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Error code: DatasetGenerationCastError Exception: DatasetGenerationCastError Message: An error occurred while generating the dataset All the data files must have the same columns, but at some point there are 3 new columns ({'n_cells', 'target_gene', 'median_umi_per_cell'}) and 1 missing columns ({'SAMD11'}). This happened while the csv dataset builder was generating data using hf://datasets/cyrilzakka/arc-institute-virtual-cell-dataset/pert_counts_Validation.csv (at revision 952b34cd3b0698a9846e9d444938e0805e76df56) Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations) Traceback: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1871, in _prepare_split_single writer.write_table(table) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 643, in write_table pa_table = table_cast(pa_table, self._schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2293, in table_cast return cast_table_to_schema(table, schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2241, in cast_table_to_schema raise CastError( datasets.table.CastError: Couldn't cast target_gene: string n_cells: int64 median_umi_per_cell: double -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 632 to {'SAMD11': Value(dtype='string', id=None)} because column names don't match During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1431, in compute_config_parquet_and_info_response parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet( File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 992, in stream_convert_to_parquet builder._prepare_split( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1742, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1873, in _prepare_split_single raise DatasetGenerationCastError.from_cast_error( datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset All the data files must have the same columns, but at some point there are 3 new columns ({'n_cells', 'target_gene', 'median_umi_per_cell'}) and 1 missing columns ({'SAMD11'}). This happened while the csv dataset builder was generating data using hf://datasets/cyrilzakka/arc-institute-virtual-cell-dataset/pert_counts_Validation.csv (at revision 952b34cd3b0698a9846e9d444938e0805e76df56) Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
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SAMD11
string |
---|
NOC2L
|
KLHL17
|
PLEKHN1
|
PERM1
|
HES4
|
ISG15
|
AGRN
|
RNF223
|
C1orf159
|
TTLL10
|
TNFRSF18
|
TNFRSF4
|
SDF4
|
B3GALT6
|
C1QTNF12
|
UBE2J2
|
SCNN1D
|
ACAP3
|
PUSL1
|
INTS11
|
CPTP
|
TAS1R3
|
DVL1
|
MXRA8
|
AURKAIP1
|
CCNL2
|
ANKRD65
|
TMEM88B
|
VWA1
|
ATAD3C
|
ATAD3B
|
ATAD3A
|
TMEM240
|
SSU72
|
FNDC10
|
MIB2
|
MMP23B
|
CDK11B
|
CDK11A
|
NADK
|
GNB1
|
CALML6
|
TMEM52
|
CFAP74
|
GABRD
|
PRKCZ
|
FAAP20
|
SKI
|
RER1
|
PEX10
|
PLCH2
|
PANK4
|
HES5
|
TNFRSF14
|
PRXL2B
|
MMEL1
|
ACTRT2
|
PRDM16
|
ARHGEF16
|
MEGF6
|
TPRG1L
|
WRAP73
|
TP73
|
CCDC27
|
SMIM1
|
LRRC47
|
CEP104
|
DFFB
|
C1orf174
|
AJAP1
|
NPHP4
|
KCNAB2
|
CHD5
|
RNF207
|
ICMT
|
HES3
|
GPR153
|
ACOT7
|
HES2
|
ESPN
|
TNFRSF25
|
PLEKHG5
|
NOL9
|
TAS1R1
|
ZBTB48
|
KLHL21
|
PHF13
|
THAP3
|
DNAJC11
|
CAMTA1
|
VAMP3
|
PER3
|
UTS2
|
TNFRSF9
|
ERRFI1
|
SLC45A1
|
RERE
|
ENO1
|
CA6
|
SLC2A7
|
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 |
... | ... | ... |
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