torchcell.datasets.scerevisiae.kuzmin2020.DmfKuzmin2020Dataset

class DmfKuzmin2020Dataset(root: str = 'data/torchcell/dmf_kuzmin2020', io_workers: int = 0, transform: Callable[[...], Any] | None = None, pre_transform: Callable[[...], Any] | None = None, **kwargs: Any)

Bases: ExperimentDataset

Double-mutant fitness experiments from Kuzmin et al. 2020.

Two record kinds (record_kind): the digenic query x array crosses, and the double-mutant QUERY strains of the trigenic screens, whose own fitness and SD Table S5 reports, one record per distinct strain.

organism: ClassVar[Organism] = 'scerevisiae'

each loader declares its own, the build’s organism switch reads it off the class (dataset_organism), and a loader that forgot it is refused by name instead of inheriting an organism it may not have.

Type:

The host organism every record describes. Annotated here WITHOUT a value

property experiment_class: type[Experiment]

Return the experiment schema class for this dataset.

property reference_class: type[ExperimentReference]

Return the experiment-reference schema class for this dataset.

property raw_file_names: list[str]

Return the raw supplementary Excel file names.

property processed_file_names: str

Return the processed output name (the LMDB directory).

download() → None

Download and extract the supplementary zip, then remove the archive.

process() → None

Read, preprocess, and serialize experiments into the LMDB store.

preprocess_raw(df_s1: DataFrame, df_s3: DataFrame, df_s5: DataFrame) → DataFrame

Merge the supplementary tables and assemble double-mutant fitness rows.

Emits both record kinds, digenic array crosses first (unchanged, so their LMDB keys and record content are preserved) then one record per distinct double-mutant query strain of the trigenic screens.

static create_experiment(dataset_name: str, row: Series) → tuple[FitnessExperiment, FitnessExperimentReference, Publication]

Build the experiment, reference, and publication objects from one row.