OSCR

Single-nucleus epigenomic dysregulation unmasks genetic risk-associated neurodegenerative glia states.

Code ↔ Paper

28 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 28 matches · 12 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › cisTopic modeling of variant-linked CRE modules ↔ code/90_topic_modeling_variant_linked_CREs_microglia/93.run_cisTopic.R, the whole file · a weak match · score 0.82 · cisTopic, frVar, eQTL, variant linked, binarized, derivatives
  2. [2] § Methods › snATAC-seq data processing ↔ code/20_dar_dgs/24.DifferentialGeneScores.R, lines 183–238 · score 0.82 · mid insula, getMarkerFeatures, PreCG, TSS enrichment, Log2FC, gene scores
  3. [3] § Methods › TF enrichment ↔ code/90_topic_modeling_variant_linked_CREs_microglia/94.topics_plot_TF_enrichR.R, lines 198–278 · score 0.80 · runAme, Homo sapiens, motif database, JASPAR, redundant, MEME
  4. [4] § Methods › MPRA and single-nucleus eQTL enrichment analysis ↔ code/90_topic_modeling_variant_linked_CREs_microglia/92.extract_CREset_w_SNPs.R, lines 1–72 · score 0.78 · 2step fdr, eGene, MPRA variants, eQTL, Yes, matched
  5. [5] § Methods › Dynamic CREs associated with GWAS variants ↔ code/30_dynamicpeaks/32.dynamic_peaks_w_PSPGWASsnps_3types.R, lines 1–43 · score 0.77 · linkage disequilibrium, dynamic CREs associated, GWAS SNP, kb, LD, Genes
  6. [6] § Results › Enrichment of MPRA-validated functional variants within dynamic peaks ↔ code/90_topic_modeling_variant_linked_CREs_microglia/94.topics_plot_TF_enrichR.R, lines 338–397 · score 0.76 · MEF2A, metabolic regulation, TFs enriched, SPIC, TFE3, homeostasis
  7. [7] § Methods › cisTopic modeling of variant-linked CRE modules ↔ code/90_topic_modeling_variant_linked_CREs_microglia/96.topics_refine_by_frVar.R, the whole file · a weak match · score 0.75 · cisTopic, frVar, eQTL, variant linked, ArchR, motif
  8. [8] § Results › Convergent gene regulatory modules regulated by MPRA frVar and eQTLs ↔ code/90_topic_modeling_variant_linked_CREs_microglia/94.topics_plot_TF_enrichR.R, lines 338–397 · score 0.75 · MEF2D, MEF2C, Topic modeling, RHOXF2, JUNB, SPI1
  9. [9] § Methods › Detection of the CRE module linked with frVars or eQTLs ↔ code/50_MPRA_eQTL/55.CREs_w_frVar_Module-subC.R, lines 1–40 · score 0.74 · depth normalized, quantile normalized, chromatin accessibility, ArchR, pseudobulked, modules
  10. [10] § Methods › Subcluster identification ↔ code/10_regulatory_element/11.subC_cA.R, the whole file · a weak match · score 0.72 · ast.C6, mg.C10, mg.C15, mg c8, resolutions, clustering
  11. [11] § Methods › Subcluster identification ↔ code/10_regulatory_element/12.subC_peak2gene.R, the whole file · a weak match · score 0.72 · ast.C6, mg.C10, mg.C15, mg c8, resolutions, clustering
  12. [12] § Results › Disease dynamic peaks improve the explanation of disease genetic variants ↔ code/50_MPRA_eQTL/55.CREs_w_frVar_Module-subC.R, lines 1–40 · score 0.72 · depth normalization, quantile normalized, defining module, Peak accessibility, pseudobulked, meta
  13. [13] § Methods › Subcluster annotation ↔ code/30_dynamicpeaks/31.dynamic_peaks_disease.R, the whole file · a weak match · score 0.68 · neu.C6, neu.C7, neu.C8, DNA, odc, OPC
  14. [14] § Methods › Subcluster identification ↔ code/20_dar_dgs/24.DifferentialGeneScores.R, lines 183–238 · score 0.66 · getMarkerFeatures, TSS enrichment, Log2FC, gene scores, FDR, clusters
  15. [15] § Methods › Cell-cell interactions analysis ↔ code/80_cellphoneDB/81.DEG_seurat.R, lines 103–150 · score 0.66 · FindMarkers, CellPhoneDB, Seurat, DEG, downsampled, Log2FC
  16. [16] § Methods › Subcluster annotation ↔ code/20_dar_dgs/24.DifferentialGeneScores.R, lines 1–81 · score 0.65 · neu.C6, neu.C7, neu.C8, odc, OPC, neurons
  17. [17] § Methods › Identification of differentially accessible regions ↔ code/00_archrproject/03.umap_callpeak.R, the whole file · a weak match · score 0.64 · addGroupCoverages, addReproduciblePeakSet, MACS2, iterative, cells
  18. [18] § Results › Convergent gene regulatory modules regulated by MPRA frVar and eQTLs ↔ code/90_topic_modeling_variant_linked_CREs_microglia/96.topics_refine_by_frVar.R, the whole file · a weak match · score 0.61 · MEF2C, frVar, Topic modeling, JUNB, motifs, sn
  19. [19] § Methods › Prediction of TF binding sites in subcluster regulatory elements (in ast.C1 and mg.C4) ↔ code/60_TFBS_TOBIAS/05.TOBIAS_footprint.sh, the whole file · a weak match · score 0.60 · FootprintScores, TOBIAS, Tn5, ATAC, ast, mg
  20. [20] § Results › Genetic heritability enriched in disorder-divergent cell states in PSP and PiD ↔ code/60_TFBS_TOBIAS/00.get_subc_barcodes.R, the whole file · a weak match · score 0.59 · neu.C9, neu.C8, ast.C1, mg c4, sub, neuron
  21. [21] § Methods › TF enrichment ↔ code/50_MPRA_eQTL/04.CREs_TFBS_FIMO.R, the whole file · a weak match · score 0.59 · Homo sapiens, motif database, MEME, TFs, peaks
  22. [22] § Results › Convergent gene regulatory modules regulated by MPRA frVar and eQTLs ↔ code/90_topic_modeling_variant_linked_CREs_microglia/95.sum_topicst_snps-ADgwas.R, lines 112–167 · score 0.59 · AD GWAS, eQTL, functional variants, smaller, MPRA, microglial
  23. [23] § Methods › Prediction of TF binding sites in subcluster regulatory elements (in ast.C1 and mg.C4) ↔ code/60_TFBS_TOBIAS/06.TOBIAS_BINDetect.sh, the whole file · a weak match · score 0.58 · BINDetect, TOBIAS, FootprintScores, Tn5, motif, ATAC
  24. [24] § Results › Disease dynamic peaks improve the explanation of disease genetic variants ↔ code/90_topic_modeling_variant_linked_CREs_microglia/95.sum_topicst_snps-ADgwas.R, lines 41–110 · score 0.58 · odds ratio, MEF2C, frVar, log10, sn, GWAS
  25. [25] § Results › Cell type-specific CREs define cell type identity ↔ code/10_regulatory_element/16.ValidateCRE.R, lines 312–378 · score 0.55 · EnhBiv, EnhWk, ENCODE, CREs, brain, overlapped
  26. [26] § Methods › CRE identification and validation ↔ code/10_regulatory_element/11.subC_cA.R, the whole file · a weak match · score 0.54 · addCoAccessibility, regulatory elements, Genomes, CREs, peak, cell
  27. [27] § Methods › Analysis of single-nucleus RNA-seq-derived subclusters ↔ code/10_regulatory_element/15.classify.PEG.R, lines 185–265 · score 0.52 · snRNA, Seurat, pre, sum, insula, seq
  28. [28] § Methods › Identification of regulatory TFs and their target genes ↔ code/20_dar_dgs/24.DifferentialGeneScores.R, lines 83–129 · score 0.52 · gene score matrix, getMarkerFeatures, Wilcoxon, FDR, PSP

Paper

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The authors' code

R · 287 lines · 10 KB · MIT · 4 matches

  1. library(rhdf5)
  2. library(data.table)
  3. library(dplyr)
  4. library(ArchR)
  5. library(parallel)
  6. library(preprocessCore)
  7. addArchRThreads(threads = 20)
  8. addArchRGenome("hg38")
  9. library(BSgenome.Hsapiens.UCSC.hg38)
  10. projdir <- "projrmSubset_subPeak"
  12. projmeta <- readRDS("projATAC_50subC_meta_584904.rds")
  13. finalSubCs <- sort(unique(projmeta$subClusters))
  15. proj <- loadArchRProject(projdir)
  16. dropSubCs <- c("undefined","ast.C6","mg.C8","mg.C10","mg.C15",
  17. "mg.C1", "mg.C5", "neu.C3", "neu.C4", "odc.C2", "neu.C2", "mg.C2")
  18. proj$subClusters[proj$subClusters %like% "neuron"] <- gsub("neuron","neu",
  19. proj$subClusters[proj$subClusters %like% "neuron"])
  20. projclean <- proj[!proj$subClusters %in% dropSubCs,]
  21. projclean$new_majorC <- projclean$subClusters
  22. projclean$new_majorC[projclean$new_majorC %in%
  23. c("neu.C6","neu.C7","neu.C8","neu.C9")] <- "IN"
  24. projclean$new_majorC[projclean$new_majorC %like% "neu"] <- "EX"
  25. projclean$new_majorC <- gsub("\\..*","", projclean$new_majorC)
  26. rm(proj)
  27. majorCs <- sort(unique(projclean$new_majorC))
  28. majorCs
  31. #subC :
  32. #"DEGtest_atacScore_subCs.rds"
  33. #"df_dxDEG_fdr0.1_subCs.rds
  34. #"df_dxDEG_Pval0.1_subCs.rds"
  35. if(T){
  36. #subCs
  37. DEGtest <- list()
  38. for (ct in c("ast","mg", "EX","IN","odc","opc")){
  39. subCs <- unique(projclean[projclean$new_majorC %like% ct,]$subClusters) %>% sort()
  40. for (subC in subCs){
  41. projsubC <- projclean[projclean$subClusters %in% subC,]
  42. for (dx in c("AD","bvFTD","PSP_S")){
  43. dmrTest <- getMarkerFeatures(
  44. ArchRProj = projsubC,
  45. useMatrix = "GeneScoreMatrix",
  46. groupBy = "Clinical.Dx",
  47. testMethod = "wilcoxon",
  48. bias = c("TSSEnrichment", "log10(nFrags)"),
  49. useGroups = dx,
  50. bgdGroups = "Control"
  51. )
  52. groupName <- paste(subC, dx, sep = "|")
  53. DEGtest[[groupName]] <- dmrTest
  54. print(groupName)
  55. }
  56. }
  57. }
  58. # saveRDS(DEGtest, file.path(projdir, paste("DEGtest_atacScore_subCs.rds")))
  59. print("saved")
  61. DEGtest <- readRDS(file.path(projdir, paste("DEGtest_atacScore_subCs.rds")) )
  63. df_differentialGene <- NULL # based on gene scores
  64. for (groupName in names(DEGtest)){
  65. dag <- DEGtest[[groupName]]
  66. # sigDAG <- getMarkers(dag, cutOff = "FDR <= 0.1", returnGR = F)
  67. sigDAG <- getMarkers(dag, cutOff = "Pval <= 0.1", returnGR = F)
  68. dx <- names(sigDAG)
  69. tmpdt <- sigDAG[[dx]]
  70. dt_group <- tmpdt[,c("name","Log2FC","FDR","MeanDiff")]
  71. dt_group <- cbind(dt_group, groupName=rep(groupName, dim(dt_group)[1]))
  72. df_differentialGene <- rbind(df_differentialGene, dt_group )
  73. }
  74. dim(df_differentialGene)
  75. df_differentialGene$subC <- gsub("\\|.*","",df_differentialGene$groupName)
  76. df_differentialGene$dx <- gsub(".*\\|","",df_differentialGene$groupName)
  77. # saveRDS(df_differentialGene, file.path(projdir, paste("df_dxDEG_fdr0.1_subCs.rds")))
  78. saveRDS(df_differentialGene, file.path(projdir, paste("df_dxDEG_Pval0.1_subCs.rds")))
  79. head(df_differentialGene)
  81. }
  83. #majorC:
  84. #"DEGtest_atacScore_majorCs.rds"
  85. #"df_dxDEG_fdr0.1_majorCs.rds"
  86. #"df_dxDEG_Pval0.1_majorCs.rds"
  87. if(T){
  88. DEGtest <- list()
  89. for (ct in majorCs){
  90. projct <- projclean[projclean$new_majorC %in% ct,]
  91. for (dx in c("AD","bvFTD","PSP_S")){
  92. dmrTest <- getMarkerFeatures(
  93. ArchRProj = projct,
  94. useMatrix = "GeneScoreMatrix",
  95. groupBy = "Clinical.Dx",
  96. testMethod = "wilcoxon",
  97. bias = c("TSSEnrichment", "log10(nFrags)"),
  98. useGroups = dx,
  99. bgdGroups = "Control"
  100. )
  101. groupName <- paste(ct, dx, sep = "|")
  102. DEGtest[[groupName]] <- dmrTest
  103. print(groupName)
  104. }
  105. }
  106. #saveRDS(DEGtest, file.path(projdir, paste("DEGtest_atacScore_majorCs.rds")))
  108. DEGtest <- readRDS(file.path(projdir, paste("DEGtest_atacScore_majorCs.rds")) )
  110. df_differentialGene <- NULL # based on gene scores
  111. for (groupName in names(DEGtest)){
  112. dag <- DEGtest[[groupName]]
  113. sigDAG <- getMarkers(dag, cutOff = "FDR <= 0.1", returnGR = F)
  114. # sigDAG <- getMarkers(dag, cutOff = "Pval <= 0.1", returnGR = F)
  115. dx <- names(sigDAG)
  116. tmpdt <- sigDAG[[dx]]
  117. # dt_group <- tmpdt[,c("name","Log2FC","FDR")]
  118. dt_group <- tmpdt[,c("name","Log2FC","FDR","MeanDiff")]
  119. dt_group <- cbind(dt_group, groupName=rep(groupName, dim(dt_group)[1]))
  120. df_differentialGene <- rbind(df_differentialGene, dt_group )
  121. }
  122. dim(df_differentialGene)
  123. df_differentialGene$subC <- gsub("\\|.*","",df_differentialGene$groupName)
  124. df_differentialGene$dx <- gsub(".*\\|","",df_differentialGene$groupName)
  125. saveRDS(df_differentialGene, file.path(projdir, paste("df_dxDEG_fdr0.1_majorCs.rds")))
  126. # saveRDS(df_differentialGene, file.path(projdir, paste("df_dxDEG_Pval0.1_majorCs.rds")))
  127. head(df_differentialGene)
  129. }
  132. # by regions - majorC:
  133. #"DEGtest_atacScore_majorCs_region.rds"
  134. #"df_dxDEG_fdr0.1_majorCs_region.rds"
  135. #"df_dxDEG_Pval0.1_majorCs_region.rds"
  136. if(T){
  137. DEGtest <- list()
  138. for (region in c("PreCG","midInsula")){
  139. projre <- projclean[projclean$region %in% region,]
  140. for (ct in majorCs){
  141. projct <- projre[projre$new_majorC %in% ct,]
  142. for (dx in c("AD","bvFTD","PSP_S")){
  143. dmrTest <- getMarkerFeatures(
  144. ArchRProj = projct,
  145. useMatrix = "GeneScoreMatrix",
  146. groupBy = "Clinical.Dx",
  147. testMethod = "wilcoxon",
  148. bias = c("TSSEnrichment", "log10(nFrags)"),
  149. useGroups = dx,
  150. bgdGroups = "Control"
  151. )
  152. groupName <- paste(region, ct, dx, sep = "|")
  153. DEGtest[[groupName]] <- dmrTest
  154. print(groupName)
  155. }
  156. }
  157. }
  158. saveRDS(DEGtest, file.path(projdir, paste("DEGtest_atacScore_majorCs_region.rds")))
  160. DEGtest <- readRDS(file.path(projdir, paste("DEGtest_atacScore_majorCs.rds")) )
  162. df_differentialGene <- NULL # based on gene scores
  163. for (groupName in names(DEGtest)){
  164. dag <- DEGtest[[groupName]]
  165. # sigDAG <- getMarkers(dag, cutOff = "FDR <= 0.1", returnGR = F)
  166. sigDAG <- getMarkers(dag, cutOff = "Pval <= 0.1", returnGR = F)
  167. dx <- names(sigDAG)
  168. tmpdt <- sigDAG[[dx]]
  169. dt_group <- tmpdt[,c("name","Log2FC","FDR","MeanDiff")]
  170. dt_group <- cbind(dt_group, groupName=rep(groupName, dim(dt_group)[1]))
  171. df_differentialGene <- rbind(df_differentialGene, dt_group )
  172. }
  173. dim(df_differentialGene)
  174. df_differentialGene$region <- gsub("\\|.*","",df_differentialGene$groupName)
  175. df_differentialGene$dx <- gsub(".*\\|","",df_differentialGene$groupName)
  176. df_differentialGene$ct <- gsub(".*\\|(.*?)\\|.*", "\\1",df_differentialGene$groupName)
  177. #saveRDS(df_differentialGene, file.path(projdir, paste("df_dxDEG_fdr0.1_majorCs_region.rds")))
  178. saveRDS(df_differentialGene, file.path(projdir, paste("df_dxDEG_Pval0.1_majorCs_region.rds")))
  179. head(df_differentialGene)
  181. }
  183. # by regions - subCs:
  184. #"DEGtest_atacScore_subCs_region.rds"
  185. #"df_dxDEG_fdr0.1_subCs_region.rds"
  186. # df_dxDEG_Pval0.1_subCs_region.rds
  187. if(T){
  188. #subCs
  189. DEGtest <- list()
  190. for (region in c("PreCG","midInsula")){
  191. projre <- projclean[projclean$region %in% region,]
  192. for (ct in majorCs){
  193. projct <- projre[projre$new_majorC %in% ct,]
  194. subCs <- unique(projct[projct$new_majorC %like% ct,]$subClusters) %>% sort()
  195. for (subC in subCs){
  196. projsubC <- projct[projct$subClusters %in% subC,]
  197. for (dx in c("AD","bvFTD","PSP_S")){
  198. dmrTest <- getMarkerFeatures(
  199. ArchRProj = projsubC,
  200. useMatrix = "GeneScoreMatrix",
  201. groupBy = "Clinical.Dx",
  202. testMethod = "wilcoxon",
  203. bias = c("TSSEnrichment", "log10(nFrags)"),
  204. useGroups = dx,
  205. bgdGroups = "Control"
  206. )
  207. groupName <- paste(region, subC, dx, sep = "|")
  208. DEGtest[[groupName]] <- dmrTest
  209. print(groupName)
  210. }
  211. }
  212. }
  213. }
  215. saveRDS(DEGtest, file.path(projdir, paste("DEGtest_atacScore_subCs_region.rds")))
  216. print("saved")
  218. DEGtest <- readRDS(file.path(projdir, paste("DEGtest_atacScore_subCs_region.rds")) )
  220. df_differentialGene <- NULL # based on gene scores
  221. for (groupName in names(DEGtest)){
  222. dag <- DEGtest[[groupName]]
  223. # sigDAG <- getMarkers(dag, cutOff = "FDR <= 0.1", returnGR = F)
  224. sigDAG <- getMarkers(dag, cutOff = "Pval <= 0.1", returnGR = F)
  225. dx <- names(sigDAG)
  226. tmpdt <- sigDAG[[dx]]
  227. dt_group <- tmpdt[,c("name","Log2FC","FDR","MeanDiff")]
  228. dt_group <- cbind(dt_group, groupName=rep(groupName, dim(dt_group)[1]))
  229. df_differentialGene <- rbind(df_differentialGene, dt_group )
  230. }
  231. dim(df_differentialGene)
  232. df_differentialGene$subC <- gsub("\\|.*","",df_differentialGene$groupName)
  233. df_differentialGene$dx <- gsub(".*\\|","",df_differentialGene$groupName)
  234. # saveRDS(df_differentialGene, file.path(projdir, paste("df_dxDEG_fdr0.1_subCs_region.rds")))
  235. saveRDS(df_differentialGene, file.path(projdir, paste("df_dxDEG_Pval0.1_subCs_region.rds")))
  236. head(df_differentialGene)
  238. }
  240. #output csv
  242. #Differential gene scores
  243. majorC_differentialGene <- readRDS( file.path(projdir, paste("df_dxDEG_fdr0.1_majorCs.rds") ) )
  244. majorC_dGS_region <- readRDS( file.path(projdir, paste("df_dxDEG_fdr0.1_majorCs_region.rds") ) )
  245. cols <- c("name","Log2FC","FDR","groupName","region","ct","dx")
  246. colnames(majorC_differentialGene)[colnames(majorC_differentialGene) %in% "subC"] <- "ct"
  247. majorC_differentialGene$region <- rep("none",dim(majorC_differentialGene)[1])
  249. majorC_DGS <- rbind(majorC_differentialGene[,cols], majorC_dGS_region[,cols])
  252. tmpDT <- as.data.frame(majorC_DGS)
  253. wide_majorC_DGS <- data.table::dcast(setDT(tmpDT),
  254. name + ct + dx ~ region,
  255. value.var=c('Log2FC','FDR'))
  257. write.csv(wide_majorC_DGS, file.path(projdir, "dxDGS_majorCs_fdr0.1.csv"))
  260. #subC
  261. subC_differentialGene <- readRDS( file.path(projdir, paste("df_dxDEG_fdr0.1_subCs.rds") ) )
  262. subC_dGS_region <- readRDS( file.path(projdir, paste("df_dxDEG_fdr0.1_subCs_region.rds") ) )
  263. cols <- c("name","Log2FC","FDR","groupName","region","subC","dx")
  265. subC_differentialGene$region <- rep("none",dim(subC_differentialGene)[1])
  267. subC_dGS_region$region <- subC_dGS_region$subC
  268. subC_dGS_region$subC <- gsub(".*\\|(.*?)\\|.*","\\1", subC_dGS_region$groupName)
  271. head(subC_differentialGene)
  272. head(subC_dGS_region)
  274. cols <- colnames(subC_differentialGene)
  275. subC_DGS <- rbind(subC_differentialGene[,cols], subC_dGS_region[,cols])
  278. tmpDT <- as.data.frame(subC_DGS)
  279. wide_subC_DGS <- data.table::dcast(setDT(tmpDT),
  280. name + subC + dx ~ region,
  281. value.var=c('Log2FC','FDR'))
  284. write.csv(wide_subC_DGS, file.path(projdir, "dxDGS_subCs_fdr0.1.csv"))

24.DifferentialGeneScores.R at commit b0c4b07, under MIT · at the source

Overview

Authors: Xia Han1, Gregory M. Rosenberg1, Vivianne M. Kisling1, Tao Zhang1, Chia-Yi Lee2, Ashvin Ravi3,4, Mikhail Melnik1, Tina Bilousova1,5, Salvatore Spina6, Alissa L. Nana6, Lea T. Grinberg6,7, William W. Seeley6,7, Karen H. Gylys5, Laura M. Huckins8, Towfique Raj3,4,9,10, Kristen J. Brennand2,3,4,8, Jessica E. Rexach1,11
ORCID iDs: Xia Han, Gregory M. Rosenberg, Chia-Yi Lee, Salvatore Spina, Alissa L. Nana, Lea T. Grinberg, William W. Seeley, Laura M. Huckins, Towfique Raj, Kristen J. Brennand, Jessica E. Rexach
  1. Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles,Los Angeles, CA USA
  2. Department of Genetics, Wu Tsai Institute, Yale University School of Medicine,New Haven, CT USA
  3. Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai,New York, NY USA
  4. Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai,New York, NY USA
  5. Department of Physiological Nursing, School of Nursing, University of California, San Francisco,San Francisco, CA USA
  6. Department of Neurology, Fein Memory and Aging Center, University of California, San Francisco,San Francisco, CA USA
  7. Department of Pathology, University of California, San Francisco,San Francisco, CA USA
  8. Department of Psychiatry, Division of Molecular Psychiatry, Yale University School of Medicine,New Haven, CT USA
  9. Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai,New York, NY USA
  10. Friedman Brain Institute, Icahn School of Medicine at Mount Sinai,New York, NY USA
  11. Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles,Los Angeles, CA USA
Institutions: University of California, Los Angeles (United States); Yale University (United States); Icahn School of Medicine at Mount Sinai (United States); University of California, San Francisco (United States)
Journal: Nature communications, volume 17, issue 1, article 6439
Dates: received 6 June 2025; accepted 23 April 2026; published online 14 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-73007-1 · PMID 42135303 · PMCID PMC13376791 · OpenAlex W4411072116
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.06.02.657512
Status: code verified
Categories: genetics / omics (modality), human (organism), other condition (population), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing
Keywords: Epigenomics, Gene regulation, Epigenetics, Dementia
MeSH: Cell Nucleus*, Epigenesis, Genetic*, Neurodegenerative Diseases*, Neuroglia*, Alzheimer Disease, Chromatin, Epigenomics, Gene Expression Regulation, Genetic Predisposition to Disease, Humans, Microglia, Neurons, tau Proteins (* major topic)
Topic: Neuroinflammation and Neurodegeneration Mechanisms (Neurology, Neuroscience), according to OpenAlex
Funding: National Institute on Aging (R01 AG075802, R01 RF1NS128800, R21AG082014, RF1AG065926, R01AG068030, R01AG050986); Rainwater Charitable Foundation, Ono Pharmaceuticals; U.S. Department of Health & Human Services | NIH | National Institute of Environmental Health Sciences (NIEHS) (R01ES033630)
Citations: cited by 2 papers (Europe PMC); 104 references in the paper
Research resources: RRID:SCR_007370

Abstract

The accumulation of abnormal tau protein selectively affects distinct brain regions and specific populations of neurons and glial cells in tau-related dementias, such as Alzheimer’s disease, Pick’s disease and progressive supranuclear palsy. Although the three disorders share the feature of tau protein pathology, the regulatory circuitry of non-coding genetic variants underlying risk-associated cell states remains to be elucidated. Using paired single-nucleus profiling of chromatin accessibility and gene expression across the three conditions, we define cell-type-specific cis-regulatory elements across six cell types and fifty subclasses. Comparing disease-dynamic cis-regulatory elements across three disorders, we find that glia overrepresent disorder-specific gene regulation related to dynamic cellular response to stress. We show that human genetic variants affecting microglial gene regulation converge into distinct and co-regulated modules affecting specific cellular functions. Moreover, polygenic risk modifiers are maximally co-accessible in disorder-specific glial states, modifying distinct pathways such as sphingomyelin regulation in Pick’s disease. Our study informs glial regulators linked to polygenic modifiers of primary tauopathy, establishing modifiable pathways governing resilience.

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 28 matches between paragraphs and lines of code.

rexachgroup/pci_snATAC

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: b0c4b07c14516724845f30e16a1983d55ff5c489, 8 May 2026
Languages: R (39), Shell (13), Python (5)
Size: 59 files, 57 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (20 files), ComplexHeatmap (17 files), data.table (17 files), ggplot2 (14 files), ggpubr (6 files), pandas (5 files), reshape2 (4 files), patchwork (3 files), SAMtools (3 files), anndata (2 files), Matplotlib (2 files), NumPy (2 files), Scanpy (2 files), xarray (2 files), circlize (1 file), Seurat (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
59 files
At the source: github.com/rexachgroup/pci_snATAC

Zenodo 18904265

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (20 files), ComplexHeatmap (17 files), data.table (17 files), ggplot2 (14 files), ggpubr (6 files), pandas (5 files), reshape2 (4 files), patchwork (3 files), SAMtools (3 files), anndata (2 files), Matplotlib (2 files), NumPy (2 files), Scanpy (2 files), xarray (2 files), circlize (1 file), Seurat (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
59 files
At the source:

Code availability

All original code generated in this study is available on GitHub [https://github.com/rexachgroup/pci_snATAC] and archived at Zenodo [10.5281/zenodo.18904265]103.

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;
  • 114 scripts, each with its path and the digest of its content;
  • 28 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

Datasets cited

Data availability

Single-nucleus RNA-seq and ATAC-seq data used in this study were previously generated and are available through Synapse under accession syn52074156 (https://www.synapse.org/Synapse:syn52074156/files/). Single-nucleus CUT&Tag data were previously generated and deposited in Synapse under accession syn53191971 (https://www.synapse.org/Synapse:syn53191971/files/). The publicly available datasets we incorporated in this study include single-cell ATAC-seq data of SEA-AD54 MTG (https://sea-ad-single-cell-profiling.s3.amazonaws.com/index.html#MTG/ATACseq/); and cell-type-specific enhancer peak sets from single-cell studies including syn26670419 (https://www.synapse.org/Synapse:syn26670419)17 (under controlled access, request for access can be made on the Synapse portal), ATAC-seq, ChIP-seq and PLAC-seq datasets for each brain cell type available on https://genome.ucsc.edu/s/nottalexi/glassLab_BrainCellTypes_hg1932, and single-cell chromatin accessibility data from the human brain available on https://catlas.org/catlas/40. Single-nucleus eQTL datasets from the human brain were obtained from two previously published studies: available through Synapse under accession code syn52335732 (10.7303/syn52335732)59 and on Zenodo (10.5281/zenodo.5543734)60. GWAS summary statistics were obtained for AD from http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90027001-GCST90028000/GCST90027158/51, for PSP from https://dss.niagads.org/open-access-data-portal/#NG0016953 and https://dss.niagads.org/datasets/ng00045/74 and for FTD on https://rdr.ucl.ac.uk/articles/dataset/IFGC_Summary-statistics_Data-sharing/1304216652.

Human reference cis-regulatory elements were obtained from the SCREEN database (https://screen.wenglab.org/) and peak sets from the ENCODE portal (https://www.encodeproject.org) with the following identifiers: ENCFF198KYT (https://www.encodeproject.org/files/ENCFF198KYT/), ENCFF791URB (https://www.encodeproject.org/files/ENCFF791URB/), ENCFF395QLP (https://www.encodeproject.org/files/ENCFF395QLP/), ENCFF815WRM (https://www.encodeproject.org/files/ENCFF815WRM/), ENCFF024XNY (https://www.encodeproject.org/files/ENCFF024XNY/), ENCFF124JXP (https://www.encodeproject.org/files/ENCFF124JXP/), ENCFF155FWO (https://www.encodeproject.org/files/ENCFF155FWO/), ENCFF243CHP (https://www.encodeproject.org/files/ENCFF243CHP/), ENCFF283LVU (https://www.encodeproject.org/files/ENCFF283LVU/), ENCFF307QYO (https://www.encodeproject.org/files/ENCFF307QYO/), ENCFF448UVY (https://www.encodeproject.org/files/ENCFF448UVY/), ENCFF509IXE (https://www.encodeproject.org/files/ENCFF509IXE/), ENCFF528AIU (https://www.encodeproject.org/files/ENCFF528AIU/), ENCFF586OIU (https://www.encodeproject.org/files/ENCFF586OIU/), ENCFF595JKW (https://www.encodeproject.org/files/ENCFF595JKW/), ENCFF693JGW (https://www.encodeproject.org/files/ENCFF693JGW/), ENCFF730HDE (https://www.encodeproject.org/files/ENCFF730HDE/), ENCFF756JDB (https://www.encodeproject.org/files/ENCFF756JDB/), ENCFF788DLD (https://www.encodeproject.org/files/ENCFF788DLD/), ENCFF810ZTP (https://www.encodeproject.org/files/ENCFF810ZTP/), ENCFF812QHM (https://www.encodeproject.org/files/ENCFF812QHM/), ENCFF860TAY (https://www.encodeproject.org/files/ENCFF860TAY/), ENCFF861YME (https://www.encodeproject.org/files/ENCFF861YME/), ENCFF878EFJ (https://www.encodeproject.org/files/ENCFF878EFJ/), and ENCFF983DQA (https://www.encodeproject.org/files/ENCFF983DQA/). Source data are provided with this paper.

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, 17 authors, 4 keywords, 13 MeSH terms, 3 funders, 103 references, 1 RRID.

Cite

This paper

Han, X., Rosenberg, G. M., Kisling, V. M., Zhang, T., Lee, C.-Y., Ravi, A., Melnik, M., Bilousova, T., Spina, S., Nana, A. L., Grinberg, L. T., Seeley, W. W., Gylys, K. H., Huckins, L. M., Raj, T., Brennand, K. J., & Rexach, J. E. (2026). Single-nucleus epigenomic dysregulation unmasks genetic risk-associated neurodegenerative glia states. Nature communications, 17(1), 6439. https://doi.org/10.1038/s41467-026-73007-1

BibTeX

@article{han2026single,
author = {Han, Xia and Rosenberg, Gregory M. and Kisling, Vivianne M. and Zhang, Tao and Lee, Chia-Yi and Ravi, Ashvin and Melnik, Mikhail and Bilousova, Tina and Spina, Salvatore and Nana, Alissa L. and Grinberg, Lea T. and Seeley, William W. and Gylys, Karen H. and Huckins, Laura M. and Raj, Towfique and Brennand, Kristen J. and Rexach, Jessica E.},
title = {{Single-nucleus epigenomic dysregulation unmasks genetic risk-associated neurodegenerative glia states}},
journal = {Nature communications},
year = {2026},
month = may,
volume = {17},
number = {1},
pages = {6439},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73007-1},
url = {https://doi.org/10.1038/s41467-026-73007-1},
pmid = {42135303},
pmcid = {PMC13376791}
}

RIS

TY - JOUR
AU - Han, Xia
AU - Rosenberg, Gregory M.
AU - Kisling, Vivianne M.
AU - Zhang, Tao
AU - Lee, Chia-Yi
AU - Ravi, Ashvin
AU - Melnik, Mikhail
AU - Bilousova, Tina
AU - Spina, Salvatore
AU - Nana, Alissa L.
AU - Grinberg, Lea T.
AU - Seeley, William W.
AU - Gylys, Karen H.
AU - Huckins, Laura M.
AU - Raj, Towfique
AU - Brennand, Kristen J.
AU - Rexach, Jessica E.
TI - Single-nucleus epigenomic dysregulation unmasks genetic risk-associated neurodegenerative glia states
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/05/14
VL - 17
IS - 1
SP - 6439
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73007-1
UR - https://doi.org/10.1038/s41467-026-73007-1
LA - en
ER -

CSL-JSON

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