DNA methylation stochasticity is linked to transcriptional variability and identifies convergent epigenetic disruption across genetically-defined subtypes of AML [scRNAseq]
Single-cell RNA-seq · human · 26 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)
- Samples
- 26 samples: 26 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.
- Paper
- DNA Methylation Stochasticity Is Linked to Transcriptional Variability and Convergent Epigenetic Disruption across Genetic Subtypes of Acute Myeloid LeukemiaCancer research · 2026
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 GSE281087_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 PRJNA1182269 and SRA study SRP543281. 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 26 sample titles: 26 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.
- GSM8610680 AML JH4198, scRNA-seq
- GSM8610681 AML JH4316, scMultiome GEX
- GSM8610682 AML JH4639, scMultiome GEX
- GSM8610683 AML JH5034, scMultiome GEX
- GSM8610684 AML JH5205, scMultiome GEX
- GSM8610685 AML JH5236, scRNA-seq
- GSM8610686 AML JH6048, scMultiome GEX
- GSM8610687 AML SU048, scRNA-seq
- GSM8610688 AML SU050, scRNA-seq
- GSM8610689 AML SU070, scRNA-seq
- GSM8610690 AML SU075, scMultiome GEX
- GSM8610691 AML SU204, scRNA-seq
- GSM8610692 AML SU209, scRNA-seq
- GSM8610693 AML SU214 Replicate 1, scRNA-seq
- GSM8610694 AML SU214 Replicate 2, scMultiome GEX
- GSM8610695 AML SU227, scMultiome GEX
- GSM8610696 AML SU270, scRNA-seq
- GSM8610697 AML SU344, scRNA-seq
- GSM8610698 AML SU462, scRNA-seq
- GSM8610699 AML SU494, scMultiome GEX
- GSM8610700 AML SU504 Replicate 1, scRNA-seq
- GSM8610701 AML SU504 Replicate 2, scMultiome GEX
- GSM8610702 AML SU758, scMultiome GEX
- GSM8610703 CD34-1, scRNA-seq
- GSM8610704 CD34-2, scRNA-seq
- GSM8610705 GMP-1, scRNA-seq
- GSE319461 DNA methylation stochasticity is linked to transcriptional variability and identifies convergent epigenetic disruption across genetically-defined subtypes of AML 82 samples shares aml, genetically-defined, stochasticity, methylation, variability, identifies, convergent, epigenetic
- GSE319444 DNA methylation stochasticity is linked to transcriptional variability and identifies convergent epigenetic disruption across genetically-defined subtypes of AML [PrimaryAML_WGBS] 20 samples shares aml, genetically-defined, stochasticity, methylation, variability, identifies, convergent, epigenetic
- GSE319448 DNA methylation stochasticity is linked to transcriptional variability and identifies convergent epigenetic disruption across genetically-defined subtypes of AML [CellCulture_WGBS] 12 samples shares aml, genetically-defined, stochasticity, methylation, variability, identifies, convergent, epigenetic
- GSE304097 DNA methylation profiling identifies long-range epigenetic silencing of clustered protocadherins as a key determinant of meningioma progression 293 samples shares methylation, identifies, epigenetic
- GSE256353 Upregulated cholesterol biosynthesis facilitates the survival of methylation-retaining AML cells following decitabine treatment [single_colony_TEM-Seq] 1728 samples shares aml, methylation
- GSE282258 Imaging-based drug screening combined with moleuclar profiling identifies signatures and drivers of therapy resistance in pediatric AML [ATAC-seq] 44 samples shares aml, identifies
- GSE282423 Imaging-based drug screening combined with moleuclar profiling identifies signatures and drivers of therapy resistance in pediatric AML [RNA-seq] 28 samples shares aml, identifies
- GSE272142 Genome‑wideBrain DNAMethylationandGeneExpression Patterns Unravel Epigenetic Reprogramming in Mesial Temporal Lobe Epilepsy 12 samples shares methylation, epigenetic
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.