Differentiation of tumor-infiltrating GZMK+ effector memory T cells associates with response to neoadjuvant immunotherapy in head and neck cancer
Single-cell RNA-seq · human · 44 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
- Organism
- Homo sapiens (human)
- Topic
- Head and neck cancerThis series is part of our curated briefing on it.
- Samples
- 44 samples: 44 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.
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 GSE296954_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 PRJNA1261988 and SRA study SRP584704. 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 44 sample titles: 44 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.
- GSM8980312 scRNA-seq of HNSCC-infiltrating T cells; 1A
- GSM8980313 scRNA-seq of HNSCC-infiltrating T cells; 1B
- GSM8980314 scRNA-seq of HNSCC-infiltrating T cells; 2A
- GSM8980315 scRNA-seq of HNSCC-infiltrating T cells; 2B
- GSM8980316 scRNA-seq of HNSCC-infiltrating T cells; 5A
- GSM8980317 scRNA-seq of HNSCC-infiltrating T cells; 5B
- GSM8980318 scRNA-seq of HNSCC-infiltrating T cells; 8A
- GSM8980319 scRNA-seq of HNSCC-infiltrating T cells; 8B
- GSM8980320 scRNA-seq of HNSCC-infiltrating T cells; 12A
- GSM8980321 scRNA-seq of HNSCC-infiltrating T cells; 12B
- GSM8980322 scRNA-seq of HNSCC-infiltrating T cells; 14A
- GSM8980323 scRNA-seq of HNSCC-infiltrating T cells; 14B
- GSM8980324 scRNA-seq of HNSCC-infiltrating T cells; 17A
- GSM8980325 scRNA-seq of HNSCC-infiltrating T cells; 17B
- GSM8980326 scRNA-seq of HNSCC-infiltrating T cells; 18A
- GSM8980327 scRNA-seq of HNSCC-infiltrating T cells; 18B
- GSM8980328 scRNA-seq of HNSCC-infiltrating T cells; 19A
- GSM8980329 scRNA-seq of HNSCC-infiltrating T cells; 19B
- GSM8980330 scRNA-seq of HNSCC-infiltrating T cells; 20A
- GSM8980331 scRNA-seq of HNSCC-infiltrating T cells; 20B
- GSM8980332 scRNA-seq of HNSCC-infiltrating T cells; 21A
- GSM8980333 scRNA-seq of HNSCC-infiltrating T cells; 21B
- GSM8980334 scVDJ-seq of HNSCC-infiltrating T cells; 1A
- GSM8980335 scVDJ-seq of HNSCC-infiltrating T cells; 1B
- GSM8980336 scVDJ-seq of HNSCC-infiltrating T cells; 2A
- GSM8980337 scVDJ-seq of HNSCC-infiltrating T cells; 2B
- GSM8980338 scVDJ-seq of HNSCC-infiltrating T cells; 5A
- GSM8980339 scVDJ-seq of HNSCC-infiltrating T cells; 5B
- GSM8980340 scVDJ-seq of HNSCC-infiltrating T cells; 8A
- GSM8980341 scVDJ-seq of HNSCC-infiltrating T cells; 8B
- GSM8980342 scVDJ-seq of HNSCC-infiltrating T cells; 12A
- GSM8980343 scVDJ-seq of HNSCC-infiltrating T cells; 12B
- GSM8980344 scVDJ-seq of HNSCC-infiltrating T cells; 14A
- GSM8980345 scVDJ-seq of HNSCC-infiltrating T cells; 14B
- GSM8980346 scVDJ-seq of HNSCC-infiltrating T cells; 17A
- GSM8980347 scVDJ-seq of HNSCC-infiltrating T cells; 17B
- GSM8980348 scVDJ-seq of HNSCC-infiltrating T cells; 18A
- GSM8980349 scVDJ-seq of HNSCC-infiltrating T cells; 18B
- GSM8980350 scVDJ-seq of HNSCC-infiltrating T cells; 19A
- GSM8980351 scVDJ-seq of HNSCC-infiltrating T cells; 19B
+ 4 more — browse all 44 samples with per-sample file links →
- GSE103322 Single cell RNA-seq analysis of head and neck cancer 5901 samples same disease briefing · shares head
- GSE139324 Immune landscape of viral- and carcinogen-drived head and neck cancer 63 samples same disease briefing · shares head
- GSE164690 Investigating Immune and Non-Immune Cell Interactions in Head and Neck Tumors by Single-Cell RNA Sequencing 51 samples same disease briefing · shares head
- GSE181919 Single-cell transcriptome profiling of the stepwise progression of head and neck cancer 37 samples same disease briefing · shares head
- GSE140042 Single-cell atlas of head and neck squamous cell carcinoma 18 samples same disease briefing · shares head
- GSE185965 Single cell RNA sequencing of head and neck squamous carcinoma (HNSCC) patient sample 3 samples same disease briefing · shares head
- GSE173647 Single-cell Sequencing Analysis of Head and Neck Squamous Cell Carcinoma 2 samples same disease briefing · shares head
- GSE162738 Immune gene signatures in oropharyngeal cancers are associated with inverse prognosis by HPV-status 51 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.