Dataset Preview
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed because of a cast error
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)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

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
End of preview.

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
... ... ...
Downloads last month
171