GEO series

Perturbation-response genes reveal signaling footprints in cancer gene expression

GSE97979 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2017/04/20 Platform GPL11154

At a glance

RNA-seq (gene expression) · human · 24 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: 24 samples: 12 conditions, mostly in duplicateGroups 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.

Summary (written by the submitters)

Aberrant cell signaling can cause cancer and other diseases and is a focal point of drug research. A common approach is to infer signaling activity of pathways from gene expression. However, mapping gene expression to pathway components disregards the effect of post-translational modifications, and downstream signatures represent very specific experimental conditions.

Read the full summary ↓

Here we present PROGENy, a method that overcomes both limitations by leveraging a large compendium of publicly available perturbation experiments to yield a common core of Pathway RespOnsive GENes. Unlike existing methods, PROGENy can (i) recover the effect of known driver mutations, (ii) provide or improve strong markers for drug indications, and (iii) distinguish between oncogenic and tumor suppressor pathways for patient survival. Collectively, these results show that PROGENy accurately infers pathway activity from gene expression.

This dataset

Open GSE97979 in finder → Find more human RNA-seq datasets →

Download · which file do I need?

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.

1 · Just have a look

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.

Series matrix ↓

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE97979_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓

3 · Start from the raw reads

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.

Raw reads on ENA ↗

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 PRJNA383562 and SRA study SRP104287. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 29295995) ↗

Study design

12 conditions, mostly in duplicate

4OHT 1h ×2 4OHT 4h ×2 BSA 1h ×2 BSA 4h ×2 IFNg 1h ×2 IFNg 4h ×2 Ly29002 1h ×2 Ly29002 4h ×2 +4 more

Supports a between-group comparison across 24 samples.

How we read this: 12 replicated groups read from 24 sample titles; they account for 24 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.

Samples in this study

GSM2584544 4OHT 1h Rep 1 4OHT 1h
GSM2584545 4OHT 1h Rep 2 4OHT 1h
GSM2584546 4OHT 4h Rep 1 4OHT 4h
GSM2584547 4OHT 4h Rep 2 4OHT 4h
GSM2584548 BSA 1h Rep 1 BSA 1h
GSM2584549 BSA 1h Rep 2 BSA 1h
GSM2584550 BSA 4h Rep 1 BSA 4h
GSM2584551 BSA 4h Rep 2 BSA 4h
GSM2584552 IFNg 1h Rep 1 IFNg 1h
GSM2584553 IFNg 1h Rep 2 IFNg 1h
GSM2584554 IFNg 4h Rep 1 IFNg 4h
GSM2584555 IFNg 4h Rep 2 IFNg 4h
GSM2584556 Ly29002 1h Rep 1 Ly29002 1h
GSM2584557 Ly29002 1h Rep 2 Ly29002 1h
GSM2584558 Ly29002 4h Rep 1 Ly29002 4h
GSM2584559 Ly29002 4h Rep 2 Ly29002 4h
GSM2584560 TGF1b 1h Rep 1 TGF1b 1h
GSM2584561 TGF1b 1h Rep 2 TGF1b 1h
GSM2584562 TGF1b 4h Rep 1 TGF1b 4h
GSM2584563 TGF1b 4h Rep 2 TGF1b 4h
GSM2584564 TNFa 1h Rep 1 TNFa 1h
GSM2584565 TNFa 1h Rep 2 TNFa 1h
GSM2584566 TNFa 4h Rep 1 TNFa 4h
GSM2584567 TNFa 4h Rep 2 TNFa 4h

Datasets on a similar topic

GSE262542 Evaluation of novel computational methods that identify RNA-binding protein footprints from structural data 20 samples shares footprints
GSE267065 Transcriptome analysis and ribosome footprints in engineered chromosome 3 and chromosome 8 isogenic aneuploidy cell model. 9 samples shares footprints
GSE279622 The macrophage response to LPS is marked by dynamic nucleosome positioning and increases in nucleosome sensitivity corresponding with immune regulatory factor footprints 24 samples shares footprints

Search all human RNA-seq datasets in GEO →

Share this dataset

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.