A unified network systems approach uncovers a core program underlying T follicular helper cell differentiation [RNA-Seq]
RNA-seq (gene expression) · human · 16 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
- 16 samples: 16 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
- A unified network systems approach uncovers a core novel program underlying T follicular helper cell differentiationbioRxiv : the preprint server for biology · 2025
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 GSE303342_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 PRJNA1294877 and SRA study SRP602824. 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 16 sample titles: 16 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.
- GSM9123829 S0001_Tfh_CXCR5++_PD1hi
- GSM9123830 S0002_Early_PreTfh_CXCR5+_PD1low
- GSM9123831 S0003_Late_PreTfh_CXCR5+_PD1int
- GSM9123832 S0004_Naive_CD45RA+CXCR5neg_PD1neg
- GSM9123833 S0005_Tfh_CXCR5++_PD1hi
- GSM9123834 S0006_Early_PreTfh_CXCR5+_PD1low
- GSM9123835 S0007_Late_PreTfh_CXCR5+_PD1int
- GSM9123836 S0008_Naive_CD45RA+CXCR5neg_PD1neg
- GSM9123837 S0013_Tfh_CXCR5++_PD1hi
- GSM9123838 S0014_Early_PreTfh_CXCR5+_PD1low
- GSM9123839 S0015_Late_PreTfh_CXCR5+_PD1int
- GSM9123840 S0016_Naive_CD45RA+CXCR5neg_PD1neg
- GSM9123841 S0017_Tfh_CXCR5++_PD1hi
- GSM9123842 S0018_Early_PreTfh_CXCR5+_PD1low
- GSM9123843 S0019_Late_PreTfh_CXCR5+_PD1int
- GSM9123844 S0020_Naive_CD45RA+CXCR5neg_PD1neg
- GSE303343 A unified network systems approach uncovers a core program underlying T follicular helper cell differentiation [CUT&RUN] 48 samples shares underlying, follicular, uncovers, unified, network, systems, program
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- GSE291471 Integrative blood profiling uncovers inflammatory network signatures in high-altitude pulmonary edema 83 samples shares uncovers, network
- GSE309174 Stem cell activation in organ culture reveals novel transcriptional programs underlying metabolic, fibrotic, vascular, and immune dysregulation in uterine leiomyomas 25 samples shares underlying, program
- GSE255902 Human iPSC 4R tauopathy model uncovers modifiers of tau propagation [bulk RNA-seq] 19 samples shares uncovers, rna-seq
- GSE261390 CRISPR Screening Uncovers a Long-Range Enhancer for ONECUT1 in Pancreatic Differentiation and Links a Diabetes Risk Variant [RNA-seq] 18 samples shares uncovers, rna-seq
- GSE288490 SLIDE analysis of the scRNA-seq data using an interpretable factor-analysis machine learning framework, that moves beyond predictive biomarkers to try and infer latent factors underlying LS pathophysiology. 16 samples shares underlying, rna-seq
- GSE134520 Dissecting the single-cell transcriptome network underlying gastric premalignant lesions and early gastric cancer [10x genomics] 13 samples shares underlying, network
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.