malariagen_data.af1.Af1.diversity_stats

Af1.diversity_stats(cohorts: str | Mapping[str, str], cohort_size: int, region: str | Region | Mapping | List[str | Region | Mapping] | Tuple[str | Region | Mapping, ...], site_mask: str | None = 'default', site_class: str | None = None, sample_query: str | None = None, sample_query_options: dict | None = None, sample_sets: Sequence[str] | str | None = None, random_seed: int = 42, n_jack: int = 200, confidence_level: float = 0.95, chunks: int | str | Tuple[int | str, ...] | Callable[[Tuple[int, ...]], int | str | Tuple[int | str, ...]] = 'native', inline_array: bool = True) → DataFrame

Compute genetic diversity summary statistics for multiple cohorts.

Parameters

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.

cohort_sizeint

Randomly down-sample to this value if the number of samples in the cohort is greater. Raise an error if the number of samples is less than this value.

regionstr or Region or Mapping or list of str or Region or Mapping or tuple of str or Region or Mapping

Region of the reference genome. Can be a contig name, region string (formatted like “{contig}:{start}-{end}”), or identifier of a genome feature such as a gene or transcript. Can also be a sequence (e.g., list) of regions.

site_maskstr or None, optional, default: ‘default’

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

site_classstr or None, optional

Select sites belonging to one of the following classes: CDS_DEG_4, (4-fold degenerate coding sites), CDS_DEG_2_SIMPLE (2-fold simple degenerate coding sites), CDS_DEG_0 (non-degenerate coding sites), INTRON_SHORT (introns shorter than 100 bp), INTRON_LONG (introns longer than 200 bp), INTRON_SPLICE_5PRIME (intron within 2 bp of 5’ splice site), INTRON_SPLICE_3PRIME (intron within 2 bp of 3’ splice site), UTR_5PRIME (5’ untranslated region), UTR_3PRIME (3’ untranslated region), INTERGENIC (intergenic, more than 10 kbp from a gene).

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.

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.

random_seedint, optional, default: 42

Random seed used for reproducible down-sampling.

n_jackint, optional, default: 200

Number of blocks to divide the data into for the block jackknife estimation of confidence intervals. N.B., larger is not necessarily better.

confidence_levelfloat, optional, default: 0.95

Confidence level to use for confidence interval calculation. E.g., 0.95 means 95% confidence interval.

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 where each row provides summary statistics and their confidence intervals for a single cohort. The columns are the value, the estimate, the bias, the standard error, the confidence interval error, the confidence interval lower value, the confidence interval upper value for each summary statistics (theta pi, Watterson’s theta and Tajima’s D), the taxon of the cohort, its year of collection, its month of collection, its country of collection, the ISO code of its first administrative level of collection, the name of its first administrative level of collection, the name of its second administrative level of collection, the longitude of its location of collection, and the latitude of its location of collection.