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--- |
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pretty_name: Virtual Cell Challenge |
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size_categories: |
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- 100K<n<1M |
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--- |
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# ARC Institute Virtual Cell Challenge |
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Please check out the [official website](https://virtualcellchallenge.org/app/rules) for the challenge rules and deadlines. |
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### About |
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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. |
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- Training set consisting of single-cell profiles for 150 gene perturbations (~150,000 cells) |
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- Validation set of 50 gene perturbations, for which entrants’ predicted transcriptomic results will be used to create a live ranking leaderboard during the challenge |
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#### Training data [15GB] |
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Gene Expression File in AnnData H5AD format. |
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**Obs** |
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| cell barcode-batch index | target_gene | guide_id | batch | |
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|--------------------------|-------------|----------|-------| |
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| AAACAAGCAACCTTGTACTTTAGG-Flex_1_01 | CHMP3 | CHMP3_P1P2_A\|CHMP3_P1P2_B | Flex_1_01 | |
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| TTTGGACGTGGTGCAGATTCGGTT-Flex_3_16 | non-targeting | non-targeting_00035\|non-targeting_03439 | Flex_3_16 | |
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**Var —** index of gene names to predict |
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adfile.var.index |
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``` |
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Index(['SAMD11', 'NOC2L', 'KLHL17', 'PLEKHN1', 'PERM1', 'HES4', 'ISG15', 'AGRN', 'RNF223', 'C1orf159', ... 'MT-ND5', 'MT-ND6', 'MT-CYB'], dtype='object', length=18080) |
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``` |
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**Control Cells** |
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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. |
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#### Validation data [1kb] |
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| Field name | Description | |
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|------------|-------------| |
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| target_gene | Gene symbol targeted for perturbation | |
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| n_cells | Recommended number of cells to predict for each perturbation to maximize model performance | |
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| median_umi_per_cell | The median number of Unique Molecular Identifiers per cell for each perturbation | |
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| target_gene | n_cells | median_umi_per_cell | |
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|-------------|---------|---------------------| |
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| SH3BP4 | 2925 | 54551.0 | |
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| ZNF581 | 2502 | 53803.5 | |
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| ANXA6 | 2496 | 55175.0 | |
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| PACSIN3 | 2101 | 54088.0 | |
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| MGST1 | 2096 | 54217.5 | |
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| IGF1R | 2056 | 53993.0 | |
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| ITGAV | 2034 | 55356.0 | |
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| SLIRP | 2000 | 54438.5 | |
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| CTSV | 1989 | 53173.0 | |
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| MTFR1 | 1787 | 53795.0 | |
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| ... | ... | ... | |
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