Single-cell RNA sequencing of ICB samples from FUSCC
Single-cell RNA-seq · human · 32 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)
- Samples
- 32 samples: 26 × E vs 6 × AGroups 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
- Interferon-induced senescent CD8(+) T cells reduce anti-PD1 immunotherapy efficacy in early triple-negative breast cancerScience translational medicine · 2025
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 GSE305078_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 PRJNA1304406. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 32 samples.
How we read this: 2 replicated groups read from 32 sample titles; they account for 32 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.
- GSM9161818 E001 E
- GSM9161819 E002 E
- GSM9161820 E003 E
- GSM9161821 E127 E
- GSM9161822 E128 E
- GSM9161823 E131 E
- GSM9161824 E004 E
- GSM9161825 E008 E
- GSM9161826 E009 E
- GSM9161827 E010 E
- GSM9161828 E011 E
- GSM9161829 E012 E
- GSM9161830 E013 E
- GSM9161831 E014 E
- GSM9161832 E015 E
- GSM9161833 E016 E
- GSM9161834 E005 E
- GSM9161835 E006 E
- GSM9161836 E132 E
- GSM9161837 E020 E
- GSM9161838 E021 E
- GSM9161839 E022 E
- GSM9161840 E023 E
- GSM9161841 E024 E
- GSM9161842 E025 E
- GSM9161843 E026 E
- GSM9161844 A001 A
- GSM9161845 A002 A
- GSM9161846 A003 A
- GSM9161847 A004 A
- GSM9161848 A005 A
- GSM9161849 A006 A
- GSE305971 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma 2105 samples shares single-cell
- GSE271107 Single-cell RNA sequencing of bone marrow aspirate samples from multiple myeloma and its precursor conditions 19 samples shares single-cell
- GSE287301 Single-cell RNA-sequencing of HNSCC-infiltrating T cells 48 samples shares single-cell
- GSE269877 Single-cell RNA-sequencing of BCG naïve and recurrent non-muscle invasive bladder cancer reveals a CD6/ALCAM-mediated immune-suppressive pathway 38 samples shares single-cell
- GSE291890 Single-Cell RNA Sequencing Unveils Macrophage Involvement in Aortic Valve Stenosis Progression 33 samples shares single-cell
- GSE243013 A single-cell atlas of immune heterogeneity in anti-PD1-treated non-small cell lung cancer 243 samples shares single-cell
- GSE263733 Single-cell transcriptome analysis reveals subtype-specific clonal evolution and microenvironmental changes in liver metastasis of pancreatic adenocarcinoma and their clinical implications 218 samples shares single-cell
- GSE282122 A longitudinal single-cell atlas of anti-tumour necrosis factor treatment in inflammatory bowel disease 216 samples shares single-cell
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.