malariagen_data.af1.Af1.pca#

Run a principal components analysis (PCA) using biallelic SNPs from the selected genome region and samples.

Changed in version 8.0.0: SNP ascertainment has changed slightly.

This function uses biallelic SNPs as input to the PCA. The ascertainment of SNPs to include has changed slightly in version 8.0.0 and therefore the results of this function may also be slightly different. Previously, SNPs were required to be biallelic and one of the observed alleles was required to be the reference allele. Now SNPs just have to be biallelic. The following additional parameters were also added in version 8.0.0: site_class, cohort_size, min_cohort_size, max_cohort_size, random_seed.

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.
n_componentsint, optional, default: 20: Number of components to return.
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.
exclude_samplesstr or int or list of str or int or tuple of str or int or None, optional: Sample identifier or index within sample set. Multiple values can also be provided as a list or tuple.
fit_exclude_samplesstr or int or list of str or int or tuple of str or int or None, optional: Sample identifier or index within sample set. Multiple values can also be provided as a list or tuple.
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#

df_pcaDataFrame: A dataframe of projections along principal components, one row per sample. The columns are: sample_id is the identifier of the sample, partner_sample_id is the identifier of the sample used by the partners who contributed it, contributor is the partner who contributed the sample, country is the country the sample was collected in, location is the location the sample was collected in, year is the year the sample was collected, month is the month the sample was collected, latitude is the latitude of the location the sample was collected in, longitude is the longitude of the location the sample was collected in, sex_call is the sex of the sample, sample_set is the sample set containing the sample, release is the release containing the sample, quarter is the quarter of the year the sample was collected, study_id* is the identifier of the study the sample set containing the sample came from, `study_url is the URL of the study the sample set containing the sample came from, terms_of_use_expiry_date is the date the terms of use for the sample expire, terms_of_use_url is the URL of the terms of use for the sample, unrestricted_use indicates whether the sample can be used without restrictions (e.g., if the terms of use of expired), mean_cov is mean value of the coverage, median_cov is the median value of the coverage, modal_cov is the mode of the coverage, mean_cov_2L is mean value of the coverage on 2L, median_cov_2L is the median value of the coverage on 2L, mode_cov_2L is the mode of the coverage on 2L, mean_cov_2R is mean value of the coverage on 2R, median_cov_2R is the median value of the coverage on 2R, mode_cov_2R is the mode of the coverage on 2R, mean_cov_3L is mean value of the coverage on 3L, median_cov_3L is the median value of the coverage on 3L, mode_cov_3L is the mode of the coverage on 3L, mean_cov_3R is mean value of the coverage on 3R, median_cov_3R is the median value of the coverage on 3R, mode_cov_3R is the mode of the coverage on 3R, mean_cov_X is mean value of the coverage on X, median_cov_X is the median value of the coverage on X, mode_cov_X is the mode of the coverage on X, frac_gen_cov is the faction of the genome covered, divergence is the divergence, contam_pct is the percentage of contamination, contam_LLR is the log-likelihood ratio of contamination, aim_species_fraction_arab is the fraction of the gambcolu vs. arabiensis AIMs that indicated arabiensis (this column is only present for Ag3), aim_species_fraction_colu is the fraction of the gambiae vs. coluzzii AIMs that indicated coluzzii (this column is only present for Ag3), aim_species_fraction_colu_no2l is the fraction of the gambiae vs. coluzzii AIMs that indicated coluzzii, not including the chromosome arm 2L which contains an introgression (this column is only present for Ag3), aim_species_gambcolu_arabiensis is the taxonomic group assigned by the gambcolu vs. arabiensis AIMs (this column is only present for Ag3), aim_species_gambiae_coluzzi is the taxonomic group assigned by the gambiae vs. coluzzii AIMs (this column is only present for Ag3), aim_species_gambcolu_arabiensis is the taxonomic group assigned by the combination of both AIMs analyses (this column is only present for Ag3), country_iso is the ISO code of the country the sample was collected in, admin1_name is the name of the first administrative level the sample was collected in, admin1_iso is the ISO code of the first administrative level the sample was collected in, admin2_name is the name of the second administrative level the sample was collected in, taxon is the taxon assigned to the sample by the combination of the AIMs analysis and the cohort analysis, cohort_admin1_year is the cohort the sample belongs to when samples are grouped by first administrative level and year, cohort_admin1_month is the cohort the sample belongs to when samples are grouped by first administrative level and month, cohort_admin1_quarter is the cohort the sample belongs to when samples are grouped by first administrative level and quarter, cohort_admin2_year is the cohort the sample belongs to when samples are grouped by second administrative level and year, cohort_admin2_month is the cohort the sample belongs to when samples are grouped by second administrative level and month, cohort_admin2_quarter is the cohort the sample belong to when samples are grouped by second administrative level and quarter. PC? is the projection along principal component ? (? being an integer between 1 and the number of components). There are as many such columns as components, pca_fit is whether this sample was used for fitting.
evrndarray: An array of explained variance ratios, one per component.

Notes#

This computation may take some time to run, depending on your computing environment. Results of this computation will be cached and re-used if the results_cache parameter was set when instantiating the API client.