Multi-omics Analysis of Skeletal Muscle Identifies Dysregulation of Hypoxia-Induced Genes in Peripheral Artery Disease [RNA-seq]
RNA-seq (gene expression) · human · 40 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
- 40 samples: 40 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
- Multiomics Analysis of Skeletal Muscle Identifies Dysregulation of Hypoxia-Induced Genes in Peripheral Artery DiseaseJournal of the American Heart Association · 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 GSE289574_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 PRJNA1223451 and SRA study SRP563854. 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 40 sample titles: 40 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.
- GSM8794432 Control, replicate 1, RNA-seq
- GSM8794433 Control, replicate 2, RNA-seq
- GSM8794434 Control, replicate 3, RNA-seq
- GSM8794435 Control, replicate 4, RNA-seq
- GSM8794436 Control, replicate 5, RNA-seq
- GSM8794437 Control, replicate 6, RNA-seq
- GSM8794438 Control, replicate 7, RNA-seq
- GSM8794439 Control, replicate 8, RNA-seq
- GSM8794440 Control, replicate 9, RNA-seq
- GSM8794441 Control, replicate 10, RNA-seq
- GSM8794442 IC, replicate 1, RNA-seq
- GSM8794443 IC-post, replicate 1, RNA-seq
- GSM8794444 IC, replicate 2, RNA-seq
- GSM8794445 IC-post, replicate 2, RNA-seq
- GSM8794446 IC, replicate 3, RNA-seq
- GSM8794447 IC-post, replicate 3, RNA-seq
- GSM8794448 IC, replicate 4, RNA-seq
- GSM8794449 IC-post, replicate 4, RNA-seq
- GSM8794450 IC, replicate 5, RNA-seq
- GSM8794451 IC-post, replicate 5, RNA-seq
- GSM8794452 IC, replicate 6, RNA-seq
- GSM8794453 IC-post, replicate 6, RNA-seq
- GSM8794454 IC, replicate 7, RNA-seq
- GSM8794455 IC-post, replicate 7, RNA-seq
- GSM8794456 IC, replicate 8, RNA-seq
- GSM8794457 IC-post, replicate 8, RNA-seq
- GSM8794458 IC, replicate 9, RNA-seq
- GSM8794459 IC-post, replicate 9, RNA-seq
- GSM8794460 IC, replicate 10, RNA-seq
- GSM8794461 IC-post, replicate 10, RNA-seq
- GSM8794462 CLI, replicate 1, RNA-seq
- GSM8794463 CLI, replicate 2, RNA-seq
- GSM8794464 CLI, replicate 3, RNA-seq
- GSM8794465 CLI, replicate 4, RNA-seq
- GSM8794466 CLI, replicate 5, RNA-seq
- GSM8794467 CLI, replicate 6, RNA-seq
- GSM8794468 CLI, replicate 7, RNA-seq
- GSM8794469 CLI, replicate 8, RNA-seq
- GSM8794470 CLI, replicate 9, RNA-seq
- GSM8794471 CLI, replicate 10, RNA-seq
- GSE287118 Multiomics identifies unique modulators of calf muscle pathophysiology in peripheral artery disease and chronic kidney disease 86 samples shares identifies, peripheral, muscle
- GSE263083 Multi-omics data analysis for the JGOG3025-TR2 ovarian cancer cohort [RNA-seq] 291 samples shares multi-omics, rna-seq
- GSE269826 Conserved patterns of transcriptional dysregulation, heterogeneity and cell states in clear cell kidney cancer [scRNA-seq] 79 samples shares dysregulation, rna-seq
- GSE273848 Definition of a multi-omics signature for Esophageal Adenocarcinoma prognosis prediction [totalRNA-seq] 40 samples shares multi-omics, rna-seq
- GSE339456 Integrated bulk and spatial transcriptomic analysis identifies progression-associated molecular signatures in biopsy-proven hypertensive nephropathy [RNA-seq] 35 samples shares identifies, rna-seq
- GSE255612 Large-scale Single-nuclei Profiling Identifies Role for ATRNL1 in Atrial Fibrillation [snRNA-seq] 34 samples shares identifies, rna-seq
- GSE310557 Multi-omics analysis identifies a microbiota–bile acid–TLR signaling axis driving bladder injury in interstitial cystitis 17 samples shares multi-omics, identifies
- GSE271408 Single-cell RNA-sequencing identifies unique cell-specific gene expression profiles in high-grade cardiac allograft vasculopathy 16 samples shares identifies, rna-seq
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.