gpsea.analysis package

class gpsea.analysis.AnalysisResult(gt_predicate: GenotypePolyPredicate, statistic: Statistic)[source]

Bases: object

AnalysisResult includes the common parts of results of all analyses.

property gt_predicate: GenotypePolyPredicate: Get the genotype predicate used in the survival analysis that produced this result.

property statistic: Statistic: Get the statistic which computed the (nominal) p values for this result.

class gpsea.analysis.MonoPhenotypeAnalysisResult(gt_predicate: GenotypePolyPredicate, phenotype: Partitioning, statistic: Statistic, data: DataFrame, pval: float)[source]

Bases: AnalysisResult

MonoPhenotypeAnalysisResult reports the outcome of an analysis that tested a single genotype-phenotype association.

GT_COL = 'genotype': Name of column for storing genotype data.

PH_COL = 'phenotype': Name of column for storing phenotype data.

DATA_COLUMNS = ('genotype', 'phenotype'): The required columns of the data data frame.

property phenotype: Partitioning: Get the Partitioning that produced the phenotype.

property data: DataFrame

Get the data frame with genotype and phenotype values for each tested individual.

The index of the data frame contains the identifiers of the tested individuals, and the values are stored in genotype and phenotype columns.

The genotype column includes the genotype category ID (cat_id) or None if the individual could not be assigned into a genotype group. The phenotype contains the phenotype values, and the data type depends on the analysis.

Here are some common phenotype data types:

a phenotype score computed in PhenotypeScoreAnalysis is a float
survival computed in SurvivalAnalysis is of type Survival

complete_records() → DataFrame[source]: Get the data rows where both genotype and phenotype columns are available (i.e. not None or NaN).

property pval: float: Get the p value of the test.

class gpsea.analysis.MultiPhenotypeAnalysisResult(gt_predicate: GenotypePolyPredicate, pheno_predicates: Iterable[PhenotypePolyPredicate[P]], statistic: Statistic, n_usable: Sequence[int], all_counts: Sequence[DataFrame], pvals: Sequence[float], corrected_pvals: Sequence[float] | None, mtc_correction: str | None)[source]

Bases: Generic[P], AnalysisResult

MultiPhenotypeAnalysisResult reports the outcome of an analysis that tested the association of genotype with two or more phenotypes.

property pheno_predicates: Sequence[PhenotypePolyPredicate[P]]: Get the phenotype predicates used in the analysis.

property phenotypes: Sequence[P]: Get the phenotypes that were tested for association with genotype in the analysis.

property n_usable: Sequence[int]: Get a sequence of numbers of patients where the phenotype was assessable, and are, thus, usable for genotype-phenotype correlation analysis.

property all_counts: Sequence[DataFrame]

Get a DataFrame sequence where each DataFrame includes the counts of patients in genotype and phenotype groups.

An example for a genotype predicate that bins into two categories (Yes and No) based on presence of a missense variant in transcript NM_123456.7, and phenotype predicate that checks presence/absence of HP:0001166 (a phenotype term):

           Has MISSENSE_VARIANT in NM_123456.7
           No       Yes
Present
Yes        1        13
No         7        5

The rows correspond to the phenotype categories, and the columns represent the genotype categories.

property pvals: Sequence[float]: Get a sequence of nominal p values for each tested phenotype. The sequence includes a NaN value for each phenotype that was not tested.

property corrected_pvals: Sequence[float] | None: Get a sequence with p values for each tested phenotype after multiple testing correction or None if the correction was not applied. The sequence includes a NaN value for each phenotype that was not tested.

n_significant_for_alpha(alpha: float = 0.05) → int | None[source]

Get the count of the corrected p values with the value being less than or equal to alpha.

Parameters:: alpha – a float with significance level.

significant_phenotype_indices(alpha: float = 0.05, pval_kind: Literal['corrected', 'nominal'] = 'corrected') → Sequence[int] | None[source]: Get the indices of phenotypes that attain significance for provided alpha.

property total_tests: int: Get total count of genotype-phenotype associations that were tested in this analysis.

property mtc_correction: str | None: Get name/code of the used multiple testing correction (e.g. fdr_bh for Benjamini-Hochberg) or None if no correction was applied.

gpsea.analysis package

Subpackages