pretty_name: Virtual Cell Challenge
size_categories:
- 100K<n<1M
ARC Institute Virtual Cell Challenge
Please check out the official website for the challenge rules and deadlines.
About
For this challenge, single-cell functional genomics was used to generate approximately 300,000 single-cell RNA-seq profiles by silencing 300 carefully selected genes using CRISPR interference (CRISPRi). 10x Genomics GEM-X Flex and Illumina sequencing were used to obtain single-cell gene expression profiles. The data are split into three groups for the Virtual Cell Challenge, to allow for training, validation of initial results, and developing a final entry for the competition.
- Training set consisting of single-cell profiles for 150 gene perturbations (~150,000 cells)
- Validation set of 50 gene perturbations, for which entrants’ predicted transcriptomic results will be used to create a live ranking leaderboard during the challenge
Training data [15GB]
Gene Expression File in AnnData H5AD format.
Obs
| cell barcode-batch index | target_gene | guide_id | batch |
|---|---|---|---|
| AAACAAGCAACCTTGTACTTTAGG-Flex_1_01 | CHMP3 | CHMP3_P1P2_A|CHMP3_P1P2_B | Flex_1_01 |
| TTTGGACGTGGTGCAGATTCGGTT-Flex_3_16 | non-targeting | non-targeting_00035|non-targeting_03439 | Flex_3_16 |
Var — index of gene names to predict adfile.var.index
Index(['SAMD11', 'NOC2L', 'KLHL17', 'PLEKHN1', 'PERM1', 'HES4', 'ISG15', 'AGRN', 'RNF223', 'C1orf159', ... 'MT-ND5', 'MT-ND6', 'MT-CYB'], dtype='object', length=18080)
Control Cells There are 38,176 unperturbed control cells in the training data denoted with a target_gene value of ‘non-targeting’. Competitors can optionally predict expression values for the control set during submission or copy expression values over from the training set.
Validation data [1kb]
| Field name | Description |
|---|---|
| target_gene | Gene symbol targeted for perturbation |
| n_cells | Recommended number of cells to predict for each perturbation to maximize model performance |
| median_umi_per_cell | The median number of Unique Molecular Identifiers per cell for each perturbation |
| target_gene | n_cells | median_umi_per_cell |
|---|---|---|
| SH3BP4 | 2925 | 54551.0 |
| ZNF581 | 2502 | 53803.5 |
| ANXA6 | 2496 | 55175.0 |
| PACSIN3 | 2101 | 54088.0 |
| MGST1 | 2096 | 54217.5 |
| IGF1R | 2056 | 53993.0 |
| ITGAV | 2034 | 55356.0 |
| SLIRP | 2000 | 54438.5 |
| CTSV | 1989 | 53173.0 |
| MTFR1 | 1787 | 53795.0 |
| ... | ... | ... |