API reference

This page provides an auto-generated summary of sgkits’s API.

IO/imports and exports

See Reading and writing genetic data

BGEN

bgen_to_zarr(input, output[, region, ...])	Convert a BGEN file to a Zarr on-disk store.
read_bgen(path[, metafile_path, ...])	Read BGEN dataset.
rechunk_bgen(ds, output, *[, chunk_length, ...])	Rechunk BGEN dataset as Zarr.

PLINK

plink_to_zarr(*[, path, bed_path, bim_path, ...])	Convert a PLINK file to a Zarr on-disk store.
read_plink(*[, path, bed_path, bim_path, ...])	Read PLINK dataset.
write_plink(ds, *[, path, bed_path, ...])	Convert a dataset to a PLINK file.
zarr_to_plink(input, *[, path, bed_path, ...])	Convert a Zarr on-disk store to a PLINK file.

VCF (reading)

Deprecated since version 0.9.0: Functions for reading VCF are deprecated, please use the bio2zarr package.

read_vcf(input, *[, target_part_size, ...])	Read VCF dataset.
vcf_to_zarr(input, output, *[, ...])	Convert VCF files to a single Zarr on-disk store.

For more low-level control:

partition_into_regions(vcf_path, *[, ...])	Calculate genomic region strings to partition a compressed VCF or BCF file into roughly equal parts.
vcf_to_zarrs(input, output, regions[, ...])	Convert VCF files to multiple Zarr on-disk stores, one per region.
concat_zarrs(urls, output, *[, ...])	Concatenate multiple Zarr stores into a single Zarr store.
zarr_array_sizes(input, *[, regions, ...])	Make a pass through a VCF/BCF file to determine sizes for storage in Zarr.

For converting from scikit-allel’s VCF Zarr representation to sgkit’s Zarr representation:

read_scikit_allel_vcfzarr(path[, field_defs])

Read a VCF Zarr file created using scikit-allel.

VCF (writing)

write_vcf(input, output, *[, vcf_header])	Convert a dataset to a VCF file.
zarr_to_vcf(input, output, *[, vcf_header])	Convert a Zarr file to a VCF file.

Dataset

load_dataset(store[, storage_options])	Load a dataset from Zarr storage.
save_dataset(ds, store[, storage_options, ...])	Save a dataset to Zarr storage.

Methods

Basic genetics statistics

call_allele_frequencies(ds, *[, ...])	Compute per sample allele frequencies from genotype calls.
cohort_allele_frequencies(ds, *[, ...])	Compute allele frequencies for each cohort.
count_call_alleles(ds, *[, call_genotype, merge])	Compute per sample allele counts from genotype calls.
count_cohort_alleles(ds, *[, ...])	Compute per cohort allele counts from per-sample allele counts, or genotype calls.
count_variant_alleles(ds, *[, ...])	Compute allele count from per-sample allele counts, or genotype calls.
count_variant_genotypes(ds, *[, ...])	Count the number of calls of each possible genotype, at each variant.
variant_stats(ds, *[, call_genotype, ...])	Compute quality control variant statistics from genotype calls.
sample_stats(ds, *[, call_genotype, merge])	Compute quality control sample statistics from genotype calls.

Population genetic statistics

divergence(ds, *[, cohort_allele_count, merge])	Compute divergence between pairs of cohorts.
diversity(ds, *[, cohort_allele_count, merge])	Compute diversity from cohort allele counts.
Fst(ds, *[, estimator, stat_divergence, merge])	Compute Fst between pairs of cohorts.
Garud_H(ds, *[, call_genotype, ...])	Compute the H1, H12, H123 and H2/H1 statistics for detecting signatures of soft sweeps, as defined in Garud et al. (2015).
individual_heterozygosity(ds, *[, ...])	Compute per call individual heterozygosity.
observed_heterozygosity(ds, *[, ...])	Compute per cohort observed heterozygosity.
pbs(ds, *[, stat_Fst, cohorts, merge])	Compute the population branching statistic (PBS) between cohort triples.
Tajimas_D(ds, *[, variant_allele_count, ...])	Compute Tajimas' D for a genotype call dataset.

Relatedness

Weir_Goudet_beta(ds, *[, ...])	Estimate pairwise beta between all pairs of samples as described in Weir and Goudet 2017 [1].
genomic_relationship(ds, *[, call_dosage, ...])	Compute a genomic relationship matrix (AKA the GRM or G-matrix).
pc_relate(ds, *[, maf, call_genotype, ...])	Compute PC-Relate as described in Conomos, et al. 2016 [1].
pedigree_contribution(ds, *[, method, ...])	Calculate the expected genomic contribution of each sample to each other sample based on pedigree structure.
pedigree_inbreeding(ds, *[, method, parent, ...])	Estimate expected inbreeding coefficients from pedigree structure.
pedigree_inverse_kinship(ds, *[, method, ...])	Calculate the inverse of the kinship matrix from pedigree structure.
pedigree_kinship(ds, *[, method, parent, ...])	Estimate expected pairwise kinship coefficients from pedigree structure.
hybrid_inverse_relationship(ds, *, ...[, ...])	Compute the inverse of a hybrid relationship matrix (AKA the HRM or H-matrix) combining pedigree and genomic information.
hybrid_relationship(ds, *, genomic_relationship)	Compute a hybrid relationship matrix (AKA the HRM or H-matrix) combining pedigree and genomic information.
identity_by_state(ds, *[, call_genotype, ...])	Compute identity by state (IBS) probabilities between all pairs of samples.

Linkage Disequilibrium

ld_matrix(ds, *[, dosage, threshold, ...])	Compute a sparse linkage disequilibrium (LD) matrix.
ld_prune(ds, *[, dosage, threshold, ...])	Prune variants in linkage disequilibrium (LD).

Genetic Association and Regression

gwas_linear_regression(ds, *, dosage, ...[, ...])	Run linear regression to identify continuous trait associations with genetic variants.
regenie(ds, *, dosage, covariates, traits[, ...])	Regenie trait transformation.
hardy_weinberg_test(ds, *[, genotype_count, ...])	Exact test for HWE as described in Wigginton et al. 2005 [1].
genee(ds, ld, *[, reg_covar])	Compute gene-ε as described in Cheng, et al. 2020 [1].

Miscellaneous

maximal_independent_set(df)	Compute a maximal independent set of variants.
pairwise_distance(x[, metric, split_every, ...])	Calculates the pairwise distance between all pairs of row vectors in the given two dimensional array x.

Utilities

convert_call_to_index(ds, *[, ...])	Convert each call genotype to a single integer value.
convert_probability_to_call(ds[, ...])	Convert genotype probabilities to hard calls.
display_genotypes(ds[, max_variants, ...])	Display genotype calls.
display_pedigree(ds[, parent, graph_attrs, ...])	Display a pedigree dataset as a directed acyclic graph.
filter_partial_calls(ds, *[, call_genotype, ...])	Replace partial genotype calls with missing values.
infer_call_ploidy(ds, *[, call_genotype, ...])	Infer the ploidy of each call genotype based on the number of fill (non-allele) values in each call genotype.
infer_sample_ploidy(ds, *[, call_genotype, ...])	Infer the ploidy of each sample across all variants based on the number of fill (non-allele) values in call genotypes.
infer_variant_ploidy(ds, *[, call_genotype, ...])	Infer the ploidy at each variant across all samples based on the number of fill (non-allele) values in call genotypes.
invert_relationship_matrix(ds, *, relationship)	Calculate the inverse relationship (sub-) matrix.
parent_indices(ds, *[, sample_id, ...])	Calculate the integer indices for the parents of each sample within the samples dimension.
pedigree_sel(ds, *, samples[, ...])	Return a new dataset with each array indexed along the 'samples' dimension using a subset of samples and the optional inclusion of their relatives.
simulate_genedrop(ds, *[, method, ...])	Generate progeny genotypes via a gene-drop simulation (MacCluer et al. 1986 [1]).
simulate_genotype_call_dataset(n_variant, ...)	Simulate genotype calls and variant/sample data.
window_by_genome(ds, *[, merge])	Add a window spanning the whole genome to a dataset.
window_by_interval(ds, *[, variant_contig, ...])	Add window information to a dataset, using arbitrary intervals.
window_by_position(ds, *, size[, step, ...])	Add window information to a dataset, measured by distance along the genome.
window_by_variant(ds, *, size[, step, ...])	Add window information to a dataset, measured by number of variants.

Variables

By convention, variable names are singular in sgkit. For example, genotype_count, not genotype_counts.

variables.call_allele_count_spec	Allele counts.
variables.call_allele_frequency_spec	Allele frequencies.
variables.call_dosage_spec	Dosages, encoded as floats, with NaN indicating a missing value.
variables.call_dosage_mask_spec	A flag for each call indicating which values are missing.
variables.call_genotype_complete_spec	Call genotypes in which partial genotype calls are replaced with completely missing genotype calls.
variables.call_genotype_complete_mask_spec	A flag for each call indicating which values are missing.
variables.call_genotype_spec	Call genotype.
variables.call_genotype_mask_spec	A flag for each call indicating which values are missing.
variables.call_genotype_fill_spec	A flag for each allele position within mixed ploidy call genotypes indicating fill (non-allele) values of lower ploidy calls.
variables.call_genotype_phased_spec	A flag for each call indicating if it is phased or not.
variables.call_genotype_probability_spec	Genotype probabilities.
variables.call_genotype_probability_mask_spec	A flag for each call indicating which values are missing.
variables.call_genotype_index_spec	Single integer values encoding each call genotype.
variables.call_genotype_index_mask_spec	A flag for each call genotype index indicating missing indices (-1) which correspond to call genotypes with missing alleles.
variables.call_heterozygosity_spec	Observed heterozygosity of each call genotype.
variables.call_ploidy_spec	Call genotype ploidy.
variables.cohort_allele_count_spec	Cohort allele counts.
variables.cohort_allele_frequency_spec	Cohort Allele frequencies.
variables.covariates_spec	Covariate variable names.
variables.interval_contig_name_spec	Interval contig name.
variables.interval_start_spec	Interval start position (inclusive).
variables.interval_stop_spec	Interval stop position (exclusive).
variables.ld_prune_index_to_drop_spec	Variant indexes to drop for LD prune.
variables.regenie_base_prediction_spec	REGENIE's base prediction (blocks, alphas, samples, outcomes).
variables.regenie_loco_prediction_spec	REGENIE's regenie_loco_prediction (contigs, samples, outcomes).
variables.regenie_meta_prediction_spec	REGENIE's regenie_meta_prediction (samples, outcomes).
variables.parent_spec	Indices of parent samples with negative values indicating unknown parents.
variables.parent_id_spec	Unique identifiers of parent samples matching those in sgkit.variables.sample_id_spec.
variables.pc_relate_phi_spec	PC Relate kinship coefficient matrix.
variables.sample_call_rate_spec	The fraction of variants with called genotypes.
variables.sample_cohort_spec	The index of the cohort that each sample belongs to.
variables.sample_id_spec	The unique identifier of the sample.
variables.sample_n_called_spec	The number of variants with called genotypes.
variables.sample_n_het_spec	The number of variants with heterozygous calls.
variables.sample_n_hom_alt_spec	The number of variants with homozygous alternate calls.
variables.sample_n_hom_ref_spec	The number of variants with homozygous reference calls.
variables.sample_n_non_ref_spec	The number of variants that are not homozygous reference calls.
variables.sample_pca_component_spec	Principal axes defined as eigenvectors for sample covariance matrix.
variables.sample_pca_explained_variance_spec	Variance explained by each principal component.
variables.sample_pca_explained_variance_ratio_spec	Ratio of variance explained to total variance for each principal component, i.e. dask_ml.decomposition.TruncatedSVD.explained_variance_ratio_.
variables.sample_pca_loading_spec	PCA loadings defined as principal axes scaled by square root of eigenvalues.
variables.sample_pca_projection_spec	Projection of samples onto principal axes.
variables.sample_ploidy_spec	Ploidy of each sample calculated from call genotypes across all variants with -1 indicating variable ploidy.
variables.stat_divergence_spec	Genetic divergence between pairs of cohorts.
variables.stat_diversity_spec	Genetic diversity (also known as "Tajima’s pi") for cohorts.
variables.stat_Fst_spec	Fixation index (Fst) between pairs of cohorts.
variables.stat_Garud_h1_spec	Garud H1 statistic for cohorts.
variables.stat_Garud_h12_spec	Garud H12 statistic for cohorts.
variables.stat_Garud_h123_spec	Garud H123 statistic for cohorts.
variables.stat_Garud_h2_h1_spec	Garud H2/H1 statistic for cohorts.
variables.stat_genomic_kinship_spec	Genomic kinship matrix with self-kinship values on the diagonal.
variables.stat_genomic_relationship_spec	Genomic relationship matrix (GRM).
variables.stat_Hamilton_Kerr_lambda_spec	The probability that two (randomly chosen without replacement) homologues, inherited from a single parent, were derived from a single chromosomal copy within that parent.
variables.stat_Hamilton_Kerr_tau_spec	Numerical contribution of each parent, for each individual, which must sum to the ploidy of the individual.
variables.stat_hybrid_relationship_spec	The hybrid relationship matrix or "H-matrix".
variables.stat_hybrid_inverse_relationship_spec	Inverse of the hybrid relationship matrix or "H-matrix".
variables.stat_identity_by_state_spec	Pairwise IBS probabilities among all samples.
variables.stat_inverse_relationship_spec	The inverse of a relationship matrix.
variables.stat_observed_heterozygosity_spec	Observed heterozygosity for cohorts.
variables.stat_pbs_spec	Population branching statistic for cohort triples.
variables.stat_pedigree_contribution_spec	Expected proportional genomic contribution to and from each pair of samples based on pedigree structure.
variables.stat_pedigree_inbreeding_spec	Expected inbreeding coefficients of samples based on pedigree structure.
variables.stat_pedigree_inverse_kinship_spec	Inverse of a kinship matrix calculated from pedigree structure.
variables.stat_pedigree_inverse_relationship_spec	Inverse of a relationship matrix calculated from pedigree structure.
variables.stat_pedigree_kinship_spec	Pairwise estimates of expected kinship among samples based on pedigree structure with self-kinship values on the diagonal.
variables.stat_pedigree_relationship_spec	Relationship matrix derived from pedigree structure.
variables.stat_Tajimas_D_spec	Tajima’s D for cohorts.
variables.stat_Weir_Goudet_beta_spec	Pairwise Weir Goudet beta statistic among all samples.
variables.traits_spec	Trait (for example phenotype) variable names.
variables.variant_allele_spec	The possible alleles for the variant.
variables.variant_allele_count_spec	Variant allele counts.
variables.variant_allele_frequency_spec	The frequency of the occurrence of each allele.
variables.variant_allele_total_spec	The number of occurrences of all alleles.
variables.variant_genotype_count_spec	The number of observations for each possible genotype at each variant.
variables.variant_linreg_beta_spec	Beta values associated with each variant and trait.
variables.variant_call_rate_spec	The fraction of samples with called genotypes.
variables.variant_contig_spec	Index corresponding to contig name for each variant.
variables.variant_hwe_p_value_spec	P values from HWE test for each variant as float in [0, 1].
variables.variant_id_spec	The unique identifier of the variant.
variables.variant_n_called_spec	The number of samples with called genotypes.
variables.variant_n_het_spec	The number of samples with heterozygous calls.
variables.variant_n_hom_alt_spec	The number of samples with homozygous alternate calls.
variables.variant_n_hom_ref_spec	The number of samples with homozygous reference calls.
variables.variant_n_non_ref_spec	The number of samples that are not homozygous reference calls.
variables.variant_linreg_p_value_spec	P values as float in [0, 1].
variables.variant_ploidy_spec	Ploidy of each variant calculated from call genotypes across all samples with -1 indicating variable ploidy.
variables.variant_position_spec	The reference position of the variant.
variables.variant_score_spec	Scores to prioritize variant selection when constructing an LD matrix.
variables.variant_linreg_t_value_spec	T statistics for each beta.
variables.window_contig_spec	The contig index of each window.
variables.window_start_spec	The index values of window start positions along the variants dimension.
variables.window_stop_spec	The index values of window stop positions along the variants dimension.