Single-cell RNA-sequencing identifies anti-cancer immune phenotypes in the early lung metastatic niche during breast cancer
Single-cell RNA-seq · mouse · 18 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
- 18 samples: 18 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.
- Paper
- Correction: Single-cell RNA-sequencing identifies anti-cancer immune phenotypes in the early lung metastatic niche during breast cancerClinical & experimental metastasis · 2023
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 GSE297298_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 PRJNA1263479 and SRA study SRP585576. Searching any of these in the dataset finder brings you back here.
Supports a case/control comparison: 9 samples read as cases, 9 as controls.
How we read this: Read from 18 sample titles: 18 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.
- GSM8988030 Day 7 Diseased, Lung, scRNAseq
- GSM8988031 Day 7 Diseased, Scaffold, scRNAseq
- GSM8988032 Day 7 Diseased, Spleen, scRNAseq
- GSM8988033 Day 7 Healthy Control, Lung, scRNAseq
- GSM8988034 Day 7 Healthy Control, Spleen, scRNAseq
- GSM8988035 Day 7 Healthy Control, Scaffold, scRNAseq
- GSM8988036 Day 14 Diseased, Scaffold, scRNAseq
- GSM8988037 Day 14 Diseased, Lung, scRNAseq
- GSM8988038 Day 14 Diseased, Spleen, scRNAseq
- GSM8988039 Day 14 Healthy Control, Scaffold, scRNAseq
- GSM8988040 Day 14 Healthy Control, Lung, scRNAseq
- GSM8988041 Day 14 Healthy Control, Spleen, scRNAseq
- GSM8988042 Day 21 Diseased, Scaffold, scRNAseq
- GSM8988043 Day 21 Healthy Control, Scaffold, scRNAseq
- GSM8988044 Day 21 Diseased, Lung, scRNAseq
- GSM8988045 Day 21 Healthy Control, Lung, scRNAseq
- GSM8988046 Day 21 Diseased, Spleen, scRNAseq
- GSM8988047 Day 21 Healthy Control, Spleen, scRNAseq
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- GSE192687 Single-cell RNAseq analysis of renal endothelial cells in murine diabetic nephropathy 5464 samples shares single-cell
- GSE277789 Spatiotemporal and genetic cell lineage tracing of endodermal organogenesis at single-cell resolution [Smart-seq3-RNA-seq-add] 830 samples shares single-cell
- GSE290574 Gene Regulatory Networks Orchestrating Oocyte Fate Bifurcation in Primordial Follicles Revealed by Single-Cell Transcriptomics 386 samples shares single-cell
- GSE166225 Inference of emergent spatio-temporal processes from single-cell sequencing reveals feedback between de novo DNA methylation and chromatin condensates [RNA-seq] 288 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.