Characterization of Chemoresistant Cell Populations Improves Risk Stratification and Therapy Prediction in Pediatric AML
RNA-seq (gene expression) · human · 75 samples
- What it is
- RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
- Organism
- Homo sapiens (human)
- Samples
- 75 samples: 65 distinct titles, 6 of them repeatedGroups 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
- Characterization of Chemoresistant Cell Populations Improves Risk Stratification and Therapy Prediction in Pediatric AMLbioRxiv : the preprint server for biology · 2025
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 GSE271137_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.
Also filed as BioProject PRJNA1130038 and SRA study SRP625327. Searching any of these in the dataset finder brings you back here.
How we read this: Read from 75 sample titles: 65 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.
- GSM8354276 TCH1_D(Bulk)
- GSM8354277 TCH1_R(Bulk)
- GSM8354278 TCH2_D(Bulk)
- GSM8354279 TCH2_R(Bulk)
- GSM8354280 TCH3_D(Bulk)
- GSM8354281 TCH3_R(Bulk)
- GSM8354282 TCH4_D(Bulk)
- GSM8354283 TCH4_R(Bulk)
- GSM8354284 TCH5_D(Bulk)
- GSM8354285 TCH5_R(Bulk)
- GSM8354286 TCH6_D(Bulk)
- GSM8354287 TCH6_R(Bulk)
- GSM8354306 TCH1-TCH3, D and R (SC)
- GSM8354307 TCH4-TCH6, D and R (SC)
- GSM8354308 PAPZCL_D (SC)
- GSM8354309 PAPZCL_R (SC)
- GSM8354310 PARBIU_D (SC)
- GSM8354311 PARBIU_R (SC)
- GSM8354312 PAUMTZ_D (SC)
- GSM8354313 PAUMTZ_R (SC)
- GSM8354314 PAUNSV_D (SC)
- GSM8354315 PAUNSV_R (SC)
- GSM8354316 PAUVAT_D (SC)
- GSM8354317 PAUVAT_R (SC)
- GSM8354318 PAVBFN_D (SC)
- GSM8354319 PAVBFN_R (SC)
- GSM8354320 PAVTLN_D (SC)
- GSM8354321 PAVTLN_R (SC)
- GSM8354322 PAWNPU_R (SC)
- GSM8354323 TCHnorm1.n.S (SC)
- GSM8354324 TCHnorm2.n.S (SC)
- GSM8354325 TCHnorm2.n.U (SC)
- GSM8370255 AML005_AraC (Bulk)
- GSM8370256 AML001_AraC (Bulk)
- GSM8370257 AML010_AraC (Bulk)
- GSM8370258 AML005_Saline (Bulk)
- GSM8370259 AML010_Saline (Bulk)
- GSM8370260 AML001_Saline (Bulk)
- GSM8370261 AML006_Saline (Bulk)
- GSM8370262 AML006_AraC (Bulk)
+ 35 more — browse all 75 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, therapy
- 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, therapy
- 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, therapy
- 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, therapy
- 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, pediatric, therapy
- 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, pediatric, therapy
- 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
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.