Integrative Gene and Isoform Co-expression Networks Reveal Regulatory Rewiring in Stress-related Psychiatric Disorders [whole blood]
RNA-seq (gene expression) · human · 37 samples
- What it is
- RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
- Organism
- Homo sapiens (human)
- Samples
- 37 samples: 37 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
- Data you can get
- A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
- Paper
- Integrative gene and isoform co-expression networks reveal regulatory rewiring in stress-related psychiatric disordersiScience · 2025
Read the full summary ↓
Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.
Processed table
The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE289144_RAW.tar (it can run to tens of gigabytes).
Raw sequencing reads
The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.
What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.
Also filed as BioProject PRJNA1221799 and SRA study SRP562858. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 37 sample titles: 37 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.
- GSM8784978 total RNA-seq from sample 23L002656_S8
- GSM8784979 total RNA-seq from sample 23L002669_S21
- GSM8784980 total RNA-seq from sample 23L002731_S83
- GSM8784981 total RNA-seq from sample 23L002650_S2
- GSM8784982 total RNA-seq from sample 23L002716_S68
- GSM8784983 total RNA-seq from sample 23L002723_S75
- GSM8784984 total RNA-seq from sample 23L002689_S41
- GSM8784985 total RNA-seq from sample 23L002714_S66
- GSM8784986 total RNA-seq from sample 23L002691_S43
- GSM8784987 total RNA-seq from sample 23L002734_S86
- GSM8784988 total RNA-seq from sample 23L002683_S35
- GSM8784989 total RNA-seq from sample 23L002681_S33
- GSM8784990 total RNA-seq from sample 23L002702_S54
- GSM8784991 total RNA-seq from sample 23L002685_S37
- GSM8784992 total RNA-seq from sample 23L002738_S90
- GSM8784993 total RNA-seq from sample 23L002721_S73
- GSM8784994 total RNA-seq from sample 23L002660_S12
- GSM8784995 total RNA-seq from sample 23L002695_S47
- GSM8784996 total RNA-seq from sample 23L002730_S82
- GSM8784997 total RNA-seq from sample 23L002719_S71
- GSM8784998 total RNA-seq from sample 23L002677_S29
- GSM8784999 total RNA-seq from sample 23L002742_S94
- GSM8785000 total RNA-seq from sample 23L002662_S14
- GSM8785001 total RNA-seq from sample 23L002664_S16
- GSM8785002 total RNA-seq from sample 23L002727_S79
- GSM8785003 total RNA-seq from sample 23L002671_S23
- GSM8785004 total RNA-seq from sample 23L002709_S61
- GSM8785005 total RNA-seq from sample 23L002680_S32
- GSM8785006 total RNA-seq from sample 23L002688_S40
- GSM8785007 total RNA-seq from sample 23L002717_S69
- GSM8785008 total RNA-seq from sample 23L002735_S87
- GSM8785009 total RNA-seq from sample 23L002725_S77
- GSM8785010 total RNA-seq from sample 23L002653_S5
- GSM8785011 total RNA-seq from sample 23L002667_S19
- GSM8785012 total RNA-seq from sample 23L002694_S46
- GSM8785013 total RNA-seq from sample 23L002700_S52
- GSM8785014 total RNA-seq from sample 23L002676_S28
- GSE314625 WSTF deficiency reprograms regulatory networks by linking locus‑specific chromatin remodeling to altered isoform expression and misdirected signaling [mRNA-seq] 18 samples shares regulatory, networks, isoform
- GSE325907 Placental gene co-expression networks and mediation analysis identifies pathways and genes associated with birthweight 319 samples shares co-expression, networks
- GSE256068 Identification of gene regulatory networks affected across drug-resistant epilepsies 162 samples shares regulatory, networks
- GSE234727 Inorganic Ions Activate Lineage-Specific Gene Regulatory Networks to Drive Stem Cells Differentiation 15 samples shares regulatory, networks
- GSE299831 KOLF2.1J iTF-Microglia: A standardized platform to study microglial transcriptional regulatory networks in CNS disease [RNA-seq] 12 samples shares regulatory, networks
- GSE276947 Massively parallel reporter assay investigates shared genetic variants of eight psychiatric disorders 10 samples shares psychiatric, disorders
- GSE263804 Chromatin accessibility mapping for functional GWAS risk variants of neuropsychiatric and neurodegenerative disorders in hiPSC-derived neural cells 141 samples shares psychiatric, disorders
- GSE299850 KOLF2.1J iTF-Microglia: A standardized platform to study microglial transcriptional regulatory networks in CNS disease 36 samples shares regulatory, networks
Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.