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Gene expression predicts histological severity and reveals distinct molecular profiles of nonalcoholic fatty liver disease

GSE130970 Homo sapiens (human) RNA-seq (gene expression) 78 samples Submitted 2019/08/15 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 78 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
78 samples: 78 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.
Summary (written by the submitters)
Transcriptomic analysis of liver biopsies that cover the spectrum of nonalcoholic fatty liver disease reveals genes that are progressively regulated over the course of the disease. This study demonstrates that progressive changes in expression of these genes can be used to approximate the histological grade of a liver biopsy. Furthermore, the relative progression profiles of these disease-responsive genes are shown to be distinct across patient biopsies. This observation suggests that there may be distinct biological processes that drive disease progression across individuals
This dataset
Open GSE130970 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 GSE130970_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 PRJNA542148 and SRA study SRP197353. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 31467298) ↗
Study design
78 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 78 sample titles: 78 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.

Samples in this study

+ 38 more — browse all 78 samples with per-sample file links →

Datasets on a similar topic

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.