OSCR

scMINA: an interpretable workflow for paired scRNA-scATAC multiome integration and regulatory prioritization.

Code ↔ Paper

9 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 9 matches
  1. [1] § Materials and methods › Benchmarking scMultiome integration methods ↔ scpair/model.py, lines 342 to 449 · score 0.86 · zero inflated, hidden layers, dropout rate, activation function, encoding, scPair · Report a mismatch
  2. [2] § Materials and methods › Data normalization, cell clustering, and differential expression analysis ↔ scripts/example_visualize_data.R, lines 46 to 105 · score 0.84 · FindVariableFeatures, FindClusters, FindNeighbors, Seurat, log, clustering · Report a mismatch
  3. [3] § Materials and methods › Benchmarking scMultiome integration methods ↔ scripts/example_process_data.py, lines 14 to 59 · score 0.79 · log1p, min_disp, highly variable genes, preprocessed, max, dimensional · Report a mismatch
  4. [4] § Materials and methods › Benchmarking scMultiome integration methods ↔ mofapy2/run/entry_point.py, lines 1772 to 1839 · score 0.74 · ard_factors, ard_weights, highly variable, fast, GPU, variance · Report a mismatch
  5. [5] § Materials and methods › Data normalization, cell clustering, and differential expression analysis ↔ scripts/integrate_scPair_multiome.R, lines 76 to 134 · score 0.65 · FindClusters, FindNeighbors, UMAP, scPair, clustering · Report a mismatch
  6. [6] § Results › scMINA overview ↔ scripts/run_FigR_analysis.R, lines 140 to 176 · score 0.61 · gene regulatory networks, TF DORC, FigR, GRNs, cis, Gradients · Report a mismatch
  7. [7] § Results › scMINA overview ↔ scripts/run_FigR_analysis.R, lines 132 to 168 · score 0.61 · gene regulatory networks, TF DORC, FigR, GRNs, cis, Gradients · Report a mismatch
  8. [8] § Results › Correlation-based downstream analysis reveals AD-associated shifts and regulatory networks in glial cell types ↔ scripts/run_FigR_analysis.R, lines 140 to 176 · score 0.58 · TF DORC, regulatory networks, FigR, interactions, enrichment, scores · Report a mismatch
  9. [9] § Results › Correlation-based downstream analysis reveals AD-associated shifts and regulatory networks in glial cell types ↔ scripts/run_FigR_analysis.R, lines 132 to 168 · score 0.58 · TF DORC, regulatory networks, FigR, interactions, enrichment, scores · Report a mismatch

Paper

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The authors' code

R · 243 lines · 8 KB · MIT · 2 matches

  1. #' Run FigR Gene Regulatory Network Analysis
  2. #'
  3. #' This function performs the full FigR analysis workflow, including:
  4. #' - Peak–gene association testing (runGenePeakcorr)
  5. #' - DORC identification and visualization
  6. #' - DORC scoring and smoothing
  7. #' - RNA smoothing using kNN graph
  8. #' - GRN inference using FigR::runFigRGRN
  9. #' - TF-DORC visualization and driver ranking
  10. #'
  11. #' It assumes that preprocessing was performed using prep_FigR_inputs().
  12. #'
  13. #' @param atac_se_rds Path to SummarizedExperiment object produced during preprocessing.
  14. #' @param rna_mat_rds Path to normalized RNA matrix (RDS).
  15. #' @param cellknn_rds Path to cell kNN matrix produced during preprocessing.
  16. #' @param genome Genome assembly for FigR ("hg38", "hg19", or "mm10"). Default: "hg38".
  17. #' @param nCores_corr Number of cores for runGenePeakcorr.
  18. #' @param nCores_smooth Number of cores for smoothing steps.
  19. #' @param nCores_grn Number of cores for GRN inference.
  20. #' @param dorc_cutoff Minimum number of significant peaks to call a DORC (default = 5).
  21. #' @param dorc_labelTop Number of DORCs to label in plots (default = 20).
  22. #' @param dorcK Proportion for DORC selection in GRN (default = 2).
  23. #' @param prefix Output filename prefix (default = "FigR").
  24. #' @param outdir Output directory (default = "./FigR_results").
  25. #' @param seed Random seed used by FigR and R, including parallel RNG streams.
  26. #' Default: 0.
  27. #'
  28. #' @return A list containing cisCorr, cisCorr.filt, dorcGenes, dorcMat.s, RNAmat.s, and figR.d.
  29. #' @export
  30. #'
  31. #' @examples
  32. #' run_FigR_analysis(
  33. #' atac_se_rds = "oligo_ATACse_FigR.rds",
  34. #' rna_mat_rds = "oligo_RNAmat_FigR.rds",
  35. #' cellknn_rds = "oligo_cellkNN_FigR.rds",
  36. #' genome = "hg38",
  37. #' prefix = "oligo"
  38. #' )
  39. #'
  41. run_FigR_analysis <- function(
  42. atac_se_rds,
  43. rna_mat_rds,
  44. cellknn_rds,
  45. genome = "hg38",
  46. nCores_corr = 64,
  47. nCores_smooth = 24,
  48. nCores_grn = 8,
  49. dorc_cutoff = 5,
  50. dorc_labelTop = 20,
  51. dorcK = 2,
  52. seed = 0,
  53. prefix = "FigR",
  54. outdir = "./FigR_results"
  55. ){
  56. suppressPackageStartupMessages({
  57. library(dplyr)
  58. library(Matrix)
  59. library(FigR)
  60. library(BuenColors)
  61. library(ggplot2)
  62. })
  64. RNGkind("L'Ecuyer-CMRG")
  65. set.seed(seed)
  67. message("Random seed: ", seed)
  69. if (!dir.exists(outdir)) dir.create(outdir, recursive = TRUE)
  71. #------------------------------------------------------
  72. # Load Inputs
  73. #------------------------------------------------------
  74. ATAC.se <- readRDS(atac_se_rds)
  75. RNAmat <- readRDS(rna_mat_rds)
  76. cellkNN <- readRDS(cellknn_rds)
  78. #------------------------------------------------------
  79. # Peak–Gene Correlation Testing
  80. #------------------------------------------------------
  81. cisCorr <- FigR::runGenePeakcorr(
  82. ATAC.se = ATAC.se,
  83. RNAmat = RNAmat,
  84. genome = genome,
  85. nCores = nCores_corr,
  86. p.cut = NULL,
  87. n_bg = 100
  88. )
  89. saveRDS(cisCorr, file.path(outdir, paste0(prefix, "_cisCorr.rds")))
  91. cisCorr.filt <- cisCorr %>% dplyr::filter(pvalZ <= 0.05)
  93. #------------------------------------------------------
  94. # DORC Identification + Visualization
  95. #------------------------------------------------------
  96. pdf(file.path(outdir, paste0(prefix, "_DORC_plot.pdf")), width = 10, height = 5)
  97. dorcGenes <- dorcJPlot(
  98. dorcTab = cisCorr.filt,
  99. cutoff = dorc_cutoff,
  100. labelTop = dorc_labelTop,
  101. returnGeneList = TRUE,
  102. force = 2
  103. )
  104. dev.off()
  106. numDorcs <- cisCorr.filt %>%
  107. group_by(Gene) %>%
  108. tally() %>%
  109. arrange(desc(n))
  110. saveRDS(numDorcs, file.path(outdir, paste0(prefix, "_numDorcs.rds")))
  112. #------------------------------------------------------
  113. # Compute DORC Scores
  114. #------------------------------------------------------
  115. dorcMat <- getDORCScores(
  116. ATAC.se = ATAC.se,
  117. dorcTab = cisCorr.filt,
  118. geneList = dorcGenes,
  119. nCores = 1
  120. )
  122. #------------------------------------------------------
  123. # Smooth DORC Scores Using kNN
  124. #------------------------------------------------------
  125. dorcMat.s <- smoothScoresNN(
  126. NNmat = cellkNN[, 1:30],
  127. mat = dorcMat,
  128. nCores = 4
  129. )
  131. #------------------------------------------------------
  132. # Smooth RNA Matrix Using kNN
  133. #------------------------------------------------------
  134. RNAmat.s <- smoothScoresNN(
  135. NNmat = cellkNN[, 1:30],
  136. mat = RNAmat,
  137. nCores = nCores_smooth
  138. )
  140. #------------------------------------------------------
  141. # FigR Gene Regulatory Network Inference
  142. #------------------------------------------------------
  143. figR.d <- runFigRGRN(
  144. ATAC.se = ATAC.se,
  145. dorcTab = cisCorr.filt,
  146. genome = genome,
  147. dorcMat = dorcMat.s,
  148. rnaMat = RNAmat.s,
  149. dorcK = dorcK,
  150. nCores = nCores_grn
  151. )
  153. saveRDS(figR.d, file.path(outdir, paste0(prefix, "_FigR_GRN.rds")))
  155. #------------------------------------------------------
  156. # Visualization: TF–DORC Scatter Plot
  157. #------------------------------------------------------
  158. pdf(file.path(outdir, paste0(prefix, "_TF_DORC_scatter.pdf")), width = 4, height = 4)
  159. figR.d %>%
  160. ggplot(aes(Corr.log10P, Enrichment.log10P, color = Score)) +
  161. ggrastr::geom_point_rast(size = 0.01, shape = 16) +
  162. theme_classic() +
  163. scale_color_gradientn(
  164. colours = jdb_palette("solar_extra"),
  165. limits = c(-3, 3),
  166. oob = scales::squish,
  167. breaks = scales::breaks_pretty(n = 3)
  168. )
  169. dev.off()
  171. #------------------------------------------------------
  172. # Visualization: Driver Ranking
  173. #------------------------------------------------------
  174. pdf(file.path(outdir, paste0(prefix, "_TF_drivers.pdf")), width = 6, height = 4)
  175. rankDrivers(figR.d, rankBy = "meanScore", interactive = FALSE)
  176. dev.off()
  178. #------------------------------------------------------
  179. # Return all key results
  180. #------------------------------------------------------
  181. return(list(
  182. cisCorr = cisCorr,
  183. cisCorr_filt = cisCorr.filt,
  184. dorcGenes = dorcGenes,
  185. numDorcs = numDorcs,
  186. dorcMat_smooth = dorcMat.s,
  187. RNAmat_smooth = RNAmat.s,
  188. figR_GRN = figR.d
  189. ))
  190. }
  192. if (sys.nframe() == 0) {
  193. # CLI wrapper for Rscript usage with --key value args
  194. args <- commandArgs(trailingOnly = TRUE)
  195. arg_list <- list()
  196. if (length(args) > 0) {
  197. i <- 1
  198. while (i <= length(args)) {
  199. key <- args[[i]]
  200. if (startsWith(key, "--")) {
  201. name <- substring(key, 3)
  202. value <- if (i + 1 <= length(args) && !startsWith(args[[i + 1]], "--")) args[[i + 1]] else TRUE
  203. arg_list[[name]] <- value
  204. i <- i + if (isTRUE(value) && (i + 1 > length(args) || startsWith(args[[i + 1]], "--"))) 1 else 2
  205. } else {
  206. i <- i + 1
  207. }
  208. }
  209. }
  211. required <- c("atac_se_rds", "rna_mat_rds", "cellknn_rds")
  212. missing <- setdiff(required, names(arg_list))
  213. if (length(missing) > 0) {
  214. stop("Missing required arguments: ", paste(missing, collapse = ", "))
  215. }
  217. genome <- if (!is.null(arg_list$genome)) arg_list$genome else "hg38"
  218. nCores_corr <- if (!is.null(arg_list$nCores_corr)) as.integer(arg_list$nCores_corr) else 64
  219. nCores_smooth <- if (!is.null(arg_list$nCores_smooth)) as.integer(arg_list$nCores_smooth) else 24
  220. nCores_grn <- if (!is.null(arg_list$nCores_grn)) as.integer(arg_list$nCores_grn) else 8
  221. dorc_cutoff <- if (!is.null(arg_list$dorc_cutoff)) as.numeric(arg_list$dorc_cutoff) else 5
  222. dorc_labelTop <- if (!is.null(arg_list$dorc_labelTop)) as.integer(arg_list$dorc_labelTop) else 20
  223. dorcK <- if (!is.null(arg_list$dorcK)) as.numeric(arg_list$dorcK) else 2
  224. seed <- if (!is.null(arg_list$seed)) as.integer(arg_list$seed) else 0L
  225. prefix <- if (!is.null(arg_list$prefix)) arg_list$prefix else "FigR"
  226. outdir <- if (!is.null(arg_list$outdir)) arg_list$outdir else "./FigR_results"
  228. run_FigR_analysis(
  229. atac_se_rds = arg_list$atac_se_rds,
  230. rna_mat_rds = arg_list$rna_mat_rds,
  231. cellknn_rds = arg_list$cellknn_rds,
  232. genome = genome,
  233. nCores_corr = nCores_corr,
  234. nCores_smooth = nCores_smooth,
  235. nCores_grn = nCores_grn,
  236. dorc_cutoff = dorc_cutoff,
  237. dorc_labelTop = dorc_labelTop,
  238. dorcK = dorcK,
  239. seed = seed,
  240. prefix = prefix,
  241. outdir = outdir
  242. )
  243. }

run_FigR_analysis.R at commit 0356836, under MIT · at the source

Overview

Authors: Shuwen Zhang1, Hongru Hu2, Xue Wang3, Chengjie Xiong4, Takahisa Kanekiyo5, Yan W Asmann3, Yingxue Ren3
ORCID iDs: Chengjie Xiong, Yingxue Ren
  1. Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, United States
  2. Integrative Genetics and Genomics, University of California, Davis, CA 95616, United States
  3. Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL 32224, United States
  4. Division of Biostatistics, Washington University School of Medicine, St. Louis, MO 63110, United States
  5. Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, United States
Institutions: Mayo Clinic (United States); University of California, Davis (United States); Mayo Clinic in Florida (United States); Washington University in St. Louis (United States)
Journal: NAR genomics and bioinformatics, volume 8, issue 4, article lqag140
Dates: received 1 June 2026; accepted 20 October 2026; published online 10 November 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/nargab/lqag140 · PMID 43011234 · PMCID PMC13748873 · OpenAlex W7225734830
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: genetics / omics (modality), human (organism), Alzheimer's / dementia (population)
Methods: Smoothing, state filtering, decompositions, Connectivity, Statistics, Machine learning
MeSH: RNA, Small Cytoplasmic*, RNA-Seq*, Single-Cell Analysis*, Software*, Alzheimer Disease, Chromatin, Gene Regulatory Networks, Humans, Multiomics, Single-Cell Gene Expression Analysis, Workflow (* major topic)
Journal subjects: Pipelines and Workflows for Biological Data Analysis
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Institute on Aging (U19 AG069701); Cure Alzheimer's Fund; Mayo Clinic Department of Quantitative Health Sciences Associate Consult Fund
Citations: not cited yet (Europe PMC); 53 references in the paper

Abstract

Single-cell multiomic technologies that jointly profile transcriptomic and epigenomic states provide unprecedented opportunities to investigate cellular heterogeneity and gene regulatory mechanisms in complex diseases. However, analysis of paired single-cell RNA-seq (scRNA-seq) and chromatin accessibility (scATAC-seq) data remains computationally challenging due to modality-specific technical variation, integration complexity, and limited interpretability of downstream regulatory inference. Here, we present scMINA (single-cell Multimodal Integration and Network Attribution), a modular and interpretable analytical workflow for the analysis of paired single-cell multiome datasets. scMINA comprises coordinated modules for multimodal integration, technical covariate correction, and regulatory feature prioritization, enabling systematic characterization of cellular states across transcriptomic and epigenomic modalities. In addition, scMINA incorporates explainable artificial intelligence-based attribution strategies to prioritize candidate genes and transcription factors associated with cell-state heterogeneity and disease-related regulatory alterations. We applied scMINA to the single-cell multiome data from the Seattle Alzheimer’s Disease Brain Cell Atlas, where the workflow robustly resolved major glial cell populations and identified regulatory features associated with Alzheimer’s disease pathology. By coupling multimodal integration with interpretable regulatory prioritization, scMINA provides a practical workflow for systematic analysis and biological interpretation of paired scRNA-seq and scATAC-seq datasets.

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 9 matches between paragraphs and lines of code.

quon-titative-biology/scpair

License: MIT
State: the link answers, verified on 26 October 2026
Evidence: files inventoried
Commit: c585949ca8ea1314f5e68b260e3d9c5b2dabe61c, 7 September 2025
Languages: Python (8)
Size: 22 files, 8 scripts
Software Heritage: not archived
Found in: the text, “Benchmarking scMultiome integration methods”
Holds: README, license file, environment (pyproject.toml, setup.py, uv.lock, legacy_env_files/environment.yml, legacy_env_files/requirements.txt), continuous integration
Not found: CITATION.cff, tests, documentation
Tools: NumPy (6 files), pandas (6 files), PyTorch (6 files), SciPy (6 files), scikit-learn (3 files), anndata (2 files), Scanpy (2 files), JAX (1 file), Matplotlib (1 file), Plotly (1 file), seaborn (1 file)
Availability: 1 check, the latest on 26 October 2026: the link answers
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10 files
At the source: github.com/quon-titative-biology/scPair/tree/main/tutorials

bioFAM/mofapy2

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Evidence: files inventoried
Commit: 90418e5021b3ae735ebf1fea5d7e07cae71c8bb7, 26 August 2026
Languages: Python (53), Jupyter (1)
Size: 66 files, 54 scripts
Software Heritage: archived
Found in: the text, “Benchmarking scMultiome integration methods”
Holds: README, license file, environment (pyproject.toml), tests, continuous integration, 1 notebook
Not found: CITATION.cff, documentation
Tools: NumPy (42 files), SciPy (36 files), pandas (11 files), h5py (4 files), CuPy (3 files), scikit-learn (2 files), anndata (1 file), Matplotlib (1 file), seaborn (1 file)
Availability: 1 check, the latest on 12 November 2026: the link answers
  • 12 November 2026: the link answers
56 files
At the source: github.com/bioFAM/mofapy2

bioFAM/mofax

License: MIT
State: the link answers, verified on 12 November 2026
Evidence: files inventoried
Commit: e4821fef8b1f9f50afd84a6acb96fcc49445c967, 22 October 2024
Languages: Python (14), Jupyter (2)
Size: 30 files, 16 scripts
Software Heritage: not archived
Found in: the text, “Benchmarking scMultiome integration methods”
Holds: README, license file, environment (pyproject.toml, setup.py), tests, continuous integration, documentation, 2 notebooks
Not found: CITATION.cff
Tools: NumPy (12 files), pandas (12 files), Matplotlib (9 files), seaborn (9 files), SciPy (5 files), h5py (4 files), Scanpy (1 file), UMAP (1 file)
Availability: 1 check, the latest on 12 November 2026: the link answers
  • 12 November 2026: the link answers
18 files
At the source: github.com/bioFAM/mofax

ShuwenZhangswz/scMINA

License: MIT
State: the link answers, verified on 12 November 2026
Evidence: files inventoried
Commit: 0356836bfbcc19aadc8c58bc8b0943a6c830b1bd, 8 September 2026
Languages: Python (6), R (5), Shell (2)
Size: 28 files, 13 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file, environment (environment.yml)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: ggplot2 (5 files), pandas (5 files), Scanpy (5 files), NumPy (4 files), tidyverse (4 files), PyTorch (3 files), Seurat (3 files), circlize (1 file), ComplexHeatmap (1 file), Plotly (1 file)
Availability: 1 check, the latest on 12 November 2026: the link answers
  • 12 November 2026: the link answers
15 files
At the source: github.com/ShuwenZhangswz/scMINA

Zenodo 19582768

License: MIT
State: the link answers, verified on 12 November 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: pandas (5 files), Scanpy (5 files), ggplot2 (4 files), NumPy (4 files), tidyverse (4 files), PyTorch (3 files), Seurat (3 files), Plotly (1 file)
Availability: 1 check, the latest on 12 November 2026: the link answers (HTTP 200)
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14 files
At the source:

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:

  • 5 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 103 scripts, each with its path and the digest of its content;
  • 9 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 multiome data used in this study can be accessed from AD knowledge Portal under ID: syn29879329: https://www.synapse.org/Synapse:syn29879329. The scMINA framework and analysis scripts are publicly available at 10.5281/zenodo.19582768 and https://github.com/ShuwenZhangswz/scMINA, including documentation and example workflows for reproducing the analyses described in this study.

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, 12 November 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 11 MeSH terms, 3 funders, 52 references.

Cite

This paper

Zhang, S., Hu, H., Wang, X., Xiong, C., Kanekiyo, T., Asmann, Y. W., & Ren, Y. (2026). scMINA: an interpretable workflow for paired scRNA-scATAC multiome integration and regulatory prioritization. NAR genomics and bioinformatics, 8(4), lqag140. https://doi.org/10.1093/nargab/lqag140

BibTeX

@article{zhang2026scmina,
author = {Zhang, Shuwen and Hu, Hongru and Wang, Xue and Xiong, Chengjie and Kanekiyo, Takahisa and Asmann, Yan W and Ren, Yingxue},
title = {{scMINA: an interpretable workflow for paired scRNA-scATAC multiome integration and regulatory prioritization}},
journal = {NAR genomics and bioinformatics},
year = {2026},
month = nov,
volume = {8},
number = {4},
pages = {lqag140},
publisher = {Oxford University Press},
issn = {2631-9268},
doi = {10.1093/nargab/lqag140},
url = {https://doi.org/10.1093/nargab/lqag140},
pmid = {43011234},
pmcid = {PMC13748873}
}

RIS

TY - JOUR
AU - Zhang, Shuwen
AU - Hu, Hongru
AU - Wang, Xue
AU - Xiong, Chengjie
AU - Kanekiyo, Takahisa
AU - Asmann, Yan W
AU - Ren, Yingxue
TI - scMINA: an interpretable workflow for paired scRNA-scATAC multiome integration and regulatory prioritization
T2 - NAR genomics and bioinformatics
J2 - NAR Genom Bioinform
PY - 2026
DA - 2026/11/10
VL - 8
IS - 4
SP - lqag140
SN - 2631-9268
PB - Oxford University Press
DO - 10.1093/nargab/lqag140
UR - https://doi.org/10.1093/nargab/lqag140
LA - en
ER -

CSL-JSON

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"id": "10.1093/nargab/lqag140",
"type": "article-journal",
"title": "scMINA: an interpretable workflow for paired scRNA-scATAC multiome integration and regulatory prioritization",
"container-title": "NAR genomics and bioinformatics",
"author": [
{
"family": "Zhang",
"given": "Shuwen"
},
{
"family": "Hu",
"given": "Hongru"
},
{
"family": "Wang",
"given": "Xue"
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{
"family": "Xiong",
"given": "Chengjie"
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{
"family": "Kanekiyo",
"given": "Takahisa"
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{
"family": "Asmann",
"given": "Yan W"
},
{
"family": "Ren",
"given": "Yingxue"
}
],
"container-title-short": "NAR Genom Bioinform",
"volume": "8",
"issue": "4",
"page": "lqag140",
"DOI": "10.1093/nargab/lqag140",
"PMID": "43011234",
"PMCID": "PMC13748873",
"ISSN": "2631-9268",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/nargab/lqag140",
"language": "en",
"issued": {
"date-parts": [
[
2026,
11,
10
]
]
}
}

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[3] doi:10.1038/s44320-026-00208-7 [code]
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Journal: Molecular systems biology
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[4] doi:10.1038/s41586-024-08453-2 [code]
Mapping cells through time and space with moscot.
Journal: Nature
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[5] doi:10.1038/s41467-024-53971-2 [code]
scPair: Boosting single cell multimodal analysis by leveraging implicit feature selection and single cell atlases.
Journal: Nature communications
In common: JAX, anndata, Scanpy, 10 other tools, genetics / omics, 6 references
[6] doi:10.1038/s41587-024-02241-z [code]
Tracking single-cell evolution using clock-like chromatin accessibility loci.
Journal: Nature biotechnology
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[7] doi:10.1016/j.isci.2024.109009 [code]
Spatial and single-nucleus transcriptomics decoding the molecular landscape and cellular organization of avian optic tectum.
Journal: iScience
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[8] doi:10.1016/j.celrep.2024.114876 [code]
Spatial, transcriptomic, and epigenomic analyses link dorsal horn neurons to chronic pain genetic predisposition.
Journal: Cell reports
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[9] doi:10.1016/j.xgen.2024.100739 [code]
CSI-GEP: A GPU-based unsupervised machine learning approach for recovering gene expression programs in atlas-scale single-cell RNA-seq data.
Journal: Cell genomics
In common: anndata, circlize, Scanpy, 14 other tools, genetics / omics, 3 references
[10] doi:10.1038/s41592-023-01938-4 [code]
SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks.
Journal: Nature methods
In common: anndata, Scanpy, ComplexHeatmap, 12 other tools, genetics / omics, 4 references

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