API reference#
This page provides an auto-generated summary of sgkits’s API.
IO/imports and exports#
See Reading and writing genetic data
BGEN#
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Convert a BGEN file to a Zarr on-disk store. |
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Read BGEN dataset. |
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Rechunk BGEN dataset as Zarr. |
PLINK#
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Convert a PLINK file to a Zarr on-disk store. |
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Read PLINK dataset. |
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Convert a dataset to a PLINK file. |
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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.
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Read VCF dataset. |
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Convert VCF files to a single Zarr on-disk store. |
For more low-level control:
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Calculate genomic region strings to partition a compressed VCF or BCF file into roughly equal parts. |
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Convert VCF files to multiple Zarr on-disk stores, one per region. |
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Concatenate multiple Zarr stores into a single Zarr store. |
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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:
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Read a VCF Zarr file created using scikit-allel. |
VCF (writing)#
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Convert a dataset to a VCF file. |
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Convert a Zarr file to a VCF file. |
Dataset#
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Load a dataset from Zarr storage. |
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Save a dataset to Zarr storage. |
Methods#
Basic genetics statistics#
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Compute per sample allele frequencies from genotype calls. |
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Compute allele frequencies for each cohort. |
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Compute per sample allele counts from genotype calls. |
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Compute per cohort allele counts from per-sample allele counts, or genotype calls. |
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Compute allele count from per-sample allele counts, or genotype calls. |
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Count the number of calls of each possible genotype, at each variant. |
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Compute quality control variant statistics from genotype calls. |
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Compute quality control sample statistics from genotype calls. |
Population genetic statistics#
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Compute divergence between pairs of cohorts. |
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Compute diversity from cohort allele counts. |
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Compute Fst between pairs of cohorts. |
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Compute the H1, H12, H123 and H2/H1 statistics for detecting signatures of soft sweeps, as defined in Garud et al. (2015). |
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Compute per call individual heterozygosity. |
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Compute per cohort observed heterozygosity. |
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Compute the population branching statistic (PBS) between cohort triples. |
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Compute Tajimas' D for a genotype call dataset. |
Linkage Disequilibrium#
Genetic Association and Regression#
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Run linear regression to identify continuous trait associations with genetic variants. |
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Regenie trait transformation. |
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Exact test for HWE as described in Wigginton et al. 2005 [1]. |
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Compute gene-ε as described in Cheng, et al. 2020 [1]. |
Miscellaneous#
Compute a maximal independent set of variants. |
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Calculates the pairwise distance between all pairs of row vectors in the given two dimensional array x. |
Utilities#
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Convert each call genotype to a single integer value. |
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Convert genotype probabilities to hard calls. |
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Display genotype calls. |
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Display a pedigree dataset as a directed acyclic graph. |
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Replace partial genotype calls with missing values. |
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Infer the ploidy of each call genotype based on the number of fill (non-allele) values in each call genotype. |
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Infer the ploidy of each sample across all variants based on the number of fill (non-allele) values in call genotypes. |
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Infer the ploidy at each variant across all samples based on the number of fill (non-allele) values in call genotypes. |
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Calculate the inverse relationship (sub-) matrix. |
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Calculate the integer indices for the parents of each sample within the samples dimension. |
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Return a new dataset with each array indexed along the 'samples' dimension using a subset of samples and the optional inclusion of their relatives. |
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Generate progeny genotypes via a gene-drop simulation (MacCluer et al. 1986 [1]). |
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Simulate genotype calls and variant/sample data. |
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Add a window spanning the whole genome to a dataset. |
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Add window information to a dataset, using arbitrary intervals. |
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Add window information to a dataset, measured by distance along the genome. |
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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
.
Allele counts. |
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Allele frequencies. |
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Dosages, encoded as floats, with NaN indicating a missing value. |
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A flag for each call indicating which values are missing. |
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Call genotypes in which partial genotype calls are replaced with completely missing genotype calls. |
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A flag for each call indicating which values are missing. |
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Call genotype. |
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A flag for each call indicating which values are missing. |
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A flag for each allele position within mixed ploidy call genotypes indicating fill (non-allele) values of lower ploidy calls. |
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A flag for each call indicating if it is phased or not. |
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Genotype probabilities. |
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A flag for each call indicating which values are missing. |
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Single integer values encoding each call genotype. |
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A flag for each call genotype index indicating missing indices (-1) which correspond to call genotypes with missing alleles. |
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Observed heterozygosity of each call genotype. |
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Call genotype ploidy. |
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Cohort allele counts. |
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Cohort Allele frequencies. |
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Covariate variable names. |
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Interval contig name. |
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Interval start position (inclusive). |
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Interval stop position (exclusive). |
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Variant indexes to drop for LD prune. |
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REGENIE's base prediction (blocks, alphas, samples, outcomes). |
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REGENIE's regenie_loco_prediction (contigs, samples, outcomes). |
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REGENIE's regenie_meta_prediction (samples, outcomes). |
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Indices of parent samples with negative values indicating unknown parents. |
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Unique identifiers of parent samples matching those in |
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PC Relate kinship coefficient matrix. |
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The fraction of variants with called genotypes. |
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The index of the cohort that each sample belongs to. |
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The unique identifier of the sample. |
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The number of variants with called genotypes. |
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The number of variants with heterozygous calls. |
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The number of variants with homozygous alternate calls. |
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The number of variants with homozygous reference calls. |
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The number of variants that are not homozygous reference calls. |
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Principal axes defined as eigenvectors for sample covariance matrix. |
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Variance explained by each principal component. |
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Ratio of variance explained to total variance for each principal component, i.e. |
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PCA loadings defined as principal axes scaled by square root of eigenvalues. |
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Projection of samples onto principal axes. |
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Ploidy of each sample calculated from call genotypes across all variants with -1 indicating variable ploidy. |
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Genetic divergence between pairs of cohorts. |
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Genetic diversity (also known as "Tajima’s pi") for cohorts. |
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Fixation index (Fst) between pairs of cohorts. |
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Garud H1 statistic for cohorts. |
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Garud H12 statistic for cohorts. |
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Garud H123 statistic for cohorts. |
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Garud H2/H1 statistic for cohorts. |
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Genomic kinship matrix with self-kinship values on the diagonal. |
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Genomic relationship matrix (GRM). |
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The probability that two (randomly chosen without replacement) homologues, inherited from a single parent, were derived from a single chromosomal copy within that parent. |
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Numerical contribution of each parent, for each individual, which must sum to the ploidy of the individual. |
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The hybrid relationship matrix or "H-matrix". |
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Inverse of the hybrid relationship matrix or "H-matrix". |
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Pairwise IBS probabilities among all samples. |
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The inverse of a relationship matrix. |
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Observed heterozygosity for cohorts. |
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Population branching statistic for cohort triples. |
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Expected proportional genomic contribution to and from each pair of samples based on pedigree structure. |
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Expected inbreeding coefficients of samples based on pedigree structure. |
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Inverse of a kinship matrix calculated from pedigree structure. |
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Inverse of a relationship matrix calculated from pedigree structure. |
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Pairwise estimates of expected kinship among samples based on pedigree structure with self-kinship values on the diagonal. |
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Relationship matrix derived from pedigree structure. |
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Tajima’s D for cohorts. |
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Pairwise Weir Goudet beta statistic among all samples. |
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Trait (for example phenotype) variable names. |
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The possible alleles for the variant. |
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Variant allele counts. |
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The frequency of the occurrence of each allele. |
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The number of occurrences of all alleles. |
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The number of observations for each possible genotype at each variant. |
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Beta values associated with each variant and trait. |
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The fraction of samples with called genotypes. |
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Index corresponding to contig name for each variant. |
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P values from HWE test for each variant as float in [0, 1]. |
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The unique identifier of the variant. |
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The number of samples with called genotypes. |
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The number of samples with heterozygous calls. |
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The number of samples with homozygous alternate calls. |
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The number of samples with homozygous reference calls. |
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The number of samples that are not homozygous reference calls. |
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P values as float in [0, 1]. |
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Ploidy of each variant calculated from call genotypes across all samples with -1 indicating variable ploidy. |
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The reference position of the variant. |
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Scores to prioritize variant selection when constructing an LD matrix. |
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T statistics for each beta. |
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The contig index of each window. |
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The index values of window start positions along the |
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The index values of window stop positions along the |