malariagen_data.ag3.Ag3.njt

Ag3.njt(region: str | Region | Mapping | List[str | Region | Mapping] | Tuple[str | Region | Mapping, ...], n_snps: int, algorithm: Literal['dynamic', 'rapid', 'canonical'] = 'dynamic', metric: Literal['cityblock', 'euclidean', 'sqeuclidean'] = 'cityblock', thin_offset: int = 0, sample_sets: Sequence[str] | str | None = None, sample_query: str | None = None, sample_query_options: dict | None = None, sample_indices: List[int] | None = None, site_mask: str | None = 'default', site_class: str | None = None, min_minor_ac: int | float | None = 2, max_missing_an: int | float | None = 0, cohort_size: int | None = None, min_cohort_size: int | None = None, max_cohort_size: int | None = None, random_seed: int = 42, inline_array: bool = True, chunks: int | str | Tuple[int | str, ...] | Callable[[Tuple[int, ...]], int | str | Tuple[int | str, ...]] = 'native') → Tuple[ndarray, ndarray, int]

Construct a neighbour-joining tree between samples using biallelic SNP genotypes.

Parameters

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.

n_snpsint

The desired number of SNPs to use when running the analysis. SNPs will be evenly thinned to approximately this number.

algorithm{‘dynamic’, ‘rapid’, ‘canonical’}, optional, default: ‘dynamic’

Neighbour-joining algorithm to use. The ‘dynamic’ algorithm is fastest.

metric{‘cityblock’, ‘euclidean’, ‘sqeuclidean’}, optional, default: ‘cityblock’

The metric to compute distance between genotypes in two samples.

thin_offsetint, optional, default: 0

Starting index for SNP thinning. Change this to repeat the analysis using a different set of SNPs.

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.

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_indiceslist of int or None, optional

Advanced usage parameter. A list of indices of samples to select, corresponding to the order in which the samples are found within the sample metadata. Either provide this parameter or sample_query, not both.

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).

min_minor_acint or float or None, optional, default: 2

The minimum minor allele count. SNPs with a minor allele count below this value will be excluded. Can also be a float, which will be interpreted as a fraction.

max_missing_anint or float or None, optional, default: 0

The maximum number of missing allele calls to accept. SNPs with more than this value will be excluded. Set to 0 to require no missing calls. Can also be a float, which will be interpreted as a fraction.

cohort_sizeint or None, optional

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.

min_cohort_sizeint or None, optional

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

max_cohort_sizeint or None, optional

Randomly down-sample to this value if the number of samples in the cohort is greater.

random_seedint, optional, default: 42

Random seed used for reproducible down-sampling.

inline_arraybool, optional, default: True

Passed through to dask from_array().

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.

Returns

Zndarray

A neighbour-joining tree encoded as a numpy array. Each row in the array contains data for one internal node in the tree, in the order in which they were created by the neighbour-joining algorithm. Within each row there are five values: left child node identifier, right child node identifier, distance to left child, distance to right child, total number of leaves. This data structure is similar to that returned by scipy’s hierarchical clustering functions, except that here we have two distance values for each internal node rather than one because distances to the children may be different.

samplesndarray

The list of the sample identifiers.

n_snps_usedint

The number of SNPs used.