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Generation of multi-omic datasets using high-throughput molecular profiling of transcriptomic human data

GSE281204 Homo sapiens (human) RNA-seq (gene expression) 39 samples Submitted 2025/12/30 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 39 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
39 samples: 30 × In vitro mixture, 9 cell-types, mixGroups 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
A robust workflow to benchmark deconvolution of multi-omic dataGenome biology · 2025
Summary (written by the submitters)
Tumor heterogeneity significantly affects cancer progression and therapeutic response, yet quantifying it from bulk molecular data remains challenging. Deconvolution algorithms, which estimate cell-type proportions in bulk samples, offer a potential solution. However, there is no consensus on the optimal algorithm for transcriptomic or methylomic data.
Read the full summary ↓
Here, we present an unbiased evaluation framework for the first comprehensive comparison of deconvolution algorithms across both omic types, including reference-based and -free approaches. Our evaluation covers raw performance, stability, and computational efficiency under varying conditions, such as missing or additional cell types and diverse sample compositions. We design a reproducible workflow using containerization and publicly available code to ensure transparency and re-usability. Our results highlight the strengths and limitations of various algorithms, providing practical guidance for selecting the best method based on data type and context. This benchmark sets a new standard for evaluating deconvolution methods and analyzing tumor heterogeneity.
Published in
A robust workflow to benchmark deconvolution of multi-omic data
Amblard E, Bertrand V, Barbot H et al. · Genome biology 2025 · PMID 41408313 · doi:10.1186/s13059-025-03897-9
This dataset
Open GSE281204 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 GSE281204_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: 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 PRJNA1182720 and SRA study SRP543639. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41408313) ↗
Study design
30 × In vitro mixture, 9 cell-types, mix

Supports a between-group comparison across 30 samples.

How we read this: 1 replicated groups read from 39 sample titles; they account for 30 of them. 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

Search all human RNA-seq datasets in GEO →

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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.