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Pooled CRISPR screening with single-cell transcriptome read-out

GSE92872 Homo sapiens (human) Single-cell RNA-seq 100 samples Submitted 2017/01/18 Platform GPL19969Platform GPL20301Platform GPL18573
At a glance

Single-cell RNA-seq · human · 100 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
100 samples: 37 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.
Summary (written by the submitters)
We combined CRISPR genome editing with single-cell RNA sequencing to assess complex phenotypes in pooled cellular screens. Our method for CRISPR droplet sequencing (CROP-seq) comprises four key components: a gRNA vector that makes individual gRNAs detectable in single-cell transcriptomes, a high-throughput assay for single-cell RNA-seq, a computational pipeline for assigning single-cell transcriptomes to gRNAs, and a bioinformatic method for analyzing and interpreting gRNA-induced transcriptional profiles.
Read the full summary ↓
CROP-seq allowed us to link gRNA expression to the associated transcriptome responses in thousands of single cells using a straightforward and broadly applicable screening workflow. Additional information are available from the CROP-seq website http://crop-seq.computational-epigenetics.org
This dataset
Open GSE92872 in finder → Find more 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 GSE92872_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 PRJNA358686 and SRA study SRP095602. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 28099430) ↗
Study design
37 conditions, mostly in duplicate
Bulk_stimulated_CTRL ×8 Bulk_unstimulated_CTRL ×8 CROP-seq_Jurkat_TCR_stimulated ×6 CROP-seq_Jurkat_TCR_unstimulated ×5 Bulk_stimulated_DOK2 ×2 Bulk_stimulated_EGR1 ×2 Bulk_stimulated_EGR2 ×2 Bulk_stimulated_EGR3 ×2 +36 more

Supports a between-group comparison across 93 samples.

How we read this: 37 replicated groups read from 100 sample titles; they account for 93 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.

Samples in this study

+ 60 more — browse all 100 samples with per-sample file links →

Datasets on a similar topic

Search all RNA-seq datasets in GEO →

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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.