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Joint analysis of multiply perturbed cells improves statistical power and cost efficiency in Perturb-seq screens

GSE337988 Homo sapiens (human) RNA-seq (gene expression) 48 samples Submitted 2026/07/10 Platform GPL24676Platform GPL34284
At a glance

RNA-seq (gene expression) · human · 48 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
48 samples: 48 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)
Perturb-seq measures transcriptomic responses to genetic perturbations at scale, but standard low-MOI designs remain resource-intensive. Conventional designs aim to recover one guide RNA per cell and often discard cells carrying multiple guides during analysis. Here, we characterize how overloading cells with guides affects signal recovery, information loss, and cost reduction.
Read the full summary ↓
Increasing guide RNA dosage eventually induced cellular stress and suppressed cell-cycle progression. Among multiplets, cells carrying two or three guides recovered perturbation responses better than higher-order guide multiplets. In a 5000-gene Perturb-seq screen across MOI levels, high MOI reduced per-cell costs by 81% while keeping information loss within 1.5-fold of a low-MOI baseline. In existing genome-wide Perturb-seq data, incorporating previously discarded multiplets increased usable cell numbers and improved statistical power. Compared with a singlet-only holdout set, adding multiplets moved signal recovery closer to the theoretical reproducibility limit. Together, these results provide an experimental and computational framework for designing cost-efficient Perturb-seq screens at scale.
This dataset
Open GSE337988 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 GSE337988_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: 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 PRJNA1492064 and SRA study SRP716389. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
48 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 48 sample titles: 48 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

+ 8 more — browse all 48 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.