Dynamic evolution of TCF3-PBX1 leukemias at the single-cell level under chemotherapy pressure [Bulk RNA-seq]
Single-cell RNA-seq · mouse · 12 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
- 12 samples: 3 × BMC_CNT vs 3 × SpinalCord_CNT vs 3 × LN_CNT vs 3 × SPLN_CNTGroups 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 GSE233937_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 PRJNA978614 and SRA study SRP440719. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 12 samples.
How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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.
- GSM7439461 BMC_CNT_1 BMC_CNT
- GSM7439462 BMC_CNT_2 BMC_CNT
- GSM7439463 BMC_CNT_3 BMC_CNT
- GSM7439464 SpinalCord_CNT_1 SpinalCord_CNT
- GSM7439465 SpinalCord_CNT_2 SpinalCord_CNT
- GSM7439466 SpinalCord_CNT_3 SpinalCord_CNT
- GSM7439467 LN_CNT_1 LN_CNT
- GSM7439468 LN_CNT_2 LN_CNT
- GSM7439469 LN_CNT_3 LN_CNT
- GSM7439470 SPLN_CNT_1 SPLN_CNT
- GSM7439471 SPLN_CNT_2 SPLN_CNT
- GSM7439472 SPLN_CNT_3 SPLN_CNT
- GSE303460 Spatiotemporal single-cell atlas of suture stem cell dynamics in craniosynostosis [scRNA-Seq] 14 samples shares single-cell, dynamic, rna-seq
- GSE277789 Spatiotemporal and genetic cell lineage tracing of endodermal organogenesis at single-cell resolution [Smart-seq3-RNA-seq-add] 830 samples shares single-cell, rna-seq
- 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, rna-seq
- GSE300288 Single-cell and spatial transcriptomics analysis of tobacco-associated lung adenocarcinoma development, and in response to targeting inflammation alone or in combination with immune checkpoint blockade in precancerous phase [scRNA-seq] 31 samples shares single-cell, rna-seq
- GSE150817 Transcriptional dynamics of the murine heart during perinatal development at single-cell resolution 30 samples shares single-cell, dynamic
- GSE262879 Single-cell RNA-sequencing of murine pancreatic tumors derived from the orthotopic transplantation of Smad4 wild-type (WT) or Smad4 knockout (KO) pancreatic ductal adenocarcinoma (PDAC) KPC organoids in nu/nu mice. 18 samples shares single-cell, rna-seq
- GSE227108 Integrated single-cell analysis defines the epigenetic basis of castration-resistant prostate luminal cells (scRNA-seq) 18 samples shares single-cell, rna-seq
- GSE264278 TIme-cource single-cell RNA-seq analysis of bleomycin and LPS-induced lung injury and fibrosis 18 samples shares single-cell, rna-seq
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.