`divbrowse.lib.genotype_data`¶

Module Contents¶

Classes¶

GenotypeData

Class for managing all genotype data related data structures and methods

Functions¶

`calculate_mean`(→ numpy.ndarray)	Calculate the mean for each variant of a variant matrix array holding the number of alternate alleles
`impute_with_mean`(→ numpy.ndarray)	variant matrix array for that missing values should be imputed (replaced) with the mean for the variant
`calc_pca_for_slice_of_variant_calls`(...)	Calculate a PCA for a variant matrix array
`calc_umap_for_slice_of_variant_calls`(...[, n_neighbors])	Calculate UMAP for a variant matrix array

divbrowse.lib.genotype_data.calculate_mean(slice_of_variant_calls: numpy.ndarray) → numpy.ndarray¶

Calculate the mean for each variant of a variant matrix array holding the number of alternate alleles

Note

Missing variant calls are excluded from the mean calculation

Parameters: slice_of_variant_calls (numpy.ndarray) – Numpy array representing a variant matrix holding the number of alternate allele calls
Returns: Numpy array holding the means per variant
Return type: numpy.ndarray

divbrowse.lib.genotype_data.impute_with_mean(slice_of_variant_calls: numpy.ndarray) → numpy.ndarray¶

variant matrix array for that missing values should be imputed (replaced) with the mean for the variant

Parameters: slice_of_variant_calls (numpy.ndarray) – Numpy array representing a variant matrix holding the number of alternate allele calls
Returns: Imputed version of the input variant matrix array
Return type: numpy.ndarray

divbrowse.lib.genotype_data.calc_pca_for_slice_of_variant_calls(slice_of_variant_calls, samples_selected)¶

Calculate a PCA for a variant matrix array

Parameters: slice_of_variant_calls (numpy.ndarray) – Numpy array representing a variant matrix holding the number of alternate allele calls
Returns: PCA result aligned with the sample IDs in the first column
Return type: numpy.ndarray

divbrowse.lib.genotype_data.calc_umap_for_slice_of_variant_calls(slice_of_variant_calls, samples_selected, n_neighbors=15)¶

Calculate UMAP for a variant matrix array

Parameters: slice_of_variant_calls (numpy.ndarray) – Numpy array representing a variant matrix holding the number of alternate allele calls
Returns: PCA result aligned with the sample IDs in the first column
Return type: numpy.ndarray

class divbrowse.lib.genotype_data.GenotypeData(config)¶

Class for managing all genotype data related data structures and methods

_load_data()¶

get_vcf_header()¶

_setup_sample_id_mapping()¶

_create_chrom_indices()¶

_create_list_of_chromosomes()¶

sample_ids_to_mask(sample_ids: list) → numpy.ndarray¶

Creates a boolean mask based on the input sample IDs that could be found in the samples array of the Zarr storage

Parameters: sample_ids (list) – List with sample IDs
Returns: Boolean mask, True for found sample IDs
Return type: numpy.ndarray

map_input_sample_ids_to_vcf_sample_ids(sample_ids: list) → list¶

Map input sample IDs to VCF sample IDs according to the configured mapping table

Parameters: sample_ids (list) – List with sample IDs
Returns: List of mapped sample IDs
Return type: list

map_vcf_sample_ids_to_input_sample_ids(sample_ids: list) → list¶

Map VCF sample IDs to input sample IDs according to the configured mapping table

Parameters: sample_ids (list) – List with sample IDs
Returns: List of mapped sample IDs
Return type: list

get_samples_mask(sample_ids)¶

Returns a tupel consisting of a boolean mask for found sample Ids and a list of mapped sample IDs

Parameters: sample_ids (list) – List with sample IDs
Returns: Boolean mask, True for found sample IDs list: mapped sample IDs
Return type: numpy.ndarray

get_posidx_by_genome_coordinate(chrom, pos) → Tuple[int, str]¶

Returns array coordinates for given physical position on a given chromosome

Parameters

chrom (str) – ID of the chromosome
pos (int) – Physical position on the chromosome

Returns

lookup (int) Array coordinate of the found physical position on the chromosome lookup_type (str): Type of the lookup, could be either ‘direct_lookup’ or ‘nearest_lookup’

count_alternate_alleles(sliced_variant_calls)¶

Returns a tupel consisting of a boolean mask for found sample Ids and a list of mapped sample IDs

Parameters: sliced_variant_calls (numpy.ndarray) – variant matrix array holding the allele calls (0/0 0/1 1/1)
Returns: variant matrix array holding the number of alternate allele calls
Return type: numpy.ndarray

count_variants_in_window(chrom, startpos, endpos) → int¶

Counts number of variants in a genomic region

Parameters

chrom (str) – The chromosome of the genomic region.
startpos (int) – The first position of the genommic region.
endpos (int) – The last position of the genommic region.

Returns

Number of variants in the genomic region

Return type

int

calculate_minor_allele_freq(numbers_of_alternate_alleles)¶

Calculates minor allele frequency

Parameters: numbers_of_alternate_alleles (numpy.ndarray) – Numpy array representing a variant matrix holding the number of alternate allele calls
Returns: Numpy array (1d) holding the calculated minor allele frequencies per each variant
Return type: numpy.ndarray

calc_variants_summary_stats(numbers_of_alternate_alleles)¶

apply_variant_filter_settings(fs, numbers_of_alternate_alleles, _slice_variant_calls)¶

get_slice_of_variant_calls(chrom, startpos=None, endpos=None, count=None, samples=None, variant_filter_settings=None)¶

divbrowse.lib.genotype_data¶

Module Contents¶

Classes¶

Functions¶

`divbrowse.lib.genotype_data`¶