Methods

Import / Export

export_elasticsearch(t, host, port, index, ...)

Export a Table to Elasticsearch.

export_gen(dataset, output[, precision, gp, ...])

Export a MatrixTable as GEN and SAMPLE files.

export_bgen(mt, output[, gp, varid, rsid, ...])

Export MatrixTable as MatrixTable as BGEN 1.2 file with 8 bits of per probability.

export_plink(dataset, output[, call, ...])

Export a MatrixTable as PLINK2 BED, BIM and FAM files.

export_vcf(dataset, output[, ...])

Export a MatrixTable or Table as a VCF file.

get_vcf_metadata(path)

Extract metadata from VCF header.

import_bed(path[, reference_genome, ...])

Import a UCSC BED file as a Table.

import_bgen(path, entry_fields[, ...])

Import BGEN file(s) as a MatrixTable.

import_fam(path[, quant_pheno, delimiter, ...])

Import a PLINK FAM file into a Table.

import_gen(path[, sample_file, tolerance, ...])

Import GEN file(s) as a MatrixTable.

import_locus_intervals(path[, ...])

Import a locus interval list as a Table.

import_matrix_table(paths[, row_fields, ...])

Import tab-delimited file(s) as a MatrixTable.

import_plink(bed, bim, fam[, ...])

Import a PLINK dataset (BED, BIM, FAM) as a MatrixTable.

import_table(paths[, key, min_partitions, ...])

Import delimited text file (text table) as Table.

import_vcf(path[, force, force_bgz, ...])

Import VCF file(s) as a MatrixTable.

index_bgen(path[, index_file_map, ...])

Index BGEN files as required by import_bgen().

read_matrix_table(path, *[, _intervals, ...])

Read in a MatrixTable written with MatrixTable.write().

read_table(path, *[, _intervals, ...])

Read in a Table written with Table.write().

Statistics

linear_mixed_model(y, x[, z_t, k, p_path, ...])

Initialize a linear mixed model from a matrix table.

linear_mixed_regression_rows(entry_expr, model)

For each row, test an input variable for association using a linear mixed model.

linear_regression_rows(y, x, covariates[, ...])

For each row, test an input variable for association with response variables using linear regression.

logistic_regression_rows(test, y, x, covariates)

For each row, test an input variable for association with a binary response variable using logistic regression.

poisson_regression_rows(test, y, x, covariates)

For each row, test an input variable for association with a count response variable using Poisson regression.

pca(entry_expr[, k, compute_loadings])

Run principal component analysis (PCA) on numeric columns derived from a matrix table.

row_correlation(entry_expr[, block_size])

Computes the correlation matrix between row vectors.

Genetics

balding_nichols_model(n_populations, ...[, ...])

Generate a matrix table of variants, samples, and genotypes using the Balding-Nichols or Pritchard-Stephens-Donnelly model.

concordance(left, right, *[, ...])

Calculate call concordance with another dataset.

filter_intervals(ds, intervals[, keep])

Filter rows with a list of intervals.

filter_alleles(mt, f)

Filter alternate alleles.

filter_alleles_hts(mt, f[, subset])

Filter alternate alleles and update standard GATK entry fields.

genetic_relatedness_matrix(call_expr)

Compute the genetic relatedness matrix (GRM).

hwe_normalized_pca(call_expr[, k, ...])

Run principal component analysis (PCA) on the Hardy-Weinberg-normalized genotype call matrix.

impute_sex(call[, aaf_threshold, ...])

Impute sex of samples by calculating inbreeding coefficient on the X chromosome.

ld_matrix(entry_expr, locus_expr, radius[, ...])

Computes the windowed correlation (linkage disequilibrium) matrix between variants.

ld_prune(call_expr[, r2, bp_window_size, ...])

Returns a maximal subset of variants that are nearly uncorrelated within each window.

compute_charr(ds[, min_af, max_af, min_dp, ...])

Compute CHARR, the DNA sample contamination estimator.

mendel_errors(call, pedigree)

Find Mendel errors; count per variant, individual and nuclear family.

de_novo(mt, pedigree, pop_frequency_prior, *)

Call putative de novo events from trio data.

nirvana(dataset, config[, block_size, name])

Annotate variants using Nirvana.

realized_relationship_matrix(call_expr)

Computes the realized relationship matrix (RRM).

sample_qc(mt[, name])

Compute per-sample metrics useful for quality control.

skat(key_expr, weight_expr, y, x, covariates)

Test each keyed group of rows for association by linear or logistic SKAT test.

lambda_gc(p_value[, approximate])

Compute genomic inflation factor (lambda GC) from an Expression of p-values.

split_multi(ds[, keep_star, left_aligned, ...])

Split multiallelic variants.

split_multi_hts(ds[, keep_star, ...])

Split multiallelic variants for datasets that contain one or more fields from a standard high-throughput sequencing entry schema.

transmission_disequilibrium_test(dataset, ...)

Performs the transmission disequilibrium test on trios.

trio_matrix(dataset, pedigree[, complete_trios])

Builds and returns a matrix where columns correspond to trios and entries contain genotypes for the trio.

variant_qc(mt[, name])

Compute common variant statistics (quality control metrics).

vep(dataset[, config, block_size, name, ...])

Annotate variants with VEP.

Relatedness

Hail provides three methods for the inference of relatedness: PLINK-style identity by descent [1], KING [2], and PC-Relate [3].

  • identity_by_descent() is appropriate for datasets containing one homogeneous population.

  • king() is appropriate for datasets containing multiple homogeneous populations and no admixture. It is also used to prune close relatives before using pc_relate().

  • pc_relate() is appropriate for datasets containing multiple homogeneous populations and admixture.

identity_by_descent(dataset[, maf, bounded, ...])

Compute matrix of identity-by-descent estimates.

king(call_expr, *[, block_size])

Compute relatedness estimates between individuals using a KING variant.

pc_relate(call_expr, min_individual_maf, *)

Compute relatedness estimates between individuals using a variant of the PC-Relate method.

Miscellaneous

grep(regex, path[, max_count, show, force, ...])

Searches given paths for all lines containing regex matches.

maximal_independent_set(i, j[, keep, ...])

Return a table containing the vertices in a near maximal independent set of an undirected graph whose edges are given by a two-column table.

rename_duplicates(dataset[, name])

Rename duplicate column keys.

segment_intervals(ht, points)

Segment the interval keys of ht at a given set of points.