divbrowse.lib.analysis¶
Module Contents¶
Functions¶
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Calculate the mean for each variant of a variant matrix array holding the number of alternate alleles |
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variant matrix array for that missing values should be imputed (replaced) with the mean for the variant |
- divbrowse.lib.analysis.calculate_mean(sliced_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
sliced_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.analysis.impute_with_mean(sliced_variant_calls: numpy.ndarray) numpy.ndarray¶
variant matrix array for that missing values should be imputed (replaced) with the mean for the variant
- Parameters
sliced_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
- class divbrowse.lib.analysis.Analysis(variant_calls_slice: divbrowse.lib.variant_calls_slice.VariantCallsSlice)¶
- get_imputed_calls()¶
- calc_distance_to_reference(samples)¶
- calc_distance_matrix(samples)¶
- pca()¶
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
- umap(n_neighbors=15)¶
Calculate UMAP for a variant matrix array
- Parameters
n_neighbors (int) – n_neighbors parameter of umap.UMAP() method
- Returns
PCA result aligned with the sample IDs in the first column
- Return type
numpy.ndarray