Single-cell transcriptional atlas of hematopoiesis reveals genetic and hierarchy-based determinants of aberrant differentiation in AML
Single-cell RNA-seq · human · 13 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
- Organism
- Homo sapiens (human)
- Topic
- Acute myeloid leukemiaThis series is part of our curated briefing on it.
- Samples
- 13 samples: 12 × AML PatientGroups 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 GSE289435_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 PRJNA1222901 and SRA study SRP563502. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 12 samples.
How we read this: 1 replicated groups read from 13 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.
- GSM8791432 AML Patient 14666 AML Patient
- GSM8791433 AML Patient 16703 AML Patient
- GSM8791434 AML Patient 17746 AML Patient
- GSM8791435 AML Patient 17843 AML Patient
- GSM8791436 AML Patient 17844 AML Patient
- GSM8791437 AML Patient 28824 AML Patient
- GSM8791438 AML Patient 28830 AML Patient
- GSM8791439 AML Patient 28855 AML Patient
- GSM8791440 AML Patient 29512 (Patient Derived Xenograft)
- GSM8791441 AML Patient 29532 AML Patient
- GSM8791442 AML Patient 29538 AML Patient
- GSM8791443 AML Patient 30862 AML Patient
- GSM8791444 AML Patient 30886 AML Patient
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- GSE303795 Halofuginone exerts broad-spectrum cytotoxic effects by regulating p-eIF2α-S100A8/A9-calcium signaling, inhibiting global protein synthesis, and reversing the resistance of idarubicin in acute myeloid leukemia 6 samples same disease briefing
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- GSE129437 Genome-wde mapping of BRD9 binding sites and gene expression changes upon BRD9 knockdown 6 samples same disease briefing
- GSE297413 Genomic and transcriptomic determinants of clinical outcomes in patients with AML and DNMT3A mutations 264 samples shares aml, determinants
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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.