Whole transcriptomic landscape and regulatory network of High-Altitude Pulmonary Edema
RNA-seq (gene expression) · human · 12 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
- 12 samples: 6 × HAPE patient vs 6 × control volunteeGroups 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
- Whole transcriptome landscape in HAPE under the stress of environment at high altitudes: new insights into the mechanisms of hypobaric hypoxia toleranceFrontiers in immunology · 2024
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 GSE260910_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 PRJNA1083989 and SRA study SRP493452. Searching any of these in the dataset finder brings you back here.
Supports a case/control comparison: 6 samples read as cases, 6 as controls.
How we read this: 2 replicated groups read from 12 sample titles; they account for 12 of them. 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.
- GSM8127887 HAPE patient 001 HAPE patient
- GSM8127888 HAPE patient 002 HAPE patient
- GSM8127889 HAPE patient 003 HAPE patient
- GSM8127890 HAPE patient 004 HAPE patient
- GSM8127891 HAPE patient 005 HAPE patient
- GSM8127892 HAPE patient 006 HAPE patient
- GSM8127893 control volunteer 001 control voluntee
- GSM8127894 control volunteer 002 control voluntee
- GSM8127895 control volunteer 003 control voluntee
- GSM8127896 control volunteer 004 control voluntee
- GSM8127897 control volunteer 005 control voluntee
- GSM8127898 control volunteer 006 control voluntee
- GSE291471 Integrative blood profiling uncovers inflammatory network signatures in high-altitude pulmonary edema 83 samples shares high-altitude, pulmonary, network, edema
- GSE288604 Whole blood transcriptomics characterize molecular phenotypes of pulmonary Mycobacterium avium complex disease 200 samples shares pulmonary, whole
- GSE215274 Identification of Whole Blood Gene Expression Signature to Predict the Therapeutic Effect of Nintedanib in Idiopathic Pulmonary Fibrosis 177 samples shares pulmonary, whole
- 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, network
- GSE260657 A smooth muscle cell gene-regulatory network critical for the development of advanced-stage and symptomatic atherosclerosis [human] 15 samples shares regulatory, network
- GSE299831 KOLF2.1J iTF-Microglia: A standardized platform to study microglial transcriptional regulatory networks in CNS disease [RNA-seq] 12 samples shares regulatory, network
- GSE327727 D-SPIN constructs regulatory network models from scRNA-seq revealing organizing principles of perturbation response 194 samples shares regulatory, network
- GSE299850 KOLF2.1J iTF-Microglia: A standardized platform to study microglial transcriptional regulatory networks in CNS disease 36 samples shares regulatory, network
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.