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Prognostic accuracy of a peripheral blood transcriptome signature in chronic hypersensitivity pneumonitis

GSE319562 Homo sapiens (human) RNA-seq (gene expression) 37 samples Submitted 2026/04/15 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 37 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
37 samples: 37 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.
Summary (written by the submitters)
The prognostic value of peripheral blood mononuclear cell (PBMC) expression profiles, when used in patients with chronic hypersensitivity pneumonitis (CHP), as an adjunct to traditional clinical assessment is unknown.
Read the full summary ↓
RNA-seq analysis on PBMC from 37 patients with CHP at initial presentation determined that (1) 74 differentially expressed transcripts at a 10% false discovery rate distinguished those with (n=10) and without (n=27) disease progression, defined as absolute FVC and/or diffusing capacity of the lungs for carbon monoxide (DLCO) decline of ≥10% and increased fibrosis on chest CT images within 24 months, and (2) classification models based on gene expression and clinical factors strongly outperform models based solely on clinical factors (baseline FVC%, DLCO% and chest CT fibrosis).
This dataset
Open GSE319562 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 GSE319562_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 PRJNA1423736 and SRA study SRP677122. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
37 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 37 sample titles: 37 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.