Perturb-seq: Dissecting molecular circuits with scalable single cell RNA profiling of pooled genetic screens
Single-cell RNA-seq · mouse · 6 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
- Organism
- Mus musculus (mouse)
- Samples
- 6 samples: 6 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.
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 GSE90063_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 PRJNA354362 and SRA study SRP093670. 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 6 sample titles: 6 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.
- GSM2396856 DC 3hr
- GSM2396857 DC 0hr
- GSM2396858 K562 TFs, 7 days
- GSM2396859 K562 TFs, 13 days
- GSM2396860 K562 TFs, High MOI
- GSM2396861 K562 cell cycle
- GSE214689 Dissecting developmental disorders caused by CTCF mutation at R567 [RNA-seq] 27 samples shares dissecting
- GSE253161 Scalable ultra-high-throughput single-cell chromatin and RNA sequencing reveals gene regulatory dynamics linking macrophage polarization to autoimmune disease [NIH-3T3_K562_RNA_without_blocking] 1 samples shares scalable
- GSE270064 In vivo intratumoral Heterogeneity in a dish: Scalable Forebrain Organoid Models of Embryonal Brain Tumors for High-Throughput Personalized Drug Discovery (human+murine organoids subset) 0 samples shares scalable
- GSE212524 Rnf20 levels modulate the VEGF-Notch signaling circuits during sprouting angiogenesis 50 samples shares circuits
- GSE299567 IT-scC&T-seq streamlines scalable, parallel profiling of protein-DNA interactions in single cells 17 samples shares scalable
- GSE268853 Conserved Transcriptional Circuits Regulate Cardiac Fibroblast-Mediated Fibrosis 12 samples shares circuits
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.