CpG island methylator phenotype classification improves risk assessment in pediatric T-cell Acute Lymphoblastic Leukemia [RNA-seq]
RNA-seq (gene expression) · human · 111 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
- 111 samples: 111 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
- CpG island methylator phenotype classification improves risk assessment in pediatric T-cell acute lymphoblastic leukemiaBlood · 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 GSE272023_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 PRJNA1134657. 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 111 sample titles: 111 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.
- GSM8391250 T-ALL Sample 201 [RNA-seq]
- GSM8391251 T-ALL Sample 202 [RNA-seq]
- GSM8391252 T-ALL Sample 203 [RNA-seq]
- GSM8391253 T-ALL Sample 204 [RNA-seq]
- GSM8391254 T-ALL Sample 205 [RNA-seq]
- GSM8391255 T-ALL Sample 206 [RNA-seq]
- GSM8391256 T-ALL Sample 207 [RNA-seq]
- GSM8391257 T-ALL Sample 208 [RNA-seq]
- GSM8391258 T-ALL Sample 209 [RNA-seq]
- GSM8391259 T-ALL Sample 210 [RNA-seq]
- GSM8391260 T-ALL Sample 211 [RNA-seq]
- GSM8391261 T-ALL Sample 212 [RNA-seq]
- GSM8391262 T-ALL Sample 213 [RNA-seq]
- GSM8391263 T-ALL Sample 214 [RNA-seq]
- GSM8391264 T-ALL Sample 215 [RNA-seq]
- GSM8391265 T-ALL Sample 216 [RNA-seq]
- GSM8391266 T-ALL Sample 217 [RNA-seq]
- GSM8391267 T-ALL Sample 218 [RNA-seq]
- GSM8391268 T-ALL Sample 219 [RNA-seq]
- GSM8391269 T-ALL Sample 220 [RNA-seq]
- GSM8391270 T-ALL Sample 221 [RNA-seq]
- GSM8391271 T-ALL Sample 222 [RNA-seq]
- GSM8391272 T-ALL Sample 223 [RNA-seq]
- GSM8391273 T-ALL Sample 224 [RNA-seq]
- GSM8391274 T-ALL Sample 225 [RNA-seq]
- GSM8391275 T-ALL Sample 226 [RNA-seq]
- GSM8391276 T-ALL Sample 227 [RNA-seq]
- GSM8391277 T-ALL Sample 228 [RNA-seq]
- GSM8391278 T-ALL Sample 229 [RNA-seq]
- GSM8391279 T-ALL Sample 230 [RNA-seq]
- GSM8391280 T-ALL Sample 231 [RNA-seq]
- GSM8391281 T-ALL Sample 232 [RNA-seq]
- GSM8391282 T-ALL Sample 233 [RNA-seq]
- GSM8391283 T-ALL Sample 235 [RNA-seq]
- GSM8391284 T-ALL Sample 237 [RNA-seq]
- GSM8391285 T-ALL Sample 238 [RNA-seq]
- GSM8391286 T-ALL Sample 239 [RNA-seq]
- GSM8391287 T-ALL Sample 242 [RNA-seq]
- GSM8391288 T-ALL Sample 243 [RNA-seq]
- GSM8391289 T-ALL Sample 244 [RNA-seq]
+ 71 more — browse all 111 samples with per-sample file links →
- GSE241405 Single cell transcriptomic landscape of pediatric B-cell acute lymphoblastic leukemia: dissection of transcriptional heterogeneity and B-cell developmental state 89 samples shares lymphoblastic, pediatric, leukemia
- GSE115656 Alternative splicing analysis of pediatric B-cell acute lymphoblastic leukemia compared to normal bone marrow derived pro-B cells 38 samples shares lymphoblastic, pediatric, leukemia
- GSE294241 T-cell dysfunction during blinatumomab therapy in pediatric acute lymphoblastic leukemia 33 samples shares lymphoblastic, pediatric, leukemia
- GSE248287 Remodeling of the immune microenvironment is linked to adverse outcome in pediatric T cell acute lymphoblastic leukemia 34 samples shares lymphoblastic, pediatric, leukemia
- GSE311877 Genotype-phenotype single-cell transcriptomics for massive parallel assessment of genetic variants [RNAseq_mutants] 80 samples shares assessment, phenotype
- GSE288566 Cup-like nuclei is a hallmark of IGH:DUX4 acute lymphoblastic leukemia and reveals cytoplasmic mitochondria accumulation 57 samples shares lymphoblastic, leukemia
- GSE273247 Label-free melanoma phenotype classification using AI-based morphological profiling 41 samples shares classification, phenotype
- GSE263709 Targeting signaling rewiring and resistant subpopulations in Philadelphia Chromosome-like Acute Lymphoblastic Leukemia [Bulk RNA-seq] 39 samples shares lymphoblastic, leukemia
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.