Create README.md

README.md

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+---
+pretty_name: Virtual Cell Challenge
+size_categories:
+- 100K<n<1M
+---
+# ARC Institute Virtual Cell Challenge
+Please check out the [official website](https://virtualcellchallenge.org/app/rules) 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 |
+| ... | ... | ... |