Multi-omic analysis reveals molecular subtypes with distinct immunological phenotypes in SLE [RNA-Seq]
RNA-seq (gene expression) · human · 804 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
- 804 samples: 804 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
- XIST expression and hypermethylation of the X chromosome in males with systemic lupus erythematosusFrontiers in immunology · 2026
Read the full summary ↓
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 GSE319130_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: 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 PRJNA1422154 and SRA study SRP676381. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from the first 400 of 804 sample titles: 400 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.
- GSM9511199 10102
- GSM9511200 10103
- GSM9511201 10104
- GSM9511202 10202
- GSM9511203 10505
- GSM9511204 10510
- GSM9511205 10514
- GSM9511206 10517
- GSM9511207 10522
- GSM9511208 10524
- GSM9511209 10526
- GSM9511210 10527
- GSM9511211 10531
- GSM9511218 10532
- GSM9511219 10533
- GSM9511220 10535
- GSM9511221 10540
- GSM9511222 10541
- GSM9511223 10542
- GSM9511224 10544
- GSM9511225 10547
- GSM9511226 10618
- GSM9511227 10703
- GSM9511228 10801
- GSM9511229 10802
- GSM9511230 10902
- GSM9511231 11201
- GSM9511232 11202
- GSM9511233 11204
- GSM9511234 11206
- GSM9511235 11207
- GSM9511236 11209
- GSM9511237 11501
- GSM9511238 11502
- GSM9511239 11506
- GSM9511240 11508
- GSM9511241 11514
- GSM9511242 11601
- GSM9511243 11603
- GSM9511244 11604
+ 764 more — browse all 804 samples with per-sample file links →
- GSE316337 Multi-omics profiling reveals microbiota, metabolite, lipid, and immunological heterogeneity underlying distinct pathophysiological mechanisms of age-related endotypes in type 1 diabetes 54 samples shares immunological, multi-omic, distinct
- GSE232381 RNA-seq of Peripheral Blood Cells Identifies Molecular Network Linked to Lymphocyte Subset Aberrations and Active Lupus Nephritis in SLE 25 samples shares sle, rna-seq
- GSE298578 Single cell RNA profiling of blood CD4+ T cells identifies distinct helper and dysfunctional regulatory clusters in children with SLE (5' PBMC validation dataset) 14 samples shares sle, distinct
- GSE275610 scRNA-seq and snATAC-seq reveal differentiation trajectories of several pancreatic islet induction protocols 341 samples shares sle, rna-seq
- GSE304878 Single-cell multi-omics profiling of PBMCs from SLE patients pre- and post-dual-target (BCMA/CD19) CAR-T therapy 30 samples shares sle, multi-omic
- GSE335464 Integrative single-cell multi-omics profiling of human pancreatic islets identifies T1D-associated genes and regulatory signals [single cell] 28 samples shares sle, multi-omic
- GSE273036 Multi-Omics Analysis and Molecular Subtypes Provide Potential Therapeutic Implications in Fumarate Hydratase-deficient Renal Cell Carcinoma 93 samples shares multi-omic, subtypes
- GSE263083 Multi-omics data analysis for the JGOG3025-TR2 ovarian cancer cohort [RNA-seq] 291 samples shares multi-omic, rna-seq
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.