Deep learning identifies Pbx1 as a network hub of hematopoietic stem cell aging [Pbx1 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. 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
- 12 samples: 3 × Young_Mock_RNAseq_D3 vs 3 × Young_Pbx1OE_RNAseq_D3 vs 3 × Old_Mock_RNAseq_D3 vs 3 × Old_Pbx1OE_RNAseq_D3Groups 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 GSE286961_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 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.
- GSM8734862 Young_Mock_RNAseq_D3_rep1 Young_Mock_RNAseq_D3
- GSM8734863 Young_Mock_RNAseq_D3_rep2 Young_Mock_RNAseq_D3
- GSM8734864 Young_Mock_RNAseq_D3_rep3 Young_Mock_RNAseq_D3
- GSM8734865 Young_Pbx1OE_RNAseq_D3_rep1 Young_Pbx1OE_RNAseq_D3
- GSM8734866 Young_Pbx1OE_RNAseq_D3_rep2 Young_Pbx1OE_RNAseq_D3
- GSM8734867 Young_Pbx1OE_RNAseq_D3_rep3 Young_Pbx1OE_RNAseq_D3
- GSM8734868 Old_Mock_RNAseq_D3_rep1 Old_Mock_RNAseq_D3
- GSM8734869 Old_Mock_RNAseq_D3_rep2 Old_Mock_RNAseq_D3
- GSM8734870 Old_Mock_RNAseq_D3_rep3 Old_Mock_RNAseq_D3
- GSM8734871 Old_Pbx1OE_RNAseq_D3_rep1 Old_Pbx1OE_RNAseq_D3
- GSM8734872 Old_Pbx1OE_RNAseq_D3_rep2 Old_Pbx1OE_RNAseq_D3
- GSM8734873 Old_Pbx1OE_RNAseq_D3_rep3 Old_Pbx1OE_RNAseq_D3
- GSE286957 Deep learning identifies Pbx1 as a network hub of hematopoietic stem cell aging [26hr RNA-seq] 8 samples shares pbx1, hematopoietic, identifies, learning, network, rna-seq, aging, deep
- 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, 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
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.