CRISPRai Perturb-seq [Series 1]
Single-cell RNA-seq · human · 16 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
- Organism
- Homo sapiens (human)
- Samples
- 16 samples: 5 conditions, mostly in duplicateGroups 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
- Bidirectional epigenetic editing reveals hierarchies in gene regulationNature biotechnology · 2025
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 GSE220974_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: 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 PRJNA912208 and SRA study SRP413101. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 12 samples.
How we read this: 5 replicated groups read from 16 sample titles; they account for 12 of them. 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.
- GSM6833424 scRNA CRISPRa single perturbation S1
- GSM6833425 scRNA CRISPRi single perturbation S2
- GSM6833426 scRNA CRISPRa or CRISPRi single perturbation S3
- GSM6833427 scRNA double perturbation rep1 scRNA double perturbati…
- GSM6833428 scRNA double perturbation rep2 scRNA double perturbati…
- GSM6833429 scRNA double perturbation rep3 scRNA double perturbati…
- GSM6833430 gRNA CRISPRa single perturbation S1 gRNA CRISPRa single per…
- GSM6833431 gRNA CRISPRi single perturbation S2 gRNA CRISPRi single per…
- GSM6833432 gRNA CRISPRa single perturbation S3 gRNA CRISPRa single per…
- GSM6833433 gRNA CRISPRi single perturbation S3 gRNA CRISPRi single per…
- GSM6833434 gRNA CRISPRa double perturbations rep1
- GSM6833435 gRNA CRISPRi double perturbation rep1 gRNA CRISPRi double per…
- GSM6833436 gRNA CRISPRa double perturbation rep2 gRNA CRISPRa double per…
- GSM6833437 gRNA CRISPRi double perturbation rep2 gRNA CRISPRi double per…
- GSM6833438 gRNA CRISPRa double perturbation rep3 gRNA CRISPRa double per…
- GSM6833439 gRNA CRISPRi double perturbation rep3 gRNA CRISPRi double per…
- GSE314342 Genome-scale perturb-seq in primary human CD4+ T cells reveals genes regulating T cell programs and human immune traits. 577 samples shares perturb-seq
- GSE306853 Perturb-seq experiment on patient-derived cancer-associated fibroblast in vitro cell line models from pancreatic ductal adenocarcinoma 70 samples shares perturb-seq
- GSE113619 Time series integrative analysis of RNA-Seq and miRNA expression data reveals key biologic pathways during keloid formation [RNA-seq] 64 samples shares series
- GSE277900 ZIKV infection series in paediaitric brain tumour and NPC cell lines 61 samples shares series
- GSE313098 Time-series RNA-seq analysis reveals dynamic transcriptional responses and crosstalk in chronic lymphocytic leukaemia (CLL) autologous and monoculture in vitro models 60 samples shares series
- GSE337988 Joint analysis of multiply perturbed cells improves statistical power and cost efficiency in Perturb-seq screens 48 samples shares perturb-seq
- GSE250130 Time-series single-cell transcriptomic profiling of luteal-phase endometrium uncovers dynamic characteristics and its dysregulation in recurrent implantation failures 28 samples shares series
- GSE274751 Method of moments approach for generalized differential gene expression analysis (Perturb-seq) 24 samples shares perturb-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.