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PerturbAI Brain-Wide In Vivo CRISPR Atlas

This dataset represents a landmark in functional genomics: spanning 8 million single cells in living tissue and hundreds of distinct neuronal cell types, this is the most expansive in vivo functional genomics resource ever created. By mapping the language of biology at an unprecedented scale, our platform provides the foundation for the next generation of AI-driven therapeutic discovery.

Manuscript: “Genome-scale functional mapping of the mammalian whole brain with in vivo Perturb-seq” on bioRxiv

Summary: Check out our blog - www.perturb.ai/news

Data: Download the full dataset on Hugging Face

Analysis: Explore the dataset with the NVIDIA AI Blueprint for Single-Cell Analysis that leverages scverse’s RAPIDS-singlecell on RTX PRO 6000 Blackwell Workstation Edition, helping PerturbAI speed up analysis from days to near real-time (link)

8M brain cells with 2000 gene knockouts

Dataset Description

Using large-scale CRISPR screening and single-nucleus RNA sequencing, we’ve built a functional map of the mouse brain's genome. Measuring the effects of nearly 2,000 disease-linked genes in their native environment, we’ve revealed the molecular logic of the neuronal circuits underlying neurodegeneration, psychiatric, and metabolic diseases.

Key Highlights:

Scale: 7.7 million cells, with single nuclear profiling data across 19,070 mRNAs and 8,588 sgRNAs.
Resolution: Brain-wide coverage, capturing the gene function across hundreds of cell types in vivo.
Causality: Moving beyond correlation to causal inference through large-scale, parallelized perturbations.

Data Structure & Formats

To support diverse workflows, this repository includes:

Format	File/Folder	Primary Use Case
Parquet (cells)	`data/*.parquet`	Distributed per-cell expression and metadata for scalable analytics and ML pipelines.
Parquet (metadata)	`metadata/all_obs.parquet`, `metadata/gene_metadata.parquet`	Curated cell-level and gene-level metadata tables.
AnnData shards	`h5ads/*.h5ad`	Per-channel AnnData files for Scanpy/scvi-tools/Seurat/SingleCellExperiment workflows.
Zarr archive (LFS)	`analysis/preprocessed_gex.zarr.tar.gz`	For NVIDIA AI Blueprint for Single-Cell Analysis
Misc	`analysis/2603_shi_manuscript/*`	Data related to reproducing figures in our manuscript. See github.com/jinlabneurogenomics/wholebrainperturbseq

Metadata Columns

The following columns describe per-cell metadata fields used across the atlas:

Column	Description
`batch`	Represents a single Flex-pool of samples.
`scp_name`	Identifier for the 10x channel where a batch was processed; each batch was processed on multiple 10x channels.
`source`	Biological source (mouse) for this cell.
`sex`	Mouse sex (`M` or `F`).
`sample_label`	Distinguishes samples from the same source (commonly left `L` and right `R` hemisphere samples).
`num_rna_umi`	Number of detected RNA UMIs in this cell.
`num_genes`	Number of unique genes detected in this cell.
`pct_mt`	Percent of UMIs coming from mitochondrial genes.
`scDblFinder.class`	Doublet call from scDblFinder (`singlet` or `doublet`).
`scDblFinder.score`	Doublet score from scDblFinder (0-1; values near 1 indicate higher doublet likelihood).
`log_ambient_mse`	Log MSE of each cell relative to channel-average expression across genes (see methods in publication).
`log_ambient_mse_norm`	`log_ambient_mse` normalized by expected log MSE under a binomial sampling assumption (see methods in publication).
`gene_target`	Gene(s) knocked out in this cell: `gene`, `gene1\|gene2\|...`, `Non_target` (non-targeting guide), or `Negative` (no sufficiently detected guide).
`num_guides`	Number of guides detected at or above a 3 UMI threshold in this cell.
`guide_call`	List of detected guides, separated by `\|` when multiple; reports `Negative` if no guide is detected.
`guide_umis`	Total number of guide UMIs detected in this cell.
`guide_umi_top`	Guide UMI count for the most highly detected guide in this cell.
`guide_umi_second`	Guide UMI count for the second-most highly detected guide in this cell.
`predicted_group`	Custom group definition for this study, created by aggregating predicted subclasses (see publication).
`predicted_class`	Predicted class from MapMyCells using Allen Institute Whole Mouse Brain Taxonomy.
`predicted_class_probability`	Predicted class probability from MapMyCells using Allen Institute Whole Mouse Brain Taxonomy.
`predicted_subclass`	Predicted subclass from MapMyCells using Allen Institute Whole Mouse Brain Taxonomy.
`predicted_subclass_probability`	Predicted subclass probability from MapMyCells using Allen Institute Whole Mouse Brain Taxonomy.
`predicted_supertype`	Predicted supertype from MapMyCells using Allen Institute Whole Mouse Brain Taxonomy.
`predicted_supertype_probability`	Predicted supertype probability from MapMyCells using Allen Institute Whole Mouse Brain Taxonomy.
`predicted_cluster`	Predicted cluster from MapMyCells using Allen Institute Whole Mouse Brain Taxonomy.
`predicted_cluster_probability`	Predicted cluster probability from MapMyCells using Allen Institute Whole Mouse Brain Taxonomy.
`neuron_type`	From Allen Institute Whole Mouse Brain Taxonomy; derived from predicted subclass (`nt_type`).
`neighborhood`	From Allen Institute Whole Mouse Brain Taxonomy; derived from predicted subclass.
`region_level1`	From Allen Institute Whole Mouse Brain Taxonomy; coarse grouping of region_level2 assignment
`region_level2`	From Allen Institute Whole Mouse Brain Taxonomy; derived from predicted cluster, highest region in CCF_broad.freq
`cluster`	Cluster ID from unsupervised clustering; primarily used for QC and to identify additional doublet clusters missed by scDblFinder.
`passes_qc`	Boolean QC flag: `num_genes >= 2000`, `scDblFinder.class == "singlet"`, `log_ambient_mse_norm > 0.09`, and `cluster` not in `{"1", "17", "2", "3", "57", "6", "83", "NA"}`.

How to Use

Hugging Face Datasets

from datasets import load_dataset

# Load the default config defined in the dataset card (data/*.parquet)
ds = load_dataset("perturbai/wholebrain_crispr_atlas", split="train", streaming=True)
first_row = next(iter(ds))
print(first_row.keys())

AnnData

import glob
import anndata
from anndata.experimental import AnnCollection

# Open all h5ad shards in backed mode and wrap them in one collection
paths = sorted(glob.glob("h5ads/*.h5ad"))
adatas = [anndata.read_h5ad(path, backed="r") for path in paths]
collection = AnnCollection(adatas)

print("# Cells:", collection.n_obs)

# Load a subset of cells from disk into an AnnData object
ad_grin2a = collection[
  (collection.obs["gene_target"] == "Grin2a")
  & (collection.obs["passes_qc"])
].to_adata()

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