API reference¶

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

IO/imports¶

PLINK¶

read_plink(*[, path, bed_path, bim_path, …])

Read PLINK dataset.

VCF¶

`partition_into_regions`(vcf_path, *[, …])	Calculate genomic region strings to partition a compressed VCF or BCF file into roughly equal parts.
`vcf_to_zarr`(input, output, *[, regions, …])	Convert VCF files to a single Zarr on-disk store.
`vcf_to_zarrs`(input, output, regions[, …])	Convert VCF files to multiple Zarr on-disk stores, one per region.
`zarrs_to_dataset`(urls[, chunk_length, …])	Combine multiple Zarr stores into a single Xarray dataset.

read_vcfzarr(path)

Read a VCF Zarr file created using scikit-allel.

Methods¶

`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.
`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, merge])	Compute the H1, H12, H123 and H2/H1 statistics for detecting signatures of soft sweeps, as defined in Garud et al.
`gwas_linear_regression`(ds, *, dosage, …[, …])	Run linear regression to identify continuous trait associations with genetic variants.
`hardy_weinberg_test`(ds, *[, …])	Exact test for HWE as described in Wigginton et al.
`regenie`(ds, *, dosage, covariates, traits[, …])	Regenie trait transformation.
`variant_stats`(ds, *[, call_genotype_mask, …])	Compute quality control variant statistics from genotype calls.
`Tajimas_D`(ds, *[, variant_allele_count, …])	Compute Tajimas’ D for a genotype call dataset.
`pc_relate`(ds, *[, maf, call_genotype, …])	Compute PC-Relate as described in Conomos, et al.

Utilities¶

`display_genotypes`(ds[, max_variants, …])	Display genotype calls.
`simulate_genotype_call_dataset`(n_variant, …)	Simulate genotype calls and variant/sample data.
`window`(ds, size, step[, merge])	Add fixed-size windowing information to a dataset.

Variables¶

`variables.base_prediction_spec`	REGENIE’s base prediction (blocks, alphas, samples, outcomes).
`variables.call_allele_count_spec`	Allele counts.
`variables.call_dosage_spec`	Dosages, encoded as floats, with NaN indicating a missing value.
`variables.call_dosage_mask_spec`	TODO
`variables.call_genotype_spec`	Call genotype.
`variables.call_genotype_mask_spec`	TODO
`variables.call_genotype_phased_spec`	A flag for each call indicating if it is phased or not.
`variables.call_genotype_probability_spec`	TODO
`variables.call_genotype_probability_mask_spec`	TODO
`variables.covariates_spec`	Covariate variable names.
`variables.dosage_spec`	Dosage variable name.
`variables.genotype_counts_spec`	Genotype counts.
`variables.loco_prediction_spec`	REGENIE’s loco_prediction (contigs, samples, outcomes).
`variables.meta_prediction_spec`	REGENIE’s meta_prediction (samples, outcomes).
`variables.pc_relate_phi_spec`	PC Relate kinship coefficient matrix.
`variables.sample_id_spec`	The unique identifier of the sample.
`variables.sample_pcs_spec`	Sample PCs (PCxS).
`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.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_beta_spec`	Beta values associated with each variant and trait.
`variables.variant_call_rate_spec`	The number of samples with heterozygous calls.
`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_p_value_spec`	P values as float in [0, 1].
`variables.variant_position_spec`	The reference position of the variant.
`variables.variant_t_value_spec`	T statistics for each beta.
`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.