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Global Transcriptomic analysis of PBMC derived from COVID-19 acute-severe patients and convalescents

GSE202805 Homo sapiens (human) Single-cell RNA-seq 80 samples Submitted 2025/04/10 Platform GPL20301
At a glance

Single-cell RNA-seq · human · 80 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Samples
80 samples: 80 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 majority of patients with COVID-19 have a self-limiting upper respiratory tract infection mostly represented by asymptomatic or mild, however a small but relevant proportion of patients develops acute respiratory distress syndrome (ARDS), characterised by an excessive, host immune response with overproduction of proinflammatory cytokines can rapidly lead to multi-organ failure and death.
Read the full summary ↓
Indeed, immune pathologies are well characterised through deep immune phenotyping, plasma analysis, sc-RNA and chip based methylation analysis, still we lack a molecular understanding of immune pathologies associated with COVID-19 which is urgently needed for new prognostic and therapeutic interventions. In this study, we first time showed a SARS-Cov2 induced severity associated deregulation of immune functions driven through a global reprogramming of immune functions through a epigenetically driven gene regulatory network
This dataset
Open GSE202805 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 GSE202805_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 PRJNA837285 and SRA study SRP374816. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
80 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 80 sample titles: 80 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

+ 40 more — browse all 80 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.