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PriOmics: integration of high_throughput proteomic data with complementary omics layers using mixed graphical modeling with group priors

GSE253910 Homo sapiens (human) RNA-seq (gene expression) 360 samples Submitted 2025/09/30 Platform GPL15520
At a glance

RNA-seq (gene expression) · human · 360 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)
Topic
Diffuse large B-cell lymphomaThis series is part of our curated briefing on it.
Samples
360 samples: 360 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
Integration of high-throughput proteomic data and complementary omics layers with PriOmicsGenome research · 2026
Summary (written by the submitters)
Mass spectrometry (MS)_based high_throughput proteomics data cover abundances of 1,000s of proteins and facilitate the study of co_ and post_translational modifications (CTMs/PTMs) such as acetylation, ubiquitination, and phosphorylation. Yet, it remains an open question how to holistically explore such data and their relationship to complementary omics layers (including the HTG EdgeSeq Pan B_Cell Lymphoma Panel) or phenotypical information.
Read the full summary ↓
Network inference methods aim for a holistic analysis of data to reveal relationships between molecular variables and to resolve underlying regulatory mechanisms. Among those, graphical models have received increased attention as they can distinguish direct from indirect relationships, aside from their generalizability to diverse data types. We propose PriOmics as a graphical modeling approach to integrate proteomics data with complementary omics layers and pheno_ and genotypical information. PriOmics models intensities of individual peptides and incorporates their protein affiliation as prior knowledge in order to resolve statistical relationships between proteins and CTMs/PTMs. We show in simulation studies that PriOmics improves the recovery of statistical associations compared to the state of the art and demonstrate that it can disentangle regulatory effects of protein modifications from those of respective protein abundances. These findings are substantiated in a dataset of Diffuse Large B-Cell Lymphomas (DLBCLs) where we integrate SWATH_MS_based proteomics data with the HTG EdgeSeq Pan B_Cell Lymphoma transcriptional Panel and phenotypic information.
Published in
Integration of high-throughput proteomic data and complementary omics layers with PriOmics
Kosch R, Limm K, Staiger AM et al. · Genome research 2026 · PMID 41253501 · doi:10.1101/gr.279487.124
This dataset
Open GSE253910 in finder → Find more human RNA-seq datasets → Diffuse large B-cell lymphoma landscape →
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 GSE253910_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 PRJNA1067652 and SRA study SRP485254. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41253501) ↗
Study design
360 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 360 sample titles: 360 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

+ 320 more — browse all 360 samples with per-sample file links →

More Diffuse large B-cell lymphoma datasets

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.