KOLF2.1J iTF-Microglia: A standardized platform to study microglial transcriptional regulatory networks in CNS disease.

Code ↔ Paper

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The 27 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted

[1] § Results › Inference of cCRE-gene associations across microglial states ↔ Scripts/cCRE-TargetGene_analysis.R, lines 1098–1184 · score 0.83 · GTPase, neutrophil degranulation, neuronal system, RHO, phase, cycle
[2] § Results › Cis-regulatory landscapes of iTF-microglia across differentiation and activation ↔ Scripts/cCRE_analysis.R, lines 227–315 · score 0.83 · CA CTCF, CA H3K4me3, CA TF, dELS, pELS, cCRE
[3] § Results › Cis-regulatory landscapes of iTF-microglia across differentiation and activation ↔ Scripts/TF_monaLisa_analysis.R, lines 199–242 · score 0.82 · CA CTCF, CA H3K4me3, CA TF, dELS, pELS, cCRE
[4] § Results › Cis-regulatory landscapes of iTF-microglia across differentiation and activation ↔ Scripts/cCRE_analysis.R, lines 419–462 · score 0.79 · cell cycle, dELS, pELS, cytokine signaling, chemokine, translation
[5] § STAR★Methods › Quantification and statistical analysis › Nomination of transcriptional regulatory networks (TRNs) › Identification of candidate cis-regulatory elements (cCREs) ↔ Scripts/TF_monaLisa_analysis.R, lines 41–81 · score 0.78 · regionCounts, windowCounts, background bins, H3K27ac, TMM, windows
[6] § STAR★Methods › Quantification and statistical analysis › Nomination of transcriptional regulatory networks (TRNs) › Identification of target genes ↔ Scripts/cCRE-TargetGene_analysis.R, lines 1098–1184 · score 0.77 · Reactome pathway, Brain microglia links, target genes, DegCre, Closest, iPSCs
[7] § Results › KOLF2.1J iTF-microglia transcriptome resembles other in vitro microglia models and brain microglia ↔ Scripts/RNAseq_analysis.R, lines 640–679 · score 0.76 · iHPCs, iMGLs, fetal microglia, CD16, WTC11, CD14
[8] § Results › Inflammatory activation of iTF-microglia by LPS + IFNγ ↔ Scripts/cCRE_analysis.R, lines 419–462 · score 0.66 · innate immune, neutrophil degranulation, interleukin, phagocytosis, iPSCs, Pathway
[9] § STAR★Methods › Quantification and statistical analysis › Nomination of transcriptional regulatory networks (TRNs) › Identification of candidate cis-regulatory elements (cCREs) ↔ Scripts/LDSC_bed_files_20250429.R, lines 84–162 · score 0.64 · GenomicRanges, findOverlaps, H3K27ac, cCREs, consensus, filtered
[10] § Results › Cis-regulatory landscapes of iTF-microglia across differentiation and activation ↔ Scripts/TF_monaLisa_analysis.R, lines 244–284 · score 0.64 · dELS, pELS, ChIP, cCREs, PLS, promoters
[11] § Results › Inflammatory activation of iTF-microglia by LPS + IFNγ ↔ Scripts/RNAseq_analysis.R, lines 170–233 · score 0.63 · HLA DRB1, CD40, CXCL9, GBP2, IDO1, CXCL10
[12] § Results › Inflammatory activation of iTF-microglia by LPS + IFNγ ↔ Scripts/TF_monaLisa_analysis.R, lines 1061–1087 · score 0.63 · HLA DRB1, CD40, CXCL9, GBP2, IDO1, CXCL10
[13] § STAR★Methods › Quantification and statistical analysis › Association of TRNs with candidate disease-risk variants ↔ Scripts/monalisa_b.R, lines 40–82 · score 0.62 · AD GWAS, risk variants, LD, H3K27ac, monaLisa, ATAC
[14] § Results › KOLF2.1J iTF-microglia transcriptome resembles other in vitro microglia models and brain microglia ↔ Scripts/RNAseq_analysis.R, lines 640–679 · score 0.61 · iHPCs, fetal microglia, DESeq2, adult, KOLF2.1J, Monocytes
[15] § STAR★Methods › Quantification and statistical analysis › Association of TRNs with candidate disease-risk variants ↔ Scripts/motifbreakR_script.R, lines 41–112 · score 0.61 · motifbreakR, ic, JASPAR, PWMs, candidate, motifs
[16] § Results › Inference of cCRE-gene associations across microglial states ↔ Scripts/cCRE-TargetGene_analysis.R, lines 1200–1263 · score 0.60 · vivo microglial, Brain microglia links, DegCre, closest, distance, enhancer
[17] § STAR★Methods › Quantification and statistical analysis › Nomination of transcriptional regulatory networks (TRNs) › Identification of candidate cis-regulatory elements (cCREs) ↔ Scripts/cCRE_disease_variant_annotation.R, lines 581–625 · score 0.60 · GenomicRanges, findOverlaps, cCRE, filtered, overlapping, cell
[18] § STAR★Methods › Quantification and statistical analysis › Nomination of transcriptional regulatory networks (TRNs) › Identification of TFs in TRNs ↔ Scripts/monalisa_b.R, lines 211–253 · score 0.59 · findMotifHits, scanning, PWMs, monaLisa, JASPAR2024, csaw
[19] § Results › Inference of cCRE-gene associations across microglial states ↔ Scripts/cCRE-TargetGene_analysis.R, lines 1524–1594 · score 0.57 · brain microglia links, cCRE, DegCre, promoter, TSS, enhancers
[20] § STAR★Methods › Quantification and statistical analysis › ATAC- and ChIP-seq processing and peak calling ↔ src/encode_task_overlap.py, lines 132–174 · score 0.56 · naive overlap, ATAC seq, shift, blacklist, ChIP, filtered
[21] § STAR★Methods › Quantification and statistical analysis › Nomination of transcriptional regulatory networks (TRNs) › Identification of TFs in TRNs ↔ Scripts/TF_monaLisa_analysis.R, lines 453–495 · score 0.56 · findMotifHits, scanning, PWMs, JASPAR2024, csaw, matrices
[22] § Results › Inference of cCRE-gene associations across microglial states ↔ Scripts/cCRE-TargetGene_analysis.R, lines 1524–1594 · score 0.56 · cCRE, target genes, DegCre, brain microglia, ratio, TSS
[23] § STAR★Methods › Quantification and statistical analysis › RNA-seq meta-analysis ↔ Scripts/motifbreakR_script.R, lines 464–533 · score 0.55 · Variance stabilizing transformation, VST, iPSCs, clustering, Gene, microglia
[24] § STAR★Methods › Quantification and statistical analysis › Association of TRNs with candidate disease-risk variants ↔ Scripts/MAGMA_expression_matrix_format.R, the whole file · a weak match · score 0.54 · GWAS summary, MAGMA, winsorized, log2, TPM, subsets
[25] § Results › iTF-microglia TRNs are enriched for neurodegenerative and autoimmune risk variants ↔ Scripts/LDSC_heatmap.R, lines 136–195 · score 0.54 · REL, H3K27ac, IBD, SCZ, SLE, cCREs
[26] § STAR★Methods › Quantification and statistical analysis › RNA-seq meta-analysis ↔ Scripts/RNAseq_analysis.R, lines 681–720 · score 0.52 · Variance stabilizing transformation, VST, clustering
[27] § STAR★Methods › Quantification and statistical analysis › ATAC- and ChIP-seq processing and peak calling ↔ src/encode_task_qc_report.py, lines 458–526 · score 0.50 · seq pipeline, mitochondrial, Picard, quality, QC, ENCODE

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The authors' code

R · 1,594 lines · 62 KB · MIT · 5 matches

# Script to compare target genes in iTF cells
# open R
srun -n 10 -p interactive --pty /bin/bash
module load cluster/gsl/1.15
module load cluster/htslib/1.9-devel
module load cluster/build_tools/llvm/8.0
module load cluster/util/gcc/8.2
module load hdf5_18/1.8.21
conda activate r_env
#conda deactivate
R
library(GenomicRanges)
library(AnnotationHub)
library(data.table)
library(dplyr)
library(tidyr)
library(rtracklayer)
library(tibble)
library(stringr)
library(DESeq2)
library(org.Hs.eg.db)
library(ggplot2)
# Set work directory
setwd("/cluster/home/irodriguez/Myers_Lab/KOLF2.1J-iTF_iMGLs_Nick_paper/manuscript/")
# Load data
iTF_CREs_ENCODE <- readRDS("batch2/Tables/cCREs_overlap_iTF_ATAC_H3K27ac_overlap.rds")
consensus_CREs <- readRDS("batch2/Tables/cCREs_overlap_consensus_peaks.rds")
gse <- readRDS("batch2/Tables/gse.rds")
load("/cluster/home/broberts/ref_genomes/STAR_Gencode_v46/gencodeV46TSSGR.rda")
Gencode.V46 <- finalTSsGR
# Load and filter gene expression data
coldata <- read.csv("/cluster/home/irodriguez/Myers_Lab/20240416_Novogene_RNAseq_Library_iTF_iMGLs_Nick_paper/R_analysis/iTF-Microglia_BulkRNA_SampleSheet_phenotype.csv")
tpm <- assay(gse, "abundance")
coldata_subset <- coldata[c(13:15, 19:24),]
tpm_subset <- tpm[, c(13:15, 19:24)]
colnames(tpm_subset) <- coldata_subset$Sample
rownames(tpm_subset) <- sapply(strsplit(rownames(tpm_subset), "[.]"), `[[`, 1)
# Compute row-wise means
expr_means <- data.frame(
iPSC = rowMeans(tpm_subset[, 1:3]),
Microglia = rowMeans(tpm_subset[, 4:6]),
Microglia_LPS_IFNG = rowMeans(tpm_subset[, 7:9])
)
# Filter genes based on expression
filtered_genes <- rownames(expr_means)[rowSums(expr_means > 1) > 0]
# Filter and process GENCODE annotations
gtf_df_filtered <- as.data.frame(Gencode.V46) %>%
filter(GeneID %in% filtered_genes) %>%
mutate(
promoter_start = ifelse(strand == "+", pmax(start - 2000, 1), end - 200),
promoter_end = ifelse(strand == "+", start + 200, pmax(end + 2000, 1))
)
Gencode.V46_tss <- GRanges(gtf_df_filtered)
# Promoters
promoter_df <- unique(gtf_df_filtered[,c(1,10:11,7)])
Gencode.V46_promoter <- GRanges(promoter_df)
# Find closest TSS and distances
closest_tss_indices <- nearest(consensus_CREs, Gencode.V46_tss)
dists <- distanceToNearest(consensus_CREs, Gencode.V46_tss)
consensus_CREs$closest_tss <- mcols(Gencode.V46_tss[closest_tss_indices])$GeneSymb
consensus_CREs$distance_tss <- mcols(dists)$distance
# Overlap with cell-type specific peaks
overlap_gr_list <- GRangesList(
lapply(names(iTF_CREs_ENCODE), function(name) {
gr_current <- iTF_CREs_ENCODE[[name]]
overlaps <- findOverlaps(consensus_CREs, gr_current)
overlapping_ranges <- consensus_CREs[queryHits(overlaps)]
mcols(overlapping_ranges)$cell.type <- mcols(gr_current)$cell.type[subjectHits(overlaps)]
unique(overlapping_ranges)
})
)
# Convert results to data frames
iTF_cCREs_df <- as.data.frame(consensus_CREs)
overlap_dfs <- lapply(overlap_gr_list, as.data.frame)
names(overlap_dfs) <- names(iTF_CREs_ENCODE)
iTF_iPSCs_cCREs_df <- overlap_dfs$iTF_iPSCs
iTF_Microglia_cCREs_df <- overlap_dfs$iTF_Microglia
iTF_Microglia.LPS.IFNG_cCREs_df <- overlap_dfs$iTF_Microglia.LPS.IFNG
# Merge keeping all rows in df1
merged_iTF_cCREs_df <- merge(iTF_cCREs_df, iTF_iPSCs_cCREs_df, by = intersect(names(iTF_cCREs_df), names(iTF_iPSCs_cCREs_df)), all.x = TRUE)
merged_iTF_cCREs_df <- merge(merged_iTF_cCREs_df, iTF_Microglia_cCREs_df, by = intersect(names(iTF_cCREs_df), names(iTF_Microglia_cCREs_df)), all.x = TRUE)
merged_iTF_cCREs_df <- merge(merged_iTF_cCREs_df, iTF_Microglia.LPS.IFNG_cCREs_df, by = intersect(names(iTF_cCREs_df), names(iTF_Microglia.LPS.IFNG_cCREs_df)), all.x = TRUE)
# Rename columns
merged_iTF_cCREs_df <- merged_iTF_cCREs_df %>% rename(
iPSC_cCRE = cell.type.x,
Microglia_cCRE = cell.type.y,
Microglia_LPS_IFNG_cCRE = cell.type
) %>% unique()
########################
# HiC loops Processing
########################
library(mariner)
library(InteractionSet)
# Define file path
dir_loop_data <- "/cluster/projects/ADFTD/HiC/Ivan_10-21-24/encode_pipeline_outputs"
loopFiles <- list.files(path = dir_loop_data, pattern = "loops_30.bedpe.gz", recursive = TRUE, full.names = TRUE)
# Read and sort loops file
loops_sorted <- fread(loopFiles[7], header = FALSE) %>%
as.data.frame() %>%
arrange(V1, V2) %>%
mutate(name = paste0("loop", row_number()))
# Convert to GInteractions and clear metadata
loops_GI <- as_ginteractions(loops_sorted)
mcols(loops_GI) <- NULL
# Find overlaps
overlap_anchor1 <- findOverlaps(anchors(loops_GI, type = "first"), Gencode.V46_promoter)
overlap_anchor2 <- findOverlaps(anchors(loops_GI, type = "second"), Gencode.V46_promoter)
# Create data.frames of overlaps
df_olap1 <- data.frame(
anchor_id = queryHits(overlap_anchor1),
gene = Gencode.V46_promoter$GeneSymb[subjectHits(overlap_anchor1)]
)
df_olap2 <- data.frame(
anchor_id = queryHits(overlap_anchor2),
gene = Gencode.V46_promoter$GeneSymb[subjectHits(overlap_anchor2)]
)
# Collapse overlapping gene names
collapsed_genes1 <- aggregate(gene ~ anchor_id, df_olap1, function(x) paste(unique(x), collapse = ","))
collapsed_genes2 <- aggregate(gene ~ anchor_id, df_olap2, function(x) paste(unique(x), collapse = ","))
# Initialize columns
mcols(loops_GI)$hic_anchor1_gene <- NA_character_
mcols(loops_GI)$hic_anchor2_gene <- NA_character_
# Assign collapsed gene names
mcols(loops_GI)$hic_anchor1_gene[collapsed_genes1$anchor_id] <-
collapsed_genes1$gene[match(collapsed_genes1$anchor_id, collapsed_genes1$anchor_id)]
mcols(loops_GI)$hic_anchor2_gene[collapsed_genes2$anchor_id] <-
collapsed_genes2$gene[match(collapsed_genes2$anchor_id, collapsed_genes2$anchor_id)]
# Overlap with cCREs
consensus_CREs_ranges <- consensus_CREs
mcols(consensus_CREs_ranges) <- NULL
# Overlap cCREs with anchor1
overlap_cCRE_anchor1 <- findOverlaps(consensus_CREs_ranges,anchors(loops_GI, type = "first"))
# Overlap cCREs with anchor2
overlap_cCRE_anchor2 <- findOverlaps(consensus_CREs_ranges,anchors(loops_GI, type = "second"))
# Add gene_name to anchor1 overlaps
consensus_CREs_anchor1 <- consensus_CREs_ranges
mcols(consensus_CREs_anchor1)$hic_anchor_gene <- NA
mcols(consensus_CREs_anchor1)$hic_anchor_gene[queryHits(overlap_cCRE_anchor1)] <- loops_GI$hic_anchor2_gene[subjectHits(overlap_cCRE_anchor1)]
# Add gene_name to anchor2 overlaps
consensus_CREs_anchor2 <- consensus_CREs_ranges
mcols(consensus_CREs_anchor2)$hic_anchor_gene <- NA
mcols(consensus_CREs_anchor2)$hic_anchor_gene[queryHits(overlap_cCRE_anchor2)] <- loops_GI$hic_anchor1_gene[subjectHits(overlap_cCRE_anchor2)]
# Create datafr
consensus_CREs_anchor1_df <- as.data.frame(consensus_CREs_anchor1)
consensus_CREs_anchor2_df <- as.data.frame(consensus_CREs_anchor2)
# Assuming df1 and df2 are the dataframes you want to rbind
df_combined <- bind_rows(consensus_CREs_anchor1_df, consensus_CREs_anchor1_df)
# Remove NA from 'hic_anchor_gene' and collapse by ',' (comma separator)
cCRE_hic_genes <- df_combined %>%
filter(!is.na(hic_anchor_gene)) %>%
group_by(across(-hic_anchor_gene)) %>% # Group by all columns except 'hic_anchor_gene'
summarise(hic_anchor_gene = paste(unique(na.omit(hic_anchor_gene)), collapse = ","), .groups = 'drop')
# Result
cCRE_hic_genes <- as.data.frame(cCRE_hic_genes)
# Merge dataframe
merged_iTF_cCREs_df <- merge(merged_iTF_cCREs_df, cCRE_hic_genes, by = intersect(names(iTF_cCREs_df), names(cCRE_hic_genes)), all.x = TRUE)
########################
#DegCre results
########################
# Define the directory
folder_path <- "/cluster/home/irodriguez/Myers_Lab/KOLF2.1J-iTF_iMGLs_Nick_paper/manuscript/batch2/Tables"
# List all files with the "_degCreGInter.rd" suffix
#rd_files <- list.files(folder_path, pattern = "_H3K27ac_overlapping_peaks_degCreGInter.rd$", full.names = TRUE)
#rd_files <- list.files(folder_path, pattern = "_ATAC_overlapping_peaks_degCreGInter.rd$", full.names = TRUE)
rd_files <- list.files(folder_path, pattern = "_ATAC_overlapping_peaks_diff_degCreGInter.rd$", full.names = TRUE)
rd_files <- list.files(folder_path, pattern = "_H3K27ac_overlapping_peaks_stim_degCreGInter.rd$", full.names = TRUE)
# Loop through the list of files and load each one
for (file in rd_files) {
# Extract the file name without extension to use as a variable name
file_name <- gsub("_H3K27ac_overlapping_peaks_stim_degCreGInter.rd$", "", basename(file))
# Load the file
temp_object_name <- load(file)
# Retrieve the object using `get`
assign(file_name, get(temp_object_name))
# Optionally remove the temporary object
rm(list = temp_object_name)
}
# After the loop, the variables corresponding to the files will be available in the environment
# Extract the second anchor (the second column of the anchor matrix)
iTF_Microglia_vs_iPSCs_CRE <- anchors(csaw_iTF_Microglia_vs_iTF_iPSCs, type = "second")
iTF_Microglia_vs_iPSCs_CRE_df <- as.data.frame(iTF_Microglia_vs_iPSCs_CRE)
iTF_Microglia.LPS.IFNG_vs_iTF_Microglia_CRE <- anchors(csaw_iTF_Microglia.LPS.IFNG_vs_iTF_Microglia, type = "second")
iTF_Microglia.LPS.IFNG_vs_iTF_Microglia_CRE_df <- as.data.frame(iTF_Microglia.LPS.IFNG_vs_iTF_Microglia_CRE)
# Extract the metadata columns: Deg_GeneSymb and Cre_direction
iTF_Microglia.LPS.IFNG_vs_iTF_Microglia_CRE_meta_data <- mcols(csaw_iTF_Microglia.LPS.IFNG_vs_iTF_Microglia)[, c("Deg_GeneSymb", "Cre_direction", "assocDist")]
iTF_Microglia_vs_iPSCs_CRE_meta_data <- mcols(csaw_iTF_Microglia_vs_iTF_iPSCs)[, c("Deg_GeneSymb", "Cre_direction", "assocDist")]
# Combine the second anchor and the selected metadata columns into a data frame
iTF_Microglia.LPS.IFNG_vs_iTF_Microglia_DegCre <- unique(cbind(iTF_Microglia.LPS.IFNG_vs_iTF_Microglia_CRE_df, iTF_Microglia.LPS.IFNG_vs_iTF_Microglia_CRE_meta_data))
iTF_Microglia_vs_iPSCs_DegCre <- unique(cbind(iTF_Microglia_vs_iPSCs_CRE_df, iTF_Microglia_vs_iPSCs_CRE_meta_data))
# Separate by cell type
iTF_Microglia.LPS.IFNG_DegCre <- iTF_Microglia.LPS.IFNG_vs_iTF_Microglia_DegCre[iTF_Microglia.LPS.IFNG_vs_iTF_Microglia_DegCre$Cre_direction == "up",]
iTF_Microglia.untreated_DegCre <- iTF_Microglia.LPS.IFNG_vs_iTF_Microglia_DegCre[iTF_Microglia.LPS.IFNG_vs_iTF_Microglia_DegCre$Cre_direction == "down",]
iTF_Microglia.diff_DegCre <- iTF_Microglia_vs_iPSCs_DegCre[iTF_Microglia_vs_iPSCs_DegCre$Cre_direction == "up",]
iTF_iPSCs_DegCre <- iTF_Microglia_vs_iPSCs_DegCre[iTF_Microglia_vs_iPSCs_DegCre$Cre_direction == "down",]
# Bind cell types
iTF_Microglia.LPS.IFNG_DegCre <- unique(iTF_Microglia.LPS.IFNG_DegCre[,c(1:6,8)])
iTF_Microglia.untreated_DegCre <- unique(iTF_Microglia.untreated_DegCre[,c(1:6,8)])
iTF_Microglia.diff_DegCre <- unique(iTF_Microglia.diff_DegCre[,c(1:6,8)])
iTF_iPSCs_DegCre <- unique(iTF_iPSCs_DegCre[,c(1:6,8)])
# Assuming df is your dataframe
iTF_Microglia.LPS.IFNG_DegCre <- iTF_Microglia.LPS.IFNG_DegCre %>%
group_by(seqnames, start, end) %>% # Group by seqnames and start
summarise(
DegCre_iTF_Microglia.LPS.IFNG = paste(sort(unique(na.omit(Deg_GeneSymb))), collapse = ","), # Sort and collapse unique Deg_GeneSymb
.groups = 'drop' # Drop grouping structure in the result
) %>%
arrange(seqnames, start, end) %>%
as.data.frame()
iTF_Microglia.untreated_DegCre <- iTF_Microglia.untreated_DegCre %>%
group_by(seqnames, start, end) %>% # Group by seqnames and start
summarise(
DegCre_iTF_Microglia.untreated = paste(sort(unique(na.omit(Deg_GeneSymb))), collapse = ","), # Collapse unique Deg_GeneSymb
.groups = 'drop' # Drop grouping structure in the result
) %>%
arrange(seqnames, start, end) %>% as.data.frame()
iTF_Microglia.diff_DegCre <- iTF_Microglia.diff_DegCre %>%
group_by(seqnames, start, end) %>% # Group by seqnames and start
summarise(
DegCre_iTF_Microglia.diff = paste(sort(unique(na.omit(Deg_GeneSymb))), collapse = ","), # Collapse unique Deg_GeneSymb
.groups = 'drop' # Drop grouping structure in the result
) %>%
arrange(seqnames, start, end) %>% as.data.frame()
iTF_iPSCs_DegCre <- iTF_iPSCs_DegCre %>%
group_by(seqnames, start, end) %>% # Group by seqnames and start
summarise(
DegCre_iTF_iPSCs = paste(sort(unique(na.omit(Deg_GeneSymb))), collapse = ","), # Collapse unique Deg_GeneSymb
.groups = 'drop' # Drop grouping structure in the result
) %>%
arrange(seqnames, start, end) %>% as.data.frame()
# Make Granges
iTF_iPSCs_DegCre_gr <- GRanges(iTF_iPSCs_DegCre)
iTF_Microglia.diff_DegCre_gr <- GRanges(iTF_Microglia.diff_DegCre)
iTF_Microglia.untreated_DegCre_gr <- GRanges(iTF_Microglia.untreated_DegCre)
iTF_Microglia.LPS.IFNG_DegCre_gr <- GRanges(iTF_Microglia.LPS.IFNG_DegCre)
# Make GrangesList
DegCre_iTF_list <- list(iTF_iPSCs_DegCre_gr,iTF_Microglia.diff_DegCre_gr,
iTF_Microglia.untreated_DegCre_gr,iTF_Microglia.LPS.IFNG_DegCre_gr)
#DegCre_iTF_list_grl <- GRangesList(DegCre_iTF_list)
# overlap
overlap_with_metadata <- function(query, subject) {
# Find overlaps between query and subject
hits <- findOverlaps(query, subject)
# Extract overlapping ranges
query_overlaps <- query[queryHits(hits)]
subject_overlaps <- subject[subjectHits(hits)]
# Combine metadata columns
combined_mcols <- cbind(mcols(query_overlaps), mcols(subject_overlaps))
# Create a new GRanges object with combined metadata
result <- query_overlaps
mcols(result) <- combined_mcols
return(result)
}
# Assuming ABC_Mac_list is a named list of GRanges objects
overlapped_list <- lapply(DegCre_iTF_list, function(gr) overlap_with_metadata(consensus_CREs_ranges, gr))
iTF_iPSCs_DegCre <- as.data.frame(overlapped_list[[1]])
iTF_Microglia.diff_DegCre <- as.data.frame(overlapped_list[[2]])
iTF_Microglia.untreated_DegCre <- as.data.frame(overlapped_list[[3]])
iTF_Microglia.LPS.IFNG_DegCre <- as.data.frame(overlapped_list[[4]])
iTF_iPSCs_DegCre <- unique(iTF_iPSCs_DegCre[,c(1:3,6)])
iTF_Microglia.diff_DegCre <- unique(iTF_Microglia.diff_DegCre[,c(1:3,6)])
iTF_Microglia.untreated_DegCre <- unique(iTF_Microglia.untreated_DegCre[,c(1:3,6)])
iTF_Microglia.LPS.IFNG_DegCre <- unique(iTF_Microglia.LPS.IFNG_DegCre[,c(1:3,6)])
# Collapse CREs
# Group by seqnames, start, and end, then collapse
iTF_iPSCs_DegCre_collapsed <- iTF_iPSCs_DegCre %>%
group_by(seqnames, start, end) %>%
summarise(DegCre_iTF_iPSCs = paste(
unique(sort(unlist(strsplit(DegCre_iTF_iPSCs, ",")))),
collapse = ","
), .groups = "drop") %>% as.data.frame()
iTF_Microglia_DegCre.diff_collapsed <- iTF_Microglia.diff_DegCre %>%
group_by(seqnames, start, end) %>%
summarise(DegCre_iTF_Microglia.diff = paste(
unique(sort(unlist(strsplit(DegCre_iTF_Microglia.diff, ",")))),
collapse = ","
), .groups = "drop") %>% as.data.frame()
iTF_Microglia_DegCre.untreated_collapsed <- iTF_Microglia.untreated_DegCre %>%
group_by(seqnames, start, end) %>%
summarise(DegCre_iTF_Microglia.untreated = paste(
unique(sort(unlist(strsplit(DegCre_iTF_Microglia.untreated, ",")))),
collapse = ","
), .groups = "drop") %>% as.data.frame()
iTF_Microglia_DegCre.LPS.IFNG_collapsed <- iTF_Microglia.LPS.IFNG_DegCre %>%
group_by(seqnames, start, end) %>%
summarise(DegCre_iTF_Microglia.LPS.IFNG = paste(
unique(sort(unlist(strsplit(DegCre_iTF_Microglia.LPS.IFNG, ",")))),
collapse = ","
), .groups = "drop") %>% as.data.frame()
# Merge dataframe
merged_iTF_cCREs_df <- merge(merged_iTF_cCREs_df, iTF_iPSCs_DegCre_collapsed, by = intersect(names(iTF_cCREs_df), names(iTF_iPSCs_DegCre_collapsed)), all.x = TRUE)
merged_iTF_cCREs_df <- merge(merged_iTF_cCREs_df, iTF_Microglia_DegCre.diff_collapsed, by = intersect(names(iTF_cCREs_df), names(iTF_Microglia_DegCre.diff_collapsed)), all.x = TRUE)
merged_iTF_cCREs_df <- merge(merged_iTF_cCREs_df, iTF_Microglia_DegCre.untreated_collapsed, by = intersect(names(iTF_cCREs_df), names(iTF_Microglia_DegCre.untreated_collapsed)), all.x = TRUE)
merged_iTF_cCREs_df <- merge(merged_iTF_cCREs_df, iTF_Microglia_DegCre.LPS.IFNG_collapsed, by = intersect(names(iTF_cCREs_df), names(iTF_Microglia_DegCre.LPS.IFNG_collapsed)), all.x = TRUE)
########################
#ABC results
########################
# Define the list of file paths
file_paths <- c(
"/cluster/home/irodriguez/software/ABC-Enhancer-Gene-Prediction/results_KOLF2.1J-iTF_HiC_20250423/iTF_iPSCs_hic_all/Predictions/EnhancerPredictionsAllPutative.tsv.gz",
"/cluster/home/irodriguez/software/ABC-Enhancer-Gene-Prediction/results_KOLF2.1J-iTF_HiC_20250423/iTF_Microglia_hic_all/Predictions/EnhancerPredictionsAllPutative.tsv.gz",
"/cluster/home/irodriguez/software/ABC-Enhancer-Gene-Prediction/results_KOLF2.1J-iTF_HiC_20250423/iTF_Microglia.LPS.IFNG_hic_all/Predictions/EnhancerPredictionsAllPutative.tsv.gz"
)
# Apply the function to each file path
cell_types <- sapply(strsplit(unlist(file_paths),"[/]"),"[[",8)
# Read all files into a list
ABC_list <- lapply(file_paths, function(fp) fread(fp, sep = "\t", header = TRUE))
names(ABC_list) <- cell_types
# ABC score threshold based on doi:10.1038/s41586-021-03446-x
ABC_filtered <- lapply(ABC_list, function(df) {
df[df$class == "promoter" & df$ABC.Score >= 0.1 |
df$class != "promoter" & df$ABC.Score >= 0.015, ]
})
ABC_filtered_subset <- lapply(ABC_filtered, function(x) {
unique(x[,c(1:3,11,31)])
})
iTF_iPSCs_ABC <- as.data.frame(ABC_filtered_subset$iTF_iPSCs_hic_all)
iTF_Microglia_ABC <- as.data.frame(ABC_filtered_subset$iTF_Microglia_hic_all)
iTF_Microglia.LPS.IFNG_ABC <- as.data.frame(ABC_filtered_subset$iTF_Microglia.LPS.IFNG_hic_all)
# Assuming df is your dataframe
iTF_Microglia.LPS.IFNG_ABC <- iTF_Microglia.LPS.IFNG_ABC %>%
group_by(chr, start, end) %>% # Group by chr and start
summarise(
ABC_iTF_Microglia.LPS.IFNG = paste(sort(unique(na.omit(TargetGene))), collapse = ","), # Sort and collapse unique TargetGene
.groups = 'drop' # Drop grouping structure in the result
) %>%
arrange(chr, start, end) %>%
as.data.frame()
iTF_Microglia_ABC <- iTF_Microglia_ABC %>%
group_by(chr, start, end) %>% # Group by chr and start
summarise(
ABC_iTF_Microglia = paste(sort(unique(na.omit(TargetGene))), collapse = ","), # Collapse unique TargetGene
.groups = 'drop' # Drop grouping structure in the result
) %>%
arrange(chr, start, end) %>% as.data.frame()
iTF_iPSCs_ABC <- iTF_iPSCs_ABC %>%
group_by(chr, start, end) %>% # Group by chr and start
summarise(
ABC_iTF_iPSCs = paste(sort(unique(na.omit(TargetGene))), collapse = ","), # Collapse unique TargetGene
.groups = 'drop' # Drop grouping structure in the result
) %>%
arrange(chr, start, end) %>% as.data.frame()
# Make Granges
iTF_iPSCs_ABC_gr <- GRanges(iTF_iPSCs_ABC)
iTF_Microglia_ABC_gr <- GRanges(iTF_Microglia_ABC)
iTF_Microglia.LPS.IFNG_ABC_gr <- GRanges(iTF_Microglia.LPS.IFNG_ABC)
# Make GrangesList
ABC_iTF_list <- list(iTF_iPSCs_ABC_gr,iTF_Microglia_ABC_gr,iTF_Microglia.LPS.IFNG_ABC_gr)
# overlap
overlap_with_metadata <- function(query, subject) {
# Find overlaps between query and subject
hits <- findOverlaps(query, subject)
# Extract overlapping ranges
query_overlaps <- query[queryHits(hits)]
subject_overlaps <- subject[subjectHits(hits)]
# Combine metadata columns
combined_mcols <- cbind(mcols(query_overlaps), mcols(subject_overlaps))
# Create a new GRanges object with combined metadata
result <- query_overlaps
mcols(result) <- combined_mcols
return(result)
}
# Assuming ABC_iTF_list is a named list of GRanges objects
overlapped_list <- lapply(ABC_iTF_list, function(gr) overlap_with_metadata(consensus_CREs_ranges, gr))
iTF_iPSCs_ABC <- as.data.frame(overlapped_list[[1]])
iTF_Microglia_ABC <- as.data.frame(overlapped_list[[2]])
iTF_Microglia.LPS.IFNG_ABC <- as.data.frame(overlapped_list[[3]])
iTF_iPSCs_ABC <- unique(iTF_iPSCs_ABC[,c(1:3,6)])
iTF_Microglia_ABC <- unique(iTF_Microglia_ABC[,c(1:3,6)])
iTF_Microglia.LPS.IFNG_ABC <- unique(iTF_Microglia.LPS.IFNG_ABC[,c(1:3,6)])
# Collapse CREs
# Group by seqnames, start, and end, then collapse ABC_THP1_Mac_0h
iTF_iPSCs_ABC_collapsed <- iTF_iPSCs_ABC %>%
group_by(seqnames, start, end) %>%
summarise(ABC_iTF_iPSCs = paste(
unique(sort(unlist(strsplit(ABC_iTF_iPSCs, ",")))),
collapse = ","
), .groups = "drop") %>% as.data.frame()
iTF_Microglia_ABC_collapsed <- iTF_Microglia_ABC %>%
group_by(seqnames, start, end) %>%
summarise(ABC_iTF_Microglia = paste(
unique(sort(unlist(strsplit(ABC_iTF_Microglia, ",")))),
collapse = ","
), .groups = "drop") %>% as.data.frame()
iTF_Microglia.LPS.IFNG_ABC_collapsed <- iTF_Microglia.LPS.IFNG_ABC %>%
group_by(seqnames, start, end) %>%
summarise(ABC_iTF_Microglia.LPS.IFNG = paste(
unique(sort(unlist(strsplit(ABC_iTF_Microglia.LPS.IFNG, ",")))),
collapse = ","
), .groups = "drop") %>% as.data.frame()
# Merge dataframe
merged_iTF_cCREs_df <- merge(merged_iTF_cCREs_df, iTF_iPSCs_ABC_collapsed, by = intersect(names(iTF_cCREs_df), names(iTF_iPSCs_ABC_collapsed)), all.x = TRUE)
merged_iTF_cCREs_df <- merge(merged_iTF_cCREs_df, iTF_Microglia_ABC_collapsed, by = intersect(names(iTF_cCREs_df), names(iTF_Microglia_ABC_collapsed)), all.x = TRUE)
merged_iTF_cCREs_df <- merge(merged_iTF_cCREs_df, iTF_Microglia.LPS.IFNG_ABC_collapsed, by = intersect(names(iTF_cCREs_df), names(iTF_Microglia.LPS.IFNG_ABC_collapsed)), all.x = TRUE)
#Save files
saveRDS(merged_iTF_cCREs_df,file="/cluster/home/irodriguez/Myers_Lab/KOLF2.1J-iTF_iMGLs_Nick_paper/manuscript/batch2/Tables/iTF_CREs_target_genes.rds")
#merged_iTF_cCREs_df <- readRDS("/cluster/home/irodriguez/Myers_Lab/KOLF2.1J-iTF_iMGLs_Nick_paper/manuscript/Tables/iTF_CREs_target_genes.rds")
#consensus_CREs <- readRDS("/cluster/home/irodriguez/Myers_Lab/KOLF2.1J-iTF_iMGLs_Nick_paper/candidate_CREs/iTF_CREs_all_ENCODE_ABC_peaks.rds")
#iTF_cCREs_df <- as.data.frame(consensus_CREs)
#######################
#snMULTIomics AD Myers lab
#######################
# Load links
links <- read.csv("/cluster/home/irodriguez/Myers_Lab/20230327_AD_variants_TF_trios_snMultiomics/mmc6_feature_links.csv")
# Subset trios
links_subset <- unique(links[,c(1:3,5,9,8)])
links_subset_MG <- links_subset[grep("Microglia",links_subset$CT),]
links_subset_MG_genes <- unique(links_subset_MG$gene)
# Summarize by unique seqnames, start, and end
links_subset_MG_summary <- links_subset_MG %>%
group_by(seqnames, start, end) %>%
summarise(
gene = paste(gene, collapse = ","),
group = paste(group, collapse = ","),
.groups = "drop"
)
links_subset_MG_summary <- unique(links_subset_MG_summary)
#colnames(links_subset_MG_summary) <- c("seqnames","start","end","Peak2gene.Anderson2023")
colnames(links_subset_MG_summary) <- c("seqnames","start","end","Peak2gene.Anderson2023","Phenotype.Anderson2023")
# Create GRanges objects
links_GR <- GRanges(links_subset_MG_summary)
#overlap with SNPs
olap_links <- findOverlaps(consensus_CREs_ranges,links_GR)
# add the metacolumns to the GR object
consensus_CREs_links <- consensus_CREs_ranges[queryHits(olap_links)]
mcols(consensus_CREs_links) <- cbind(mcols(consensus_CREs_links), mcols(links_GR[subjectHits(olap_links)]))
# Create df from the GR overlap
iTF_cCREs_links <- data.frame(consensus_CREs_links)
iTF_cCREs_links <- unique(iTF_cCREs_links[,c(1:3,6:7)])
# Collapse CREs
# Group by seqnames, start, and end
iTF_cCREs_links_collapsed <- iTF_cCREs_links %>%
group_by(seqnames, start, end) %>%
summarise(Peak2gene.Anderson2023 = paste(
unlist(strsplit(Peak2gene.Anderson2023, ",")), collapse = ","),
Phenotype.Anderson2023 = paste(
unlist(strsplit(Phenotype.Anderson2023, ",")), collapse = ","),
.groups = "drop") %>% as.data.frame()
iTF_cCREs_links_collapsed <- unique(iTF_cCREs_links_collapsed)
# Merge dataframe
merged_iTF_cCREs_df <- merge(merged_iTF_cCREs_df, iTF_cCREs_links_collapsed, by = intersect(names(iTF_cCREs_df), names(iTF_cCREs_links_collapsed)), all.x = TRUE)
#Save files
saveRDS(merged_iTF_cCREs_df,file="/cluster/home/irodriguez/Myers_Lab/KOLF2.1J-iTF_iMGLs_Nick_paper/manuscript/batch2/Tables/iTF_CREs_target_genes.rds")
#merged_iTF_cCREs_df <- readRDS("/cluster/home/irodriguez/Myers_Lab/KOLF2.1J-iTF_iMGLs_Nick_paper/manuscript/batch2/Tables/iTF_CREs_target_genes.rds")
#########################################
# Nott PLACseq brain
#########################################
library(readxl)
read_excel_allsheets <- function(filename, tibble = FALSE) {
# I prefer straight data.frames
# but if you like tidyverse tibbles (the default with read_excel)
# then just pass tibble = TRUE
sheets <- readxl::excel_sheets(filename)
x <- lapply(sheets, function(X) readxl::read_excel(filename, sheet = X))
if(!tibble) x <- lapply(x, as.data.frame)
names(x) <- sheets
x
}
# Load PLACseq files
Nott_PLAC <- read_excel_allsheets("/cluster/home/irodriguez/Myers_Lab/GWAS_AD_summary_statistics/variant_annotation/Nott_files/aay0793-nott-table-s5.xlsx")
names(Nott_PLAC)
PLAC_mic <- as.data.frame(Nott_PLAC[[2]])
colnames(PLAC_mic) <- PLAC_mic[2,]
PLAC_mic <- PLAC_mic[-c(1:2),1:6]
PLACseq <- unique(PLAC_mic)
PLACseq$start1 <- as.numeric(PLACseq$start1)
PLACseq$start2 <- as.numeric(PLACseq$start2)
# Convert to GInteractions and clear metadata
loops_GI <- as_ginteractions(PLACseq)
mcols(loops_GI) <- NULL
# Liftover loops
# transformation to hg38 coordinates
ch = import.chain("/cluster/home/irodriguez/software/hg19ToHg38.over.chain")
str(ch[[1]])
# Perform liftOver without flattening
lifted1_list <- liftOver(anchors(loops_GI, type = "first"), ch)
lifted2_list <- liftOver(anchors(loops_GI, type = "second"), ch)
# Get indices where exactly one mapping was returned
valid_idx <- elementNROWS(lifted1_list) == 1 & elementNROWS(lifted2_list) == 1
# Subset to valid interactions
loops_valid <- loops_GI[valid_idx]
# Extract the lifted anchors
lifted1 <- unlist(lifted1_list[valid_idx])
lifted2 <- unlist(lifted2_list[valid_idx])
lifted_loops_GI <- GInteractions(anchor1 = lifted1,
anchor2 = lifted2,
metadata = mcols(loops_valid),
mode = "strict") # optional
# Find overlaps
overlap_anchor1 <- findOverlaps(anchors(lifted_loops_GI, type = "first"), Gencode.V46_promoter)
overlap_anchor2 <- findOverlaps(anchors(lifted_loops_GI, type = "second"), Gencode.V46_promoter)
# Create data.frames of overlaps
df_olap1 <- data.frame(
anchor_id = queryHits(overlap_anchor1),
gene = Gencode.V46_promoter$GeneSymb[subjectHits(overlap_anchor1)]
)
df_olap2 <- data.frame(
anchor_id = queryHits(overlap_anchor2),
gene = Gencode.V46_promoter$GeneSymb[subjectHits(overlap_anchor2)]
)
# Collapse overlapping gene names
collapsed_genes1 <- aggregate(gene ~ anchor_id, df_olap1, function(x) paste(unique(x), collapse = ","))
collapsed_genes2 <- aggregate(gene ~ anchor_id, df_olap2, function(x) paste(unique(x), collapse = ","))
# Initialize columns
mcols(lifted_loops_GI)$PLAC_anchor1_gene <- NA_character_
mcols(lifted_loops_GI)$PLAC_anchor2_gene <- NA_character_
# Assign collapsed gene names
mcols(lifted_loops_GI)$PLAC_anchor1_gene[collapsed_genes1$anchor_id] <-
collapsed_genes1$gene[match(collapsed_genes1$anchor_id, collapsed_genes1$anchor_id)]
mcols(lifted_loops_GI)$PLAC_anchor2_gene[collapsed_genes2$anchor_id] <-
collapsed_genes2$gene[match(collapsed_genes2$anchor_id, collapsed_genes2$anchor_id)]
# Overlap cCREs with anchor1
overlap_cCRE_anchor1 <- findOverlaps(consensus_CREs_ranges,anchors(lifted_loops_GI, type = "first"))
# Overlap cCREs with anchor2
overlap_cCRE_anchor2 <- findOverlaps(consensus_CREs_ranges,anchors(lifted_loops_GI, type = "second"))
# Add gene_name to anchor1 overlaps
consensus_CREs_anchor1 <- consensus_CREs_ranges
mcols(consensus_CREs_anchor1)$Nott_PLAC_anchor_gene <- NA
mcols(consensus_CREs_anchor1)$Nott_PLAC_anchor_gene[queryHits(overlap_cCRE_anchor1)] <- lifted_loops_GI$PLAC_anchor2_gene[subjectHits(overlap_cCRE_anchor1)]
# Add gene_name to anchor2 overlaps
consensus_CREs_anchor2 <- consensus_CREs_ranges
mcols(consensus_CREs_anchor2)$Nott_PLAC_anchor_gene <- NA
mcols(consensus_CREs_anchor2)$Nott_PLAC_anchor_gene[queryHits(overlap_cCRE_anchor2)] <- lifted_loops_GI$PLAC_anchor1_gene[subjectHits(overlap_cCRE_anchor2)]
# Create datafr
consensus_CREs_anchor1_df <- as.data.frame(consensus_CREs_anchor1)
consensus_CREs_anchor2_df <- as.data.frame(consensus_CREs_anchor2)
# Assuming df1 and df2 are the dataframes you want to rbind
df_combined <- bind_rows(consensus_CREs_anchor1_df, consensus_CREs_anchor1_df)
# Remove NA from 'hic_anchor_gene' and collapse by ',' (comma separator)
cCRE_hic_genes <- df_combined %>%
filter(!is.na(Nott_PLAC_anchor_gene)) %>%
group_by(across(-Nott_PLAC_anchor_gene)) %>% # Group by all columns except 'hic_anchor_gene'
summarise(Nott_PLAC_anchor_gene = paste(unique(na.omit(Nott_PLAC_anchor_gene)), collapse = ","), .groups = 'drop')
# Result
cCRE_hic_genes <- as.data.frame(cCRE_hic_genes)
# Merge dataframe
merged_iTF_cCREs_df <- merge(merged_iTF_cCREs_df, cCRE_hic_genes, by = intersect(names(iTF_cCREs_df), names(cCRE_hic_genes)), all.x = TRUE)
#Save files
saveRDS(merged_iTF_cCREs_df,file="/cluster/home/irodriguez/Myers_Lab/KOLF2.1J-iTF_iMGLs_Nick_paper/manuscript/batch2/Tables/iTF_CREs_target_genes.rds")
#############
# monaLisa TF
#############
# monaLisa
monaLisa_iTF_Microglia.LPS.IFNG_vs_iTF_Microglia <- readRDS("/cluster/home/irodriguez/Myers_Lab/KOLF2.1J-iTF_iMGLs_Nick_paper/manuscript/batch2/Tables/cCRE_specific_monaLisa_9050_iTF_Microglia.LPS.IFNG_vs_iTF_Microglia_H3K27ac_overlapping_peaks.rds")
monaLisa_iTF_Microglia_vs_iTF_iPSC <- readRDS("/cluster/home/irodriguez/Myers_Lab/KOLF2.1J-iTF_iMGLs_Nick_paper/manuscript/batch2/Tables/cCRE_specific_monaLisa_9054_iTF_Microglia_vs_iTF_iPSCs_ATAC_overlapping_peaks.rds")
grl <- c(monaLisa_iTF_Microglia.LPS.IFNG_vs_iTF_Microglia,monaLisa_iTF_Microglia_vs_iTF_iPSC)
grl <- GRangesList(lapply(names(grl), function(nm) {
gr <- grl[[nm]]
mcols(gr)$TF <- nm
gr
}))
unlisted_gr <- unlist(grl)
# Remove row names
names(unlisted_gr) <- NULL
# Convert to data.frame without row names
df <- as.data.frame(unlisted_gr, row.names = NULL)
df <- unique(df[,c(1:5,15)])
# Arrange the data frame by seqnames, start, end, and TF
df_sorted <- df %>%
arrange(seqnames, start, end, TF)
df_summarized <- df_sorted %>%
group_by(seqnames, start, end, width, strand) %>%
summarise(
monaLisa.TF = paste(unique(TF), collapse = ","),
.groups = 'drop'
)
monalisa_gr <- GRanges(df_summarized)
mcols(monalisa_gr)$monaLisa.TF <- toupper(mcols(monalisa_gr)$monaLisa.TF)
# Find overlaps
overlaps <- findOverlaps(consensus_CREs_ranges, monalisa_gr)
# Create a new column with overlapping names
iTF_cCREs_TF <- consensus_CREs_ranges[queryHits(overlaps)]
mcols(iTF_cCREs_TF) <- cbind(mcols(iTF_cCREs_TF), mcols(monalisa_gr[subjectHits(overlaps)]))
iTF_cCREs_TF_df <- as.data.frame(iTF_cCREs_TF)
iTF_cCREs_TF_df <- unique(iTF_cCREs_TF_df)
iTF_cCREs_TF_df_collapsed <- iTF_cCREs_TF_df %>%
group_by(seqnames, start, end) %>%
summarise(monaLisa.TF = paste(
unique(sort(unlist(strsplit(monaLisa.TF, ",")))),
collapse = ","
), .groups = "drop") %>% as.data.frame()
# Merge dataframe
merged_iTF_cCREs_df <- merge(merged_iTF_cCREs_df, iTF_cCREs_TF_df_collapsed,
by = intersect(names(iTF_cCREs_df), names(iTF_cCREs_TF_df_collapsed)),
all.x = TRUE)
########################
# Filter target genes based on their expression
########################
# Load and filter gene expression data
coldata <- read.csv("/cluster/home/irodriguez/Myers_Lab/20240416_Novogene_RNAseq_Library_iTF_iMGLs_Nick_paper/R_analysis/iTF-Microglia_BulkRNA_SampleSheet_phenotype.csv")
gse <- readRDS("batch2/Tables/gse.rds")
tpm <- assay(gse, "abundance")
coldata_subset <- coldata[c(13:15, 19:24),]
tpm_subset <- tpm[, c(13:15, 19:24)]
colnames(tpm_subset) <- coldata_subset$Sample
#rownames(tpm_subset) <- sapply(strsplit(rownames(tpm_subset), "[.]"), `[[`, 1)
# Replace ensgid to gene symbol
gene_map <- readRDS("Tables/Gencode_v46_gene_map.rds")
# Create a lookup table
gene_lookup <- setNames(gene_map$gene_name, gene_map$gene_id)
# Replace ENSG IDs with gene symbols where possible
new_rownames <- gene_lookup[rownames(tpm_subset)]
# Keep ENSG IDs for missing symbols
new_rownames[is.na(new_rownames)] <- rownames(tpm_subset)[is.na(new_rownames)]
# Assign new row names
rownames(tpm_subset) <- new_rownames
# Split columns into groups
iPSC_cols <- tpm_subset[,1:3]
Microglia_cols <- tpm_subset[,4:6]
Microglia_LPS_IFNG_cols <- tpm_subset[,7:9]
# Compute row-wise means for each group
iPSC_mean <- rowMeans(iPSC_cols)
Microglia_mean <- rowMeans(Microglia_cols)
Microglia_LPS_IFNG_mean <- rowMeans(Microglia_LPS_IFNG_cols)
# Get rows where any group has an average expression > 1
iPSC_filtered_rows <- (iPSC_mean > 1)
Microglia_filtered_rows <- (Microglia_mean > 1)
Microglia_LPS_IFNG_filtered_rows <- (Microglia_LPS_IFNG_mean > 1)
# Extract gene names from row names
iPSC_filtered_genes <- rownames(iPSC_cols)[iPSC_filtered_rows]
Microglia_filtered_genes <- rownames(Microglia_cols)[Microglia_filtered_rows]
Microglia_LPS_IFNG_filtered_genes <- rownames(Microglia_LPS_IFNG_cols)[Microglia_LPS_IFNG_filtered_rows]
filtered_genes <- c(iPSC_filtered_genes,Microglia_filtered_genes,Microglia_LPS_IFNG_filtered_genes)
filtered_genes <- unique(filtered_genes)
# Define gene list
gene_list <- iPSC_filtered_genes #iPSCs
gene_list <- Microglia_filtered_genes #Microglia
gene_list <- Microglia_LPS_IFNG_filtered_genes #Microglia_LPS_IFNG
gene_list <- filtered_genes #all
# Define columns that need filtering
gene_cols <- c("DegCre_iTF_iPSCs", "ABC_iTF_iPSCs") # iPSCs
gene_cols <- c("DegCre_iTF_Microglia.diff", "DegCre_iTF_Microglia.untreated", "ABC_iTF_Microglia") # Microglia
gene_cols <- c("DegCre_iTF_Microglia.LPS.IFNG", "ABC_iTF_Microglia.LPS.IFNG") # MicrogliaLPS.IFNG
gene_cols <- c("monaLisa.TF") # filtered_genes
# Function to filter gene symbols, handling "::" cases
filter_gene_symbols <- function(col) {
ifelse(is.na(col), NA,
sapply(strsplit(col, ","), function(genes) {
# Process each gene entry
filtered_genes <- sapply(genes, function(g) {
# Split by "::" and check if any part is in gene_list
parts <- unlist(strsplit(g, "::"))
if (any(parts %in% gene_list)) g else NA
})
# Remove NA entries and collapse back
paste(na.omit(filtered_genes), collapse = ",")
})
)
}
# Apply the filtering function to the specified columns
#filtered_iTF_cCREs_df <- merged_iTF_cCREs_df
filtered_iTF_cCREs_df <- filtered_iTF_cCREs_df %>%
mutate(across(all_of(gene_cols), filter_gene_symbols))
#Save files
saveRDS(filtered_iTF_cCREs_df,file="/cluster/home/irodriguez/Myers_Lab/KOLF2.1J-iTF_iMGLs_Nick_paper/manuscript/batch2/Tables/iTF_CREs_target_genes.rds")
filtered_iTF_cCREs_df <- readRDS("/cluster/home/irodriguez/Myers_Lab/KOLF2.1J-iTF_iMGLs_Nick_paper/manuscript/batch2/Tables/iTF_CREs_target_genes.rds")
filtered_iTF_cCREs_df %>%
filter(if_any(everything(), ~ grepl("^GRN", .)))
########################
# Count cCREs and target genes
########################
# Format dataframe
iTF_cCREs_split_df <- filtered_iTF_cCREs_df
iTF_cCREs_split_df <- unique(iTF_cCREs_split_df[,c(1:3,6,12:19,21)])
# Remove"Nott_PLAC_anchor_gene" column
iTF_cCREs_split_df <- unique(iTF_cCREs_split_df[,-13])
# List of columns to separate
cols_to_separate <- c(
"closest_tss",
"DegCre_iTF_iPSCs",
"DegCre_iTF_Microglia.diff",
"DegCre_iTF_Microglia.untreated",
"DegCre_iTF_Microglia.LPS.IFNG",
"ABC_iTF_iPSCs",
"ABC_iTF_Microglia",
"ABC_iTF_Microglia.LPS.IFNG",
"Peak2gene.Anderson2023")
cols_to_separate <- c(
"closest_tss",
"DegCre_iTF_iPSCs",
"DegCre_iTF_Microglia.diff",
"DegCre_iTF_Microglia.untreated",
"DegCre_iTF_Microglia.LPS.IFNG",
"ABC_iTF_iPSCs",
"ABC_iTF_Microglia",
"ABC_iTF_Microglia.LPS.IFNG",
"Peak2gene.Anderson2023",
"Nott_PLAC_anchor_gene")
# Apply separate_rows to each specified column
iTF_cCREs_split_long <- iTF_cCREs_split_df
# Convert to data.table
iTF_cCREs_split_long_dt <- as.data.table(iTF_cCREs_split_long)
# Function to split columns into long format
split_columns_to_long <- function(dt, cols_to_separate) {
# Initialize an empty list to store the long-format data
long_dt_list <- list()
# Iterate through each column you want to separate
for (col in cols_to_separate) {
# For each column, split the comma-separated values into separate rows
temp_dt <- dt[!is.na(get(col)) & get(col) != "",
.(seqnames, start, end,
target_gene = unlist(tstrsplit(get(col), ",", fixed = TRUE)),
Mapping = col)
]
# Append the processed data.table chunk to the list
long_dt_list[[col]] <- temp_dt
}
# Combine all the chunks into one final long-format data.table
final_long_dt <- rbindlist(long_dt_list, fill = TRUE)
# Order the final data.table by seqnames and start
setorder(final_long_dt, seqnames, start)
return(final_long_dt)
}
# Apply the function to your data.table
iTF_cCREs_split_long_dt <- split_columns_to_long(iTF_cCREs_split_long_dt, cols_to_separate)
iTF_cCREs_split_long_dt <-unique(iTF_cCREs_split_long_dt)
# View the first few rows of the transformed data
head(iTF_cCREs_split_long_dt)
# Remove NA
iTF_cCREs_clean <- iTF_cCREs_split_long_dt[complete.cases(iTF_cCREs_split_long_dt),]
iTF_cCREs_clean <- iTF_cCREs_clean %>% distinct()
iTF_cCREs_clean <- iTF_cCREs_clean %>%
mutate(cell.type = case_when(
grepl("iTF_iPSCs", Mapping) ~ "iTF-iPSCs",
grepl("iTF_Microglia$", Mapping) ~ "iTF-Microglia",
grepl("iTF_Microglia.diff", Mapping) ~ "iTF-Microglia",
grepl("iTF_Microglia.untreated", Mapping) ~ "iTF-Microglia",
grepl("LPS.IFNG", Mapping) ~ "iTF-Microglia LPS+IFNG",
grepl("Anderson2023", Mapping) ~ "Brain microglia",
TRUE ~ NA_character_ # For rows that don't match any condition
))
iTF_cCREs_clean <- iTF_cCREs_clean %>%
mutate(mapping = case_when(
grepl("DegCre", Mapping) ~ "DegCre",
grepl("ABC_iTF", Mapping) ~ "ABC",
grepl("closest_tss", Mapping) ~ "closest tss",
grepl("Peak2gene", Mapping) ~ "Brain microglia link",
TRUE ~ NA_character_ # For rows that don't match any condition
))
iTF_cCREs_clean <- iTF_cCREs_clean %>%
mutate(mapping = case_when(
grepl("DegCre", Mapping) ~ "DegCre",
grepl("ABC_iTF", Mapping) ~ "ABC",
grepl("closest_tss", Mapping) ~ "closest tss",
grepl("Peak2gene", Mapping) ~ "Brain microglia link",
grepl("Nott", Mapping) ~ "PLAC-seq (Nott)",
TRUE ~ NA_character_ # For rows that don't match any condition
))
iTF_cCREs_clean$cell.type <- as.factor(iTF_cCREs_clean$cell.type)
iTF_cCREs_clean$mapping <- as.factor(iTF_cCREs_clean$mapping)
#iTF_cCREs_clean <- unique(iTF_cCREs_clean[,-5])
iTF_cCREs_class <- iTF_cCREs_clean
# Split the data by cell.type
cell_type_list <- split(iTF_cCREs_clean, iTF_cCREs_clean$cell.type)
iTF_cCREs_class_gr <- GRanges(iTF_cCREs_class)
# Overlap with specific cell peaks
overlap_with_metadata <- function(query, subject) {
# Find overlaps between query and subject
hits <- findOverlaps(query, subject)
# Extract overlapping ranges
query_overlaps <- query[queryHits(hits)]
subject_overlaps <- subject[subjectHits(hits)]
# Combine metadata columns
combined_mcols <- cbind(mcols(query_overlaps), mcols(subject_overlaps))
# Create a new GRanges object with combined metadata
result <- query_overlaps
mcols(result) <- combined_mcols
return(result)
}
# Assuming ABC_iTF_list is a named list of GRanges objects
overlapped_list <- lapply(iTF_CREs_ENCODE, function(gr) overlap_with_metadata(iTF_cCREs_class_gr, gr))
iTF_iPSCs_class <- as.data.frame(overlapped_list$iTF_iPSCs)
iTF_Microglia_class <- as.data.frame(overlapped_list$iTF_Microglia)
iTF_Microglia.LPS.IFNG_class <- as.data.frame(overlapped_list$iTF_Microglia.LPS.IFNG)
iTF_iPSCs_closest.tss <- iTF_iPSCs_class[iTF_iPSCs_class$mapping == "closest tss",]
iTF_iPSCs_DegCre <- iTF_iPSCs_class[iTF_iPSCs_class$cell.type == "iTF-iPSCs" & iTF_iPSCs_class$mapping == "DegCre",]
iTF_iPSCs_ABC <- iTF_iPSCs_class[iTF_iPSCs_class$cell.type == "iTF-iPSCs" & iTF_iPSCs_class$mapping == "ABC",]
iTF_iPSCs_Brain.MG.link <- iTF_iPSCs_class[iTF_iPSCs_class$mapping == "Brain microglia link",]
#iTF_iPSCs_PLAC <- iTF_iPSCs_class[iTF_iPSCs_class$mapping == "PLAC-seq (Nott)",]
iTF_Microglia_closest.tss <- iTF_Microglia_class[iTF_Microglia_class$mapping == "closest tss",]
iTF_Microglia.diff_DegCre <- iTF_Microglia_class[iTF_Microglia_class$Mapping == "DegCre_iTF_Microglia.diff",]
iTF_Microglia.unt_DegCre <- iTF_Microglia_class[iTF_Microglia_class$Mapping == "DegCre_iTF_Microglia.untreated",]
iTF_Microglia_ABC <- iTF_Microglia_class[iTF_Microglia_class$Mapping == "ABC_iTF_Microglia",]
iTF_Microglia_Brain.MG.link <- iTF_Microglia_class[iTF_Microglia_class$mapping == "Brain microglia link",]
#iTF_Microglia_PLAC <- iTF_Microglia_class[iTF_Microglia_class$mapping == "PLAC-seq (Nott)",]
iTF_Microglia.LPS.IFNG_closest.tss <- iTF_Microglia.LPS.IFNG_class[iTF_Microglia.LPS.IFNG_class$Mapping == "closest_tss",]
iTF_Microglia.LPS.IFNG_DegCre <- iTF_Microglia.LPS.IFNG_class[iTF_Microglia.LPS.IFNG_class$Mapping == "DegCre_iTF_Microglia.LPS.IFNG",]
iTF_Microglia.LPS.IFNG_ABC <- iTF_Microglia.LPS.IFNG_class[iTF_Microglia.LPS.IFNG_class$Mapping == "ABC_iTF_Microglia.LPS.IFNG",]
iTF_Microglia.LPS.IFNG_Brain.MG.link <- iTF_Microglia.LPS.IFNG_class[iTF_Microglia.LPS.IFNG_class$mapping == "Brain microglia link",]
#iTF_Microglia.LPS.IFNG_PLAC <- iTF_Microglia.LPS.IFNG_class[iTF_Microglia.LPS.IFNG_class$mapping == "PLAC-seq (Nott)",]
dim(unique(iTF_iPSCs_closest.tss[,1:3]))
length(unique(iTF_iPSCs_closest.tss[,6]))
dim(unique(iTF_iPSCs_DegCre[,1:3])) #42753
length(unique(iTF_iPSCs_DegCre[,6])) #6645
dim(unique(iTF_iPSCs_ABC[,1:3])) #39486
length(unique(iTF_iPSCs_ABC[,6])) #14453
dim(unique(iTF_iPSCs_Brain.MG.link[,1:3])) #16175
length(unique(iTF_iPSCs_Brain.MG.link[,6])) #11613
dim(unique(iTF_iPSCs_PLAC[,1:3])) #16175
length(unique(iTF_iPSCs_PLAC[,6])) #11613
dim(unique(iTF_Microglia_closest.tss[,1:3])) #62011
length(unique(iTF_Microglia_closest.tss[,6])) #26899
dim(unique(iTF_Microglia.diff_DegCre[,1:3])) #22377
length(unique(iTF_Microglia.diff_DegCre[,6])) #9885
dim(unique(iTF_Microglia.unt_DegCre[,1:3])) #22377
length(unique(iTF_Microglia.unt_DegCre[,6])) #9885
dim(unique(iTF_Microglia_ABC[,1:3])) #43971
length(unique(iTF_Microglia_ABC[,6])) #15724
dim(unique(iTF_Microglia_Brain.MG.link[,1:3])) #23788
length(unique(iTF_Microglia_Brain.MG.link[,6])) #12240
dim(unique(iTF_Microglia_PLAC[,1:3])) #16175
length(unique(iTF_Microglia_PLAC[,6])) #11613
dim(unique(iTF_Microglia.LPS.IFNG_closest.tss[,1:3])) #62470
length(unique(iTF_Microglia.LPS.IFNG_closest.tss[,6])) #26387
dim(unique(iTF_Microglia.LPS.IFNG_DegCre[,1:3])) #26298
length(unique(iTF_Microglia.LPS.IFNG_DegCre[,6])) #10020
dim(unique(iTF_Microglia.LPS.IFNG_ABC[,1:3])) #39913
length(unique(iTF_Microglia.LPS.IFNG_ABC[,6])) #15695
dim(unique(iTF_Microglia.LPS.IFNG_Brain.MG.link[,1:3])) #23861
length(unique(iTF_Microglia.LPS.IFNG_Brain.MG.link[,6])) #12191
dim(unique(iTF_Microglia.LPS.IFNG_PLAC[,1:3])) #16175
length(unique(iTF_Microglia.LPS.IFNG_PLAC[,6])) #11613
########################
#Cluster profiler target genes
########################
# Load required packages
library(clusterProfiler)
library(ReactomePA)
library(ggplot2)
library(dplyr)
library(ComplexUpset)
library(purrr)
# Consolidate gene lists
gene_lists <- list(
iTF_iPSCs = list(
`closest TSS` = unique(iTF_iPSCs_closest.tss[, 6]),
DegCre = unique(iTF_iPSCs_DegCre[, 6]),
ABC = unique(iTF_iPSCs_ABC[, 6]),
`Brain microglia link` = unique(iTF_iPSCs_Brain.MG.link[, 6])#,
#`PLAC-seq (Nott)` = unique(iTF_iPSCs_PLAC[, 6])
),
iTF_Microglia = list(
`closest TSS` = unique(iTF_Microglia_closest.tss[, 6]),
DegCre.diff = unique(iTF_Microglia.diff_DegCre[, 6]),
DegCre.unt = unique(iTF_Microglia.unt_DegCre[, 6]),
ABC = unique(iTF_Microglia_ABC[, 6]),
`Brain microglia link` = unique(iTF_Microglia_Brain.MG.link[, 6])#,
#`PLAC-seq (Nott)` = unique(iTF_Microglia_PLAC[, 6])
),
iTF_Microglia_LPS_IFNG = list(
`closest TSS` = unique(iTF_Microglia.LPS.IFNG_closest.tss[, 6]),
DegCre = unique(iTF_Microglia.LPS.IFNG_DegCre[, 6]),
ABC = unique(iTF_Microglia.LPS.IFNG_ABC[, 6]),
`Brain microglia link` = unique(iTF_Microglia.LPS.IFNG_Brain.MG.link[, 6])#,
#`PLAC-seq (Nott)` = unique(iTF_Microglia.LPS.IFNG_PLAC[, 6])
)
)
# Function to map gene names/IDs to Entrez IDs
convert_to_entrez <- function(gene_vector, gene_map) {
gene_df <- data.frame(gene_name = gene_vector, stringsAsFactors = FALSE)
# Ensure gene_map has unique mappings
gene_map_filtered <- gene_map %>%
select(gene_name, entrez_id) %>%
distinct() %>%
filter(!is.na(entrez_id)) # Remove NA Entrez IDs
# Perform the mapping
mapped_genes <- left_join(gene_df, gene_map_filtered, by = "gene_name")
# Return only Entrez IDs (removing NAs)
return(na.omit(mapped_genes$entrez_id))
}
# Apply function to each list element
gene_lists_entrez <- map_depth(gene_lists, 2, ~convert_to_entrez(.x, gene_map))
# Check structure of converted lists
str(gene_lists_entrez)
# Perform Reactome enrichment analysis and save results separately
csv_file_paths <- list()
for (cell_type in names(gene_lists_entrez)) {
reactome_results <- compareCluster(
geneCluster = gene_lists_entrez[[cell_type]],
fun = "enrichPathway",
pvalueCutoff = 0.05,
readable = TRUE
)
# Save results to CSV
csv_file_path <- paste0("/cluster/home/irodriguez/Myers_Lab/KOLF2.1J-iTF_iMGLs_Nick_paper/manuscript/batch2/Tables/Reactome_Enrichment_target_gene_", cell_type, ".csv")
write.csv(reactome_results@compareClusterResult, csv_file_path, row.names = FALSE)
csv_file_paths[[cell_type]] <- csv_file_path
}
# Combine all results for dot plot
all_reactome_results <- do.call(rbind, lapply(csv_file_paths, read.csv))
all_reactome_results$mapped.cell <- ifelse(grepl("iTF_iPSCs", rownames(all_reactome_results)), "iTF-iPSCs",
ifelse(grepl("iTF_Microglia_LPS_IFNG", rownames(all_reactome_results)), "iTF-Microglia\nLPS+IFNG",
ifelse(grepl("iTF_Microglia.", rownames(all_reactome_results)), "iTF-Microglia", NA)))
# Define pathways of interest
pathways <- c(
"RHO GTPase cycle","Translation","S Phase",
"Transcriptional regulation of pluripotent stem cells","Neuronal System",
"Neutrophil degranulation","Inflammasomes","Signaling by Interleukins",
"Interferon gamma signaling")
# Subset Reactome results
reactome_results_subset <- all_reactome_results %>% filter(Description %in% pathways)
# Ensure Cluster is a factor with the desired order
reactome_results_subset$Cluster <- factor(reactome_results_subset$Cluster, levels = c("closest TSS", "DegCre",
"DegCre.diff","DegCre.unt",
"ABC", "Brain microglia link"#,
#"PLAC-seq (Nott)"
))
# Order pathways as a factor
reactome_results_subset$Description <- factor(reactome_results_subset$Description, levels = rev(pathways))
pdf("/cluster/home/irodriguez/Myers_Lab/KOLF2.1J-iTF_iMGLs_Nick_paper/manuscript/batch2/Figures/Reactome_Pathway_target_genes_DotPlot.pdf", width = 11, height = 9)
#pdf("/cluster/home/irodriguez/Myers_Lab/KOLF2.1J-iTF_iMGLs_Nick_paper/manuscript/batch2/Figures/Reactome_Pathway_target_genes_DotPlot.pdf", width = 10, height = 12)
#pdf("/cluster/home/irodriguez/Myers_Lab/KOLF2.1J-iTF_iMGLs_Nick_paper/manuscript/batch2/Figures/Reactome_Pathway_target_genes_DotPlot.pdf", width = 14, height = 12)
ggplot(reactome_results_subset, aes(x = Cluster, y = Description, size = Count, fill = p.adjust)) +
geom_point(shape = 21, color = "black", stroke = 0.5) + # Black outline for each dot
scale_fill_gradient(low = "blue", high = "red") +
facet_wrap(~ mapped.cell, scales = "free_x") +
theme_minimal() +
labs(title = NULL, x = NULL, y = NULL) +
theme(
text = element_text(size = 16),
axis.text.x = element_text(size = 14, angle = 45, hjust = 1),
axis.text.y = element_text(size = 16),
plot.title = element_text(size = 18, face = "bold"),
strip.background = element_rect(fill = "grey80", color = "black"), # Grey background with black border for facet title
strip.text = element_text(size = 16, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1) # Outline around each facet
)
dev.off()
########################
#Overlap CRE-gene pair models
########################
iTF_cCREs_split_df <- unique(filtered_iTF_cCREs_df[, c(1:3,6,12:19,21)])
iTF_cCREs_split_df <- unique(filtered_iTF_cCREs_df[, c(1:3,6,12:19)])
# List of columns to separate
cols_to_separate <- c(
"closest_tss", "DegCre_iTF_iPSCs", "DegCre_iTF_Microglia.diff",
"DegCre_iTF_Microglia.untreated",
"DegCre_iTF_Microglia.LPS.IFNG", "ABC_iTF_iPSCs", "ABC_iTF_Microglia",
"ABC_iTF_Microglia.LPS.IFNG", "Peak2gene.Anderson2023"#, "Nott_PLAC_anchor_gene"
)
# Convert to data.table
iTF_cCREs_split_long_dt <- as.data.table(iTF_cCREs_split_df)
# Function to split columns into long format
split_columns_to_long <- function(dt, cols_to_separate) {
long_dt_list <- lapply(cols_to_separate, function(col) {
dt[!is.na(get(col)) & get(col) != "", .(
seqnames, start, end,
target_gene = unlist(tstrsplit(get(col), ",", fixed = TRUE)),
Mapping = col
)]
})
final_long_dt <- rbindlist(long_dt_list, fill = TRUE)
setorder(final_long_dt, seqnames, start)
unique(final_long_dt)
}
# Apply function
iTF_cCREs_clean <- split_columns_to_long(iTF_cCREs_split_long_dt, cols_to_separate)
iTF_cCREs_clean <- iTF_cCREs_clean[complete.cases(iTF_cCREs_clean),] %>% distinct()
# Process promoters
gencode_promoters <- gtf_df_filtered %>%
dplyr::select(seqnames, promoter_start, promoter_end, strand, GeneID, GeneSymb) %>%
unique() %>%
filter(seqnames != "chrM")
promoters_V46 <- GRanges(gencode_promoters[, c("seqnames", "promoter_start", "promoter_end", "GeneSymb")])
mcols(promoters_V46)$class <- "promoter"
# Merge with promoters
iTF_cCREs_clean_promoter <- merge(iTF_cCREs_clean, gencode_promoters,
by.x = "target_gene", by.y = "GeneSymb",
allow.cartesian = TRUE) %>%
dplyr::select(seqnames = seqnames.x, start, end, target_gene, Mapping)
# Convert to GRanges
iTF_cCREs_clean_gr <- GRanges(iTF_cCREs_clean_promoter)
# Find overlaps
olap_2KB <- findOverlaps(iTF_cCREs_clean_gr, promoters_V46)
cCREs_2KB <- iTF_cCREs_clean_gr[queryHits(olap_2KB)]
mcols(cCREs_2KB) <- cbind(mcols(cCREs_2KB), mcols(promoters_V46[subjectHits(olap_2KB)]))
# Convert to DataFrame
cCREs_2KB_df <- as.data.frame(cCREs_2KB) %>%
dplyr::select(seqnames, start, end, GeneSymb) %>%
distinct() %>%
mutate(class = "promoter") %>%
arrange(seqnames, start)
# Save results
saveRDS(cCREs_2KB_df, "/cluster/home/irodriguez/Myers_Lab/KOLF2.1J-iTF_iMGLs_Nick_paper/manuscript/batch2/Tables/iTF_cCREs_promoter_2KB.rds")
# Merge and classify for distance
iTF_cCREs_clean_prom <- merge(as.data.frame(iTF_cCREs_clean_gr), cCREs_2KB_df,
by = c("seqnames", "start", "end"), all.x = TRUE) %>%
mutate(
cCreType = case_when(
class == "promoter" & target_gene == GeneSymb ~ "SelfPromoter",
class == "promoter" & target_gene != GeneSymb ~ "OtherPromoter",
TRUE ~ "enhancer"
),
cell.type = case_when(
grepl("iTF_iPSCs", Mapping) ~ "iTF-iPSCs",
grepl("iTF_Microglia$", Mapping) ~ "iTF-Microglia",
grepl("LPS.IFNG", Mapping) ~ "iTF-Microglia LPS+IFNG",
grepl("Anderson2023", Mapping) ~ "Brain microglia",
#grepl("Nott_PLAC", Mapping) ~ "Ex vivo microglia",
TRUE ~ NA_character_
),
mapping = case_when(
grepl("DegCre", Mapping) ~ "DegCre",
grepl("ABC_iTF", Mapping) ~ "ABC",
grepl("closest_tss", Mapping) ~ "closest TSS",
grepl("Peak2gene", Mapping) ~ "Brain microglia link",
#grepl("Nott", Mapping) ~ "PLAC-seq (Nott)",
TRUE ~ NA_character_
)
) %>%
mutate(across(c(cCreType, cell.type, mapping), as.factor))
# Merge and classify for upset plot
iTF_cCREs_clean_prom <- merge(as.data.frame(iTF_cCREs_clean_gr), cCREs_2KB_df,
by = c("seqnames", "start", "end"), all.x = TRUE) %>%
mutate(
cCreType = case_when(
class == "promoter" & target_gene == GeneSymb ~ "SelfPromoter",
class == "promoter" & target_gene != GeneSymb ~ "OtherPromoter",
TRUE ~ "enhancer"
)
) %>%
mutate(
cell.type = case_when(
grepl("iTF_iPSCs", Mapping) ~ "iTF-iPSCs",
grepl("iTF_Microglia$", Mapping) ~ "iTF-Microglia",
grepl("iTF_Microglia.diff", Mapping) ~ "iTF-Microglia",
grepl("iTF_Microglia.untreated", Mapping) ~ "iTF-Microglia",
grepl("LPS.IFNG", Mapping) ~ "iTF-Microglia LPS+IFNG",
grepl("Anderson2023", Mapping) ~ "Brain microglia",
#grepl("Nott_PLAC", Mapping) ~ "Ex vivo microglia",
TRUE ~ NA_character_
),
mapping = case_when(
grepl("DegCre", Mapping) ~ "DegCre",
grepl("ABC_iTF", Mapping) ~ "ABC",
grepl("closest_tss", Mapping) ~ "closest TSS",
grepl("Peak2gene", Mapping) ~ "Brain microglia link",
#grepl("Nott", Mapping) ~ "PLAC-seq (Nott)",
TRUE ~ NA_character_
)
) %>%
mutate(across(c(cCreType, cell.type, mapping), as.factor))
# Convert to GRanges
iTF_cCREs_class_gr <- GRanges(iTF_cCREs_clean_prom[, c(1:7, 10, 12)])
# Overlap function
overlap_with_metadata <- function(query, subject) {
hits <- findOverlaps(query, subject)
query_overlaps <- query[queryHits(hits)]
subject_overlaps <- subject[subjectHits(hits)]
result <- query_overlaps
mcols(result) <- cbind(mcols(query_overlaps), mcols(subject_overlaps))
result
}
# Process overlaps
overlapped_list <- lapply(iTF_CREs_ENCODE, function(gr) overlap_with_metadata(iTF_cCREs_class_gr, gr))
# Extract specific cell type classes
iTF_iPSCs_class <- as.data.frame(overlapped_list$iTF_iPSCs) %>%
filter(Mapping %in% c(#"Nott_PLAC_anchor_gene",
"DegCre_iTF_iPSCs", "ABC_iTF_iPSCs", "Peak2gene.Anderson2023", "closest_tss")) %>%
distinct()
iTF_Microglia_class <- as.data.frame(overlapped_list$iTF_Microglia) %>%
filter(Mapping %in% c(#"Nott_PLAC_anchor_gene",
"DegCre_iTF_Microglia.diff", "DegCre_iTF_Microglia.untreated", "ABC_iTF_Microglia", "Peak2gene.Anderson2023", "closest_tss")) %>%
distinct()
iTF_Microglia.LPS.IFNG_class <- as.data.frame(overlapped_list$iTF_Microglia.LPS.IFNG) %>%
filter(Mapping %in% c(#"Nott_PLAC_anchor_gene",
"DegCre_iTF_Microglia.LPS.IFNG", "ABC_iTF_Microglia.LPS.IFNG", "Peak2gene.Anderson2023", "closest_tss")) %>%
distinct()
# Function to process data
process_data <- function(df, dataset_name) {
df <- df %>%
group_by(across(-c(cCreType))) %>%
filter(!(cCreType == "OtherPromoter" & "SelfPromoter" %in% cCreType)) %>%
ungroup() %>%
mutate(CREtoGene = paste(seqnames, start, end, target_gene, sep = "_"),
Dataset = dataset_name) %>%
as.data.frame()
return(df)
}
# Process all datasets
iTF_cCRE_gene_pair_iPSCs <- process_data(iTF_iPSCs_class, "iTF-iPSCs")
iTF_cCRE_gene_pair_Microglia <- process_data(iTF_Microglia_class, "iTF-Microglia")
iTF_cCRE_gene_pair_Microglia_LPS_IFNG <- process_data(iTF_Microglia.LPS.IFNG_class, "iTF-Microglia LPS+IFNG")
# Combine datasets
combined_df <- bind_rows(iTF_cCRE_gene_pair_iPSCs,
iTF_cCRE_gene_pair_Microglia,
iTF_cCRE_gene_pair_Microglia_LPS_IFNG)
# Compute distances with correct sign
cCRE_gr <- GRanges(
seqnames = combined_df$seqnames,
ranges = IRanges(start = combined_df$start, end = combined_df$end),
strand = "*",
target_gene = combined_df$target_gene
)
# Match TSS positions based on the target gene
matched_tss <- Gencode.V46[match(cCRE_gr$target_gene, Gencode.V46$GeneSymb)]
# Compute raw distances
distances <- distance(cCRE_gr, matched_tss)
# Determine the direction based on gene strand
strand_info <- as.character(strand(matched_tss))
# Adjust distances: negative for upstream (on + strand), positive for downstream
signed_distances <- ifelse(
strand_info == "+",
start(cCRE_gr) - start(matched_tss),
start(matched_tss) - start(cCRE_gr)
)
# Assign distances back
combined_df$distance_to_TSS <- distances
combined_df$signed_distances <- signed_distances
# Change sign in distances based on strand
combined_df$distance_to_TSS <- ifelse(
combined_df$distance_to_TSS == 0,
0,
ifelse(
combined_df$signed_distances < 0,
abs(combined_df$distance_to_TSS) * -1,
abs(combined_df$distance_to_TSS)
)
)
# Mapping column transformation
combined_df <- combined_df %>%
mutate(Mapping = case_when(
grepl("closest", Mapping) ~ "closest TSS",
grepl("DegCre", Mapping) ~ "DegCre",
grepl("ABC", Mapping) ~ "ABC",
grepl("Anderson2023", Mapping) ~ "Brain microglia link",
#grepl("Nott", Mapping) ~ "PLAC-seq (Nott)",
TRUE ~ NA_character_
))
combined_df <- unique(combined_df[,-7])
# Order
orderMapping <- c(#"PLAC-seq (Nott)",
"DegCre","ABC","Brain microglia link", "closest TSS")
combined_df$mapping <- factor(combined_df$mapping, levels = rev(orderMapping))
# Define colors
custom_colors <- c(
#"PLAC-seq (Nott)" = "#000000",
"Brain microglia link" = "#009E73",
"DegCre" = "#F0E442",
"ABC" = "#0072B2",
"closest TSS" = "#D55E00"
)
# Define your custom linetypes per mapping
custom_linetypes <- c(
"DegCre" = "dotdash",
"ABC" = "dashed",
"closest TSS" = "solid",
"Brain microglia link" = "dotted"#,
#"PLAC-seq (Nott)" = "twodash"
)
# Create plot
library(scales) # For custom transformation
# Define output files
Distance_plot_file_log <- "/cluster/home/irodriguez/Myers_Lab/KOLF2.1J-iTF_iMGLs_Nick_paper/manuscript/batch2/Figures/Distance_CREs_target_genes_log.pdf"
# Define a signed log transformation
signed_log_trans <- trans_new(
name = "signed_log10",
transform = function(x) sign(x) * log10(abs(x) + 1),
inverse = function(x) sign(x) * (10^abs(x) - 1)
)
# Manually define tick positions (log scale, symmetric around zero)
log_ticks <- c(-1e5, -1e3, 0, 1e3, 1e5)
# Create plot
pdf(Distance_plot_file_log, width=12, height=5)
# Loop over each unique dataset to generate and plot individually
ggplot(combined_df, aes(x = distance_to_TSS, color = mapping, linetype = mapping)) +
geom_density(linewidth = 1.2) +
facet_wrap(~ Dataset) +
scale_x_continuous(trans = signed_log_trans, breaks = log_ticks) +
scale_color_manual(values = custom_colors) +
scale_linetype_manual(values = custom_linetypes) +
theme_minimal() +
theme(
text = element_text(size = 16),
axis.text.x = element_text(angle = 45, hjust = 1, size = 14),
axis.text.y = element_text(size = 14),
strip.text = element_text(size = 18, face = "bold"),
legend.text = element_text(size = 14)
) +
labs(
x = "cCRE distance to target TSS",
y = "Density",
color = "Mapping",
linetype = "Mapping"
)
dev.off()
#######################
# Overlap target genes
#######################
library(ComplexUpset)
library(ggplot2)
# Convert to the appropriate format
upset_data <- iTF_cCRE_gene_pair_iPSCs %>%
mutate(CREtoGene = factor(CREtoGene))
upset_data <- iTF_cCRE_gene_pair_Microglia %>%
mutate(CREtoGene = factor(CREtoGene))
upset_data <- iTF_cCRE_gene_pair_Microglia_LPS_IFNG %>%
mutate(CREtoGene = factor(CREtoGene))
# Upset plot
upset_data_subset <- unique(upset_data[,c(1:5,8:11)])
upset_data_collapsed <- upset_data_subset %>%
group_by(seqnames, start, end, width, strand, cCreType, cell.type, CREtoGene) %>%
summarise(mapping = paste(mapping, collapse = ","), .groups = "drop")
upset_data_collapsed <- upset_data_collapsed %>%
mutate(
`closest TSS` = ifelse(grepl("closest", mapping), 1, 0),
ABC = ifelse(grepl("ABC", mapping), 1, 0),
DegCre = ifelse(grepl("DegCre", mapping), 1, 0),
`Brain microglia link` = ifelse(grepl("Brain", mapping), 1, 0)#,
#`PLAC-seq (Nott)` = ifelse(grepl("Nott", mapping), 1, 0),
) %>% as.data.frame()
cats <- colnames(upset_data_collapsed)[10:13]
orderBind <- c("SelfPromoter","OtherPromoter","enhancer")
orderBind <- rev(orderBind)
upset_data_collapsed$cCreType <- factor(upset_data_collapsed$cCreType,levels = orderBind)
Upset_plot_file = "/cluster/home/irodriguez/Myers_Lab/KOLF2.1J-iTF_iMGLs_Nick_paper/manuscript/batch2/Figures/Upset_CREs_target_genes_iTF_iPSCs.pdf"
Upset_plot_file = "/cluster/home/irodriguez/Myers_Lab/KOLF2.1J-iTF_iMGLs_Nick_paper/manuscript/batch2/Figures/Upset_CREs_target_genes_iTF_Microglia.pdf"
Upset_plot_file = "/cluster/home/irodriguez/Myers_Lab/KOLF2.1J-iTF_iMGLs_Nick_paper/manuscript/batch2/Figures/Upset_CREs_target_genes_iTF_Microglia_LPS_IFNG.pdf"
library(grid)
pdf(Upset_plot_file, width = 10, height = 5)
p <- upset(
upset_data_collapsed,
cats,
n_intersections = 10,
# Custom themes
themes = upset_modify_themes(list(
'intersections_matrix' = theme(text = element_text(size = 16))
)),
# Customize intersection size bar
base_annotations = list(
'cCREs-TargetGene' = intersection_size(
counts = FALSE,
mapping = aes(fill = cCreType)
) +
scale_fill_manual(values = c(
"SelfPromoter" = "red1",
"OtherPromoter" = "purple",
"enhancer" = "gold"
)) +
theme(
text = element_text(size = 18),
axis.text.y = element_text(size = 14),
legend.position = "top",
panel.grid = element_blank()
)
),
# Customize set size bar
set_sizes = (
upset_set_size() +
theme(
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, size = 16),
text = element_blank()
)
),
width_ratio = 0.2,
# Highlight specific sets
queries = list(
#upset_query(set = 'PLAC-seq (Nott)', fill = '#000000'),
upset_query(set = 'Brain microglia link', fill = '#009E73'),
upset_query(set = 'ABC', fill = '#0072B2'),
upset_query(set = 'DegCre', fill = '#F0E442'),
upset_query(set = 'closest TSS', fill = '#D55E00')
)
)
# Ensure the plot is printed to the device and only one page is used
print(p)
dev.off()
# Calculate average number of genes per cCRE
genes_per_cCRE <- upset_data %>%
group_by(Mapping, seqnames, start, end) %>%
summarise(num_genes = n_distinct(target_gene), .groups = "drop") %>%
group_by(Mapping) %>%
summarise(avg_genes_per_cCRE = mean(num_genes))
# Calculate average number of cCREs per gene
cCREs_per_gene <- upset_data %>%
group_by(Mapping, target_gene) %>%
summarise(num_cCREs = n_distinct(paste(seqnames, start, end, sep = "_")), .groups = "drop") %>%
group_by(Mapping) %>%
summarise(avg_cCREs_per_gene = mean(num_cCREs))
# Combine both results
result_iPSC <- full_join(genes_per_cCRE, cCREs_per_gene, by = "Mapping")
result_Microglia <- full_join(genes_per_cCRE, cCREs_per_gene, by = "Mapping")
result_LPS_IFNG <- full_join(genes_per_cCRE, cCREs_per_gene, by = "Mapping")

cCRE-TargetGene_analysis.R at commit 83d4caa, under MIT · at the source

Overview

Authors: Brianne B. Rogers¹, Ashlyn G. Anderson^1,2, Ivan Rodriguez-Nunez¹, Samuel C. Bartley^1,2, S. Quinn Johnston¹, Jared W. Taylor¹, Sarah K. Meadows¹, Kimberly M. Newberry¹, Richard M. Myers¹, J. Nicholas Cochran¹

ORCID iDs: J. Nicholas Cochran

Institutions: HudsonAlpha Institute for Biotechnology (United States); University of Alabama at Birmingham (United States)

Journal: iScience, volume 29, issue 7, article 116412

Dates: received 24 September 2025; accepted 29 May 2026; published online 16 June 2026

Type: Research article · Language: English

License: CC BY-NC

Identifiers: DOI 10.1016/j.isci.2026.116412 · PMID 42338472 · PMCID PMC13285636 · OpenAlex W7164886869

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: genetics / omics (modality)

Methods: Statistics, Machine learning

Keywords: neuroscience, techniques in neuroscience, stem cells research

Topic: Neuroinflammation and Neurodegeneration Mechanisms (Neurology, Neuroscience), according to OpenAlex

Funding: NIH (5R00AG068271); National Institute on Aging (5R01AG085357); Alzheimer’s Association (AARG-24-1308947); American Parkinson Disease Association (1269685); NIGMS (5T32GM146611); HudsonAlpha Foundation

Citations: not cited yet (Europe PMC); 99 references in the paper

Research resources: Alexa 488 Goat-AntiMouse RRID:AB_2534069, Anti-H3K27ac RRID:AB_2561016, IBA1 Monoclonal Antibody (GT10312) RRID:AB_2735228, RRID:AB_469901

Abstract

Understanding transcriptional regulatory networks (TRNs) in microglia is essential for elucidating mechanisms underlying central nervous system (CNS) disorders. Human induced pluripotent stem cell (iPSC)-derived models enable mechanistic studies of microglia but often suffer from variability across lines. Here, we use the standardized KOLF2.1J iPSC line, engineered to inducibly express six transcription factors that allow rapid generation of microglia-like cells (iTF-microglia). We profile TRNs under homeostatic and inflammatory conditions and show that iTF-microglia resemble primary brain microglia at transcriptomic and epigenomic levels. Integrative analyses identify microglia-enriched candidate cis-regulatory elements (cCREs) and reveal dynamic enhancer remodeling during differentiation and stimulation with lipopolysaccharide (LPS) or interferon-gamma (IFNγ), involving NF-κB, IRF, and STAT transcription factors. TRNs active in iTF-microglia are enriched for genetic variants linked to Alzheimer’s disease and related CNS disorders. These findings establish KOLF2.1J iTF-microglia as a reproducible, genetically tractable system for dissecting microglial gene regulation and TRN remodeling in disease.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 27 matches between paragraphs and lines of code.

HudsonAlpha/iTF-Microglia

License: MIT

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 83d4caaea2aab82e4da550cf5a5f377e5ed611d3, 11 September 2025

Languages: R (14)

Size: 23 files, 14 scripts

Software Heritage: not archived

Found in: “Data and code availability”

Holds: README, license file

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

Tools: tidyverse (12 files), ggplot2 (8 files), data.table (6 files), ComplexHeatmap (5 files), DESeq2 (5 files), ggpubr (4 files), circlize (3 files), clusterProfiler (2 files), edgeR (2 files), pheatmap (1 file)

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

27 September 2026: the link answers

16 files

Scripts/DESeq2_tss_annotation_for_DegCre.R, R, 80 lines
Scripts/DegCre_analysis_20250427.R, R, 169 lines
Scripts/LDSC_bed_files_20250429.R, R, 194 lines, 1 match
Scripts/LDSC_heatmap.R, R, 333 lines, 1 match
Scripts/MAGMA_expression_matrix_format.R, R, 84 lines, 1 match
Scripts/MAGMA_plot.R, R, 128 lines
Scripts/Partitioned_heritability_files.R, R, 83 lines
Scripts/RNAseq_analysis.R, R, 987 lines, 4 matches
Scripts/TF_monaLisa_analysis.R, R, 1,238 lines, 5 matches
Scripts/cCRE-TargetGene_analysis.R, R, 1,594 lines, 5 matches
Scripts/cCRE_analysis.R, R, 825 lines, 3 matches
Scripts/cCRE_disease_variant_annotation.R, R, 1,866 lines, 1 match
Scripts/monalisa_b.R, R, 928 lines, 2 matches
Scripts/motifbreakR_script.R, R, 535 lines, 2 matches
LICENSE, License, 21 lines
README.md, Text, 32 lines

At the source: github.com/HudsonAlpha/iTF-Microglia

ENCODE-DCC/atac-seq-pipeline

License: MIT

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 47ba8dff9c332e24b48e767303e9fcac98589cf2, 15 February 2024

Languages: Python (45), Shell (9)

Size: 204 files, 54 scripts

Software Heritage: not archived

Found in: the text, “Key resources table”

Holds: README, license file, environment (scripts/requirements.macs2.txt, scripts/requirements.python2.txt, scripts/requirements.spp.txt, scripts/requirements.txt, dev/docker_image/Dockerfile), tests, continuous integration, documentation

Not found: CITATION.cff

Tools: Matplotlib (6 files), NumPy (6 files), pandas (3 files), SAMtools (2 files), SciPy (2 files), BEDTools (1 file)

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

27 September 2026: the link answers

56 files

dev/build_on_dx_dockerhub.sh, Shell, 60 lines
dev/test/run_cromwell_server_on_gc.sh, Shell, 15 lines
dev/test/test_py/__init__.py, Python, 1 line
dev/test/test_workflow/ref_output/sync.sh, Shell, 3 lines
scripts/build_genome_data.sh, Shell, 351 lines
scripts/download_genome_data.sh, Shell, 297 lines
scripts/install_conda_env.sh, Shell, 88 lines
scripts/uninstall_conda_env.sh, Shell, 12 lines
scripts/update_conda_env.sh, Shell, 21 lines
src/assign_multimappers.py, Python, 73 lines
src/detect_adapter.py, Python, 84 lines
src/dev_check_sync_atac.sh, Shell, 21 lines
src/encode_lib_blacklist_filter.py, Python, 117 lines
src/encode_lib_common.py, Python, 399 lines
src/encode_lib_frip.py, Python, 114 lines
src/encode_lib_genomic.py, Python, 767 lines
src/encode_lib_log_parser.py, Python, 660 lines
src/encode_lib_qc_category.py, Python, 259 lines
src/encode_task_annot_enrich.py, Python, 109 lines
src/encode_task_bam2ta.py, Python, 163 lines
src/encode_task_bam_to_pbam.py, Python, 64 lines
src/encode_task_bowtie2.py, Python, 192 lines
src/encode_task_bwa.py, Python, 280 lines
src/encode_task_choose_ctl.py, Python, 181 lines
src/encode_task_compare_signal_to_roadmap.py, Python, 137 lines
src/encode_task_count_signal_track.py, Python, 115 lines
src/encode_task_filter.py, Python, 438 lines
src/encode_task_frac_mito.py, Python, 86 lines
src/encode_task_fraglen_stat_pe.py, Python, 244 lines
src/encode_task_gc_bias.py, Python, 143 lines
src/encode_task_idr.py, Python, 213 lines
src/encode_task_jsd.py, Python, 133 lines
src/encode_task_macs2_atac.py, Python, 139 lines
src/encode_task_macs2_chip.py, Python, 172 lines
src/encode_task_macs2_signal_track_atac.py, Python, 190 lines
src/encode_task_macs2_signal_track_chip.py, Python, 215 lines
src/encode_task_merge_fastq.py, Python, 105 lines
src/encode_task_overlap.py, Python, 178 lines, 1 match
src/encode_task_pool_ta.py, Python, 76 lines
src/encode_task_post_align.py, Python, 92 lines
src/encode_task_post_call_peak_atac.py, Python, 104 lines
src/encode_task_post_call_peak_chip.py, Python, 107 lines
src/encode_task_preseq.py, Python, 174 lines
src/encode_task_qc_report.py, Python, 941 lines, 1 match
src/encode_task_reproducibility.py, Python, 204 lines
src/encode_task_spp.py, Python, 133 lines
src/encode_task_spr.py, Python, 176 lines
src/encode_task_subsample_ctl.py, Python, 66 lines
src/encode_task_trim_adapter.py, Python, 214 lines
src/encode_task_trim_fastq.py, Python, 73 lines
src/encode_task_trimmomatic.py, Python, 204 lines
src/encode_task_tss_enrich.py, Python, 178 lines
src/encode_task_xcor.py, Python, 156 lines
src/trimfastq.py, Python, 255 lines
LICENSE, License, 21 lines
README.md, Text, 184 lines

At the source: github.com/ENCODE-DCC/atac-seq-pipeline

The paper's code and data availability statement is in the Data section.

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;
68 scripts, each with its path and the digest of its content;
27 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:GSE299850, at NCBI GEO; found in the text, “Key resources table”

Data and code availability

• RNA-sequencing, ATAC-sequencing, ChIP-sequencing and Hi-C data have been deposited at GEO database under GEO Superseries: GSE299850 and are publicly available. • The analysis scripts used to generate the images and statistical output in this study are available on GitHub (https://github.com/HudsonAlpha/iTF-Microglia) • Any additional information required to reanalyze the data reported in this study is available from the lead contact upon request.

Reproduced under the paper's license (CC BY-NC), 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 2, 28 September 2026

Authors: added J. Nicholas Cochran (0000-0002-9852-5504); removed J. Nicholas Cochran

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 3 keywords, 6 funders, 99 references, 4 RRIDs.

Cite

This paper

Rogers, B. B., Anderson, A. G., Rodriguez-Nunez, I., Bartley, S. C., Johnston, S. Q., Taylor, J. W., Meadows, S. K., Newberry, K. M., Myers, R. M., & Cochran, J. N. (2026). KOLF2.1J iTF-Microglia: A standardized platform to study microglial transcriptional regulatory networks in CNS disease. iScience, 29(7), 116412. https://doi.org/10.1016/j.isci.2026.116412

BibTeX

@article{rogers2026kolf2, author = {Rogers, Brianne B. and Anderson, Ashlyn G. and Rodriguez-Nunez, Ivan and Bartley, Samuel C. and Johnston, S. Quinn and Taylor, Jared W. and Meadows, Sarah K. and Newberry, Kimberly M. and Myers, Richard M. and Cochran, J. Nicholas}, title = {{KOLF2.1J iTF-Microglia: A standardized platform to study microglial transcriptional regulatory networks in CNS disease}}, journal = {iScience}, year = {2026}, month = jun, volume = {29}, number = {7}, pages = {116412}, publisher = {Elsevier}, issn = {2589-0042}, doi = {10.1016/j.isci.2026.116412}, url = {https://doi.org/10.1016/j.isci.2026.116412}, pmid = {42338472}, pmcid = {PMC13285636} }

RIS

TY - JOUR AU - Rogers, Brianne B. AU - Anderson, Ashlyn G. AU - Rodriguez-Nunez, Ivan AU - Bartley, Samuel C. AU - Johnston, S. Quinn AU - Taylor, Jared W. AU - Meadows, Sarah K. AU - Newberry, Kimberly M. AU - Myers, Richard M. AU - Cochran, J. Nicholas TI - KOLF2.1J iTF-Microglia: A standardized platform to study microglial transcriptional regulatory networks in CNS disease T2 - iScience J2 - iScience PY - 2026 DA - 2026/06/16 VL - 29 IS - 7 SP - 116412 SN - 2589-0042 PB - Elsevier DO - 10.1016/j.isci.2026.116412 UR - https://doi.org/10.1016/j.isci.2026.116412 LA - en ER -

CSL-JSON

{ "id": "10.1016/j.isci.2026.116412", "type": "article-journal", "title": "KOLF2.1J iTF-Microglia: A standardized platform to study microglial transcriptional regulatory networks in CNS disease", "container-title": "iScience", "author": [ { "family": "Rogers", "given": "Brianne B." }, { "family": "Anderson", "given": "Ashlyn G." }, { "family": "Rodriguez-Nunez", "given": "Ivan" }, { "family": "Bartley", "given": "Samuel C." }, { "family": "Johnston", "given": "S. Quinn" }, { "family": "Taylor", "given": "Jared W." }, { "family": "Meadows", "given": "Sarah K." }, { "family": "Newberry", "given": "Kimberly M." }, { "family": "Myers", "given": "Richard M." }, { "family": "Cochran", "given": "J. Nicholas" } ], "container-title-short": "iScience", "volume": "29", "issue": "7", "page": "116412", "DOI": "10.1016/j.isci.2026.116412", "PMID": "42338472", "PMCID": "PMC13285636", "ISSN": "2589-0042", "publisher": "Elsevier", "URL": "https://doi.org/10.1016/j.isci.2026.116412", "language": "en", "issued": { "date-parts": [ [ 2026, 6, 16 ] ] } }

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