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Transcriptomic analysis in autism spectrum disorder suggests three molecular subtypes with distinct phenotypic profiles and functional pathways.

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 · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Identification of genes associated with ASD core symptoms ↔ 1.rlm_single_task.R, lines 1–73 · score 0.96 · considered symptom related, randomly subsampled, rlm function, MASS package, ADOS modules, core symptom
  2. [2] § Methods › RNA-seq dataset for subtyping analysis ↔ 1.rlm_single_task.R, lines 1–73 · score 0.60 · ASD symptoms, core symptom, genes associated, ADOS, ADI, phenotypic
  3. [3] § Methods › Validation analysis ↔ code/02_DEGenesIsoforms/02_01_A_DEGenes.R, lines 1–47 · score 0.56 · SeqBatch, limma, ancestry, covariates, pipeline, sex
  4. [4] § Methods › Validation analysis ↔ code/02_DEGenesIsoforms/02_01_B_DEGenes.R, lines 1–44 · score 0.56 · SeqBatch, limma, ancestry, covariates, pipeline, sex
  5. [5] § Methods › Subtyping using nonnegative matrix factorization (NMF) ↔ 2.NMF_random_n30_1711ASD_5pheno_686_genes_overlap_genes.R, the whole file · a weak match · score 0.55 · optimal factorization rank, NMF, matrix, gene
  6. [6] § Methods › Validation analysis ↔ code/01_RNAseqProcessing/01_02_A_CountsProcessing.R, lines 207–251 · score 0.51 · Brain Bank, age, BA41, BA17, seq, ASD

Paper

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The authors' code

R · 129 lines · 5.6 KB · no license · 2 matches

  1. ## This script is to obtain genes related to trait.
  2. ## And we performed 100 replicates, each time randomly subsampled 90% of the participants (N = 1,540)
  3. ## and testing the associations between gene expression and ASD symptoms using the rlm function from the MASS package.
  4. ## Each replicate may take up to 2 hours, so we recommend running multiple tasks simultaneously.
  5. ## Once all replicates have finished, a data frame containing the coefficients and p-values will be generated.
  6. ## And Genes associated with one core symptom measure (P value < 0.05) across all replicates were considered symptom-related genes,
  7. ## and genes related to at least one core symptom were retained.
  8. rm(list = ls())
  9. gc()
  11. # -------------------------- Load packages & parse command line arguments --------------------------
  12. library(data.table)
  13. library(MASS)
  14. library(sfsmisc)
  15. library(optparse) # Parse command line arguments (install if needed: install.packages("optparse"))
  17. # Parse command line arguments
  18. option_list <- list(
  19. make_option(c("-p", "--pheno"), type = "character", help = "Phenotype name", metavar = "character"),
  20. make_option(c("-b", "--boot_id"), type = "integer", help = "Bootstrap ID", metavar = "integer"),
  21. make_option(c("-d", "--data_path"), type = "character", help = "Path to data file", metavar = "character"),
  22. make_option(c("-o", "--output_dir"), type = "character", help = "Output directory", metavar = "character"),
  23. make_option(c("-r", "--sample_ratio"), type = "numeric", default = 0.9, help = "Sampling ratio [default: 0.9]")
  24. )
  25. opt <- parse_args(OptionParser(option_list = option_list))
  27. # Validate required arguments
  28. if (is.null(opt$pheno) || is.null(opt$boot_id) || is.null(opt$data_path) || is.null(opt$output_dir)) {
  29. stop("Must specify: --pheno phenotype name --boot_id bootstrap ID --data_path data path --output_dir output directory")
  30. }
  32. # Define parameters (simplify subsequent code)
  33. pheno <- opt$pheno
  34. boot_id <- opt$boot_id
  35. data_path <- opt$data_path
  36. output_dir <- opt$output_dir
  37. sample_ratio <- opt$sample_ratio
  39. # -------------------------- Data reading and preprocessing --------------------------
  40. cat(paste0("[", Sys.time(), "] Start processing: phenotype = ", pheno, " | Bootstrap = ", boot_id, "\n"))
  42. # Read data
  43. dat_raw <- fread(data_path, na.strings = c("", "NA", "NaN"))
  44. dat <- as.data.frame(dat_raw)
  46. # Preprocessing
  47. dat$sex <- factor(dat$sex)
  48. dat$age_at_ados <- as.numeric(dat$age_at_ados)
  49. # New: handle ados_module type (adjust according to actual data type; if numeric, change to as.numeric)
  50. dat$ados_module <- factor(dat$ados_module)
  51. colnames(dat) <- gsub("-", "_", colnames(dat))
  52. genes <- colnames(dat)[2:15534] # Gene column range (adjust as needed)
  54. # Filter missing phenotype + missing covariates (critical: avoid model errors)
  55. # Construct dynamic filtering condition: for adi_r phenotypes additionally require non‑missing ados_module
  56. filter_cond <- !is.na(dat[[pheno]]) & !is.na(dat$sex)
  57. if (grepl("^adi_r", pheno)) { # Check if phenotype starts with adi_r
  58. filter_cond <- filter_cond & !is.na(dat$age_at_ados) & !is.na(dat$ados_module)
  59. } else {
  60. filter_cond <- filter_cond & !is.na(dat$age_at_ados)
  61. }
  62. dat_pheno <- dat[filter_cond, ]
  63. cat(paste0("[", Sys.time(), "] Sample size after filtering: ", nrow(dat_pheno), "\n"))
  65. # -------------------------- Core functions --------------------------
  66. # Generate bootstrap sample
  67. #load(paste0("../bin/bootstrap_2023_samples_list_",pheno,".Rdat"))
  68. #load("bootstrap_samples_list.Rdat")
  69. load("bootstrap_1711_samples_list_5_pheno.Rdat")
  70. dat_boot_ids <- sampled_id_list[[boot_id]]
  71. #dat_boot_ids <- random_samples[[boot_id]]
  72. dat_boot <- dat_pheno[match(dat_boot_ids, dat_pheno$V1), ]
  73. cat(paste0("[", Sys.time(), "] Bootstrap sample size: ", nrow(dat_boot), "\n"))
  75. # 2. RLM analysis (with tryCatch error handling)
  76. run_rlm <- function(gene, pheno, data) {
  77. # Filter out invalid gene values
  78. idx_valid <- !is.infinite(data[[gene]]) & !is.na(data[[gene]])
  79. data_valid <- data[idx_valid, ]
  81. # Core modification: dynamically construct formula based on phenotype prefix
  82. if (grepl("^adi_r", pheno)) {
  83. # adi_r phenotype: covariates = interaction of ados_module and age_at_ados + sex
  84. formula_str <- as.formula(paste(pheno, "~", gene, "+ ados_module:age_at_ados + sex"))
  85. } else {
  86. # Non‑adi_r phenotype: keep original covariates (age_at_ados + sex)
  87. formula_str <- as.formula(paste(pheno, "~", gene, "+ age_at_ados + sex"))
  88. }
  90. rlm_model <- rlm(formula_str, data = data_valid)
  92. # Extract coefficient results
  93. coef_summary <- summary(rlm_model)$coefficients
  95. # Wald test (f.robftest)
  96. wald_test <- f.robftest(rlm_model, var = gene)
  98. # Compile results
  99. res <- data.frame(
  100. gene = gene,
  101. phenotype = pheno,
  102. value = coef_summary[2, "Value"],
  103. SE = coef_summary[2, "Std. Error"],
  104. t_value = coef_summary[2, "t value"],
  105. p_value = as.numeric(wald_test$p.value), # Wald test p‑value
  106. bootstrap_id = NA # Will be filled with bootstrap ID later
  107. )
  108. return(res)
  109. }
  111. # -------------------------- Execute analysis --------------------------
  112. # Batch run RLM
  113. rlm_res <- lapply(genes, function(g) run_rlm(g, pheno, dat_boot))
  114. rlm_res_df <- do.call(rbind, rlm_res)
  116. # Fill bootstrap ID
  117. rlm_res_df$bootstrap_id <- boot_id
  119. # -------------------------- Save results --------------------------
  120. # Output file name: phenotype_BootstrapID.Rdat
  121. output_file <- file.path(output_dir, sprintf("RLM_%s_boot_%d.Rdat", pheno, boot_id))
  122. save(rlm_res_df, file = output_file)
  124. cat(paste0("[", Sys.time(), "] Done! Results saved to: ", output_file, "\n"))
  125. cat(paste0("Number of valid results: ", sum(!is.na(rlm_res_df$p_value)), "/", nrow(rlm_res_df), "\n"))
  127. # Clean memory
  128. rm(list = ls())
  129. gc()

1.rlm_single_task.R at commit ad4b502, no license · at the source

Overview

Authors: Tao Pang1, Xiangyu Zheng1, Jia-Jia Liu2, Lin Lu1, Li Yang1,3, Suhua Chang1,3,4
ORCID iDs: Jia-Jia Liu, Lin Lu, Li Yang, Suhua Chang
  1. Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
  2. School of Nursing, Peking University, Beijing, China
  3. Beijing Key Laboratory for Big Data Innovative Application of Child and Adolescent Mental Disorders, Beijing, China
  4. Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan China
Institutions: Peking University Sixth Hospital (China); Peking University (China); Henan Psychiatric Hospital (China); Henan Medical University (China)
Journal: Communications biology, volume 9, issue 1, article 883
Dates: received 17 September 2025; accepted 2 April 2026; published online 27 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s42003-026-10059-5 · PMID 42045359 · PMCID PMC13324688 · OpenAlex W7155723103
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), autism (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Connectivity
Keywords: Transcriptomics, Autism spectrum disorders
MeSH: Autism Spectrum Disorder*, Gene Expression Profiling*, Transcriptome*, Clustering Algorithms, Humans, Phenotype (* major topic)
Topic: Autism Spectrum Disorder Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Natural Science Foundation of China (National Science Foundation of China) (82471565)
Citations: cited by 1 paper (Europe PMC); 62 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 6 matches between paragraphs and lines of code.

dhglab/Broad-transcriptomic-dysregulation-across-the-cerebral-cortex-in-ASD

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 6821cc55aaf17879a4e0d8eec454d98b2f87dd6a, 10 August 2022
Languages: R (58), C (10), C/C++ (6), Shell (1)
Size: 232 files, 75 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, environment (code/01_RNAseqProcessing/earth-infGenes/DESCRIPTION), tests, documentation
Not found: license file, CITATION.cff, continuous integration
Tools: limma (12 files), ggplot2 (10 files), WGCNA (8 files), reshape2 (6 files), mgcv (3 files), edgeR (2 files), ggpubr (2 files), lmerTest (2 files), FastQC (1 file), igraph (1 file), randomForest (1 file), SAMtools (1 file), Seurat (1 file), STAR (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
76 files
At the source: github.com/dhglab/Broad-transcriptomic-dysregulation-across-the-cerebral-cortex-in-ASD

PerperPKU/SSC_subtype_NMF

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: ad4b50205b0466f1433ba194552f45ad1be2ae9f, 24 March 2026
Languages: R (3)
Size: 5 files, 3 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: data.table (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
4 files
At the source: github.com/PerperPKU/SSC_subtype_NMF

Zenodo 19124178

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file, 0 scripts
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
At the source:

Zenodo 19124179

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: R (3)
Size: 3 files, 3 scripts
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: data.table (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
3 files
At the source:

Code availability statement

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Read it in the paper: doi.org/10.1038/s42003-026-10059-5.

Tracing map

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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;
  • 81 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);
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Data

Datasets cited

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s42003-026-10059-5.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 2 keywords, 6 MeSH terms, 1 funder, 60 references.

Cite

This paper

Pang, T., Zheng, X., Liu, J.-J., Lu, L., Yang, L., & Chang, S. (2026). Transcriptomic analysis in autism spectrum disorder suggests three molecular subtypes with distinct phenotypic profiles and functional pathways. Communications biology, 9(1), 883. https://doi.org/10.1038/s42003-026-10059-5

BibTeX

@article{pang2026transcriptomic,
author = {Pang, Tao and Zheng, Xiangyu and Liu, Jia-Jia and Lu, Lin and Yang, Li and Chang, Suhua},
title = {{Transcriptomic analysis in autism spectrum disorder suggests three molecular subtypes with distinct phenotypic profiles and functional pathways}},
journal = {Communications biology},
year = {2026},
month = apr,
volume = {9},
number = {1},
pages = {883},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-10059-5},
url = {https://doi.org/10.1038/s42003-026-10059-5},
pmid = {42045359},
pmcid = {PMC13324688}
}

RIS

TY - JOUR
AU - Pang, Tao
AU - Zheng, Xiangyu
AU - Liu, Jia-Jia
AU - Lu, Lin
AU - Yang, Li
AU - Chang, Suhua
TI - Transcriptomic analysis in autism spectrum disorder suggests three molecular subtypes with distinct phenotypic profiles and functional pathways
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/04/27
VL - 9
IS - 1
SP - 883
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-10059-5
UR - https://doi.org/10.1038/s42003-026-10059-5
LA - en
ER -

CSL-JSON

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