Sorted bulk RNA-seq profiling of human peripheral blood mononuclear cell populations across diverse in vitro immune stimuli
RNA-seq (gene expression) · human · 150 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
- 150 samples: 150 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.
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 GSE327707_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 PRJNA1451900 and SRA study SRP690951. 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 150 sample titles: 150 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.
- GSM9663679 Donor 464, Whole PBMC, Baseline
- GSM9663680 Donor 467, Whole PBMC, Baseline
- GSM9663681 Donor 470, Whole PBMC, Baseline
- GSM9663682 Donor 474, Whole PBMC, Baseline
- GSM9663683 Donor 477, Whole PBMC, Baseline
- GSM9663684 Donor 479, Whole PBMC, Baseline
- GSM9663685 Donor 464, CD14, Baseline
- GSM9663686 Donor 467, CD14, Baseline
- GSM9663687 Donor 470, CD14, Baseline
- GSM9663688 Donor 474, CD14, Baseline
- GSM9663689 Donor 477, CD14, Baseline
- GSM9663690 Donor 479, CD14, Baseline
- GSM9663691 Donor 464, CD8, Baseline
- GSM9663692 Donor 467, CD8, Baseline
- GSM9663693 Donor 470, CD8, Baseline
- GSM9663694 Donor 474, CD8, Baseline
- GSM9663695 Donor 477, CD8, Baseline
- GSM9663696 Donor 479, CD8, Baseline
- GSM9663697 Donor 464, CD4, Baseline
- GSM9663698 Donor 467, CD4, Baseline
- GSM9663699 Donor 470, CD4, Baseline
- GSM9663700 Donor 474, CD4, Baseline
- GSM9663701 Donor 477, CD4, Baseline
- GSM9663702 Donor 479, CD4, Baseline
- GSM9663703 Donor 464, B cell, Baseline
- GSM9663704 Donor 467, B cell, Baseline
- GSM9663705 Donor 470, B cell, Baseline
- GSM9663706 Donor 474, B cell, Baseline
- GSM9663707 Donor 477, B cell, Baseline
- GSM9663708 Donor 479, B cell, Baseline
- GSM9663709 Donor 464, Whole PBMC, Cytostim
- GSM9663710 Donor 467, Whole PBMC, Cytostim
- GSM9663711 Donor 470, Whole PBMC, Cytostim
- GSM9663712 Donor 474, Whole PBMC, Cytostim
- GSM9663713 Donor 477, Whole PBMC, Cytostim
- GSM9663714 Donor 479, Whole PBMC, Cytostim
- GSM9663715 Donor 464, CD14, Cytostim
- GSM9663716 Donor 467, CD14, Cytostim
- GSM9663717 Donor 470, CD14, Cytostim
- GSM9663718 Donor 474, CD14, Cytostim
+ 110 more — browse all 150 samples with per-sample file links →
- GSE270387 Single cell profiling of peripheral blood mononuclear cells in diverse men with prostate cancer reveals lethal disease-associated immune signatures 165 samples shares mononuclear, peripheral, diverse
- GSE267718 Single cell RNA sequencing (scRNAseq) of urine-derived cells, peripheral blood mononuclear (PBMC), and tumor cells from bladder cancer patients. 33 samples shares mononuclear, peripheral
- GSE298173 Circulating immune cell signature analysis in HFpEF across species [RNA-Seq - human] 18 samples shares rna-seq, across
- GSE207625 RNA Sequencing Analysis of Peripheral Blood Mononuclear Cells Associated with COVID-19 Deterioration 12 samples shares mononuclear, peripheral
- GSE318490 Single-cell RNA-seq of tumor samples from six advanced HGSOC patients across treatment stages (pre-NACT, post-NACT and platinum-sensitive recurrence) 10 samples shares rna-seq, across
- GSE269269 Single-Cell RNA Sequencing of Peripheral Blood Mononuclear Cells From Acute Myocardial Infarction 10 samples shares mononuclear, peripheral
- GSE254176 Dynamic changes of immune cells in systemic lupus erythematosus with nephritis using longitudinal peripheral blood single-cell RNA-seq 57 samples shares peripheral, rna-seq
- GSE217313 The evolution of the type I IFN transcriptional response across human populations 46 samples shares populations, across
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.