Single-cell transcriptomics reveals etiology-specific T-cell heterogeneity in hepatocellular carcinoma and implicates regulatory
Single-cell RNA-seq · human · 18 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
- Organism
- Homo sapiens (human)
- Topic
- Hepatocellular carcinomaThis series is part of our curated briefing on it.
- Samples
- 18 samples: 8 × con vs 5 × PT vs 5 × TGroups 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.
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 GSE319709_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.
Also filed as BioProject PRJNA1418916 and SRA study SRP674727. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 18 samples.
How we read this: 3 replicated groups read from 18 sample titles; they account for 18 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.
- GSM9524176 PT-1 PT
- GSM9524177 T-1 T
- GSM9524178 con-1 con
- GSM9524179 con-2 con
- GSM9524180 PT-2 PT
- GSM9524181 T-2 T
- GSM9524182 con-3 con
- GSM9524183 PT-3 PT
- GSM9524184 T-3 T
- GSM9524185 con-4 con
- GSM9524186 con-5 con
- GSM9524187 PT-4 PT
- GSM9524188 T-4 T
- GSM9524189 con-6 con
- GSM9524190 con-7 con
- GSM9524191 con-8 con
- GSM9524192 PT-5 PT
- GSM9524193 T-5 T
- GSE105130 Comprehensive analysis of Transcriptome Profiles in Hepatocellular Carcinoma patients 52 samples same disease briefing · shares hepatocellular
- GSE140228 Landscape and Dynamics of Single Immune Cells in Hepatocellular Carcinoma 41 samples same disease briefing · shares hepatocellular
- GSE14520 Gene expression data of human hepatocellular carcinoma (HCC) 488 samples same disease briefing · shares hepatocellular
- GSE10143 Gene Expression in Fixed Tissues and Outcome in Hepatocellular Carcinoma 387 samples same disease briefing · shares hepatocellular
- GSE107170 Molecular Signature of Hepatitis D Virus-Associated Hepatocellular Carcinoma 307 samples same disease briefing · shares hepatocellular
- GSE109211 Molecular predictors of prevention of recurrence in hepatocellular carcinoma with sorafenib as adjuvant treatment in the phase 3 STORM trial 140 samples same disease briefing · shares hepatocellular
- GSE10186 Integrative Transcriptome Analysis Reveals Common Molecular Subtypes of Human Hepatocellular Carcinoma 118 samples same disease briefing · shares hepatocellular
- GSE114564 Identification of novel serum biomarkers in early-stage HCC patients. 118 samples same disease briefing
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.