Molecular Characterization of Chemoresistant Cell Populations Improves Risk Stratification and Therapy Response Prediction in Pediatric AML [Treatment_Study_RNA-seq]
Single-cell RNA-seq · human · 17 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
- Homo sapiens (human)
- Samples
- 17 samples
- 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
- Characterization of Chemoresistant Cell Populations Improves Risk Stratification and Therapy Prediction in Pediatric AMLbioRxiv : the preprint server for biology · 2025
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 GSE308825_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 PRJNA1332760 and SRA study SRP625327. Searching any of these in the dataset finder brings you back here.
The sample list for this study is not cached yet. Press Load samples and it will be fetched from NCBI.
+ 17 more — browse all 17 samples with per-sample file links →
- GSE270894 Molecular Characterization of Chemoresistant Cell Populations Improves Risk Stratification and Therapy Response Prediction in Pediatric AML [pAML Patients scRNA-seq] 20 samples shares aml, chemoresistant, stratification, populations, prediction, pediatric, improves
- GSE271134 Molecular Characterization of Chemoresistant Cell Populations Improves Risk Stratification and Therapy Response Prediction in Pediatric AML [pAML PDX Bulk RNA-seq] 14 samples shares aml, chemoresistant, stratification, populations, prediction, pediatric, improves
- GSE270892 Molecular Characterization of Chemoresistant Cell Populations Improves Risk Stratification and Therapy Response Prediction in Pediatric AML [pAML Patients Bulk RNA-seq] 12 samples shares aml, chemoresistant, stratification, populations, prediction, pediatric, improves
- GSE271136 Molecular Characterization of Chemoresistant Cell Populations Improves Risk Stratification and Therapy Response Prediction in Pediatric AML [pAML PDX scRNA-seq] 12 samples shares aml, chemoresistant, stratification, populations, prediction, pediatric, improves
- GSE289786 Improved Prediction of Venetoclax and Azacitidine Efficacy in Acute Myeloid Leukemia through Machine Learning [AML patients] 142 samples shares aml, prediction
- GSE296649 Clonal evolution permits outgrowth of chemoresistant LMPP-like early progenitors in relapsed NPM1c AML [scRNA-Seq] 58 samples shares aml, chemoresistant
- GSE296381 Clonal evolution permits outgrowth of chemoresistant LMPP-like early progenitors in relapsed NPM1c AML 20 samples shares aml, chemoresistant
- GSE289713 Superior preclinical efficacy of co-treatment with BRG1/BRM and FLT3 inhibitor against AML cells with FLT3 mutations 11 samples shares aml, treatment
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.