Deep learning identifies Pbx1 as a network hub of hematopoietic stem cell aging [26hr RNA-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. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
- Organism
- Mus musculus (mouse)
- Samples
- 8 samples: 2 × young fresh HSCs, vs 2 × young cultured HSCs, vs 2 × old fresh HSCs, vs 2 × old cultured HSCs,Groups 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 GSE286957_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.
Supports a between-group comparison across 8 samples.
How we read this: 4 replicated groups read from 8 sample titles; they account for 8 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.
- GSM8734824 RNA-seq of Mus musculus : young fresh HSCs, rep1 young fresh HSCs,
- GSM8734825 RNA-seq of Mus musculus : young fresh HSCs, rep2 young fresh HSCs,
- GSM8734826 RNA-seq of Mus musculus : young cultured HSCs, rep1 young cultured HSCs,
- GSM8734827 RNA-seq of Mus musculus : young cultured HSCs, rep2 young cultured HSCs,
- GSM8734828 RNA-seq of Mus musculus : old fresh HSCs, rep1 old fresh HSCs,
- GSM8734829 RNA-seq of Mus musculus : old fresh HSCs, rep2 old fresh HSCs,
- GSM8734830 RNA-seq of Mus musculus : old cultured HSCs, rep1 old cultured HSCs,
- GSM8734831 RNA-seq of Mus musculus : old cultured HSCs, rep2 old cultured HSCs,
- GSE286968 Deep learning identifies Pbx1 as a network hub of hematopoietic stem cell aging 60 samples shares pbx1, hematopoietic, identifies, learning, network, aging
- GSE266509 A Bcl11a-driven mechanism maintaining hematopoietic stem cell functionality gives rise to dysfunction during aging [RNA-Seq] 52 samples shares hematopoietic, rna-seq, aging
- GSE284988 Epigenetic profiling of hematopoietic stem cells identifies KDR and PU.1 as regulators of aging transcriptome and caloric restriction response 58 samples shares hematopoietic, identifies, aging
- GSE265909 yHSC and oHSC scRNAseq with oHSC AThi ATlo Index Sorting. Assessing the transcriptional changes associated with autophagy engagement in old hematopoietic stem cells at single cell resolution. [scRNA-Seq] 563 samples shares hematopoietic, rna-seq
- GSE299686 Longitudinal liquid biopsy identifies an early predictive biomarker of immune checkpoint blockade response in head and neck squamous cell carcinoma [bulk RNA-seq] 178 samples shares identifies, rna-seq
- GSE293874 Anchored screening identifies transcription factor blueprints underlying dendritic cell diversity and subset-specific anti-tumor immunity [mouse bulk RNA-seq] 167 samples shares identifies, rna-seq
- GSE299683 Longitudinal liquid biopsy identifies an early predictive biomarker of immune checkpoint blockade response in head and neck squamous cell carcinoma [scRNA-seq] 125 samples shares identifies, rna-seq
- GSE98969 Single cell RNA-seq identifies a unique microglia type associated with Alzheimer’s disease [RNA] 97 samples shares identifies, 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.