Deep learning identifies Pbx1 as a network hub of hematopoietic stem cell aging [scRNA-seq]
Single-cell RNA-seq · mouse · 8 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
- 8 samples: 8 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.
Read the full summary ↓
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 GSE286966_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 PRJNA1202576. 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 8 sample titles: 8 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.
- GSM8734946 scRNA-seq of Mus musculus: Young_HSC
- GSM8734947 scRNA-seq of Mus musculus: 7mo
- GSM8734948 scRNA-seq of Mus musculus: 1yo
- GSM8734949 scRNA-seq of Mus musculus: 1half
- GSM8734950 scRNA-seq of Mus musculus: Old_HSC
- GSM8734951 scRNA-seq of Mus musculus: H2B_high
- GSM8734952 scRNA-seq of Mus musculus: H2B_low
- GSM8734953 scRNA-seq of Mus musculus: Young_CD41
- GSE286968 Deep learning identifies Pbx1 as a network hub of hematopoietic stem cell aging 60 samples shares pbx1, hematopoietic, identifies, learning, network, aging, deep
- GSE266509 A Bcl11a-driven mechanism maintaining hematopoietic stem cell functionality gives rise to dysfunction during aging [RNA-Seq] 52 samples shares hematopoietic, aging
- GSE245838 Anchored screening identifies transcription factor blueprints underlying dendritic cell diversity and subset-specific anti-tumor immunity [mouse scRNA-seq] 21 samples shares identifies, scrna-seq
- GSE299487 LY6D Identifies Persistent Tumor-Initiating Cells Driving Pancreatic Tumorigenesis [scRNA-seq] 12 samples shares identifies, scrna-seq
- GSE264105 Plate-based scRNA-seq of plasma-positive microglia and plasma-negative microglia in young mice 1111 samples shares scrna-seq
- GSE271100 Stem cells tightly regulate dead cell clearance to maintain tissue fitness [scRNA-seq] 1037 samples shares scrna-seq
- GSE326100 scRNA-seq of mouse cochlea organoid cells 768 samples shares scrna-seq
- GSE217662 Decoding Trp53 regulatory program with knockout cell transcriptomic landscape analyses [scRNA-seq] 247 samples shares scrna-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.