torchcell.datasets.scerevisiae.sameith2015.SmMicroarraySameith2015Dataset

class SmMicroarraySameith2015Dataset(root: str = 'data/torchcell/sm_microarray_sameith2015', io_workers: int = 0, process_workers: int = 0, batch_size: int = 10, transform: Callable[[...], Any] | None = None, pre_transform: Callable[[...], Any] | None = None, **kwargs: Any)

Bases: ExperimentDataset

Single mutant GSTF expression profiling from Sameith et al. 2015.

Microarray gene expression data for 82 single mutant yeast strains with deletions in general stress transcription factors (GSTFs). All single mutants are from the yeast deletion library (BY4742, MATalpha, KanMX marker). Title tokens resolve to ORFs only through the release’s Additional file 1 (release_symbol_map), and perturbed_gene_name is the workbook’s symbol.

Data source: GEO accession GSE42536 Paper: Sameith et al. (2015) BMC Biology DOI: 10.1186/s12915-015-0222-5

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]

Experiment model produced by this dataset.

property reference_class: type[ExperimentReference]

Experiment-reference model produced by this dataset.

property raw_file_names: list[str]

Raw GEO SOFT family file required before processing.

download() → None

Download single mutant microarray expression data from GEO.

process() → None

Parse GEO expression data into single-mutant experiments and write LMDB.

preprocess_raw(df: DataFrame, preprocess: dict[str, Any] | None = None) → DataFrame

Preprocess raw data - for Sameith this is handled in process().

create_experiment() → None

Required by base class but not used.

static create_single_mutant_expression_experiment(dataset_name: str, sample_info: dict[str, Any], mutant_data: SortedDict, refpool_data: SortedDict, log2_ratio_data: SortedDict, log2_ratio_se: SortedDict | None = None, log2_ratio_variance: SortedDict | None = None, n_replicates: SortedDict | None = None, *, refpool_n_replicates: SortedDict, gene_symbols: dict[str, str]) → tuple[Any, Any, Any]

Create experiment for single deletion mutant.

The reference expression is the mean refpool signal over the record’s arrays, so the reference n_replicates of a gene is the number of arrays whose refpool value entered that mean (1 or 2 on GEO for a single mutant), counted per gene: a gene missing from one array’s refpool channel counts only the arrays that carry it (#630; the WT RNA batch and the additional WT cultures are not the stored value, see the module comment at the reference pool quotes).

Parameters:

• dataset_name – Name of the dataset

• sample_info – Sample metadata dictionary

• mutant_data – Expression values for deletion mutant

• refpool_data – Mean refpool expression over the record’s arrays

• log2_ratio_data – log2(mutant/refpool) ratios

• log2_ratio_se – Standard error for log2 ratios (optional)

• log2_ratio_variance – Variance for log2 ratios (optional)

• n_replicates – Number of replicates per gene (optional)

• refpool_n_replicates – Arrays in each gene’s refpool mean, same keys as refpool_data

• gene_symbols – The workbook’s ORF -> gene symbol map (release_gene_symbols); perturbed_gene_name is that symbol, or the ORF when the workbook gives the ORF no symbol