malariagen_data.af1.Af1.aa_allele_frequencies

Af1.aa_allele_frequencies(transcript: str, cohorts: str | Mapping[str, str], sample_query: str | None = None, sample_query_options: dict | None = None, min_cohort_size: int | None = 10, site_mask: str | None = None, sample_sets: Sequence[str] | str | None = None, drop_invariant: bool = True, include_counts: bool = False, chunks: int | str | Tuple[int | str, ...] | Callable[[Tuple[int, ...]], int | str | Tuple[int | str, ...]] = 'native', inline_array: bool = True) → DataFrame

Compute amino acid substitution frequencies for a gene transcript.

Parameters

transcriptstr

Gene transcript identifier.

cohortsstr or Mapping[str, str]

Either a string giving the name of a predefined cohort set (e.g., “admin1_month”) or a dict mapping custom cohort labels to sample queries.

sample_querystr or None, optional

A pandas query string to be evaluated against the sample metadata, to select samples to be included in the returned data.

sample_query_optionsdict or None, optional

A dictionary of arguments that will be passed through to pandas query() or eval(), e.g. parser, engine, local_dict, global_dict, resolvers.

min_cohort_sizeint or None, optional, default: 10

Minimum cohort size. Raise an error if the number of samples is less than this value.

site_maskstr or None, optional

Which site filters mask to apply. See the site_mask_ids property for available values.

sample_setssequence of str or str or None, optional

List of sample sets and/or releases. Can also be a single sample set or release.

drop_invariantbool, optional, default: True

If True, drop variants not observed in the selected samples.

include_countsbool, optional, default: False

Include columns with allele counts and number of non-missing allele calls (nobs).

chunksint or str or tuple of int or str or Callable[[typing.Tuple[int, …]], int or str or tuple of int or str], optional, default: ‘native’

Define how input data being read from zarr should be divided into chunks for a dask computation. If ‘native’, use underlying zarr chunks. If a string specifying a target memory size, e.g., ‘300 MiB’, resize chunks in arrays with more than one dimension to match this size. If ‘auto’, let dask decide chunk size. If ‘ndauto’, let dask decide chunk size but only for arrays with more than one dimension. If ‘ndauto0’, as ‘ndauto’ but only vary the first chunk dimension. If ‘ndauto1’, as ‘ndauto’ but only vary the second chunk dimension. If ‘ndauto01’, as ‘ndauto’ but only vary the first and second chunk dimensions. Also, can be a tuple of integers, or a callable which accepts the native chunks as a single argument and returns a valid dask chunks value.

inline_arraybool, optional, default: True

Passed through to dask from_array().

Returns

DataFrame

A dataframe of amino acid allele frequencies, one row per variant. The variants are indexed by their amino acid change, their contig, their position. The columns are split into two categories: there is 1 column for each cohort containing the frequency of the amino acid change within the cohort, additionally there is a column max_af containing the maximum frequency of the amino acide change accross all cohorts; finally, there are 9 columns describing the variant allele: transcript contains the gene transcript used for this analysis, effect is the effect of the allele change, impact`is the impact of the allele change, `ref_allele is the reference allel, alt_allele is the alternate allele, aa_pos is the position of the amino acid, ref_aa is the reference amino acid, alt_aa is the altered amino acid with the varaint allele, and label is the label of the variant allele.

Notes

Cohorts with fewer samples than min_cohort_size will be excluded from output data frame.