A Phase II Trial of Pembrolizumab plus Granulocyte Macrophage Colony Stimulating Factor in Advanced Biliary Cancer [CITE-Seq]
Single-cell RNA-seq · human · 168 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
- MelanomaThis series is part of our curated briefing on it.
- Samples
- 168 samples: 168 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.
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 GSE317605_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 PRJNA1414535 and SRA study SRP666483. 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 168 sample titles: 168 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.
- GSM9474997 Patient 10-02_GEX timepoint T01 scRNAseq
- GSM9474998 Patient 10-03_GEX timepoint T02 replicate 1 scRNAseq
- GSM9474999 Patient 10-03_GEX timepoint T02 replicate 2 scRNAseq
- GSM9475000 Patient 10-05_GEX timepoint T03 scRNAseq
- GSM9475001 Patient 10-06_GEX timepoint T04 replicate 1 scRNAseq
- GSM9475002 Patient 10-06_GEX timepoint T04 replicate 2 scRNAseq
- GSM9475003 Patient 11-02_GEX timepoint T01 scRNAseq
- GSM9475004 Patient 11-03_GEX timepoint T02 scRNAseq
- GSM9475005 Patient 11-05_GEX timepoint T03 scRNAseq
- GSM9475006 Patient 11-06_GEX timepoint T04 replicate 1 scRNAseq
- GSM9475007 Patient 11-06_GEX timepoint T04 replicate 2 scRNAseq
- GSM9475008 Patient 12-01_GEX timepoint T01 scRNAseq
- GSM9475009 Patient 12-02_GEX timepoint T02 scRNAseq
- GSM9475010 Patient 12-04_GEX timepoint T03 scRNAseq
- GSM9475011 Patient 12-05_GEX timepoint T04 scRNAseq
- GSM9475012 Patient 13-02_GEX timepoint T01 scRNAseq
- GSM9475013 Patient 13-03_GEX timepoint T02 scRNAseq
- GSM9475014 Patient 13-04_GEX timepoint T03 scRNAseq
- GSM9475015 Patient 13-05_GEX timepoint T04 scRNAseq
- GSM9475016 Patient 14-01_GEX timepoint T01 scRNAseq
- GSM9475017 Patient 14-02_GEX timepoint T02 scRNAseq
- GSM9475018 Patient 14-03_GEX timepoint T03 scRNAseq
- GSM9475019 Patient 14-04_GEX timepoint T04 replicate 1 scRNAseq
- GSM9475020 Patient 14-04_GEX timepoint T04 replicate 2 scRNAseq
- GSM9475021 Patient 15-02_GEX timepoint T01 scRNAseq
- GSM9475022 Patient 15-03_GEX timepoint T02 scRNAseq
- GSM9475023 Patient 15-04_GEX timepoint T03 replicate 1 scRNAseq
- GSM9475024 Patient 15-04_GEX timepoint T03 replicate 2 scRNAseq
- GSM9475025 Patient 15-05_GEX timepoint T04 scRNAseq
- GSM9475026 Patient 16-02_GEX timepoint T01 scRNAseq
- GSM9475027 Patient 16-03_GEX timepoint T02 scRNAseq
- GSM9475028 Patient 16-04_GEX timepoint T03 replicate 1 scRNAseq
- GSM9475029 Patient 16-04_GEX timepoint T03 replicate 2 scRNAseq
- GSM9475030 Patient 16-05_GEX timepoint T04 scRNAseq
- GSM9475031 Patient 17-01_GEX timepoint T01 scRNAseq
- GSM9475032 Patient 17-02_GEX timepoint T02 scRNAseq
- GSM9475033 Patient 17-03_GEX timepoint T03 scRNAseq
- GSM9475034 Patient 17-04_GEX timepoint T04 replicate 1 scRNAseq
- GSM9475035 Patient 17-04_GEX timepoint T04 replicate 2 scRNAseq
- GSM9475036 Patient 18-01_GEX timepoint T01 scRNAseq
+ 128 more — browse all 168 samples with per-sample file links →
- GSE142751 Genome-wide maps of chromatin state in 142 cancer cell lines [cell line] 855 samples same disease briefing
- GSE12391 Melanoma: comparison between common nevi, radial/vertical growth phase melanoma, metastases and dysplastic nevi 114 samples same disease briefing
- GSE91061 Molecular portraits of tumor mutational and micro-environmental sculpting by immune checkpoint blockade therapy 109 samples same disease briefing
- GSE134459 Genome-wide maps of transcriptomic and epigenomic state in melanoma cell lines 73 samples same disease briefing
- GSE230489 RNA-sequencing of cutaneous melanoma and non-small cell lung cancer biopsies 38 samples same disease briefing
- GSE100797 Mutational and neoantigen load predict clinical benefit of adoptive T cell therapy in melanoma 25 samples same disease briefing
- GSE123728 Clinical and Immunologic Activity of a Single Dose of Neoadjuvant PD-1 Blockade and Predictors of Clinical Outcomes in Resectable Melanoma 24 samples same disease briefing
- GSE130956 Investigation of ST8SIA1 expression in icaritin-treated and untreated melanoma cell lines 12 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.