID (ensure cell names match exactly)
[email hidden] = meta_df
# check
marksToPlot <- c('NRGN', 'SYT1', 'SNAP25', 'GAD1', 'GAD2', 'DRD1', 'DRD2', 'FOXP2', 'TAC1', 'PENK', 'SST', 'NPY', 'MOG', 'PLP1', 'MBP', 'MOBP', 'SLC1A2', 'SLC1A3', 'APOE', 'GPC5', 'GLI3', 'AQP4', 'CSF1R', 'ARHGAP15', 'PLDL1', 'CASZ1', 'PTPRZ1', 'PCDH15', 'SOX6', 'FYN', 'BCAS1', 'ENPP6', 'GPR17', 'FLT1', 'DUSP1', 'COBLL1', 'CFTR', 'CHAT', 'ADARB2', 'LHX6', 'MEIS2', 'TAC3', 'VIP', 'TH', 'CCK', 'TTC34', 'CROCC', 'FHAD1', 'SOX4', 'PDGFRB', 'PDGFRA', 'SATB2', 'SLC17A7', 'EYA2', 'PDGFD', 'PTHLH', 'PVALB', 'SPARCL1', 'RSPO2', 'RMST', 'LMO3', 'TSHZ2', "CALB1", "CALB2", "NOS1", "TSHZ1", "OPRM1", "CHST9","GRM8", "PPP1R1B", 'GRIK3', 'CXCL14')
# Plot previously identified markers
DefaultAssay(b_keep_2) = 'RNA'
b_keep_2 = NormalizeData(b_keep_2)
pdf(paste0("merged_clean_AllCellTypes_AllTissues_seu_detailedAnnot_DOTPLOT.pdf"), width = 20, height = 10)
DotPlot(b_keep_2, features = marksToPlot, group.by = "detailed_annot") +
xlab('') +
ylab('') +
theme(axis.text.x=element_text(size=20, face = 'bold'),
axis.text.y=element_text(size=20, face = 'bold'),
legend.text = element_text(size=20, face = 'bold'),
legend.title = element_text(size=20, face = 'bold')) +
rotate_x_text(45)
dev.off()
# save
saveRDS(b_keep_2,"/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/seu_objs/merged_clean_AllCellTypes_AllTissues_seu.RDS")
##### generate pseudobulk counts
file_outdir = "/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/seu_objs/"
species_list <- c("human", "chimp", "macaque", "marmoset", "mouse", "bat", "ferret")
primates_list <- c("human", "chimp", "macaque", "marmoset")
#### new obj AFTER GLIA CLEANUP
b = readRDS("/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/seu_objs/merged_clean_AllCellTypes_AllTissues_seu.RDS")
sub_obj_new_list = SplitObject(b, split.by = "Species")
names(sub_obj_new_list) = tolower(names(sub_obj_new_list))
meta_df = [email hidden]
### run function to generate pseudobulk count matrices for each species and celltype. Keep cellTypes which are present in at least 3 of the samples for a given Species-Tissue as well as genes with at least 1 copy of at least 3 of the samples for a given Species-Tissue. Generate the pseudobulk count matrix for all species-cellTypes except ferret which has only 2 samples using utilities function: pseudobulk_species = function(seurObj, ctype, features = c())
pseudobulk_species
function(seurObj, ctype, n_copy, sample_number){
require(plyr)
require(dplyr)
require(tidyverse)
require(tidyr)
require(Seurat)
require(ggpubr)
require(reshape2)
require(data.table)
require(rio)
require(scran)
require(scater)
require(SingleCellExperiment)
require(edgeR)
require(DESeq2)
if(!('Sample' %in% colnames(seurObj[[]]))){stop('Please put your donors into Sample column')}
if(!('CellType' %in% colnames(seurObj[[]]))){stop('Please put your cell types into CellType column')}
# Load RNA
meta = [email hidden]
metakeep = c('CellType', 'Sample', 'Species')
meta = meta[,metakeep]
# Subset the given cell type
subSeur = subset(seurObj, subset = CellType %in% ctype)
# Create SCE assay
DefaultAssay(subSeur) = 'RNA'
subSCE = as.SingleCellExperiment(subSeur)
# Pseudobulk SCE assay
subGroups = colData(subSCE)[, c("Species", "Sample", "CellType")]
subPseudoSCE = sumCountsAcrossCells(subSCE, subGroups)
subGroups = colData(subPseudoSCE)[, c("Species", "Sample", "CellType")]
subGroups$Sample = factor(subGroups$Sample)
subAggMat = subPseudoSCE@assays@data$sum
colnames(subAggMat) = subGroups$Sample
# binarize the matrix
binary_subAggMat = as.matrix((subAggMat >= n_copy) + 0) # genes with at least "n_copy" copy(ies) will be 1
exp_list = list()
# Keep genes expressed in at least "sample_number" samples of any Tissue
for(tissue in unique(subSeur$Tissue)){
tissue_columns = grepl(tissue, colnames(binary_subAggMat), ignore.case = TRUE)
exp_list[[tissue]] = which(rowSums(binary_subAggMat[, tissue_columns]) >= sample_number) %>% names
}
expgns = Reduce(intersect, exp_list)
cat("Length of genes", unique(subSeur$Species), ctype, "=", length(expgns), "\n")
subAggMat = subAggMat[expgns,]
return(subAggMat)
}
pb_count_list = list()
for (sp in species_list) {
sub_obj_new = sub_obj_new_list[[sp]]
celltypes = unique(sub_obj_new$CellType)
for (ctype in celltypes) {
if (sp != "ferret") {
pb_count_list[[sp]][[ctype]] <- pseudobulk_species(
seurObj = sub_obj_new,
ctype = ctype,
n_copy = 1,
sample_number = 3
)
} else {
pb_count_list[["ferret"]][[ctype]] <- NULL
}
}
}
#save
saveRDS(pb_count_list, file = "/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/seu_objs/pseudobulk_count_matrices_AllSpecies_AllCellTypes_broadannot_PostgliaCleanup.RDS")
### Genes were filtered to prep the pseudobulk counts (> 1 copy in >= 3 samples of that Species-Tissue) To continue with downstream analysis, get only the common genes across primates
common_genes_per_celltype = list()
primate_pb_count_list = pb_count_list[primates_list]
for (ctype in names(primate_pb_count_list$human)) {
# Extract the gene vectors for this cell type across all species
genes_across_species <- lapply(primate_pb_count_list, function(species_count) {
rownames(species_count[[ctype]])
})
# Remove any NULLs (if some species lack this cell type)
genes_across_species <- genes_across_species[!sapply(genes_across_species, is.null)]
# Take intersection across species
common_genes_per_celltype[[ctype]] <- Reduce(intersect, genes_across_species)
}
# Inspect results
str(common_genes_per_celltype)
# save
saveRDS(common_genes_per_celltype, file = "/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/seu_objs/pseudobulk_common_genes_withinPrimates_per_celltype_broadannot_postgliaCleanup.RDS")
common_genes_per_celltype = readRDS("/project/Neuroinformatics_Core/Konopka_lab/s422071/workdir_pr/s422071/projects/comparative_striatum/seu_objs/pseudobulk_common_genes_withinPrimates_per_celltype_broadannot_postgliaCleanup.RDS")
##### subset each count matrix with these genes
sub_seu_pb_count_list = list()
pb_meta_list = list()
meta_df_list = list()
## Generate singleCellExperiment object
for (sp in primates_list){
for (t in unique(sub_obj_new_list[[sp]]$Tissue)){
# change COP to OPC
sub_obj_new_list[[sp]]$CellType <- gsub("COP", "OPC", sub_obj_new_list[[sp]]$CellType)
for (ctype in unique(sub_obj_new_list[[sp]]$CellType)){
# get the seu obj
seu = sub_obj_new_list[[sp]]
# subset for the given cell type and tissue
sub_seu = subset(seu, subset = CellType %in% ctype & Tissue %in% t)
# subset pseudobulk count mat with common genes
sub_seu_pb_count = primate_pb_count_list[[sp]][[ctype]][, grepl(t, colnames(primate_pb_count_list[[sp]][[ctype]]))]
# subset the pseudocount matrix with common genes for each celltype
sub_seu_pb_count_list[[t]][[ctype]][[sp]] = sub_seu_pb_count[common_genes_per_celltype[[ctype]],]
# generate pseudobulk metadata
sub_seu_meta <- [email hidden] %>%
as.data.frame() %>%
dplyr::select(Species, Sample, Tissue, Sex, Age, Age_in_years, Humanized_age, CellType, id, nCount_RNA)
## calculate the sum of the total UMI at the log scale for each sample
tibble_meta <- as_tibble(sub_seu_meta) %>%
dplyr::group_by(Sample) %>%
dplyr::summarise(
log10_sum_ncount_RNA = log10(sum(nCount_RNA, na.rm = TRUE)),
n = dplyr::n(),
.groups = "drop"
)
tibble_meta = as.data.frame(tibble_meta)
# aggregate based on sample
a <- sub_seu_meta %>% distinct(Sample, .keep_all = TRUE)
rownames(a) = a$Sample
# merge two dataframes
meta_df_sample = merge(a,tibble_meta ,by = "Sample", all.x = TRUE)
rownames(meta_df_sample) = meta_df_sample$Sample
# Check matching of matrix columns and metadata rows
all(colnames(sub_seu_pb_count) == rownames(meta_df_sample))
# if it says FALSE due to order not being the same. Order:
# order the metadata rownames based on the col names of the count matrix
#rownames(meta_df_sample) <- rownames(meta_df_sample)[order(match(rownames(meta_df_sample), colnames(sub_seu_pb_count)))]
# Check matching of matrix columns and metadata rows
#all(colnames(sub_seu_pb_count) == rownames(meta_df_sample))
# save in a list
pb_meta_list[[t]][[ctype]][[sp]] = meta_df_sample
meta_df_list[[t]][[ctype]][[sp]] = [email hidden]
#rownames(meta_df_list[[t]][[ctype]][[sp]]) <- meta_df_list[[t]][[ctype]][[sp]]$Sample
# get sub_seu_pb_count_list for the next step
}}}
saveRDS(pb_meta_list, file = paste0(file_outdir,"primates_pb_meta_list_PostgliaCleanup.RDS"))
saveRDS(meta_df_list, file = paste0(file_outdir,"primates_meta_df_list_PostgliaCleanup.RDS"))
saveRDS(sub_seu_pb_count_list, file = paste0(file_outdir,"primates_sub_seu_pb_count_list_PostgliaCleanup.RDS"))
### generate a list of metadata and pseudobulk counts for the same tissue and celltype
merged_pb_meta <- list()
merged_sub_seu_pb_count <- list()
merged_meta_df <- list()
for (t in names(pb_meta_list)){
for (ctype in names(pb_meta_list[[t]])){
# Combine all species' metadata rows
merged_pb_meta[[t]][[ctype]] <- do.call(rbind, pb_meta_list[[t]][[ctype]])
merged_sub_seu_pb_count[[t]][[ctype]] <- do.call(cbind, sub_seu_pb_count_list[[t]][[ctype]])
merged_meta_df[[t]][[ctype]] <- do.call(rbind, meta_df_list[[t]][[ctype]])
}}
# save
saveRDS(merged_pb_meta, file = paste0(file_outdir,"primates_merged_pb_meta_PostgliaCleanup.RDS"))
saveRDS(merged_sub_seu_pb_count, file = paste0(file_outdir,"primates_merged_sub_seu_pb_count_PostgliaCleanup.RDS"))
saveRDS(merged_meta_df, file = paste0(file_outdir,"primates_merged_meta_df_PostgliaCleanup.RDS"))
############################################################
sessionInfo()
R version 4.2.3 (2023-03-15)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux Server 7.9 (Maipo)
Matrix products: default
BLAS/LAPACK: /cm/shared/apps/rstudio-desktop/2022.12.0/lib/libopenblasp-r0.3.21.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] fastDummies_1.7.3 flashClust_1.01-2
[3] WGCNA_1.73 fastcluster_1.3.0
[5] dynamicTreeCut_1.63-1 curl_5.2.2
[7] Matrix.utils_0.9.8 variancePartition_1.28.9
[9] BiocParallel_1.32.5 edgeR_3.40.2
[11] limma_3.54.0 ggrepel_0.9.3
[13] DESeq2_1.38.3 harmony_1.1.0
[15] Rcpp_1.0.10 data.table_1.14.8
[17] rio_1.0.1 reshape2_1.4.4
[19] ggpubr_0.6.0 lubridate_1.9.3
[21] forcats_1.0.0 stringr_1.5.0
[23] purrr_1.0.1 readr_2.1.4
[25] tidyr_1.3.0 tibble_3.2.1
[27] tidyverse_2.0.0 plyr_1.8.9
[29] ggplot2_3.4.4 Matrix_1.6-5
[31] dplyr_1.1.4 DropletQC_0.0.0.9000
[33] DropletUtils_1.18.1 SingleCellExperiment_1.20.0
[35] SummarizedExperiment_1.28.0 Biobase_2.58.0
[37] GenomicRanges_1.50.0 GenomeInfoDb_1.34.9
[39] IRanges_2.32.0 S4Vectors_0.36.0
[41] BiocGenerics_0.44.0 MatrixGenerics_1.10.0
[43] matrixStats_0.63.0 rhdf5_2.42.1
[45] BPCells_0.3.1 Seurat_5.3.0
[47] SeuratObject_5.1.0 sp_1.6-0
[49] patchwork_1.3.0.9000
loaded via a namespace (and not attached):
[1] scattermore_1.2 R.methodsS3_1.8.2
[3] knitr_1.48 bit64_4.0.5
[5] irlba_2.3.5.1 DelayedArray_0.24.0
[7] R.utils_2.12.2 rpart_4.1.19
[9] KEGGREST_1.38.0 RCurl_1.98-1.12
[11] doParallel_1.0.17 generics_0.1.3
[13] preprocessCore_1.60.2 RhpcBLASctl_0.23-42
[15] cowplot_1.1.1 RSQLite_2.3.1
[17] RANN_2.6.1 future_1.33.1
[19] bit_4.0.5 tzdb_0.3.0
[21] spatstat.data_3.0-0 httpuv_1.6.9
[23] xfun_0.47 hms_1.1.2
[25] evaluate_0.20 promises_1.2.0.1
[27] fansi_1.0.6 progress_1.2.2
[29] caTools_1.18.2 igraph_1.4.2
[31] DBI_1.1.3 geneplotter_1.76.0
[33] htmlwidgets_1.6.2 spatstat.geom_3.0-6
[35] ellipsis_0.3.2 RSpectra_0.16-1
[37] backports_1.4.1 annotate_1.76.0
[39] aod_1.3.3 deldir_1.0-6
[41] sparseMatrixStats_1.10.0 vctrs_0.6.5
[43] ROCR_1.0-11 abind_1.4-5
[45] cachem_1.0.7 withr_2.5.2
[47] grr_0.9.5 progressr_0.13.0
[49] checkmate_2.3.0 sctransform_0.4.1
[51] prettyunits_1.1.1 mclust_6.0.0
[53] goftest_1.2-3 cluster_2.1.4
[55] dotCall64_1.1-0 lazyeval_0.2.2
[57] crayon_1.5.2 spatstat.explore_3.0-6
[59] pkgconfig_2.0.3 nlme_3.1-162
[61] nnet_7.3-18 rlang_1.1.4
[63] globals_0.16.2 lifecycle_1.0.3
[65] miniUI_0.1.1.1 polyclip_1.10-4
[67] RcppHNSW_0.4.1 lmtest_0.9-40
[69] carData_3.0-5 Rhdf5lib_1.20.0
[71] boot_1.3-28.1 zoo_1.8-11
[73] base64enc_0.1-3 ggridges_0.5.4
[75] png_0.1-8 viridisLite_0.4.1
[77] bitops_1.0-7 R.oo_1.25.0
[79] KernSmooth_2.23-20 spam_2.10-0
[81] rhdf5filters_1.10.1 Biostrings_2.66.0
[83] blob_1.2.3 DelayedMatrixStats_1.20.0
[85] parallelly_1.35.0 spatstat.random_3.1-3
[87] remaCor_0.0.16 rstatix_0.7.2
[89] ggsignif_0.6.4 beachmat_2.14.0
[91] scales_1.3.0 memoise_2.0.1
[93] magrittr_2.0.3 ica_1.0-3
[95] gplots_3.1.3 zlibbioc_1.44.0
[97] compiler_4.2.3 dqrng_0.3.0
[99] RColorBrewer_1.1-3 lme4_1.1-35.1
[101] fitdistrplus_1.1-8 cli_3.6.2
[103] XVector_0.38.0 listenv_0.9.0
[105] pbapply_1.7-0 htmlTable_2.4.2
[107] Formula_1.2-5 MASS_7.3-58.3
[109] tidyselect_1.2.0 stringi_1.7.12
[111] locfit_1.5-9.8 grid_4.2.3
[113] tools_4.2.3 timechange_0.2.0
[115] future.apply_1.10.0 parallel_4.2.3
[117] rstudioapi_0.14 foreign_0.8-85
[119] foreach_1.5.2 gridExtra_2.3
[121] EnvStats_2.8.1 farver_2.1.1
[123] Rtsne_0.16 digest_0.6.31
[125] shiny_1.7.4 car_3.1-2
[127] broom_1.0.3 scuttle_1.8.0
[129] later_1.3.0 RcppAnnoy_0.0.20
[131] httr_1.4.5 AnnotationDbi_1.60.2
[133] Rdpack_2.6 colorspace_2.1-0
[135] XML_3.99-0.14 tensor_1.5
[137] reticulate_1.42.0 splines_4.2.3
[139] uwot_0.1.14 spatstat.utils_3.1-4
[141] plotly_4.10.1 xtable_1.8-4
[143] jsonlite_1.8.4 nloptr_2.0.3
[145] R6_2.5.1 Hmisc_5.1-1
[147] pillar_1.9.0 htmltools_0.5.8.1
[149] mime_0.12 glue_1.6.2
[151] fastmap_1.1.1 minqa_1.2.6
[153] codetools_0.2-19 mvtnorm_1.2-3
[155] utf8_1.2.4 lattice_0.21-8
[157] spatstat.sparse_3.0-0 pbkrtest_0.5.2
[159] gtools_3.9.4 GO.db_3.16.0
[161] survival_3.5-3 rmarkdown_2.28
[163] munsell_0.5.0 GenomeInfoDbData_1.2.9
[165] iterators_1.0.14 impute_1.72.3
[167] HDF5Array_1.26.0 gtable_0.3.3
[169] rbibutils_2.2.16

00_prep_pseudobulk_counts.R at commit b8e3704, under MIT · at the source

Overview

Authors: Gozde Buyukkahraman¹, Emre Caglayan^2,3, Stephen G Hörpel⁴, Yaqiang Zhang², Ine A van Tussenbroek⁴, Carlos G Orozco², Emily Oh², William D Hopkins⁵, Chet C Sherwood⁶, Todd F Roberts², Sonja C Vernes⁴, Genevieve Konopka^1,2

ORCID iDs: Gozde Buyukkahraman, Emre Caglayan, Stephen G Hörpel, Yaqiang Zhang, Carlos G Orozco, Emily Oh, William D Hopkins, Chet C Sherwood, Todd F Roberts, Sonja C Vernes, Genevieve Konopka

Institutions: University of California, Los Angeles (United States); Boston Children's Hospital (United States); Harvard University (United States); The University of Texas Southwestern Medical Center (United States); University of St Andrews (United Kingdom); The University of Texas MD Anderson Cancer Center (United States); George Washington University (United States)

Journal: Nature communications, volume 17, issue 1, article 6793

Dates: received 27 April 2025; accepted 6 May 2026; published online 25 May 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1038/s41467-026-73305-8 · PMID 42185272 · PMCID PMC13385833 · OpenAlex W7162321241

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: genetics / omics (modality), human (organism), mouse (organism), other (organism), non-human primate (organism), cellular / molecular (subfield)

Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Connectivity, Spectral & time-frequency

Keywords: Molecular evolution, Genetics of the nervous system

MeSH: Corpus Striatum*, Animals, Callithrix, Chiroptera, Humans, Macaca mulatta, Male, Medium Spiny Neurons, Mice, Neuroglia, Neurons, Pan troglodytes, Species Specificity (* major topic)

Topic: Neurogenesis and neuroplasticity mechanisms (Developmental Neuroscience, Neuroscience), according to OpenAlex

Funding: U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS) (NS115821, NS126143); NHGRI NIH HHS (R01 HG011641); NINDS NIH HHS (RF1 NS126143, R01 NS126143, R24 NS092988, UF1 NS115821); James S. McDonnell Foundation (McDonnell Foundation) (220020467); National Science Foundation (NSF) (DRL-2219759); U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) (MH134809, MH103517, MH126481); EC | EU Framework Programme for Research and Innovation H2020 | H2020 Euratom (H2020 Euratom Research and Training Programme 2014-2018) (101001702); NIMH NIH HHS (R01 MH103517, R01 MH126481, R01 MH134809); U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI) (HG011641)

Citations: cited by 2 papers (Europe PMC); 101 references in the paper

Abstract

The dorsal striatum is important for highly specialized functions including movement, learning, and habit formation. However, it is not known if species-specialized behaviors are associated with cellular specializations in the striatum. Here, we compared single-nucleus RNA sequencing (snRNA-seq) data from human, chimpanzee, rhesus macaque, common marmoset, and pale spear-nosed bat caudate (CN) and putamen (Pu) separately as well as mouse caudoputamen (C-Pu), which represents divergence among species spanning approximately 94 million years of evolution. We observed a lower neuron-to-glia ratio in primate striata compared to non-primates, reflecting the allometric scaling of neuron density and relative glia density invariance in larger brains. Among neurons, eccentric spiny projection neurons (eSPNs) - an SPN of unknown function - showed significantly lower proportions in non-primate striata for both CN and Pu. Focusing on the heterogeneity within interneurons, we identified two bat striatal interneuron cell types that are nearly absent in other species: which express LMO3, and co-express FOXP2 and TSHZ2. Other striatal interneurons also exhibited differential abundance between primates and non-primates. In summary, we provide a comprehensive snRNA-seq dataset of dorsal striatum, identify two distinct, previously uncharacterized populations of bat interneurons, and uncover fundamental cellular composition differences between primate and non-primate striata.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 23 matches between paragraphs and lines of code.

konopkalab/Comparative_striatum

License: MIT

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Commit: b8e37042199a12756ee1ae145790fc9862f6cfc6, 15 April 2026

Languages: Shell (43), R (36), Python (3), Jupyter (2)

Size: 87 files, 84 scripts

Software Heritage: not archived

Found in: “Code availability”

Holds: README, license file, 2 notebooks

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

Tools: Seurat (36 files), ggpubr (34 files), reshape2 (34 files), tidyverse (34 files), Cell Ranger (24 files), data.table (19 files), ggplot2 (19 files), Harmony (18 files), DESeq2 (8 files), edgeR (8 files), anndata (5 files), Matplotlib (5 files), pandas (5 files), patchwork (5 files), rpy2 (5 files), Scanpy (5 files), WGCNA (5 files), SingleCellExperiment (4 files), clusterProfiler (3 files), NumPy (2 files), pheatmap (2 files), ComplexHeatmap (1 file), cowplot (1 file), SAMtools (1 file)

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

28 September 2026: the link answers

86 files

01_Generate_NHP_gtfs/LIFTOFF_HUMAN_TO_CHIMP.sh, Shell, 22 lines
01_Generate_NHP_gtfs/LIFTOFF_HUMAN_TO_MACAQUE.sh, Shell, 30 lines
01_Generate_NHP_gtfs/LIFTOFF_HUMAN_TO_MARMOSET.sh, Shell, 22 lines
01_Generate_NHP_gtfs/MKREF_LIFTOFF_CHIMP.sh, Shell, 48 lines, 1 match
01_Generate_NHP_gtfs/MKREF_LIFTOFF_MACAQUE.sh, Shell, 48 lines
01_Generate_NHP_gtfs/MKREF_LIFTOFF_MARMOSET.sh, Shell, 48 lines
02_Bat/00_DEMULTIPLEX.sh, Shell, 145 lines
02_Bat/01_GENERATE_COUNT_MATRIX.sh, Shell, 49 lines
02_Bat/02_CELLBENDER.sh, Shell, 13 lines
02_Bat/03_ANNOTATION.R, R, 950 lines
02_Bat/04_WGCNA.R, R, 547 lines, 1 match
02_Chimp/00_mkfastq_1.sh, Shell, 54 lines
02_Chimp/00_mkfastq_2.sh, Shell, 54 lines
02_Chimp/01_CELLRANGER_COUNT_CHIMP.sh, Shell, 51 lines
02_Chimp/01_CELLRANGER_COUNT_CHIMP_2.sh, Shell, 54 lines
02_Chimp/01_CELLRANGER_COUNT_CHIMP_3.sh, Shell, 53 lines
02_Chimp/01_CELLRANGER_COUNT_CHIMP_4.sh, Shell, 44 lines
02_Chimp/02_CELLBENDER.sh, Shell, 13 lines
02_Chimp/03_ANNOTATE_CHIMP_CAUDATE.R, R, 396 lines
02_Chimp/03_ANNOTATE_CHIMP_PUTAMEN.R, R, 391 lines
02_Chimp/04_INTERNEURON_CLUSTER.R, R, 292 lines
02_Chimp/05_NEURON_CLUSTER.R, R, 636 lines
02_Ferret/00_CONVERT_BAM_TO_10X_FASTQ.sh, Shell, 72 lines
02_Ferret/01_GENERATE_COUNT_MATRIX.sh, Shell, 48 lines
02_Ferret/02_PREP_FOR_CELLBENDER.R, R, 118 lines
02_Ferret/03_CELLBENDER.sh, Shell, 43 lines
02_Ferret/04_ANNOTATION.R, R, 325 lines
02_Human/00_mkfastq.sh, Shell, 40 lines
02_Human/01_COUNT_CELLRANGER_1.sh, Shell, 58 lines
02_Human/01_COUNT_CELLRANGER_2.sh, Shell, 61 lines
02_Human/01_COUNT_CELLRANGER_3.sh, Shell, 59 lines
02_Human/01_COUNT_CELLRANGER_4.sh, Shell, 54 lines
02_Human/01_COUNT_CELLRANGER_5.sh, Shell, 54 lines
02_Human/02_CELLBENDER.sh, Shell, 19 lines
02_Human/03_CAUDATEPUTAMEN_CLUSTER.R, R, 356 lines
02_Human/04_NEURON_CLUSTER.R, R, 481 lines
02_Human/05_INTERNEURON_CLUSTER.R, R, 226 lines
02_Human/06_WGCNA.R, R, 510 lines, 3 matches
02_Macaque/01_CELLRANGER_COUNT_1.sh, Shell, 66 lines
02_Macaque/02_CELLBENDER.sh, Shell, 13 lines
02_Macaque/03_ANNOTATION_MACAQUE_CAUDATE.R, R, 525 lines
02_Macaque/03_ANNOTATION_MACAQUE_PUTAMEN.R, R, 306 lines
02_Macaque/04_INTEGRATION_CAUDATE.R, R, 212 lines
02_Macaque/04_INTEGRATION_PUTAMEN.R, R, 219 lines
02_Macaque/05_CAUDATE_PUTAMEN_INTEGRATE_ANNOTATE.R, R, 408 lines
02_Macaque/He_et_al_Macaque/00_fasterq_dump.sh, Shell, 74 lines
02_Macaque/He_et_al_Macaque/01_CELLRANGER_COUNT_1.sh, Shell, 51 lines
02_Macaque/He_et_al_Macaque/01_CELLRANGER_COUNT_2.sh, Shell, 51 lines
02_Macaque/He_et_al_Macaque/01_CELLRANGER_COUNT_3.sh, Shell, 51 lines
02_Macaque/He_et_al_Macaque/02_CELLBENDER.sh, Shell, 13 lines
02_Macaque/He_et_al_Macaque/03_ANNOTATION_CAUDATE.R, R, 361 lines
02_Macaque/He_et_al_Macaque/03_ANNOTATION_PUTAMEN.R, R, 445 lines
02_Marmoset/03_INTEGRATE_ANNOTATE_DATASETS.R, R, 538 lines
02_Marmoset/Krienen_et_al/00_DOWNLOAD_FASTQ_FROM_URL.sh, Shell, 34 lines
02_Marmoset/Krienen_et_al/01_ARRANGE_FASTQs.sh, Shell, 81 lines
02_Marmoset/Krienen_et_al/02_GENERATE_COUNT_MATRICES.sh, Shell, 69 lines
02_Marmoset/Krienen_et_al/03_CELLBENDER.sh, Shell, 11 lines
02_Marmoset/Krienen_et_al/04_ANNOTATION.R, R, 793 lines
02_Marmoset/Lin_et_al/00_DOWNLOAD_FASTQs.sh, Shell, 77 lines
02_Marmoset/Lin_et_al/01_GENERATE_COUNT_MATRIX.sh, Shell, 45 lines
02_Marmoset/Lin_et_al/02_CELLBENDER.sh, Shell, 11 lines
02_Marmoset/Lin_et_al/03_ANNOTATE.R, R, 538 lines
02_Mouse/01_GENERATE_COUNT_MATRIX_NEXTFLOW.sh, Shell, 51 lines
02_Mouse/02_PREPARE_FOR_CELLBENDER.R, R, 49 lines
02_Mouse/03_CELLBENDER.sh, Shell, 15 lines
02_Mouse/04_CAUDOPUTAMEN_ANNOTATE.R, R, 279 lines
03_SPNs/00_get_orthologs.sh, Shell, 90 lines, 1 match
03_SPNs/01_INTEGRATION_AND_CLUSTERING.R, R, 2,281 lines, 1 match
03_SPNs/02_eSPN_markers.R, R, 444 lines, 1 match
03_SPNs/03_eSPN_D1D2_hybrid_correlation.R, R, 186 lines, 2 matches
03_SPNs/04_SPN_seurat_obj_convert_seurat_obj_to_AnnData.R, R, 32 lines
03_SPNs/05_Cell_type_proportion_analysis_scCODA.ipynb, Jupyter, 460 lines
04_Interneurons/01_Integration&Clustering.R, R, 3,782 lines, 2 matches
04_Interneurons/02_Interneurons_scCODA.ipynb, Jupyter, 586 lines
04_Interneurons/03_DEG_Analysis.R, R, 467 lines, 1 match
04_Interneurons/04_Correlation_withinBatPutamen.R, R, 104 lines, 1 match
04_Interneurons/05_Fig5A_scCODA.py, Python, 77 lines
05_DGE_analysis/00_prep_pseudobulk_counts.R, R, 1,667 lines, 3 matches
05_DGE_analysis/01_primates_pseudobulk_DESeq2.R, R, 353 lines, 2 matches
05_DGE_analysis/02_DEG_figures.R, R, 827 lines, 1 match
GLIA_TO_NEURON.R, R, 517 lines
QC_plots.R, R, 1,047 lines, 1 match
python_functions.py, Python, 394 lines
scCODA_Glia_to_Neuron.py, Python, 86 lines
LICENSE, License, 21 lines
README.md, Text, 15 lines

At the source: github.com/konopkalab/Comparative_striatum

Zenodo 19266844

License: MIT

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Size: 1 file

Software Heritage: not checked

Found in: the references

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

Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)

28 September 2026: the link answers (HTTP 200)

86 files

01_Generate_NHP_gtfs/LIFTOFF_HUMAN_TO_CHIMP.sh, Shell, 22 lines
01_Generate_NHP_gtfs/LIFTOFF_HUMAN_TO_MACAQUE.sh, Shell, 30 lines
01_Generate_NHP_gtfs/LIFTOFF_HUMAN_TO_MARMOSET.sh, Shell, 22 lines
01_Generate_NHP_gtfs/MKREF_LIFTOFF_CHIMP.sh, Shell, 48 lines
01_Generate_NHP_gtfs/MKREF_LIFTOFF_MACAQUE.sh, Shell, 48 lines
01_Generate_NHP_gtfs/MKREF_LIFTOFF_MARMOSET.sh, Shell, 48 lines
02_Bat/00_DEMULTIPLEX.sh, Shell, 145 lines
02_Bat/01_GENERATE_COUNT_MATRIX.sh, Shell, 49 lines
02_Bat/02_CELLBENDER.sh, Shell, 13 lines
02_Bat/03_ANNOTATION.R, R, 950 lines
02_Bat/04_WGCNA.R, R, 547 lines
02_Chimp/00_mkfastq_1.sh, Shell, 54 lines
02_Chimp/00_mkfastq_2.sh, Shell, 54 lines
02_Chimp/01_CELLRANGER_COUNT_CHIMP.sh, Shell, 51 lines
02_Chimp/01_CELLRANGER_COUNT_CHIMP_2.sh, Shell, 54 lines
02_Chimp/01_CELLRANGER_COUNT_CHIMP_3.sh, Shell, 53 lines
02_Chimp/01_CELLRANGER_COUNT_CHIMP_4.sh, Shell, 44 lines
02_Chimp/02_CELLBENDER.sh, Shell, 13 lines
02_Chimp/03_ANNOTATE_CHIMP_CAUDATE.R, R, 396 lines
02_Chimp/03_ANNOTATE_CHIMP_PUTAMEN.R, R, 391 lines
02_Chimp/04_INTERNEURON_CLUSTER.R, R, 292 lines
02_Chimp/05_NEURON_CLUSTER.R, R, 636 lines
02_Ferret/00_CONVERT_BAM_TO_10X_FASTQ.sh, Shell, 72 lines
02_Ferret/01_GENERATE_COUNT_MATRIX.sh, Shell, 48 lines
02_Ferret/02_PREP_FOR_CELLBENDER.R, R, 118 lines
02_Ferret/03_CELLBENDER.sh, Shell, 43 lines
02_Ferret/04_ANNOTATION.R, R, 325 lines
02_Human/00_mkfastq.sh, Shell, 40 lines
02_Human/01_COUNT_CELLRANGER_1.sh, Shell, 58 lines
02_Human/01_COUNT_CELLRANGER_2.sh, Shell, 61 lines
02_Human/01_COUNT_CELLRANGER_3.sh, Shell, 59 lines
02_Human/01_COUNT_CELLRANGER_4.sh, Shell, 54 lines
02_Human/01_COUNT_CELLRANGER_5.sh, Shell, 54 lines
02_Human/02_CELLBENDER.sh, Shell, 19 lines
02_Human/03_CAUDATEPUTAMEN_CLUSTER.R, R, 356 lines
02_Human/04_NEURON_CLUSTER.R, R, 481 lines
02_Human/05_INTERNEURON_CLUSTER.R, R, 226 lines
02_Human/06_WGCNA.R, R, 510 lines
02_Macaque/01_CELLRANGER_COUNT_1.sh, Shell, 66 lines
02_Macaque/02_CELLBENDER.sh, Shell, 13 lines
02_Macaque/03_ANNOTATION_MACAQUE_CAUDATE.R, R, 525 lines
02_Macaque/03_ANNOTATION_MACAQUE_PUTAMEN.R, R, 306 lines
02_Macaque/04_INTEGRATION_CAUDATE.R, R, 212 lines
02_Macaque/04_INTEGRATION_PUTAMEN.R, R, 219 lines
02_Macaque/05_CAUDATE_PUTAMEN_INTEGRATE_ANNOTATE.R, R, 408 lines
02_Macaque/He_et_al_Macaque/00_fasterq_dump.sh, Shell, 74 lines
02_Macaque/He_et_al_Macaque/01_CELLRANGER_COUNT_1.sh, Shell, 51 lines
02_Macaque/He_et_al_Macaque/01_CELLRANGER_COUNT_2.sh, Shell, 51 lines
02_Macaque/He_et_al_Macaque/01_CELLRANGER_COUNT_3.sh, Shell, 51 lines
02_Macaque/He_et_al_Macaque/02_CELLBENDER.sh, Shell, 13 lines
02_Macaque/He_et_al_Macaque/03_ANNOTATION_CAUDATE.R, R, 361 lines
02_Macaque/He_et_al_Macaque/03_ANNOTATION_PUTAMEN.R, R, 445 lines
02_Marmoset/03_INTEGRATE_ANNOTATE_DATASETS.R, R, 538 lines
02_Marmoset/Krienen_et_al/00_DOWNLOAD_FASTQ_FROM_URL.sh, Shell, 34 lines
02_Marmoset/Krienen_et_al/01_ARRANGE_FASTQs.sh, Shell, 81 lines
02_Marmoset/Krienen_et_al/02_GENERATE_COUNT_MATRICES.sh, Shell, 69 lines
02_Marmoset/Krienen_et_al/03_CELLBENDER.sh, Shell, 11 lines
02_Marmoset/Krienen_et_al/04_ANNOTATION.R, R, 793 lines
02_Marmoset/Lin_et_al/00_DOWNLOAD_FASTQs.sh, Shell, 77 lines
02_Marmoset/Lin_et_al/01_GENERATE_COUNT_MATRIX.sh, Shell, 45 lines
02_Marmoset/Lin_et_al/02_CELLBENDER.sh, Shell, 11 lines
02_Marmoset/Lin_et_al/03_ANNOTATE.R, R, 538 lines
02_Mouse/01_GENERATE_COUNT_MATRIX_NEXTFLOW.sh, Shell, 51 lines
02_Mouse/02_PREPARE_FOR_CELLBENDER.R, R, 49 lines
02_Mouse/03_CELLBENDER.sh, Shell, 15 lines
02_Mouse/04_CAUDOPUTAMEN_ANNOTATE.R, R, 279 lines
03_SPNs/00_get_orthologs.sh, Shell, 90 lines
03_SPNs/01_INTEGRATION_AND_CLUSTERING.R, R, 2,281 lines
03_SPNs/02_eSPN_markers.R, R, 444 lines
03_SPNs/03_eSPN_D1D2_hybrid_correlation.R, R, 180 lines, 2 matches
03_SPNs/04_SPN_seurat_obj_convert_seurat_obj_to_AnnData.R, R, 32 lines
03_SPNs/05_Cell_type_proportion_analysis_scCODA.ipynb, Jupyter, 460 lines
04_Interneurons/01_Integration&Clustering.R, R, 3,782 lines
04_Interneurons/02_Interneurons_scCODA.ipynb, Jupyter, 586 lines
04_Interneurons/03_DEG_Analysis.R, R, 467 lines
04_Interneurons/04_Correlation_withinBatPutamen.R, R, 104 lines
04_Interneurons/05_Fig5A_scCODA.py, Python, 77 lines
05_DGE_analysis/00_prep_pseudobulk_counts.R, R, 1,667 lines
05_DGE_analysis/01_primates_pseudobulk_DESeq2.R, R, 353 lines
05_DGE_analysis/02_DEG_figures.R, R, 827 lines
GLIA_TO_NEURON.R, R, 517 lines
QC_plots.R, R, 1,047 lines
python_functions.py, Python, 394 lines
scCODA_Glia_to_Neuron.py, Python, 86 lines
LICENSE, License, 21 lines
README.md, Text, 15 lines

At the source:

Code availability

All the scripts used in the study were deposited to https://github.com/konopkalab/Comparative_striatum and Zenodo101.

Reproduced under the paper's license (CC BY), from the paper cited above.

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;
168 scripts, each with its path and the digest of its content;
23 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

Datasets cited

geo:GSE293075, at NCBI GEO; found in “Data availability”

Data Availability Statement

The human, chimpanzee, and pale spear-nosed bat dorsal striatum snRNA-seq data generated in this study have been deposited in the GEO database under accession code GSE293075 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE293075). The processed data are available at UCSC cell browser [https://mammal-striatum-evo.cells.ucsc.edu]. The following published dorsal striatum snRNA-seq data used in this study were downloaded from the GEO database: rhesus macaque dataset with accession number GSE167920 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE167920), marmoset datasets with accession numbers GSE151761 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE151761) and GSE165578 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE165578), mouse and ferret datasets with accession number GSE151761 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE151761). Source data are provided with this paper.

All the scripts used in the study were deposited to https://github.com/konopkalab/Comparative_striatum and Zenodo101.

Reproduced under the paper's license (CC BY), from the paper cited above.

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, 12 authors, 2 keywords, 13 MeSH terms, 9 funders, 99 references.

Cite

This paper

Buyukkahraman, G., Caglayan, E., Hörpel, S. G., Zhang, Y., van Tussenbroek, I. A., Orozco, C. G., Oh, E., Hopkins, W. D., Sherwood, C. C., Roberts, T. F., Vernes, S. C., & Konopka, G. (2026). Comparative analysis of the cellular landscape in mammalian striatum. Nature communications, 17(1), 6793. https://doi.org/10.1038/s41467-026-73305-8

BibTeX

@article{buyukkahraman2026comparative, author = {Buyukkahraman, Gozde and Caglayan, Emre and Hörpel, Stephen G and Zhang, Yaqiang and van Tussenbroek, Ine A and Orozco, Carlos G and Oh, Emily and Hopkins, William D and Sherwood, Chet C and Roberts, Todd F and Vernes, Sonja C and Konopka, Genevieve}, title = {{Comparative analysis of the cellular landscape in mammalian striatum}}, journal = {Nature communications}, year = {2026}, month = may, volume = {17}, number = {1}, pages = {6793}, publisher = {Nature Publishing Group}, issn = {2041-1723}, doi = {10.1038/s41467-026-73305-8}, url = {https://doi.org/10.1038/s41467-026-73305-8}, pmid = {42185272}, pmcid = {PMC13385833} }

RIS

TY - JOUR AU - Buyukkahraman, Gozde AU - Caglayan, Emre AU - Hörpel, Stephen G AU - Zhang, Yaqiang AU - van Tussenbroek, Ine A AU - Orozco, Carlos G AU - Oh, Emily AU - Hopkins, William D AU - Sherwood, Chet C AU - Roberts, Todd F AU - Vernes, Sonja C AU - Konopka, Genevieve TI - Comparative analysis of the cellular landscape in mammalian striatum T2 - Nature communications J2 - Nat Commun PY - 2026 DA - 2026/05/25 VL - 17 IS - 1 SP - 6793 SN - 2041-1723 PB - Nature Publishing Group DO - 10.1038/s41467-026-73305-8 UR - https://doi.org/10.1038/s41467-026-73305-8 LA - en ER -

CSL-JSON

{ "id": "10.1038/s41467-026-73305-8", "type": "article-journal", "title": "Comparative analysis of the cellular landscape in mammalian striatum", "container-title": "Nature communications", "author": [ { "family": "Buyukkahraman", "given": "Gozde" }, { "family": "Caglayan", "given": "Emre" }, { "family": "Hörpel", "given": "Stephen G" }, { "family": "Zhang", "given": "Yaqiang" }, { "family": "van Tussenbroek", "given": "Ine A" }, { "family": "Orozco", "given": "Carlos G" }, { "family": "Oh", "given": "Emily" }, { "family": "Hopkins", "given": "William D" }, { "family": "Sherwood", "given": "Chet C" }, { "family": "Roberts", "given": "Todd F" }, { "family": "Vernes", "given": "Sonja C" }, { "family": "Konopka", "given": "Genevieve" } ], "container-title-short": "Nat Commun", "volume": "17", "issue": "1", "page": "6793", "DOI": "10.1038/s41467-026-73305-8", "PMID": "42185272", "PMCID": "PMC13385833", "ISSN": "2041-1723", "publisher": "Nature Publishing Group", "URL": "https://doi.org/10.1038/s41467-026-73305-8", "language": "en", "issued": { "date-parts": [ [ 2026, 5, 25 ] ] } }

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Comparative analysis of the cellular landscape in mammalian striatum.

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konopkalab/Comparative_striatum

Zenodo 19266844

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Version 1, 28 September 2026: the first record

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