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Integrated transcriptomic identification and validation reveal key autophagy-associated biomarkers in sleep deprivation.

Code ↔ Paper

11 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 11 matches
  1. [1] § Results › Immune cell infiltration analysis ↔ 05_Immune_Infiltration.R, lines 1–35 · score 0.88 · myeloid derived suppressor, plasmacytoid dendritic cells, central memory CD4, natural killer, CD8, monocytes
  2. [2] § Results › Immune cell infiltration analysis ↔ 05_Immune_Infiltration.R, lines 1–35 · score 0.83 · myeloid derived suppressor, plasmacytoid dendritic cells, memory CD8, memory CD4, macrophages, effector
  3. [3] § Results › Expression patterns of predictor genes in single cells ↔ 06_Single_Cell_Analysis and GSEA Analysis.R, lines 71–128 · score 0.83 · Cx3cr1, GABAergic, Acta2, Aldoc, Cldn11, Dlk1
  4. [4] § Methods › scRNA-seq ↔ 06_Single_Cell_Analysis and GSEA Analysis.R, lines 130–217 · score 0.75 · FindAllMarkers, logfc.threshold, min.pct, clustering, cell, genes
  5. [5] § Results › Diagnostic value of key predictor genes and external cohort validation ↔ 04_Diagnostic_Model.R, lines 306–375 · score 0.70 · calibration curve, predicted probabilities, diagnostic model, ideal, CDKN1A, NR4A1
  6. [6] § Methods › GSEA functional enrichment analysis ↔ 06_Single_Cell_Analysis and GSEA Analysis.R, lines 289–340 · score 0.65 · c5.go.v2024.1.Hs.symbols.gmt, GSEA, enrichment, genes
  7. [7] § Results › Functional enrichment analysis ↔ 02_Enrichment_Analysis.R, lines 99–154 · score 0.65 · Cellular Component, Biological Process, Molecular Function, enrichment, genes
  8. [8] § Methods › Data collection ↔ 01Data Cleaning & Differential Analysis.R, lines 101–172 · score 0.64 · removeBatchEffect, batch correction, limma, matrix, GSE33302, GSE9442
  9. [9] § Methods › Immune infiltration analysis ↔ 05_Immune_Infiltration.R, lines 37–122 · score 0.61 · ssGSEA, immune cell, GSVA, scores, infiltration, matrix
  10. [10] § Methods › Screening of predictor genes for SD ↔ 03_Machine_Learning_Hub.R, lines 1–54 · score 0.60 · SVM RFE, Machine, glmnet, error, fold, LASSO
  11. [11] § Methods › Identification and validation of predictor genes ↔ 04_Diagnostic_Model.R, lines 58–119 · score 0.56 · diagnostic model, CI, GSE37667, confidence, interval, AUC

Paper

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The authors' code

R · 122 lines · 4.3 KB · no license · 3 matches

  1. ######ssGSEA
  2. #BiocManager::install('devtools')
  3. library(devtools)
  4. #BiocManager::install('Biostrings')
  5. #BiocManager::install("GSVA")
  6. #BiocManager::install('GenomeInfoDbData')
  7. library(dplyr)
  8. library(tidyverse)
  9. library(GSVA)
  10. library(ggplot2)
  11. geneSet <- read.csv("CellReports.txt",header = F,sep = "\t",)
  12. table(is.na(geneSet ))#
  13. class(geneSet)
  14. geneSet <- geneSet %>%column_to_rownames("V1")%>%t()
  15. a <- geneSet
  16. a <- a[1:nrow(a),]
  17. set <- colnames(a)
  19. l <- list()
  20. #i <- "Activated CD8 T cell"
  21. for (i in set) {
  22. x <- as.character(a[,i])
  23. x <- x[nchar(x)!=0]
  24. x <- as.character(x)
  25. l[[i]] <-x
  26. }
  27. Imu = as.list(c(l[["Central memory CD8 T cell"]],l[["Effector memeory CD8 T cell"]],l[["Activated CD4 T cell"]],
  28. l[["Central memory CD4 T cell"]],l[["Effector memeory CD4 T cell"]],l[["T follicular helper cell"]],
  29. l[["Gamma delta T cell"]],l[["Type 1 T helper cell"]],l[["Type 17 T helper cell"]],l[["Type 2 T helper cell"]],
  30. l[["Regulatory T cell"]],l[["Activated B cell"]],l[["Immature B cell"]],l[["Memory B cell"]],l[["Natural killer cell"]],
  31. l[["CD56bright natural killer cell"]],l[["CD56dim natural killer cell"]],l[["Myeloid derived suppressor cell"]],
  32. l[["Natural killer T cell"]],l[["Activated dendritic cell"]],l[["Plasmacytoid dendritic cell"]],l[["Immature dendritic cell"]],
  33. l[["Macrophage"]],l[["Eosinophil"]],l[["Mast cell"]],l[["Monocyte"]],l[["Neutrophil"]]))
  35. Imu = c(l[["Central memory CD8 T cell"]],l[["Effector memeory CD8 T cell"]],l[["Activated CD4 T cell"]])
  37. # BiocManager::install('limma')
  38. library(limma)
  39. load("D:/项目/24110426/SD_exp_combined-human.Rdata")
  40. exprSet<-as.matrix(exp2)
  41. exprSet=exprSet[,-42]
  42. exprSet[1:6,1:6]
  43. str(exprSet)
  44. exprSet2 = as.data.frame(exprSet)
  45. for (i in 1:ncol(exprSet)) {
  46. exprSet2[,i] = as.numeric(exprSet2[,i])
  47. }
  48. str(exprSet2)
  49. exprSet2 = as.matrix(exprSet2)
  50. ssgsea<- gsva(exprSet2, l,method='ssgsea',kcdf='Gaussian',abs.ranking=TRUE)
  51. Immune = as.data.frame(t(ssgsea))
  52. Immune2 = Immune
  53. Immune2$id <- rownames(Immune2)
  54. exprSet2=as.data.frame(t(exprSet2))
  55. exprSet2$id<-rownames(exprSet2)
  56. Immune2 = merge(Immune2,exprSet2,by = 'id')
  57. Immune2 = cbind(group_list,Immune2)
  58. Immune3 <- Immune %>% as.data.frame() %>%
  59. rownames_to_column("Sample") %>%
  60. gather(key = 'Immune Cell Type',value = 'Estimating Score',-Sample)
  61. Immune3$Group <- ifelse(Immune3$Sample %in% Immune2$id ,Immune2$group_list,0)
  62. #Immune3$Group=ifelse(Immune3$Group==1,'Control','SD')
  63. Immune3=na.omit(Immune3)
  64. table(Immune3$Group)
  67. library(ggplot2)
  68. library(tidyverse)
  69. library(ggpubr)
  70. # plot
  71. colnames(Immune3)
  72. ggplot(Immune3,aes(x = reorder(`Immune Cell Type`,-`Estimating Score`) , y = `Estimating Score`, fill = Group)) +
  74. geom_violin(position = position_dodge(0.9),alpha = 1.2,
  75. width = 1.8,trim = T,
  76. color = NA) +
  78. geom_boxplot(width = 0.35,show.legend = F,
  79. position = position_dodge(0.9),
  80. color = 'black',alpha = 1.2,
  81. outlier.shape = 21) +
  82. theme_bw(base_size = 16) +
  83. labs(x = "Immune Cell Type", y = 'ssGSEA Estimating Score') +
  84. theme(axis.text.x = element_text(angle = 65,hjust = 1,color = 'black'),
  85. legend.position = 'top',
  86. aspect.ratio = 0.4) +
  87. scale_fill_manual(values = c("#FF0000",'#00CCFF'))+
  89. stat_compare_means(aes(group = Group,label = ..p.signif..),method = "t.test")#kruskal.test p.signif
  90. ggsave(filename = './ssGSEA.pdf',width = 16,height = 10)
  93. library(corrplot)
  94. library(Hmisc)
  95. library(pheatmap)
  96. mygene <- read.csv('ML-交集基因.csv',sep=',')
  97. mygene=mygene[,-1]
  98. exp2=exp2[,-42]
  99. nc = t(rbind(ssgsea,exp2[mygene,])) ;
  100. m = rcorr(nc)$r[1:nrow(ssgsea),(ncol(nc)-length(mygene)+1):ncol(nc)]
  103. p = rcorr(nc)$P[1:nrow(ssgsea),(ncol(nc)-length(mygene)+1):ncol(nc)]
  104. head(p)
  107. tmp <- matrix(case_when(as.vector(p) < 0.01 ~ "**",
  108. as.vector(p) < 0.05 ~ "*",
  109. TRUE ~ ""), nrow = nrow(p))
  112. p1 <- pheatmap(t(m),
  113. display_numbers =t(tmp),
  114. angle_col =45,
  115. color = colorRampPalette(c("#92b7d1", "white", "#d71e22"))(100),
  116. border_color = "white",
  117. cellwidth = 20,
  118. cellheight = 20,
  119. width = 7,
  120. height=9.1,
  121. treeheight_col = 0,
  122. treeheight_row = 0)

05_Immune_Infiltration.R at commit 9433f3a, no license · at the source

Overview

Authors: Lutong Gan1, Zerui You1, Wanyue Tan2, Simeng Feng1, Yixian Cai1, Xian Shi1, Xia Ma3, Jiaqi Yu1, Jiyang Pan1
  1. Sleep Medicine Centre, Department of Psychiatry, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
  2. Department of Anesthesiology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
  3. Nephrology Department, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, Guangdong, China
Institutions: First Affiliated Hospital of Jinan University (China); Second Affiliated Hospital of Guangzhou Medical University (China); Jinan University (China)
Journal: PeerJ, volume 14, article e21426
Dates: received 19 November 2025; accepted 30 April 2026; published online 3 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7717/peerj.21426 · PMID 42254657 · PMCID PMC13242190 · OpenAlex W7163327509
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism), rat (organism), sleep disorders (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity
Keywords: Sleep deprivation, Insomnia, Autophagy, Single-cell sequencing, ML
MeSH: Autophagy*, Sleep Deprivation*, Transcriptome*, Animals, Biomarkers, Brain, Gene Expression Profiling, Humans, Male, Mice, Rats, Rats, Sprague-Dawley (* major topic)
Journal subjects: Bioinformatics, Genomics, Neurology, Statistics
Topic: Sleep and related disorders (Experimental and Cognitive Psychology, Psychology), according to OpenAlex
Funding: National Key R&D Program of China (2022YFC2503902)
Citations: not cited yet (Europe PMC); 63 references in the paper

Abstract

Background: Sleep deprivation (SD) is harmful to individuals, but its pathogenesis is not clarified.

Objective: This study seeks to identify SD-linked autophagy genes via integrated transcriptomic and experimental validation approaches.

Methods: Primary SD transcriptomic datasets (GSE33302 and GSE9442), derived from murine brain tissue, were retrieved from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). Murine gene symbols were subsequently mapped to their human orthologs to enable downstream bioinformatic analyses and integration with the GeneCards database. The converted DEGs were intersected with autophagy-related genes (ARGs) obtained from GeneCards to identify autophagy-associated DEGs, which were then subjected to functional enrichment analyses. Candidate predictor genes were selected using machine learning (ML) algorithms. Their expression was rigorously validated in both internal and external datasets (GSE9441 and GSE3767), encompassing murine brain tissue and human peripheral blood samples, respectively. In parallel, an SD rat model was established by exposing male Sprague-Dawley rats to continuous sleep deprivation for seven consecutive days. Brain tissues from the prefrontal cortex and hippocampus were harvested, and the expression levels of rat orthologs of the candidate genes were quantified using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The diagnostic performance of the identified genes was evaluated through nomogram construction and receiver operating characteristic (ROC) curve analysis. In addition, the immune landscape associated with SD was inferred using single-sample gene set enrichment analysis (ssGSEA). The cellular distribution and functional roles of the candidate genes were further explored via single-cell RNA sequencing (scRNA-seq; GSE37665) and gene set enrichment analysis (GSEA). Finally, potential therapeutic targets associated with these genes were predicted.

Results: Three significantly dysregulated predictor genes: CDKN1A, HSPA5, and NR4A1, were identified, and a diagnostic model incorporating these genes demonstrated strong predictive performance. Bioinformatic analysis of immune cell infiltration indicated a potential association between the three key predictor genes and modifications in the immune microenvironment. Moreover, single-cell transcriptomic profiling revealed that these genes were preferentially and highly expressed in endothelial cells, glial cells, and neurons, respectively, implying distinct functional roles across different cellular subpopulations.

Conclusion: CDKN1A, HSPA5, and NR4A1 emerge as crucial pathogenic biomarkers and potential therapeutic targets for SD. This study provides novel molecular targets for elucidating the mechanisms underlying SD-induced autophagy modulation, immune response, and neurovascular injury.

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

Repositories

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tobejoy613/Autophagy-Genes-as-Sleep-Deprivation-Biomarkers

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Languages: R (6)
Size: 8 files, 6 scripts
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supp:PMC13242190/peerj-14-21426-s010.r

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Data

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 9 authors, 5 keywords, 12 MeSH terms, 1 funder, 63 references.

Cite

This paper

Gan, L., You, Z., Tan, W., Feng, S., Cai, Y., Shi, X., Ma, X., Yu, J., & Pan, J. (2026). Integrated transcriptomic identification and validation reveal key autophagy-associated biomarkers in sleep deprivation. PeerJ, 14, e21426. https://doi.org/10.7717/peerj.21426

BibTeX

@article{gan2026integrated,
author = {Gan, Lutong and You, Zerui and Tan, Wanyue and Feng, Simeng and Cai, Yixian and Shi, Xian and Ma, Xia and Yu, Jiaqi and Pan, Jiyang},
title = {{Integrated transcriptomic identification and validation reveal key autophagy-associated biomarkers in sleep deprivation}},
journal = {PeerJ},
year = {2026},
month = jun,
volume = {14},
pages = {e21426},
publisher = {PeerJ, Inc},
issn = {2167-8359},
doi = {10.7717/peerj.21426},
url = {https://doi.org/10.7717/peerj.21426},
pmid = {42254657},
pmcid = {PMC13242190}
}

RIS

TY - JOUR
AU - Gan, Lutong
AU - You, Zerui
AU - Tan, Wanyue
AU - Feng, Simeng
AU - Cai, Yixian
AU - Shi, Xian
AU - Ma, Xia
AU - Yu, Jiaqi
AU - Pan, Jiyang
TI - Integrated transcriptomic identification and validation reveal key autophagy-associated biomarkers in sleep deprivation
T2 - PeerJ
J2 - PeerJ
PY - 2026
DA - 2026/06/03
VL - 14
SP - e21426
SN - 2167-8359
PB - PeerJ, Inc
DO - 10.7717/peerj.21426
UR - https://doi.org/10.7717/peerj.21426
LA - en
ER -

CSL-JSON

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