Comparison of differential gene expression in blood PBMCs from healthy controls, patients with rheumatoid arthritis,and patients with lupus using single-cell transcriptome sequencing analysis.
Single-cell RNA-seq · human · 10 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
- 10 samples: 4 × Healthy control vs 3 × Rheumatoid arthritis vs 3 × Systemic lupus erythematosusGroups 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.
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE266852_RAW.tar (it can run to tens of gigabytes).
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.
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 PRJNA1108432 and SRA study SRP506030. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 10 samples.
How we read this: 3 replicated groups read from 10 sample titles; they account for 10 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.
- GSM8254794 Healthy control-01 Healthy control
- GSM8254795 Healthy control-02 Healthy control
- GSM8254796 Healthy control-03 Healthy control
- GSM8254797 Healthy control-04 Healthy control
- GSM8254798 Rheumatoid arthritis-01 Rheumatoid arthritis
- GSM8254799 Rheumatoid arthritis-02 Rheumatoid arthritis
- GSM8254800 Rheumatoid arthritis-04 Rheumatoid arthritis
- GSM8254801 Systemic lupus erythematosus-02 Systemic lupus erythema…
- GSM8254802 Systemic lupus erythematosus-03 Systemic lupus erythema…
- GSM8254803 Systemic lupus erythematosus-04 Systemic lupus erythema…
- GSE161245 Comparison of bronchial epithelial cells obtained by bronchoscopy from mild asthma patients, moderate asthma patients, severe asthma patients and healthy controls 55 samples shares comparison, controls, healthy
- GSE296117 A single-cell RNA-seq dataset of synovial fluid from rheumatoid arthritis treated with TNF-α/JAK inhibitor 18 samples shares single-cell, rheumatoid, arthritis
- GSE334588 Single-cell transcriptomic atlas of adult dermatomyositis, cutaneous lupus erythematosus and healthy skin 48 samples shares single-cell, healthy
- GSE260666 Differential analysis of gene expression in liver tissues of patients with nonalcoholic fatty liver disease and controls. 16 samples shares differential, controls
- GSE307297 Single cell RNAseq on rheumatoid arthritis-related interstitial lung disease 12 samples shares rheumatoid, arthritis
- GSE298602 Single-cell RNA-Seq data from preeclamptic and healthy placentas 11 samples shares single-cell, healthy
- GSE233321 Single-cell map of the healthy human immune system across the lifespan reveals unique infant immune signatures 118 samples shares single-cell, healthy
- GSE279392 Single-cell RNA and TCR sequencing from skin, blood and cerebral spinal fluid (CSF) samples from Degos disease patients 116 samples shares single-cell, blood
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.