OSCR

scTWAS: a powerful statistical framework for single-cell transcriptome-wide association studies.

Code ↔ Paper

6 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 6 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Real data applications › Prediction model training and evaluation ↔ realdata_code/ROSMAP/GE_generate_CTS.R, lines 1–61 · score 0.68 · excitatory neuron, inhibitory neuron, Onek1k, OPC, astrocyte, oligodendrocyte
  2. [2] § Methods › Data sets › Genotype and scRNA-seq data › The OneK1K study ↔ realdata_code/Onek1k/CTS_GE_generate.R, the whole file · a weak match · score 0.66 · MonoNC, MonoC, BMem, DC, donors, CD4
  3. [3] § Methods › Data sets › Genotype and scRNA-seq data › The ROSMAP study ↔ realdata_code/ROSMAP/GE_generate_CTS.R, lines 1–61 · score 0.64 · excitatory neurons, inhibitory neurons, cell subtypes, OPCs, astrocytes, oligodendrocytes
  4. [4] § Methods › Data sets › Genotype and scRNA-seq data › The OneK1K study ↔ realdata_code/Onek1k/summary_result.R, the whole file · a weak match · score 0.60 · MonoNC, MonoC, BMem, DC, CD4, filtered
  5. [5] § Methods › Other TWAS methods under comparison ↔ realdata_code/ROSMAP/GE_generate_CTS.R, lines 178–241 · score 0.56 · log transformation, pseudo bulk, TMM, aggregation, matrix, TWAS
  6. [6] § Methods › Real data applications › Prediction model training and evaluation ↔ realdata_code/ROSMAP/GE_generate_BULK.R, lines 31–65 · score 0.54 · inhibitory neuron, external, log, OPC, astrocyte, oligodendrocyte

Paper

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The authors' code

R · 320 lines · 14 KB · no license · 3 matches

  1. library(Seurat)
  2. library(SeuratDisk) # new, for h5Seurat objects
  3. library(dplyr)
  4. library(data.table)
  5. library(Matrix)
  6. require(SeqArray)
  8. source('../../R/scTWAS_IRLS.R')
  9. gene_info <- fread('../Onek1k/1k1k_gene_GRCh37.txt')
  11. suppressMessages(library("optparse"))
  12. option_list = list(
  13. make_option("--run_subtype", action="store", default="FALSE", type="character",
  14. help="whether to analyze cell subtypes"),
  15. make_option("--ct", action="store", default="Microglia", type="character",
  16. help="which cell type"),
  17. make_option("--match_with_ct", action="store", default="FALSE", type="character",
  18. help="whether to match the gene evauated in subtypes with cell type's"))
  20. opt = parse_args(OptionParser(option_list=option_list))
  21. run_subtype <- (opt$run_subtype == 'TRUE')
  22. cts <- opt$ct
  23. match_with_ct <- (opt$match_with_ct == 'TRUE')
  24. file_suffix <- ifelse(match_with_ct, '_matched', '')
  26. sprintf('%s, run subtype: %s, %s', ct, run_subtype, file_suffix) %>% print
  28. data_dir <- './data/ROSMAP/Gene Expression (snRNAseq - DLPFC, Experiment 2)/processed (March 2024 update)'
  29. if(run_subtype){
  30. cell_annotation <- read.csv(sprintf('%s/cell-annotation.n424.csv', data_dir))
  31. # handle Ex and cux2+, cux2-
  32. annotation_ct <- ifelse(grepl('cux2', ct), 'Excitatory Neurons', ct)
  33. # get gene expression data for all subtypes
  34. sub_cts <- table(cell_annotation$state[cell_annotation$cell.type == annotation_ct]) %>% sort(decreasing=T) %>% names
  35. print(sprintf('%s subtypes:', ct))
  36. print(sub_cts)
  37. }
  38. ct_seurat_fn <- c('microglia', 'astrocytes',
  39. 'cux2+', 'cux2-',
  40. 'oligodendroglia', 'oligodendroglia',
  41. 'inhibitory',
  42. 'vascular.niche', 'vascular.niche', 'vascular.niche', 'vascular.niche', 'excitatory')
  43. names(ct_seurat_fn) <- c('Microglia', 'Astrocyte',
  44. 'cux2+', 'cux2-',
  45. 'Oligodendrocytes', 'OPCs',
  46. 'Inhibitory Neurons',
  47. 'Endothelial', 'Fibroblast', 'Pericytes', 'SMC', 'excitatory')
  48. subj_var <- 'individualID'
  49. batch_var <- 'batch_combined'
  52. if(ct == 'excitatory'){
  53. obj_list <- list()
  54. obj_list[['cux2-']] <- LoadH5Seurat(sprintf("%s/%s.h5Seurat", data_dir, 'cux2-'), assays='RNA')
  55. obj_list[['cux2+']] <- LoadH5Seurat(sprintf("%s/%s.h5Seurat", data_dir, 'cux2+'), assays='RNA')
  56. obj <- merge(obj_list[['cux2-']], obj_list[['cux2+']], add.cell.ids = c("cux2-", "cux2+"))
  57. print('cux2- and cux2+ combined')
  58. print(dim(obj))
  59. }else{
  60. obj <- LoadH5Seurat(sprintf("%s/%s.h5Seurat", data_dir, ct_seurat_fn[ct]), assays='RNA')
  61. }
  63. # handle Ex:
  64. # only part of Ex subtypes are saved in either cux2+/cux2-
  65. # focus on those subtypes
  66. if(grepl('cux2', ct) & run_subtype){
  67. print('handle Excitatory neurons data')
  68. # subset cell subtypes for Ex
  69. covered_state <- unique(cell_annotation$state[cell_annotation$barcode %in% colnames(obj)]) %>% sort()
  70. sub_cts <- sub_cts[sub_cts %in% covered_state]
  71. print('overlapped cell states:')
  72. print(sub_cts)
  73. }
  75. # handle cell types underlying vascular niche:
  76. # the seurat object contain cell types other than those annotated in cell_annotation
  77. # remove those cells when performing cell-type level aggregation
  78. if(ct_seurat_fn[ct] %in% c('vascular.niche', 'oligodendroglia')){
  79. print('handle vascular niche / oligodendroglia data')
  80. cell_annotation <- read.csv(sprintf('%s/cell-annotation.n424.csv', data_dir))
  81. obj$barcode <- rownames([email hidden])
  82. obj <- subset(obj, subset = barcode %in% cell_annotation$barcode[cell_annotation$cell.type == ct])
  83. }
  86. # -
  87. # remove cells that failed to be mapped to an individual (individualID='NA') or sequenced in a duplicate batch
  88. # following Fujita et al., 2024
  89. # -
  90. print(sprintf('total #cells: %i', ncol(obj)))
  91. # remove cells that were not matched to the 424 subjects
  92. obj <- subset(obj, subset = individualID != 'NA')
  93. cell_annotation <- read.csv(sprintf('%s/cell-annotation.n424.csv', data_dir))
  94. obj$barcode <- rownames([email hidden])
  95. obj <- subset(obj, subset = barcode %in% cell_annotation$barcode)
  96. print(sprintf('#cells matched to 424 subjects: %i', ncol(obj)))
  97. # Combine ...-A and ...-B, e.g. 190403-B4-A and 190403-B4-B as one batch
  98. # "Libraries from four channels were pooled and sequenced on one lane of the Illu- mina HiSeq X "
  99. # As there are a total of eight channels on 10x GEM machine, we assume that -A represents the first four libraries, and -B the second set of four libraries.
  100. [email hidden]$batch_combined <- sapply(1:nrow([email hidden]),
  101. function(i){
  102. x = [email hidden]$batch[i]
  103. substr(x, 1, nchar(x)-2)})
  104. # Identify individualIDs sampled in multiple batches
  105. meta_data1 <- [email hidden] %>% select('individualID', 'batch_combined') %>% unique()
  106. inds_with_dup_batches <- names(which(table(meta_data1$individualID)>1))
  107. keep_batch_list <- character(length(inds_with_dup_batches))
  108. names(keep_batch_list) <- inds_with_dup_batches
  109. for(ind in inds_with_dup_batches){
  110. keep_batch_list[ind] <- names(which.max(table(obj$batch_combined[obj$individualID == ind])))
  111. }
  112. # Remove the batch with less samples
  113. # following the pre-processing in the original paper
  114. # "Among the remaining 436 specimens, 12 individuals were sequenced twice in distinct batches. After comparing sequencing metrics, one of these duplicates was excluded from further analyses."
  115. keep_cell_inds <- rep(T, ncol(obj))
  116. for(ind in names(keep_batch_list)){
  117. subset_inds <- (obj$individualID == ind & obj$batch_combined != keep_batch_list[ind])
  118. keep_cell_inds[subset_inds] <- F
  119. }
  120. obj$keep_cell_inds <- keep_cell_inds
  121. obj <- subset(obj, subset = keep_cell_inds)
  122. print(sprintf('#cells with de-duplicated batch: %i', ncol(obj)))
  126. # -
  127. # match ID between snRNA-seq and WGS data
  128. # -
  129. # load WGS IDs
  130. wgs_samples <- seqVCF_SampID(sprintf('%s/ROSMAP/WGS/DEJ_11898_B01_GRM_WGS_2017-05-15_15.recalibrated_variants.vcf.gz', data_dir))
  131. clinical_covar <- fread(sprintf('%s/ROSMAP_clinical.csv', data_dir))
  132. specimen_covar <- fread(sprintf('%s/ROSMAP_biospecimen_metadata.csv', data_dir))
  133. merged_covar <- merge(clinical_covar, specimen_covar, by = 'individualID', all = TRUE)
  134. merged_covar$newID <- paste0(merged_covar$Study, merged_covar$projid)
  135. # load gene IDs
  136. gene_samples <- unique(obj$individualID)
  137. #colnames(Agg_obj[['RNA']]$counts)
  138. # extract covariate data that covers the gene sample
  139. gene_covar <- merged_covar[merged_covar$individualID %in% gene_samples,]
  140. gene_covar$geno_ID <- rep(NA, nrow(gene_covar))
  141. # some WGS genotype ID are based on Study+projid
  142. gene_covar$geno_ID[gene_covar$newID %in% wgs_samples] <- gene_covar$newID[gene_covar$newID %in% wgs_samples]
  143. # others are based on specimenID
  144. gene_covar$geno_ID[gene_covar$specimenID %in% wgs_samples] <- gene_covar$specimenID[gene_covar$specimenID %in% wgs_samples]
  145. geno_covar_uni <- unique(gene_covar[!is.na(gene_covar$geno_ID), c('individualID', 'geno_ID')])
  146. print(dim(geno_covar_uni)) # some individuals with multiple WGS samples
  147. geno_covar_matched <- geno_covar_uni[match(gene_samples, geno_covar_uni$individualID),] # remove one of the WGS samples for those who have more than one
  148. print(dim(geno_covar_matched))
  150. obj$geno_ID <- geno_covar_matched$geno_ID[match(obj$individualID, geno_covar_matched$individualID)] # match the new geno_IDs to cells
  151. subj_var <- 'geno_ID'
  153. if(ct == 'microglia'){
  154. # save the matching between WGS ID and snRNAseq ID
  155. write.table(geno_covar_matched, sprintf('%s/WGS_snRNAseq_sample_matching.txt', data_dir))
  156. # create a fam file for WGS samples that are matched
  157. wgs_samples_matched <- wgs_samples[match(geno_covar_matched$geno_ID, wgs_samples)]
  158. print(length(wgs_samples_matched))
  159. write.table(data.frame(FID=0, IID=wgs_samples_matched),
  160. sprintf('%s/ROSMAP/WGS/plink_files/match_WGS_samples_subset.txt', data_dir), sep = '\t', quote = F, col.names = F, row.names = F)
  161. }
  162. if(ct %in% c('Endothelial', 'Fibroblast', 'Pericytes', 'SMC')){
  163. print(dim(obj))
  164. saveRDS(dim(obj), sprintf('%s/scTransform_by_celltype_afterAgg/dimension_%s.rds', data_dir, ct))
  165. q()
  166. }
  170. if(!run_subtype){
  171. sub_cts <- cts
  172. }else{
  173. sub_ct_sum <- matrix(nrow=length(sub_cts), ncol=3)
  174. rownames(sub_ct_sum) <- sub_cts
  175. colnames(sub_ct_sum) <- c('ncells', 'ngenes', 'nsubj')
  176. }
  178. # -
  179. # save pseudo-bulk data by cel ltype
  180. # -
  181. for(sub_ct in sub_cts){
  182. print('---------------------------')
  183. print(sprintf('-----------%s----------', sub_ct))
  184. print('---------------------------')
  185. # -
  186. # save scTWAS object
  187. # -
  188. if(run_subtype){
  189. obj_sub <- subset(obj, barcode %in% cell_annotation$barcode[cell_annotation$state == sub_ct])
  190. }else{
  191. obj_sub <- obj
  192. }
  193. sprintf('#cells in %s: %i', sub_ct, ncol(obj_sub))
  194. if(run_subtype) sub_ct_sum[sub_ct, 1] <- ncol(obj_sub)
  196. Agg_obj <- AggregateExpression(object=obj_sub, assay = 'RNA', group.by=subj_var, return.seurat = TRUE)
  198. meta_data1 <- [email hidden] %>% select(all_of(subj_var), batch_combined) %>% unique()
  199. meta_data1 <- meta_data1[match(colnames(Agg_obj),meta_data1[[subj_var]]),]
  200. rownames(meta_data1) <- meta_data1[[subj_var]]
  201. [email hidden] <- meta_data1
  203. ## scTransform normalization
  204. data <- SCTransform(object = Agg_obj, return.only.var.genes = FALSE)
  206. sprintf('#individual samples with %s cells after aggregation: %i', sub_ct, ncol(data)) %>% print
  208. sub_ct_save <- ifelse(grepl('cux2', ct),
  209. sprintf('%s_%s', ct,sub_ct),
  210. sub_ct)
  212. # scTWAS object
  213. saveRDS(data, file = sprintf('%s/scTransform_by_celltype_afterAgg/%s.rds',data_dir,sub_ct_save))
  215. # -
  216. # AN-TWAS
  217. # -
  218. ## TMM+log+batch correct
  219. library(edgeR) # for TMM
  220. library(sva)
  221. pb_mat <- GetAssayData(Agg_obj, assay = 'RNA', layer = 'counts')
  222. # select genes
  223. if(match_with_ct){
  224. print(sprintf('Match with %s', ct))
  225. # use the same genes for subtypes that were selected at the cell type level
  226. # this is used for microglia subtypes to enable evaluating the same genes as in microglia
  227. ct_PB_df <- read.table(sprintf('%s/scTransform_by_celltype_afterAgg/Expression_matrices/%s_AN.tsv',data_dir,ct), header = T)
  228. min_count <- which(!rownames(pb_mat) %in% ct_PB_df$gene_name)
  229. }else{
  230. # otherwise, select genes based on total gene counts
  231. min_count <- which(rowSums(pb_mat)<1000)
  232. }
  233. pb_mat <- pb_mat[-min_count,]
  234. pb_mat <- pb_mat[order(rowSums(pb_mat),decreasing=T),]
  236. print(dim(pb_mat))
  238. dgList <- DGEList(pb_mat, genes=rownames(pb_mat))
  239. dgList <- calcNormFactors(dgList, method = "TMM")
  240. expr_norm = log_transform(dgList) # same as voom(dgList)$E
  241. donor_pool <- ([email hidden])[[batch_var]]
  243. # SKIP quantile normalization
  244. # https://support.bioconductor.org/p/77664/#77665
  245. expr_norm_inrt <- matrix(NA, nrow = nrow(expr_norm), ncol = ncol(expr_norm))
  246. for(i in 1:nrow(expr_norm)){
  247. expr_norm_inrt[i,] <- INT(expr_norm[i,])
  248. }
  249. rownames(expr_norm_inrt) = rownames(expr_norm)
  250. colnames(expr_norm_inrt) = colnames(expr_norm)
  253. PB_df = data.frame(gene_name = rownames(expr_norm_inrt))
  254. PB_df = cbind(PB_df,expr_norm_inrt)
  255. PB_df = merge(PB_df,gene_info %>% dplyr::select(-ensembl_gene_id),by.x='gene_name',by.y='external_gene_name',sort=FALSE)
  256. PB_df = PB_df %>% relocate(gene_name, chromosome_name, start_position, end_position)
  257. fwrite(PB_df, sprintf('%s/scTransform_by_celltype_afterAgg/Expression_matrices/%s_AN%s.tsv',data_dir,sub_ct_save, file_suffix),sep='\t')
  259. write.table(colnames(PB_df),sprintf('%s/scTransform_by_celltype_afterAgg/Expression_matrices/%s%s.header',
  260. data_dir,sub_ct_save,file_suffix),quote=F,row.names=F,col.names=F)
  262. if(run_subtype){
  263. sub_ct_sum[sub_ct,2] <- nrow(expr_norm)
  264. sub_ct_sum[sub_ct,3] <- ncol(expr_norm)
  265. }
  267. # -
  268. # NA-TWAS
  269. # -
  270. print('scTWAS')
  271. print(dim(obj_sub))
  272. sct_obj <- NormalizeData(obj_sub,normalization.method = "LogNormalize", scale.factor = 1e6)
  273. unique_donor <- sort(unique([email hidden][[subj_var]]))
  274. # match with PBINT
  275. print(all(unique_donor == colnames(expr_norm_inrt)))
  276. unique_donor <- unique_donor[match(colnames(expr_norm_inrt), unique_donor)]
  277. print(all(unique_donor == colnames(expr_norm_inrt)))
  278. pr = matrix(NA, nrow = nrow(sct_obj), ncol = length(unique_donor))
  279. print(length(unique_donor))
  280. colnames(pr) = unique_donor; rownames(pr) = rownames(sct_obj)
  281. n_cells <- numeric(length(unique_donor))
  282. names(n_cells) <- unique_donor
  283. for(i in unique_donor){
  284. ind = which([email hidden][[subj_var]] == i)
  285. pr[,i] = rowMeans((sct_obj[['RNA']]$data[,ind,drop=FALSE]))
  286. n_cells[i] = length(ind)
  287. }
  288. if(grepl('cux', sub_ct_save)){
  289. saveRDS(pr, sprintf('%s/scTransform_by_celltype_afterAgg/Expression_matrices/%s_pr_mean%s.rds',data_dir,sub_ct_save, file_suffix))
  290. saveRDS(n_cells, sprintf('%s/scTransform_by_celltype_afterAgg/Expression_matrices/%s_pr_n_count%s.rds', data_dir,sub_ct_save,file_suffix))
  291. print('save PR mean before INT normalization')
  292. }
  293. # # SKIP quantile normalization
  294. # # https://support.bioconductor.org/p/77664/#77665
  295. pr_inrt <- matrix(NA, nrow = nrow(pr), ncol = ncol(pr))
  296. print(anyNA(pr))
  297. for(i in 1:nrow(pr)){
  298. pr_inrt[i,] <- INT(pr[i,])
  299. }
  300. rownames(pr_inrt) = rownames(pr)
  301. colnames(pr_inrt) = colnames(pr)
  303. PR_df = data.frame(gene_name = rownames(pr_inrt))
  304. PR_df = cbind(PR_df,pr_inrt)
  305. PR_df = merge(PR_df,gene_info %>% dplyr::select(-ensembl_gene_id),by.x='gene_name',by.y='external_gene_name',sort=FALSE)
  306. PR_df = PR_df %>% relocate(gene_name, chromosome_name, start_position, end_position)
  307. PR_df = PR_df[match(PB_df$gene_name,PR_df$gene_name),]
  308. fwrite(PR_df, sprintf('%s/scTransform_by_celltype_afterAgg/Expression_matrices/%s_NA%s.tsv',
  309. data_dir,sub_ct_save,file_suffix),sep='\t')
  310. }
  312. print('Data saved')
  314. if(run_subtype){
  315. write.table(sub_ct_sum, sprintf('%s/scTransform_by_celltype_afterAgg/%s_subtypes_summary%s.txt',
  316. data_dir,ct,file_suffix))
  317. print(sprintf('%s/scTransform_by_celltype_afterAgg/%s_subtypes_summary%s.txt',data_dir,ct,file_suffix))
  318. }

GE_generate_CTS.R at commit f4120fa, no license · at the source

Overview

Authors: Zhaotong Lin1, Chang Su2,3
ORCID iDs: Zhaotong Lin, Chang Su
  1. Department of Statistics, Florida State University,Tallahassee, FL USA
  2. Department of Biostatistics and Bioinformatics, Emory University,Atlanta, GA USA
  3. Department of Human Genetics, Emory University,Atlanta, GA USA
Institutions: Florida State University (United States); Emory University (United States)
Journal: Nature communications, volume 17, issue 1, article 3853
Dates: received 1 May 2025; accepted 25 February 2026; published online 12 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-70374-7 · PMID 41820391 · PMCID PMC13121454 · OpenAlex W7135087009
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing
Keywords: Genome-wide association studies, Gene expression profiling, Gene expression, Statistical methods, Computational models
MeSH: Blood*, Brain*, Single-Cell Gene Expression Analysis*, Aging, Datasets as Topic, Dementia, Gene Expression Regulation, Genetic Association Studies, Humans, Leukocytes, Mononuclear (* major topic)
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: URC Research Award at Emory University
Citations: cited by 3 papers (Europe PMC); 114 references in the paper

Abstract

Transcriptome-wide association studies (TWAS) have successfully identified genes associated with complex traits and diseases, but most have been performed using bulk gene expression data, which aggregate signals across heterogeneous cell types. Population-scale single-cell RNA sequencing data now make it possible to perform TWAS at the cell-type resolution, but present unique challenges due to strong noises, technical variations, and high sparsity. Here, we propose scTWAS, a statistical method to conduct cell-type-specific TWAS using single-cell data. Leveraging a latent-variable model and moment-based estimation to address the challenges of single-cell data, scTWAS consistently improves the prediction of genetically regulated gene expression across cell types in both blood and brain tissues. Compared to existing methods, scTWAS identifies substantially more gene-trait associations across 29 hematological traits and three immune-related diseases in immune cell types. An application to Alzheimer’s disease also reveals cell-subtype-specific associations, including MS4A6A in the disease-associated microglial subtype and PPP1R37 in the inflammatory microglial subtype.

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

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ZhaotongL/scTWAS

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Tools: data.table (8 files), tidyverse (8 files), Seurat (7 files), edgeR (4 files), caret (2 files), glmnet (2 files), ggplot2 (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
17 files
At the source:

Code availability

R package for scTWAS is available at https://github.com/ZhaotongL/scTWAS113. Code for real data analyses is available at https://github.com/ZhaotongL/scTWAS_paper114.

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:

  • 4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 44 scripts, each with its path and the digest of its content;
  • 6 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

Data availability

The GReX prediction models for immune and brain cell types generated in this study have been deposited in Figshare at [10.6084/m9.figshare.31329184] and [10.6084/m9.figshare.31329139]111,112. The availability for datasets used to train or validate GReX models is as follows: OneK1K genotype and scRNA-seq data are publicly available from the OneK1K study17. Genotype and gene expression data of immune cell types from the DICE project16 are available under controlled access in dbGap under accession code phs001703.v5.p1 [https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001703.v5.p1], access can be obtained by requesting Authorized Access on dbGap. WGS and snRNA-seq data from the ROSMAP study are available on Synapse under controlled access with accession codes syn11724057 [https://www.synapse.org/Synapse:syn11724057], syn53366818 [https://www.synapse.org/Synapse:syn53366818], and syn52293417 [https://www.synapse.org/Synapse:syn52293417], access can be obtained by submitting a Data Use Certificate to Synapse.

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 5 keywords, 10 MeSH terms, 1 funder, 112 references.

Cite

This paper

Lin, Z., & Su, C. (2026). scTWAS: a powerful statistical framework for single-cell transcriptome-wide association studies. Nature communications, 17(1), 3853. https://doi.org/10.1038/s41467-026-70374-7

BibTeX

@article{lin2026sctwas,
author = {Lin, Zhaotong and Su, Chang},
title = {{scTWAS: a powerful statistical framework for single-cell transcriptome-wide association studies}},
journal = {Nature communications},
year = {2026},
month = mar,
volume = {17},
number = {1},
pages = {3853},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-70374-7},
url = {https://doi.org/10.1038/s41467-026-70374-7},
pmid = {41820391},
pmcid = {PMC13121454}
}

RIS

TY - JOUR
AU - Lin, Zhaotong
AU - Su, Chang
TI - scTWAS: a powerful statistical framework for single-cell transcriptome-wide association studies
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/03/12
VL - 17
IS - 1
SP - 3853
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-70374-7
UR - https://doi.org/10.1038/s41467-026-70374-7
LA - en
ER -

CSL-JSON

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"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "3853",
"DOI": "10.1038/s41467-026-70374-7",
"PMID": "41820391",
"PMCID": "PMC13121454",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-70374-7",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
12
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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