GEO series

D-SPIN constructs regulatory network models from scRNA-seq revealing organizing principles of perturbation response

GSE327727 Homo sapiens (human) Single-cell RNA-seq 194 samples Submitted 2026/05/11 Platform GPL24676Platform GPL20301Platform GPL15520

At a glance

Single-cell RNA-seq · human · 194 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: 194 samples: 194 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)

Gene regulatory networks modulate the expression of the genome in response to signals and environmental conditions. Reconstructions of such networks can reveal the control principles cells use to maintain homeostasis and execute cell-state transitions. Here, we introduce a computational framework, D-SPIN, that infers mechanistically interpretable and generative models of gene regulatory networks from single-cell mRNA-seq datasets collected across thousands of perturbation conditions.

Read the full summary ↓

The models explain how perturbations modulate cell state proportions by reconfiguring underlying regulatory interactions. Using large Perturb-seq and drug-response datasets, D-SPIN models reveal key regulators of cell-fate decisions and the coordination of distant cellular pathways in response to gene knockdowns and drug treatments, elucidate how combinations of immunomodulatory drugs induce combinatorial cell states through additive recruitment of gene expression programs, and simulate shifts in immune cell population structures across unobserved drug dosage combinations. D-SPIN provides a computational framework for revealing principles of cellular information processing and physiological control.

This dataset

Open GSE327727 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 GSE327727_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 PRJNA1449626 and SRA study SRP690278. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

194 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 194 sample titles: 194 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

GSM9664244 scRNA-seq human PBMC MULT-6-1 library
GSM9664245 scRNA-seq human PBMC MULT-6-2 library
GSM9664246 scRNA-seq human PBMC MULT-6-3 library
GSM9664247 scRNA-seq human PBMC MULT-6-4 library
GSM9664248 scRNA-seq human PBMC MULT-6-5 library
GSM9664249 scRNA-seq human PBMC MULT-6-6 library
GSM9664250 scRNA-seq human PBMC MULT-6-7 library
GSM9664251 MULTI-seq hashtag MULT-6-1 TAGS library
GSM9664252 MULTI-seq hashtag MULT-6-2 TAGS library
GSM9664253 MULTI-seq hashtag MULT-6-3 TAGS library
GSM9664254 MULTI-seq hashtag MULT-6-4 TAGS library
GSM9664255 MULTI-seq hashtag MULT-6-5 TAGS library
GSM9664256 MULTI-seq hashtag MULT-6-6 TAGS library
GSM9664257 MULTI-seq hashtag MULT-6-7 TAGS library
GSM9664258 scRNA-seq human PBMC MULT-7-1 library
GSM9664259 scRNA-seq human PBMC MULT-7-2 library
GSM9664260 scRNA-seq human PBMC MULT-7-3 library
GSM9664261 scRNA-seq human PBMC MULT-7-4 library
GSM9664262 scRNA-seq human PBMC MULT-7-5 library
GSM9664263 scRNA-seq human PBMC MULT-7-6 library
GSM9664264 scRNA-seq human PBMC MULT-7-7 library
GSM9664265 scRNA-seq human PBMC MULT-7-8 library
GSM9664266 MULTI-seq hashtag MULT-7-1 TAGS library
GSM9664267 MULTI-seq hashtag MULT-7-2 TAGS library
GSM9664268 MULTI-seq hashtag MULT-7-3 TAGS library
GSM9664269 MULTI-seq hashtag MULT-7-4 TAGS library
GSM9664270 MULTI-seq hashtag MULT-7-5 TAGS library
GSM9664271 MULTI-seq hashtag MULT-7-6 TAGS library
GSM9664272 MULTI-seq hashtag MULT-7-7 TAGS library
GSM9664273 MULTI-seq hashtag MULT-7-8 TAGS library
GSM9664274 scRNA-seq human PBMC MULT-TC-1 library
GSM9664275 scRNA-seq human PBMC MULT-TC-2 library
GSM9664276 scRNA-seq human PBMC MULT-TC-3 library
GSM9664277 scRNA-seq human PBMC MULT-TC-4 library
GSM9664278 scRNA-seq human PBMC MULT-TC-5 library
GSM9664279 scRNA-seq human PBMC MULT-TC-6 library
GSM9664280 MULTI-seq hashtag MULT-TC-1 TAGS library
GSM9664281 MULTI-seq hashtag MULT-TC-2 TAGS library
GSM9664282 MULTI-seq hashtag MULT-TC-3 TAGS library
GSM9664283 MULTI-seq hashtag MULT-TC-4 TAGS library

+ 154 more — browse all 194 samples with per-sample file links →

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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.