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Genomic and single-cell landscape of transformed cutaneous T-cell lymphoma

GSE272005 Homo sapiens (human) Single-cell RNA-seq 32 samples Submitted 2024/07/12 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 32 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
32 samples: 32 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
Genomic and Single-Cell Landscape Reveals Novel Drivers and Therapeutic Vulnerabilities of Transformed Cutaneous T-cell LymphomaCancer discovery · 2022
Summary (written by the submitters)
Cutaneous T-cell lymphoma (CTCL) is a malignancy of skin-homing T cells. A subgroup of patients develops large cell transformation with progression to an aggressive lymphoma and with poor survival. We aimed to study the transformed CTCL (tCTCL) ecosystem using integrative approaches spanning whole-exome sequencing (WES), single-cell RNAseq, and immune profiling in a unique cohort of 56 patients with tCTCL
Published in
Genomic and Single-Cell Landscape Reveals Novel Drivers and Therapeutic Vulnerabilities of Transformed Cutaneous T-cell Lymphoma
Song X, Chang S, Seminario-Vidal L et al. · Cancer discovery 2022 · PMID 35247891 · doi:10.1158/2159-8290.CD-21-1207
This dataset
Open GSE272005 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

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.

1 · Just have a look

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.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE272005_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

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.

Raw reads on ENA ↗

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 PRJNA754592 and SRA study SRP332550. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 35247891) ↗
Study design
32 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 32 sample titles: 32 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.

Samples in this study
Datasets on a similar topic

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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.