Integrative proteomics and transcriptomics identify novel fibrosis-related biomarkers of patients with post-HBV hepatitis cirrhosis
RNA-seq (gene expression) · human · 11 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
- 11 samples: 6 × bHSCt vs 5 × nHSCtGroups 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.
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 GSE221083_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 PRJNA912724 and SRA study SRP413299. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 11 samples.
How we read this: 2 replicated groups read from 11 sample titles; they account for 11 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.
- GSM6842780 nHSCt_1 nHSCt
- GSM6842781 nHSCt_2 nHSCt
- GSM6842783 nHSCt_3 nHSCt
- GSM6842784 nHSCt_4 nHSCt
- GSM6842785 nHSCt_5 nHSCt
- GSM6842786 bHSCt 1 bHSCt
- GSM6842787 bHSCt 2 bHSCt
- GSM6842788 bHSCt 3 bHSCt
- GSM6842790 bHSCt 4 bHSCt
- GSM6842791 bHSCt 5 bHSCt
- GSM6842792 bHSCt 6 bHSCt
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- GSE291257 Gene Expression Profiling Identifies Potential Biomarkers for Vaso-Occlusive Episodes in Sickle Cell Disease 414 samples shares biomarkers
- GSE318225 Development of an Optimized Epigenetic Silencer to Transcriptionally Inactivate Viral DNA in Chronic Hepatitis B [RNAseq] 218 samples shares hepatitis
- GSE325054 Time-Resolved Multi-Omics Identify Biomarkers of Immediate Reactions to mRNA Vaccination [RNA-Seq] 187 samples shares biomarkers
- GSE251778 Sex-specific and multiomic integration enhance accuracy of peripheral blood biomarkers of major depressive disorder [RNA-Seq] 169 samples shares biomarkers
- GSE203071 Machine-learning detects non-invasive 5-hydroxymeythylcytosine biomarkers for predicting response to chemotherapy in patients with metastatic colorectal cancer 155 samples shares biomarkers
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.