Subclonal evolution revealed by single-cell profiles of expressed mutations identifies adaptation mechanisms to immunotherapy in melanoma.
Single-cell RNA-seq · mouse · 1497 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
- Organism
- Mus musculus (mouse)
- Samples
- 1497 samples: 34 conditions, mostly with about 12 replicates eachGroups 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
- Stochastic modeling of single-cell gene expression adaptation reveals non-genomic contribution to evolution of tumor subclonesCell systems · 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 GSE215963_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 PRJNA891346 and SRA study SRP403101. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 400 samples.
How we read this: 34 replicated groups read from the first 400 of 1497 sample titles; they account for 400 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.
- GSM6650185 103_P05_A01 103_P05_A
- GSM6650186 103_P05_A02 103_P05_A
- GSM6650187 103_P05_A03 103_P05_A
- GSM6650188 103_P05_A04 103_P05_A
- GSM6650189 103_P05_A05 103_P05_A
- GSM6650190 103_P05_A06 103_P05_A
- GSM6650191 103_P05_A07 103_P05_A
- GSM6650192 103_P05_A08 103_P05_A
- GSM6650193 103_P05_A09 103_P05_A
- GSM6650194 103_P05_A10 103_P05_A
- GSM6650195 103_P05_A11 103_P05_A
- GSM6650196 103_P05_A12 103_P05_A
- GSM6650197 103_P05_B01 103_P05_B
- GSM6650198 103_P05_B02 103_P05_B
- GSM6650199 103_P05_B03 103_P05_B
- GSM6650200 103_P05_B04 103_P05_B
- GSM6650201 103_P05_B05 103_P05_B
- GSM6650202 103_P05_B06 103_P05_B
- GSM6650203 103_P05_B07 103_P05_B
- GSM6650204 103_P05_B08 103_P05_B
- GSM6650205 103_P05_B09 103_P05_B
- GSM6650206 103_P05_B10 103_P05_B
- GSM6650207 103_P05_B11 103_P05_B
- GSM6650208 103_P05_B12 103_P05_B
- GSM6650209 103_P05_C01 103_P05_C
- GSM6650210 103_P05_C02 103_P05_C
- GSM6650211 103_P05_C03 103_P05_C
- GSM6650212 103_P05_C04 103_P05_C
- GSM6650213 103_P05_C05 103_P05_C
- GSM6650214 103_P05_C06 103_P05_C
- GSM6650215 103_P05_C07 103_P05_C
- GSM6650216 103_P05_C08 103_P05_C
- GSM6650217 103_P05_C09 103_P05_C
- GSM6650218 103_P05_C10 103_P05_C
- GSM6650219 103_P05_C11 103_P05_C
- GSM6650220 103_P05_C12 103_P05_C
- GSM6650221 103_P05_D01 103_P05_D
- GSM6650222 103_P05_D02 103_P05_D
- GSM6650223 103_P05_D03 103_P05_D
- GSM6650224 103_P05_D04 103_P05_D
+ 1457 more — browse all 1497 samples with per-sample file links →
- GSE239477 Single-cell genomics of the mouse olfactory cortex reveals contrasts with neocortex and ancestral signatures of cell type evolution 72 samples shares single-cell, evolution
- GSE269748 A single-cell atlas deconstructs heterogeneity across multiple models in murine traumatic brain injury and identifies novel cell-specific targets 36 samples shares single-cell, identifies
- GSE297298 Single-cell RNA-sequencing identifies anti-cancer immune phenotypes in the early lung metastatic niche during breast cancer 18 samples shares single-cell, identifies
- GSE329941 Single-cell transcriptomics identifies ergothioneine as a mitochondrial protector to prevent AKI-to-CKD progression 9 samples shares single-cell, identifies
- GSE304763 A pan-immunotherapy signature to predict intratumoral CD8+ T cell expansions [scTCR_immunotherapies] 91 samples shares immunotherapy
- GSE255371 Characterization of cuprizone mouse model at single-cell and spatial transcriptomics level 86 samples shares single-cell
- GSE334890 Discovery of Tcf7 regulators with clonally-resolved CRISPR screens identifies Trim28 as a mediator of CD8 T cell differentiation in tumors 48 samples shares identifies
- GSE294093 Single-cell transcriptomics and translatomics dual-omics analysis reveals the potential mechanism of HFD-induced maternal RNA degradation abnormalities 42 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.