Machine learning identifies activation of RUNX/AP-1 as drivers of mesenchymal and fibrotic regulatory programs in gastric cancer (RNA-Seq)
RNA-seq (gene expression) · human · 13 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
- 13 samples: 11 distinct titles, 1 of them repeatedGroups 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
- Machine learning identifies activation of RUNX/AP-1 as drivers of mesenchymal and fibrotic regulatory programs in gastric cancerGenome research · 2024
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 GSE266159_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 PRJNA1106027 and SRA study SRP504720. Searching any of these in the dataset finder brings you back here.
How we read this: Read from 13 sample titles: 11 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.
- GSM8241634 RNA-seq SH-10-TC
- GSM8241635 RNA-seq HS746T
- GSM8241636 RNA-seq LMSU
- GSM8241637 RNA-seq GES1
- GSM8241638 RNA-seq NUGC3
- GSM8241639 RNA-seq NCC_STC_K140
- GSM8241640 RNA-seq YCC2
- GSM8241641 RNA-seq CLS145
- GSM8241642 RNA-seq YCC6
- GSM8241643 RNA-seq AGS
- GSM8241644 RNA-seq GSU
- GSM8241645 RNA-seq NCC20
- GSM8241646 RNA-seq YCC10
- GSE264550 Machine learning identifies activation of RUNX/AP-1 as drivers of mesenchymal and fibrotic regulatory programs in gastric cancer 25 samples shares runx, ap-1, mesenchymal, identifies, activation, regulatory, learning, fibrotic
- GSE295823 Integrative multi-omics identifies AP-1 transcription factor as a targetable mediator of acquired osimertinib resistance in non-small cell lung cancer 20 samples shares ap-1, identifies
- GSE318816 Blood-based Molecular Endotyping of Fibrotic Hypersensitivity Pneumonitis Identifies Two Immune Subtypes with Divergent Clinical Outcomes 126 samples shares identifies, fibrotic
- GSE298578 Single cell RNA profiling of blood CD4+ T cells identifies distinct helper and dysfunctional regulatory clusters in children with SLE (5' PBMC validation dataset) 14 samples shares identifies, regulatory
- GSE305558 Single-cell profiling reveals RAB13+ endothelial cells and profibrotic mesenchymal cells in aged human bone marrow 12 samples shares mesenchymal, fibrotic
- GSE325670 Promoter mutagenesis and a massively parallel reporter screen of the MAPT locus identifies cis-regulatory elements and genetic variation effects 140 samples shares identifies, regulatory
- GSE290367 Single-cell multiomic integration identifies widespread, cell-type resolved fetal reactivation in the diseased human heart 90 samples shares identifies, activation
- GSE335464 Integrative single-cell multi-omics profiling of human pancreatic islets identifies T1D-associated genes and regulatory signals [single cell] 28 samples shares identifies, regulatory
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.