Differential analysis of gene expression in liver tissues of patients with nonalcoholic fatty liver disease and controls.
RNA-seq (gene expression) · human · 16 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
- 16 samples: 16 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
- Unveiling the role of disulfidptosis-related genes in the pathogenesis of non-alcoholic fatty liver diseaseFrontiers 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 GSE260666_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 PRJNA1082656 and SRA study SRP492791. 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 16 sample titles: 16 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.
- GSM8122035 Control_rep1 [21120D1]
- GSM8122036 Control_rep2 [21124D1]
- GSM8122037 Control_rep3 [22068D1]
- GSM8122038 Control_rep4 [22085D1]
- GSM8122039 Control_rep5 [22113D1]
- GSM8122040 Control_rep6 [22114D1]
- GSM8122041 NAFL_rep1 [21149D1]
- GSM8122042 NAFL_rep2 [21151D1]
- GSM8122043 NAFL_rep3 [22105D1]
- GSM8122044 NAFL_rep4 [22120D1]
- GSM8122045 NAFL_rep5 [22123D1]
- GSM8122046 NAFL_rep6 [22130D1]
- GSM8122047 NASH_rep1 [22054D1]
- GSM8122048 NASH_rep2 [22092D1]
- GSM8122049 NASH_rep3 [22093D1]
- GSM8122050 NASH_rep4 [22095D1]
- GSE130970 Gene expression predicts histological severity and reveals distinct molecular profiles of nonalcoholic fatty liver disease 78 samples shares nonalcoholic, liver, fatty
- GSE135251 TRANSCRIPTOMIC PROFILING ACROSS THE SPECTRUM OF NON-ALCOHOLIC FATTY LIVER DISEASE 216 samples shares liver, fatty
- GSE167523 Transcriptome profiling of human non-alcoholic fatty liver disease 98 samples shares liver, fatty
- GSE202379 Single Nuclei RNA-Seq to map progression of non-alcoholic fatty liver disease (NAFLD) in patients II 59 samples shares liver, fatty
- GSE266852 Comparison of differential gene expression in blood PBMCs from healthy controls, patients with rheumatoid arthritis,and patients with lupus using single-cell transcriptome sequencing analysis. 10 samples shares differential, controls
- GSE278723 Differential Roles of Astrocytic CSF1 in Alzheimer's Disease and Cerebral Amyloid Angiopathy: Insights from Transcriptomic Analysis 248 samples shares differential
- GSE276114 Integrative proteo-transcriptomic characterization of advanced fibrosis in chronic liver disease across etiologies 177 samples shares liver
- GSE306664 Revealing differential type I and type II IFN responses in different immune cells in various human diseases by a new IFNs transcriptomic atlas 131 samples shares differential
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.