genes listlengths 361 12.3k | expressions listlengths 361 12.3k | cell_id stringlengths 35 35 | batch stringclasses 2
values | scp_name stringclasses 4
values | source stringclasses 26
values | sex stringclasses 2
values | sample_label stringclasses 52
values | num_rna_umi float32 498 371k | num_genes int32 361 12.3k | pct_mt float32 0 17.2 | scDblFinder.class stringclasses 2
values | scDblFinder.score float64 0 1 | log_ambient_mse float32 -3.65 8.43 | log_ambient_mse_norm float32 -0.07 7.69 | gene_target stringlengths 2 181 | num_guides int64 0 28 | guide_call stringlengths 4 237 | guide_umis float32 0 835 | guide_umi_top float32 0 757 | guide_umi_second float32 0 160 | predicted_group stringclasses 31
values | predicted_class stringclasses 34
values | predicted_class_probability float64 0.11 1 | predicted_subclass stringclasses 318
values | predicted_subclass_probability float64 0.12 1 | predicted_supertype stringlengths 12 42 | predicted_supertype_probability float64 0.13 1 | predicted_cluster stringlengths 12 42 | predicted_cluster_probability float64 0.09 1 | neuron_type stringclasses 10
values | neighborhood stringclasses 11
values | region_level1 stringclasses 8
values | region_level2 stringclasses 17
values | cluster stringclasses 87
values | passes_qc bool 2
classes |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
[
44,
69,
86,
95,
141,
196,
257,
355,
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507,
523,
604,
662,
689,
731,
783,
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796,
829,
873,
910,
916,
937,
989,
1021,
1064,
1123,
1182,
1234,
1336,
1366,
1371,
1396,
1460,
1465,
1479,
1496,
1499,
1505,
1523,
154... | [
1,
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1,
1... | AAACCAATCAAGCCAGTCCGTGCGTA-1:SCP038 | WB8588_1 | WB8588_1_1 | mouse2 | M | 2L | 560 | 431 | 0.892857 | singlet | 0.000002 | -2.955118 | 0.573097 | Negative | 0 | Negative | 0 | 0 | 0 | 329 Vascular Immune | 34 Immune | 0.29 | 337 DC NN | 0.39 | 1197 DC NN_1 | 1 | 5318 DC NN_1 | 0.73 | Non-Neuron | NN-IMN-GC | NA | NA | NA | false |
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82,
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86,
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94,
95,
97,
98,
99,
100,
10... | [
10,
1,
2,
16,
5,
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8,
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4,
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2,
42,
1,
9,
14,
3,
10,
6,
5,
4,
4,
2,
1,
2,
9,
6,
1,... | AAACCAATCAATTCGTACGTCAACCA-1:SCP038 | WB8588_1 | WB8588_1_1 | mouse4 | F | 4L | 51,222 | 9,114 | 0.312366 | doublet | 0.999982 | 2.003015 | 1.015242 | En1 | 1 | En1_3 | 17 | 14 | 1 | 008 L2-3 IT ENT PPP RSP Glut | 01 IT-ET Glut | 0.5 | 008 L2/3 IT ENT Glut | 1 | 0036 L2/3 IT ENT Glut_4 | 1 | 0130 L2/3 IT ENT Glut_4 | 0.59 | Glut | Pallium-Glut | Cortex | RHP | NA | false |
[
0,
4,
6,
9,
12,
14,
23,
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29,
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48,
63,
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69,
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72,
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81,
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83,
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86,
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88,
95,
97,
98,
105,
107,
112,
113,
114,
115,
118,
120,
121,
123,
127,
128,
130,
1... | [
1,
3,
4,
2,
4,
5,
2,
2,
5,
1,
2,
1,
1,
1,
2,
2,
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1,
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1,
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1,
1,
2,
3,
1,
1,
... | AAACCAATCAGCGGACACACCTGCTG-1:SCP038 | WB8588_1 | WB8588_1_1 | mouse6 | F | 6L | 15,945 | 5,974 | 0.219505 | singlet | 0.000433 | 0.747898 | 0.927149 | Negative | 0 | Negative | 4 | 2 | 2 | 066 CNU-HYa HY GABA | 12 HY GABA | 1 | 093 RT-ZI Gnb3 Gaba | 1 | 0432 RT-ZI Gnb3 Gaba_2 | 0.51 | 1586 RT-ZI Gnb3 Gaba_2 | 0.51 | GABA | HY-EA-Glut-GABA | Interbrain | TH | 32 | true |
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95,
97,
98,
99,
100,
103,
105,
106,
107,
112,
113,
115,
118,
119,
120,
121... | [
4,
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3,
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1,
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1,
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1,
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9,
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1,
4,
25,
3,
2,
4,
12,
2,
1,
2,
1,
3,
3,
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1,
2,
2,
2,
3,
1,
2,
1,
3,
2,
8,
7,
7,
1,
5,
1,
3,
1,
1,
1,
4,
1,
2,
1,
4,
4,
2,
... | AAACCAATCAGCGGACCTCAGAGGTA-1:SCP038 | WB8588_1 | WB8588_1_1 | mouse7 | F | 7L | 22,700 | 6,800 | 0.026432 | singlet | 0.000002 | 1.133991 | 0.960022 | Flvcr1 | 1 | Flvcr1_4 | 15 | 13 | 1 | 005 L4-5 IT CTX Glut | 01 IT-ET Glut | 1 | 006 L4/5 IT CTX Glut | 1 | 0023 L4/5 IT CTX Glut_1 | 1 | 0074 L4/5 IT CTX Glut_1 | 0.89 | Glut | Pallium-Glut | Cortex | Isocortex | 21 | true |
[0,3,4,6,9,12,13,17,18,19,23,24,25,31,32,33,38,39,41,42,43,45,47,48,60,63,68,69,70,71,73,76,77,78,81(...TRUNCATED) | [3,2,4,2,2,5,3,1,2,2,7,6,5,2,3,2,2,1,1,10,3,3,1,2,2,3,1,12,6,2,3,5,1,4,1,1,3,2,8,1,2,4,3,4,9,10,1,1,(...TRUNCATED) | AAACCAATCAGGATACCTCCCAACAC-1:SCP038 | WB8588_1 | WB8588_1_1 | mouse6 | F | 6L | 27,635 | 7,453 | 0.018093 | singlet | 0.000003 | 1.39458 | 1.023894 | Map2k1 | 1 | Map2k1_1 | 24 | 23 | 1 | 151 TH Prkcd Grin2c Glut | 18 TH Glut | 1 | 151 TH Prkcd Grin2c Glut | 1 | 0666 TH Prkcd Grin2c Glut_13 | 1 | 2687 TH Prkcd Grin2c Glut_13 | 1 | Glut | TH-EPI-Glut | Interbrain | TH | 26 | true |
[0,4,5,6,9,12,13,14,16,19,23,24,31,33,38,39,42,43,45,47,48,60,63,68,69,70,71,72,73,75,76,77,78,81,86(...TRUNCATED) | [3,1,1,8,5,3,1,1,4,2,1,7,1,1,2,4,3,1,1,3,2,3,4,4,8,1,4,1,3,1,5,5,2,3,1,14,1,1,4,2,1,2,6,3,2,2,3,1,10(...TRUNCATED) | AAACCAATCAGGATACTGCAGTTGGT-1:SCP038 | WB8588_1 | WB8588_1_1 | mouse2 | M | 2L | 20,831 | 6,534 | 0.009601 | singlet | 0.000002 | 1.076538 | 0.988493 | Dtnbp1 | 1 | Dtnbp1_4 | 38 | 38 | 0 | 005 L4-5 IT CTX Glut | 01 IT-ET Glut | 1 | 006 L4/5 IT CTX Glut | 1 | 0026 L4/5 IT CTX Glut_4 | 1 | 0090 L4/5 IT CTX Glut_4 | 0.82 | Glut | Pallium-Glut | Cortex | Isocortex | 21 | true |
[0,3,4,5,6,9,12,13,14,16,18,23,24,25,38,41,42,44,45,47,48,60,69,71,73,75,76,78,82,83,85,87,89,94,95,(...TRUNCATED) | [6,2,2,1,5,4,7,1,1,4,2,2,4,7,4,1,2,3,2,3,2,1,13,1,2,1,4,2,1,1,4,14,1,1,3,2,3,3,1,1,1,2,1,8,4,4,3,2,1(...TRUNCATED) | AAACCAATCCAATGAAAGCGAACCCT-1:SCP038 | WB8588_1 | WB8588_1_1 | mouse5 | F | 5L | 21,846 | 6,689 | 0.004577 | singlet | 0.000012 | 1.141899 | 1.006278 | Negative | 0 | Negative | 1 | 1 | 0 | 009 L2-3 IT PIR AON ENT Glut | 01 IT-ET Glut | 1 | 009 L2/3 IT PIR-ENTl Glut | 1 | 0041 L2/3 IT PIR-ENTl Glut_3 | 1 | 0154 L2/3 IT PIR-ENTl Glut_3 | 1 | Glut | Pallium-Glut | Cortex | OLF | 13 | true |
[5,6,12,15,24,32,43,44,45,48,60,63,71,76,77,78,87,89,95,97,99,103,107,108,109,112,113,118,122,128,13(...TRUNCATED) | [4,1,3,1,3,2,1,3,1,1,6,1,2,1,3,1,8,1,1,4,1,2,1,2,1,1,3,1,4,1,2,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,6,3,2(...TRUNCATED) | AAACCAATCCAATGAATGTTGGTAAG-1:SCP038 | WB8588_1 | WB8588_1_1 | mouse6 | F | 6L | 8,618 | 4,113 | 0 | singlet | 0.000001 | 0.932996 | 1.727539 | Negative | 0 | Negative | 0 | 0 | 0 | 316 Glia | 30 Astro-Epen | 1 | 319 Astro-TE NN | 0.99 | 1163 Astro-TE NN_3 | 1 | 5225 Astro-TE NN_3 | 1 | Non-Neuron | NN-IMN-GC | Cortex | Isocortex | 57 | false |
[0,3,4,6,9,12,13,18,23,24,25,33,41,42,43,44,45,46,48,59,63,68,69,70,72,73,75,76,78,81,85,87,88,89,90(...TRUNCATED) | [1,1,2,6,2,1,1,1,3,4,4,1,1,1,7,1,3,1,2,1,1,1,7,3,1,2,3,3,1,1,2,3,1,1,1,3,1,4,1,3,1,1,1,6,5,1,3,2,1,1(...TRUNCATED) | AAACCAATCCACGCATACCTGGTTGT-1:SCP038 | WB8588_1 | WB8588_1_1 | mouse4 | F | 4L | 13,350 | 5,945 | 0.029963 | singlet | 0.000001 | 0.342071 | 0.698951 | Atp2a2|Nos1ap|Rp9 | 3 | Atp2a2_1|Nos1ap_3|Rp9_4 | 37 | 14 | 13 | 046 CTX-CGE GABA | 06 CTX-CGE GABA | 1 | 049 Lamp5 Gaba | 1 | 0199 Lamp5 Gaba_1 | 1 | 0710 Lamp5 Gaba_1 | 0.69 | GABA | Subpallium-GABA | Cortex | Isocortex | 12 | true |
[0,3,4,6,9,12,13,14,16,18,23,24,25,31,38,41,42,43,44,45,46,48,60,63,69,70,71,76,77,78,81,83,85,86,87(...TRUNCATED) | [2,1,2,3,4,3,1,2,1,1,1,1,4,2,1,1,8,3,6,1,1,2,1,3,6,2,2,8,7,1,6,1,1,2,9,3,1,5,2,2,4,3,1,2,2,4,5,3,2,1(...TRUNCATED) | AAACCAATCCATGACCACAAGCTTGT-1:SCP038 | WB8588_1 | WB8588_1_1 | mouse4 | F | 4L | 23,680 | 7,139 | 0.219595 | singlet | 0.000042 | 2.561425 | 2.345191 | Negative | 0 | Negative | 0 | 0 | 0 | 053 Sst Gaba | 07 CTX-MGE GABA | 1 | 053 Sst Gaba | 1 | 0215 Sst Gaba_2 | 1 | 0765 Sst Gaba_2 | 0.83 | GABA | Subpallium-GABA | Cortex | CTXsp | 18 | true |
End of preview. Expand in Data Studio
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)
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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