Ag3 API#

This page provides documentation for functions in the malariagen_data Python package for accessing Anopheles gambiae data.

Ag3()#

malariagen_data.Ag3(bokeh_output_notebook=True, results_cache=None, log=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>, debug=False, show_progress=True, check_location=True, cohorts_analysis=None, species_analysis=None, site_filters_analysis=None, pre=False, **kwargs)#

Provides access to data from Ag3.x releases.

Parameters

urlstr: Base path to data. Give “gs://vo_agam_release/” to use Google Cloud Storage, or a local path on your file system if data have been downloaded.
cohorts_analysisstr: Cohort analysis version.
species_analysis{“aim_20200422”, “pca_20200422”}, optional: Species analysis version.
site_filters_analysisstr, optional: Site filters analysis version.
bokeh_output_notebookbool, optional: If True (default), configure bokeh to output plots to the notebook.
results_cachestr, optional: Path to directory on local file system to save results.
logstr or stream, optional: File path or stream output for logging messages.
debugbool, optional: Set to True to enable debug level logging.
show_progressbool, optional: If True, show a progress bar during longer-running computations.
check_locationbool, optional: If True, use ipinfo to check the location of the client system.
**kwargs: Passed through to fsspec when setting up file system access.

Examples

Access data from Google Cloud Storage (default):

>>> import malariagen_data
>>> ag3 = malariagen_data.Ag3()

Access data downloaded to a local file system:

>>> ag3 = malariagen_data.Ag3("/local/path/to/vo_agam_release/")

Access data from Google Cloud Storage, with caching on the local file system in a directory named “gcs_cache”:

>>> ag3 = malariagen_data.Ag3(
...     "simplecache::gs://vo_agam_release",
...     simplecache=dict(cache_storage="gcs_cache"),
... )

Set up caching of some longer-running computations on the local file system, in a directory named “results_cache”:

>>> ag3 = malariagen_data.Ag3(results_cache="results_cache")

sample_sets()#

Ag3.sample_sets(release=None)#

Access a dataframe of sample sets.

Parameters

releasestr, optional: Release identifier, e.g. give “3.0” to access the v3.0 data release.

Returns

dfpandas.DataFrame: A dataframe of sample sets, one row per sample set.

sample_metadata()#

Ag3.sample_metadata(sample_sets=None, sample_query=None)#

Access sample metadata for one or more sample sets.

Parameters

sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
sample_querystr, optional: A pandas query string which will be evaluated against the sample metadata e.g., “country == ‘Burkina Faso’”.

Returns

df_samplespandas.DataFrame: A dataframe of sample metadata, one row per sample.

sample_cohorts()#

Ag3.sample_cohorts(sample_sets=None)#

Access cohorts metadata for one or more sample sets.

Parameters

sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.

Returns

dfpandas.DataFrame: A dataframe of cohort metadata, one row per sample.

count_samples()#

Ag3.count_samples(sample_sets=None, sample_query=None, index=('country', 'admin1_iso', 'admin1_name', 'admin2_name', 'year'), columns='taxon')#

Create a pivot table showing numbers of samples available by space, time and taxon.

Parameters

sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
sample_querystr, optional: A pandas query string which will be evaluated against the sample metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.
indexstr or tuple of str: Sample metadata columns to use for the index.
columnsstr or tuple of str: Sample metadata columns to use for the columns.

Returns

dfpandas.DataFrame: Pivot table of sample counts.

plot_samples_interactive_map()#

Ag3.plot_samples_interactive_map(sample_sets=None, sample_query=None, basemap=None, center=(- 2, 20), zoom=3, min_samples=1)#

Plot an interactive map showing sampling locations using ipyleaflet.

Parameters

sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
sample_querystr, optional: A pandas query string which will be evaluated against the sample metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.
basemapdict: Basemap description coming from ipyleaflet.basemaps.
centertuple of int, optional: Location to center the map.
zoomint, optional: Initial zoom level.
min_samplesint, optional: Minimum number of samples required to show a marker for a given location.

Returns

samples_mapipyleaflet.Map: Ipyleaflet map widget.

cross_metadata()#

Ag3.cross_metadata()#

Load a dataframe containing metadata about samples in colony crosses, including which samples are parents or progeny in which crosses.

Returns

dfpandas.DataFrame: A dataframe of sample metadata for colony crosses.

species_calls()#

Ag3.species_calls(sample_sets=None)#

Access species calls for one or more sample sets.

Parameters

sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”] or a release identifier (e.g., “3.0”) or a list of release identifiers.

Returns

dfpandas.DataFrame: A dataframe of species calls for one or more sample sets, one row per sample.

genome_sequence()#

Ag3.genome_sequence(region, inline_array=True, chunks='native')#

Access the reference genome sequence.

Parameters

region: str or list of str or Region or list of Region: Contig name (e.g., “2L”), gene name (e.g., “AGAP007280”), genomic region defined with coordinates (e.g., “2L:44989425-44998059”) or a named tuple with genomic location Region(contig, start, end). Multiple values can be provided as a list, in which case data will be concatenated, e.g., [“3R”, “3L”].
inline_arraybool, optional: Passed through to dask.array.from_array().
chunksstr, optional: If ‘auto’ let dask decide chunk size. If ‘native’ use native zarr chunks. Also, can be a target size, e.g., ‘200 MiB’.

Returns

ddask.array.Array: An array of nucleotides giving the reference genome sequence for the given contig.

geneset()#

Ag3.geneset(*args, **kwargs)#: Deprecated, this method has been renamed to genome_features().

snp_calls()#

Ag3.snp_calls(region, sample_sets=None, sample_query=None, site_mask=None, site_class=None, inline_array=True, chunks='native', cohort_size=None, random_seed=42)#

Access SNP sites, site filters and genotype calls.

Parameters

region: str or list of str or Region or list of Region: Contig name (e.g., “2L”), gene name (e.g., “AGAP007280”), genomic region defined with coordinates (e.g., “2L:44989425-44998059”) or a named tuple with genomic location Region(contig, start, end). Multiple values can be provided as a list, in which case data will be concatenated, e.g., [“3R”, “3L”].
sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
sample_querystr, optional: A pandas query string which will be evaluated against the sample metadata e.g., “country == ‘Burkina Faso’”.
site_maskstr, optional: Site filters mask to apply, e.g. “gamb_colu”
site_classstr, 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).
inline_arraybool, optional: Passed through to dask.array.from_array().
chunksstr, optional: If ‘auto’ let dask decide chunk size. If ‘native’ use native zarr chunks. Also, can be a target size, e.g., ‘200 MiB’.
cohort_sizeint, optional: If provided, randomly down-sample to the given cohort size.
random_seedint, optional: Random seed used for down-sampling.

Returns

dsxarray.Dataset: A dataset containing SNP sites, site filters and genotype calls.

snp_sites()#

Ag3.snp_sites(region, field, site_mask=None, inline_array=True, chunks='native')#

Access SNP site data (positions and alleles).

Parameters

region: str or list of str or Region or list of Region: Contig name (e.g., “2L”), gene name (e.g., “AGAP007280”), genomic region defined with coordinates (e.g., “2L:44989425-44998059”) or a named tuple with genomic location Region(contig, start, end). Multiple values can be provided as a list, in which case data will be concatenated, e.g., [“3R”, “3L”].
field{“POS”, “REF”, “ALT”}: Array to access.
site_maskstr, optional: Site filters mask to apply, e.g. “gamb_colu”
inline_arraybool, optional: Passed through to dask.array.from_array().
chunksstr, optional: If ‘auto’ let dask decide chunk size. If ‘native’ use native zarr chunks. Also, can be a target size, e.g., ‘200 MiB’.

Returns

ddask.array.Array: An array of either SNP positions, reference alleles or alternate alleles.

snp_genotypes()#

Ag3.snp_genotypes(region, sample_sets=None, sample_query=None, field='GT', site_mask=None, inline_array=True, chunks='native')#

Access SNP genotypes and associated data.

Parameters

region: str or list of str or Region or list of Region: Contig name (e.g., “2L”), gene name (e.g., “AGAP007280”), genomic region defined with coordinates (e.g., “2L:44989425-44998059”) or a named tuple with genomic location Region(contig, start, end). Multiple values can be provided as a list, in which case data will be concatenated, e.g., [“3R”, “3L”].
sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
sample_querystr, optional: A pandas query string which will be evaluated against the sample metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.
field{“GT”, “GQ”, “AD”, “MQ”}: Array to access.
site_maskstr, optional: Site filters mask to apply, e.g. “gamb_colu”
inline_arraybool, optional: Passed through to dask.array.from_array().
chunksstr, optional: If ‘auto’ let dask decide chunk size. If ‘native’ use native zarr chunks. Also, can be a target size, e.g., ‘200 MiB’.

Returns

ddask.array.Array: An array of either genotypes (GT), genotype quality (GQ), allele depths (AD) or mapping quality (MQ) values.

site_filters()#

Ag3.site_filters(region, mask, field='filter_pass', inline_array=True, chunks='native')#

Access SNP site filters.

Parameters

region: str or list of str or Region or list of Region: Contig name (e.g., “2L”), gene name (e.g., “AGAP007280”), genomic region defined with coordinates (e.g., “2L:44989425-44998059”) or a named tuple with genomic location Region(contig, start, end). Multiple values can be provided as a list, in which case data will be concatenated, e.g., [“3R”, “3L”].
maskstr: Mask to use, e.g. “gamb_colu”
fieldstr, optional: Array to access.
inline_arraybool, optional: Passed through to dask.from_array().
chunksstr, optional: If ‘auto’ let dask decide chunk size. If ‘native’ use native zarr chunks. Also, can be a target size, e.g., ‘200 MiB’.

Returns

ddask.array.Array: An array of boolean values identifying sites that pass the filters.

is_accessible()#

Ag3.is_accessible(region, site_mask)#

Compute genome accessibility array.

Parameters

region: str or list of str or Region or list of Region: Contig name (e.g., “2L”), gene name (e.g., “AGAP007280”), genomic region defined with coordinates (e.g., “2L:44989425-44998059”) or a named tuple with genomic location Region(contig, start, end). Multiple values can be provided as a list, in which case data will be concatenated, e.g., [“3R”, “3L”].
site_maskstr: Site filters mask to apply, e.g. “gamb_colu”

Returns

anumpy.ndarray: An array of boolean values identifying accessible genome sites.

snp_effects()#

Ag3.snp_effects(transcript, site_mask=None)#

Compute variant effects for a gene transcript.

Parameters

transcriptstr: Gene transcript ID (AgamP4.12), e.g., “AGAP004707-RA”.
site_maskstr, optional: Site filters mask to apply, e.g. “gamb_colu”

Returns

dfpandas.DataFrame: A dataframe of all possible SNP variants and their effects, one row per variant.

site_annotations()#

Ag3.site_annotations(region, site_mask=None, inline_array=True, chunks='auto')#

Load site annotations.

Parameters

region: str or list of str or Region or list of Region: Contig name (e.g., “2L”), gene name (e.g., “AGAP007280”), genomic region defined with coordinates (e.g., “2L:44989425-44998059”) or a named tuple with genomic location Region(contig, start, end). Multiple values can be provided as a list, in which case data will be concatenated, e.g., [“3R”, “3L”].
site_maskstr: Site filters mask to apply, e.g. “gamb_colu”
inline_arraybool, optional: Passed through to dask.from_array().
chunksstr, optional: If ‘auto’ let dask decide chunk size. If ‘native’ use native zarr chunks. Also, can be a target size, e.g., ‘200 MiB’.

Returns

dsxarray.Dataset: A dataset of site annotations.

cnv_hmm()#

Ag3.cnv_hmm(region, sample_sets=None, sample_query=None, max_coverage_variance=0.2, inline_array=True, chunks='native')#

Access CNV HMM data from CNV calling.

Parameters

region: str or list of str or Region or list of Region: Chromosome arm (e.g., “2L”), gene name (e.g., “AGAP007280”), genomic region defined with coordinates (e.g., “2L:44989425-44998059”) or a named tuple with genomic location Region(contig, start, end). Multiple values can be provided as a list, in which case data will be concatenated, e.g., [“3R”, “3L”].
sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
sample_querystr, optional: A pandas query string which will be evaluated against the sample metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.
max_coverage_variancefloat, optional: Remove samples if coverage variance exceeds this value.
inline_arraybool, optional: Passed through to dask.array.from_array().
chunksstr, optional: If ‘auto’ let dask decide chunk size. If ‘native’ use native zarr chunks. Also, can be a target size, e.g., ‘200 MiB’.

Returns

dsxarray.Dataset: A dataset of CNV HMM calls and associated data.

cnv_coverage_calls()#

Ag3.cnv_coverage_calls(region, sample_set, analysis, inline_array=True, chunks='native')#

Access CNV HMM data from genome-wide CNV discovery and filtering.

Parameters

region: str or list of str or Region or list of Region: Chromosome arm (e.g., “2L”), gene name (e.g., “AGAP007280”), genomic region defined with coordinates (e.g., “2L:44989425-44998059”) or a named tuple with genomic location Region(contig, start, end). Multiple values can be provided as a list, in which case data will be concatenated, e.g., [“3R”, “3L”].
sample_setstr: Sample set identifier.
analysis{‘gamb_colu’, ‘arab’, ‘crosses’}: Name of CNV analysis.
inline_arraybool, optional: Passed through to dask.array.from_array().
chunksstr, optional: If ‘auto’ let dask decide chunk size. If ‘native’ use native zarr chunks. Also, can be a target size, e.g., ‘200 MiB’.

Returns

dsxarray.Dataset: A dataset of CNV alleles and genotypes.

cnv_discordant_read_calls()#

Ag3.cnv_discordant_read_calls(contig, sample_sets=None, inline_array=True, chunks='native')#

Access CNV discordant read calls data.

Parameters

contigstr or list of str: Chromosome arm, e.g., “3R”. Multiple values can be provided as a list, in which case data will be concatenated, e.g., [“2R”, “3R”].
sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
inline_arraybool, optional: Passed through to dask.array.from_array().
chunksstr, optional: If ‘auto’ let dask decide chunk size. If ‘native’ use native zarr chunks. Also, can be a target size, e.g., ‘200 MiB’.

Returns

dsxarray.Dataset: A dataset of CNV alleles and genotypes.

gene_cnv()#

Ag3.gene_cnv(region, sample_sets=None, sample_query=None, max_coverage_variance=0.2)#

Compute modal copy number by gene, from HMM data.

Parameters

region: str or list of str or Region or list of Region: Chromosome arm (e.g., “2L”), gene name (e.g., “AGAP007280”), genomic region defined with coordinates (e.g., “2L:44989425-44998059”) or a named tuple with genomic location Region(contig, start, end). Multiple values can be provided as a list, in which case data will be concatenated, e.g., [“3R”, “3L”].
sample_setsstr or list of str: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
sample_querystr, optional: A pandas query string which will be evaluated against the sample metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.
max_coverage_variancefloat, optional: Remove samples if coverage variance exceeds this value.

Returns

dsxarray.Dataset: A dataset of modal copy number per gene and associated data.

haplotypes()#

Ag3.haplotypes(region, analysis, sample_sets=None, sample_query=None, inline_array=True, chunks='native', cohort_size=None, random_seed=42)#

Access haplotype data.

Parameters

region: str or list of str or Region or list of Region: Chromosome arm (e.g., “2L”), gene name (e.g., “AGAP007280”), genomic region defined with coordinates (e.g., “2L:44989425-44998059”) or a named tuple with genomic location Region(contig, start, end). Multiple values can be provided as a list, in which case data will be concatenated, e.g., [“3R”, “3L”].
analysis{“arab”, “gamb_colu”, “gamb_colu_arab”}: Which phasing analysis to use. If analysing only An. arabiensis, the “arab” analysis is best. If analysing only An. gambiae and An. coluzzii, the “gamb_colu” analysis is best. Otherwise, use the “gamb_colu_arab” analysis.
sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
sample_querystr, optional: A pandas query string which will be evaluated against the sample metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.
inline_arraybool, optional: Passed through to dask.array.from_array().
chunksstr, optional: If ‘auto’ let dask decide chunk size. If ‘native’ use native zarr chunks. Also, can be a target size, e.g., ‘200 MiB’.
cohort_sizeint, optional: If provided, randomly down-sample to the given cohort size.
random_seedint, optional: Random seed used for down-sampling.

Returns

dsxarray.Dataset: A dataset of haplotypes and associated data.

snp_allele_frequencies()#

Ag3.snp_allele_frequencies(transcript, cohorts, sample_query=None, min_cohort_size=10, site_mask=None, sample_sets=None, drop_invariant=True, effects=True)#

Compute per variant allele frequencies for a gene transcript.

Parameters

transcriptstr: Gene transcript ID (AgamP4.12), e.g., “AGAP004707-RD”.
cohortsstr or dict: If a string, gives the name of a predefined cohort set, e.g., one of {“admin1_month”, “admin1_year”, “admin2_month”, “admin2_year”}. If a dict, should map cohort labels to sample queries, e.g., {“bf_2012_col”: “country == ‘Burkina Faso’ and year == 2012 and taxon == ‘coluzzii’”}.
sample_querystr, optional: A pandas query string which will be evaluated against the sample metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.
min_cohort_sizeint: Minimum cohort size. Any cohorts below this size are omitted.
site_mask{“gamb_colu_arab”, “gamb_colu”, “arab”}: Site filters mask to apply.
sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
drop_invariantbool, optional: If True, variants with no alternate allele calls in any cohorts are dropped from the result.
effectsbool, optional: If True, add SNP effect columns.

Returns

dfpandas.DataFrame: A dataframe of SNP frequencies, one row per variant.

Notes

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

aa_allele_frequencies()#

Ag3.aa_allele_frequencies(transcript, cohorts, sample_query=None, min_cohort_size=10, site_mask=None, sample_sets=None, drop_invariant=True)#

Compute per amino acid allele frequencies for a gene transcript.

Parameters

transcriptstr: Gene transcript ID (AgamP4.12), e.g., “AGAP004707-RA”.
cohortsstr or dict: If a string, gives the name of a predefined cohort set, e.g., one of {“admin1_month”, “admin1_year”, “admin2_month”, “admin2_year”}. If a dict, should map cohort labels to sample queries, e.g., {“bf_2012_col”: “country == ‘Burkina Faso’ and year == 2012 and taxon == ‘coluzzii’”}.
sample_querystr, optional: A pandas query string which will be evaluated against the sample metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.
min_cohort_sizeint: Minimum cohort size, below which allele frequencies are not calculated for cohorts.
site_mask{“gamb_colu_arab”, “gamb_colu”, “arab”}: Site filters mask to apply.
sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
drop_invariantbool, optional: If True, variants with no alternate allele calls in any cohorts are dropped from the result.

Returns

dfpandas.DataFrame: A dataframe of amino acid allele frequencies, one row per replacement.

Notes

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

gene_cnv_frequencies()#

Ag3.gene_cnv_frequencies(region, cohorts, sample_query=None, min_cohort_size=10, sample_sets=None, drop_invariant=True, max_coverage_variance=0.2)#

Compute modal copy number by gene, then compute the frequency of amplifications and deletions in one or more cohorts, from HMM data.

Parameters

region: str or list of str or Region or list of Region: Chromosome arm (e.g., “2L”), gene name (e.g., “AGAP007280”), genomic region defined with coordinates (e.g., “2L:44989425-44998059”) or a named tuple with genomic location Region(contig, start, end). Multiple values can be provided as a list, in which case data will be concatenated, e.g., [“3R”, “3L”].
cohortsstr or dict: If a string, gives the name of a predefined cohort set, e.g., one of {“admin1_month”, “admin1_year”, “admin2_month”, “admin2_year”}. If a dict, should map cohort labels to sample queries, e.g., {“bf_2012_col”: “country == ‘Burkina Faso’ and year == 2012 and taxon == ‘coluzzii’”}.
sample_querystr, optional: A pandas query string which will be evaluated against the sample metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.
min_cohort_sizeint: Minimum cohort size, below which cohorts are dropped.
sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
drop_invariantbool, optional: If True, drop any rows where there is no evidence of variation.
max_coverage_variancefloat, optional: Remove samples if coverage variance exceeds this value.

Returns

dfpandas.DataFrame: A dataframe of CNV amplification (amp) and deletion (del) frequencies in the specified cohorts, one row per gene and CNV type (amp/del).

plot_frequencies_heatmap()#

Ag3.plot_frequencies_heatmap(df, index='label', max_len=100, x_label='Cohorts', y_label='Variants', colorbar=True, col_width=40, width=None, row_height=20, height=None, text_auto='.0%', aspect='auto', color_continuous_scale='Reds', title=True, **kwargs)#

Plot a heatmap from a pandas DataFrame of frequencies, e.g., output from Ag3.snp_allele_frequencies() or Ag3.gene_cnv_frequencies(). It’s recommended to filter the input DataFrame to just rows of interest, i.e., fewer rows than max_len.

Parameters

dfpandas DataFrame: A DataFrame of frequencies, e.g., output from snp_allele_frequencies() or gene_cnv_frequencies().
indexstr or list of str: One or more column headers that are present in the input dataframe. This becomes the heatmap y-axis row labels. The column/s must produce a unique index.
max_lenint, optional: Displaying large styled dataframes may cause ipython notebooks to crash.
x_labelstr, optional: This is the x-axis label that will be displayed on the heatmap.
y_labelstr, optional: This is the y-axis label that will be displayed on the heatmap.
colorbarbool, optional: If False, colorbar is not output.
col_widthint, optional: Plot width per column in pixels (px).
widthint, optional: Plot width in pixels (px), overrides col_width.
row_heightint, optional: Plot height per row in pixels (px).
heightint, optional: Plot height in pixels (px), overrides row_height.
text_autostr, optional: Formatting for frequency values.
aspectstr, optional: Control the aspect ratio of the heatmap.
color_continuous_scalestr, optional: Color scale to use.
titlebool or str, optional: If True, attempt to use metadata from input dataset as a plot title. Otherwise, use supplied value as a title.
**kwargs: Other parameters are passed through to px.imshow().

Returns

figplotly.graph_objects.Figure

plot_frequencies_time_series()#

Ag3.plot_frequencies_time_series(ds, height=None, width=None, title=True, **kwargs)#

Create a time series plot of variant frequencies using plotly.

Parameters

dsxarray.Dataset: A dataset of variant frequencies, such as returned by Ag3.snp_allele_frequencies_advanced(), Ag3.aa_allele_frequencies_advanced() or Ag3.gene_cnv_frequencies_advanced().
heightint, optional: Height of plot in pixels (px).
widthint, optional: Width of plot in pixels (px).
titlebool or str, optional: If True, attempt to use metadata from input dataset as a plot title. Otherwise, use supplied value as a title.
**kwargs: Passed through to px.line().

Returns

figplotly.graph_objects.Figure: A plotly figure containing line graphs. The resulting figure will have one panel per cohort, grouped into columns by taxon, and grouped into rows by area. Markers and lines show frequencies of variants.

plot_frequencies_interactive_map()#

Ag3.plot_frequencies_interactive_map(ds, center=(- 2, 20), zoom=3, title=True, epilogue=True)#

Create an interactive map with markers showing variant frequencies or cohorts grouped by area (space), period (time) and taxon.

Parameters

dsxarray.Dataset: A dataset of variant frequencies, such as returned by Ag3.snp_allele_frequencies_advanced(), Ag3.aa_allele_frequencies_advanced() or Ag3.gene_cnv_frequencies_advanced().
centertuple of int, optional: Location to center the map.
zoomint, optional: Initial zoom level.
titlebool or str, optional: If True, attempt to use metadata from input dataset as a plot title. Otherwise, use supplied value as a title.
epiloguebool or str, optional: Additional text to display below the map.

Returns

outipywidgets.Widget: An interactive map with widgets for selecting which variant, taxon and time period to display.

plot_frequencies_map_markers()#

Ag3.plot_frequencies_map_markers(m, ds, variant, taxon, period, clear=True)#

Plot markers on a map showing variant frequencies for cohorts grouped by area (space), period (time) and taxon.

Parameters

mipyleaflet.Map: The map on which to add the markers.
dsxarray.Dataset: A dataset of variant frequencies, such as returned by Ag3.snp_allele_frequencies_advanced(), Ag3.aa_allele_frequencies_advanced() or Ag3.gene_cnv_frequencies_advanced().
variantint or str: Index or label of variant to plot.
taxonstr: Taxon to show markers for.
periodpd.Period: Time period to show markers for.
clearbool, optional: If True, clear all layers (except the base layer) from the map before adding new markers.

snp_allele_frequencies_advanced()#

Ag3.snp_allele_frequencies_advanced(transcript, area_by, period_by, sample_sets=None, sample_query=None, min_cohort_size=10, drop_invariant=True, variant_query=None, site_mask=None, nobs_mode='called', ci_method='wilson')#

Group samples by taxon, area (space) and period (time), then compute SNP allele counts and frequencies.

Parameters

transcriptstr: Gene transcript ID (AgamP4.12), e.g., “AGAP004707-RD”.
area_bystr: Column name in the sample metadata to use to group samples spatially. E.g., use “admin1_iso” or “admin1_name” to group by level 1 administrative divisions, or use “admin2_name” to group by level 2 administrative divisions.
period_by{“year”, “quarter”, “month”}: Length of time to group samples temporally.
sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
sample_querystr, optional: A pandas query string which will be evaluated against the sample metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.
min_cohort_sizeint, optional: Minimum cohort size. Any cohorts below this size are omitted.
drop_invariantbool, optional: If True, variants with no alternate allele calls in any cohorts are dropped from the result.
variant_querystr, optional
site_maskstr, optional: Site filters mask to apply.
nobs_mode{“called”, “fixed”}: Method for calculating the denominator when computing frequencies. If “called” then use the number of called alleles, i.e., number of samples with non-missing genotype calls multiplied by 2. If “fixed” then use the number of samples multiplied by 2.
ci_method{“normal”, “agresti_coull”, “beta”, “wilson”, “binom_test”}, optional: Method to use for computing confidence intervals, passed through to statsmodels.stats.proportion.proportion_confint.

Returns

dsxarray.Dataset: The resulting dataset contains data has dimensions “cohorts” and “variants”. Variables prefixed with “cohort” are 1-dimensional arrays with data about the cohorts, such as the area, period, taxon and cohort size. Variables prefixed with “variant” are 1-dimensional arrays with data about the variants, such as the contig, position, reference and alternate alleles. Variables prefixed with “event” are 2-dimensional arrays with the allele counts and frequency calculations.

aa_allele_frequencies_advanced()#

Ag3.aa_allele_frequencies_advanced(transcript, area_by, period_by, sample_sets=None, sample_query=None, min_cohort_size=10, variant_query=None, site_mask=None, nobs_mode='called', ci_method='wilson')#

Group samples by taxon, area (space) and period (time), then compute amino acid change allele counts and frequencies.

Parameters

transcriptstr: Gene transcript ID (AgamP4.12), e.g., “AGAP004707-RD”.
area_bystr: Column name in the sample metadata to use to group samples spatially. E.g., use “admin1_iso” or “admin1_name” to group by level 1 administrative divisions, or use “admin2_name” to group by level 2 administrative divisions.
period_by{“year”, “quarter”, “month”}: Length of time to group samples temporally.
sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
sample_querystr, optional: A pandas query string which will be evaluated against the sample metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.
min_cohort_sizeint, optional: Minimum cohort size. Any cohorts below this size are omitted.
variant_querystr, optional
site_maskstr, optional: Site filters mask to apply.
nobs_mode{“called”, “fixed”}: Method for calculating the denominator when computing frequencies. If “called” then use the number of called alleles, i.e., number of samples with non-missing genotype calls multiplied by 2. If “fixed” then use the number of samples multiplied by 2.
ci_method{“normal”, “agresti_coull”, “beta”, “wilson”, “binom_test”}, optional: Method to use for computing confidence intervals, passed through to statsmodels.stats.proportion.proportion_confint.

Returns

dsxarray.Dataset: The resulting dataset contains data has dimensions “cohorts” and “variants”. Variables prefixed with “cohort” are 1-dimensional arrays with data about the cohorts, such as the area, period, taxon and cohort size. Variables prefixed with “variant” are 1-dimensional arrays with data about the variants, such as the contig, position, reference and alternate alleles. Variables prefixed with “event” are 2-dimensional arrays with the allele counts and frequency calculations.

gene_cnv_frequencies_advanced()#

Ag3.gene_cnv_frequencies_advanced(region, area_by, period_by, sample_sets=None, sample_query=None, min_cohort_size=10, variant_query=None, drop_invariant=True, max_coverage_variance=0.2, ci_method='wilson')#

Group samples by taxon, area (space) and period (time), then compute gene CNV counts and frequencies.

Parameters

region: str or list of str or Region or list of Region: Chromosome arm (e.g., “2L”), gene name (e.g., “AGAP007280”), genomic region defined with coordinates (e.g., “2L:44989425-44998059”) or a named tuple with genomic location Region(contig, start, end). Multiple values can be provided as a list, in which case data will be concatenated, e.g., [“3R”, “3L”].
area_bystr: Column name in the sample metadata to use to group samples spatially. E.g., use “admin1_iso” or “admin1_name” to group by level 1 administrative divisions, or use “admin2_name” to group by level 2 administrative divisions.
period_by{“year”, “quarter”, “month”}: Length of time to group samples temporally.
sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
sample_querystr, optional: A pandas query string which will be evaluated against the sample metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.
min_cohort_sizeint, optional: Minimum cohort size. Any cohorts below this size are omitted.
variant_querystr, optional
drop_invariantbool, optional: If True, drop any rows where there is no evidence of variation.
max_coverage_variancefloat, optional: Remove samples if coverage variance exceeds this value.
ci_method{“normal”, “agresti_coull”, “beta”, “wilson”, “binom_test”}, optional: Method to use for computing confidence intervals, passed through to statsmodels.stats.proportion.proportion_confint.

Returns

dsxarray.Dataset: The resulting dataset contains data has dimensions “cohorts” and “variants”. Variables prefixed with “cohort” are 1-dimensional arrays with data about the cohorts, such as the area, period, taxon and cohort size. Variables prefixed with “variant” are 1-dimensional arrays with data about the variants, such as the contig, position, reference and alternate alleles. Variables prefixed with “event” are 2-dimensional arrays with the allele counts and frequency calculations.

plot_genes()#

Ag3.plot_genes(region, width=800, height=120, show=True, toolbar_location='above', x_range=None, title='Genes')#

Plot a genes track, using bokeh.

Parameters

regionstr or Region: Contig name (e.g., “2L”), gene name (e.g., “AGAP007280”) or genomic region defined with coordinates (e.g., “2L:44989425-44998059”).
widthint, optional: Plot width in pixels (px).
heightint, optional: Plot height in pixels (px).
showbool, optional: If true, show the plot.
toolbar_locationstr, optional: Location of bokeh toolbar.
x_rangebokeh.models.Range1d, optional: X axis range (for linking to other tracks).
titlestr, optional: Plot title.

Returns

figFigure: Bokeh figure.

plot_transcript()#

Ag3.plot_transcript(transcript, width=800, height=120, show=True, x_range=None, toolbar_location='above', title=True)#

Plot a transcript, using bokeh.

Parameters

transcriptstr: Transcript identifier, e.g., “AGAP004707-RD”.
widthint, optional: Plot width in pixels (px).
heightint, optional: Plot height in pixels (px).
showbool, optional: If true, show the plot.
toolbar_locationstr, optional: Location of bokeh toolbar.
x_rangebokeh.models.Range1d, optional: X axis range (for linking to other tracks).
titlestr, optional: Plot title.

Returns

figFigure: Bokeh figure.

plot_cnv_hmm_coverage()#

Ag3.plot_cnv_hmm_coverage(sample, region, sample_set=None, y_max='auto', width=800, track_height=170, genes_height=100, circle_kwargs=None, line_kwargs=None, show=True)#

Plot CNV HMM data for a single sample, together with a genes track, using bokeh.

Parameters

samplestr or int: Sample identifier or index within sample set.
regionstr: Chromosome arm (e.g., “2L”), gene name (e.g., “AGAP007280”) or genomic region defined with coordinates (e.g., “2L:44989425-44998059”).
sample_setstr, optional: Sample set identifier.
y_maxstr or int, optional: Maximum Y axis value.
widthint, optional: Plot width in pixels (px).
track_heightint, optional: Height of CNV HMM track in pixels (px).
genes_heightint, optional: Height of genes track in pixels (px).
circle_kwargsdict, optional: Passed through to bokeh circle() function.
line_kwargsdict, optional: Passed through to bokeh line() function.
showbool, optional: If true, show the plot.

Returns

figFigure: Bokeh figure.

plot_cnv_hmm_heatmap()#

Ag3.plot_cnv_hmm_heatmap(region, sample_sets=None, sample_query=None, max_coverage_variance=0.2, width=800, row_height=7, track_height=None, genes_height=100, show=True)#

Plot CNV HMM data for multiple samples as a heatmap, with a genes track, using bokeh.

Parameters

regionstr: Chromosome arm (e.g., “2L”), gene name (e.g., “AGAP007280”) or genomic region defined with coordinates (e.g., “2L:44989425-44998059”).
sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
sample_querystr, optional: A pandas query string which will be evaluated against the sample metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.
max_coverage_variancefloat, optional: Remove samples if coverage variance exceeds this value.
widthint, optional: Plot width in pixels (px).
row_heightint, optional: Plot height per row (sample) in pixels (px).
track_heightint, optional: Absolute plot height for HMM track in pixels (px), overrides row_height.
genes_heightint, optional: Height of genes track in pixels (px).
showbool, optional: If true, show the plot.

Returns

figFigure: Bokeh figure.

resolve_region()#

Ag3.resolve_region(region)#

Convert a genome region into a standard data structure.

Parameters

region: str: Contig name (e.g., “2L”), gene name (e.g., “AGAP007280”) or genomic region defined with coordinates (e.g., “2L:44989425-44998059”).

Returns

outRegion: A named tuple with attributes contig, start and end.

igv()#

Ag3.igv(region, tracks=None)#

Create an IGV browser and display it within the notebook.

Parameters

region: str or Region: Genomic region defined with coordinates, e.g., “2L:2422600-2422700”.
trackslist of dict, optional: Configuration for any additional tracks.

Returns

browserigv_notebook.Browser

view_alignments()#

Ag3.view_alignments(region, sample, visibility_window=20000)#

Launch IGV and view sequence read alignments and SNP genotypes from the given sample.

Parameters

region: str or Region: Genomic region defined with coordinates, e.g., “2L:2422600-2422700”.
samplestr: Sample identifier, e.g., “AR0001-C”.
visibility_windowint, optional: Zoom level in base pairs at which alignment and SNP data will become visible.

Notes

Only samples from the Ag3.0 release are currently supported.

wgs_data_catalog()#

Ag3.wgs_data_catalog(sample_set)#

Load a data catalog providing URLs for downloading BAM, VCF and Zarr files for samples in a given sample set.

Parameters

sample_setstr: Sample set identifier.

Returns

dfpandas.DataFrame: One row per sample, columns provide URLs.

snp_allele_counts()#

Ag3.snp_allele_counts(region, sample_sets=None, sample_query=None, site_mask=None, site_class=None, cohort_size=None, random_seed=42)#

Compute SNP allele counts. This returns the number of times each SNP allele was observed in the selected samples.

Parameters

regionstr or Region: Contig name (e.g., “2L”), gene name (e.g., “AGAP007280”) or genomic region defined with coordinates (e.g., “2L:44989425-44998059”).
sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
sample_querystr, optional: A pandas query string which will be evaluated against the sample metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.
site_mask{“gamb_colu_arab”, “gamb_colu”, “arab”}: Site filters mask to apply.
site_classstr, 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).
cohort_sizeint, optional: If provided, randomly down-sample to the given cohort size before computing allele counts.
random_seedint, optional: Random seed used for down-sampling.

Returns

acnp.ndarray: A numpy array of shape (n_variants, 4), where the first column has the reference allele (0) counts, the second column has the first alternate allele (1) counts, the third column has the second alternate allele (2) counts, and the fourth column has the third alternate allele (3) counts.

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 Ag3 class.

pca()#

Ag3.pca(region, n_snps, thin_offset=0, sample_sets=None, sample_query=None, site_mask='default', min_minor_ac=2, max_missing_an=0, n_components=20)#

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

Parameters

regionstr: Contig name (e.g., “2L”), gene name (e.g., “AGAP007280”) or genomic region defined with coordinates (e.g., “2L:44989425-44998059”).
n_snpsint: The desired number of SNPs to use when running the analysis. SNPs will be evenly thinned to approximately this number.
thin_offsetint, optional: Starting index for SNP thinning. Change this to repeat the analysis using a different set of SNPs.
sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
sample_querystr, optional: A pandas query string which will be evaluated against the sample metadata e.g., “country == ‘Burkina Faso’”.
site_maskstr, optional: Site filters mask to apply, e.g. “gamb_colu”
min_minor_acint, optional: The minimum minor allele count. SNPs with a minor allele count below this value will be excluded prior to thinning.
max_missing_anint, optional: The maximum number of missing allele calls to accept. SNPs with more than this value will be excluded prior to thinning. Set to 0 (default) to require no missing calls.
n_componentsint, optional: Number of components to return.

Returns

df_pcapandas.DataFrame: A dataframe of sample metadata, with columns “PC1”, “PC2”, “PC3”, etc., added.
evrnp.ndarray: An array of explained variance ratios, one per component.

Notes

plot_pca_variance()#

Ag3.plot_pca_variance(evr, width=900, height=400, **kwargs)#

Plot explained variance ratios from a principal components analysis (PCA) using a plotly bar plot.

Parameters

evrnp.ndarray: An array of explained variance ratios, one per component.
widthint, optional: Plot width in pixels (px).
heightint, optional: Plot height in pixels (px).
**kwargs: Passed through to px.bar().

Returns

figFigure: A plotly figure.

plot_pca_coords()#

Ag3.plot_pca_coords(data, x='PC1', y='PC2', color=None, symbol=None, jitter_frac=0.02, random_seed=42, width=900, height=600, marker_size=10, **kwargs)#

Plot sample coordinates from a principal components analysis (PCA) as a plotly scatter plot.

Parameters

datapandas.DataFrame: A dataframe of sample metadata, with columns “PC1”, “PC2”, “PC3”, etc., added.
xstr, optional: Name of principal component to plot on the X axis.
ystr, optional: Name of principal component to plot on the Y axis.
colorstr, optional: Name of column in the input dataframe to use to color the markers.
symbolstr, optional: Name of column in the input dataframe to use to choose marker symbols.
jitter_fracfloat, optional: Randomly jitter points by this fraction of their range.
random_seedint, optional: Random seed for jitter.
widthint, optional: Plot width in pixels (px).
heightint, optional: Plot height in pixels (px).
marker_sizeint, optional: Marker size.

Returns

figFigure: A plotly figure.

plot_pca_coords_3d()#

Ag3.plot_pca_coords_3d(data, x='PC1', y='PC2', z='PC3', color=None, symbol=None, jitter_frac=0.02, random_seed=42, width=900, height=600, marker_size=5, **kwargs)#

Plot sample coordinates from a principal components analysis (PCA) as a plotly 3D scatter plot.

Parameters

datapandas.DataFrame: A dataframe of sample metadata, with columns “PC1”, “PC2”, “PC3”, etc., added.
xstr, optional: Name of principal component to plot on the X axis.
ystr, optional: Name of principal component to plot on the Y axis.
zstr, optional: Name of principal component to plot on the Z axis.
colorstr, optional: Name of column in the input dataframe to use to color the markers.
symbolstr, optional: Name of column in the input dataframe to use to choose marker symbols.
jitter_fracfloat, optional: Randomly jitter points by this fraction of their range.
random_seedint, optional: Random seed for jitter.
widthint, optional: Plot width in pixels (px).
heightint, optional: Plot height in pixels (px).
marker_sizeint, optional: Marker size.

Returns

figFigure: A plotly figure.

plot_snps()#

Ag3.plot_snps(region, sample_sets=None, sample_query=None, site_mask='default', width=800, track_height=80, genes_height=120, max_snps=200000, show=True)#

Plot SNPs in a given genome region. SNPs are shown as rectangles, with segregating and non-segregating SNPs positioned on different levels, and coloured by site filter.

Parameters

regionstr: Contig name (e.g., “2L”), gene name (e.g., “AGAP007280”) or genomic region defined with coordinates (e.g., “2L:44989425-44998059”).
sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
sample_querystr, optional: A pandas query string which will be evaluated against the sample metadata e.g., “country == ‘Burkina Faso’”.
site_maskstr, optional: Site filters mask to apply, e.g. “gamb_colu”
widthint, optional: Width of plot in pixels (px).
track_heightint, optional: Height of SNPs track in pixels (px).
genes_heightint, optional: Height of genes track in pixels (px).
max_snpsint, optional: Maximum number of SNPs to show.
showbool, optional: If True, show the plot.

Returns

figFigure: Bokeh figure.

aim_variants()#

Ag3.aim_variants(aims)#

Open ancestry informative marker variants.

Parameters

aims{‘gamb_vs_colu’, ‘gambcolu_vs_arab’}: Which ancestry informative markers to use.

Returns

dsxarray.Dataset: A dataset containing AIM positions and discriminating alleles.

aim_calls()#

Ag3.aim_calls(aims, sample_sets=None, sample_query=None)#

Access ancestry informative marker SNP sites, alleles and genotype calls.

Parameters

aims{‘gamb_vs_colu’, ‘gambcolu_vs_arab’}: Which ancestry informative markers to use.
sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
sample_querystr, optional: A pandas query string which will be evaluated against the sample metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.

Returns

dsxarray.Dataset: A dataset containing AIM SNP sites, alleles and genotype calls.

plot_aim_heatmap()#

Ag3.plot_aim_heatmap(aims, sample_sets=None, sample_query=None, sort=True, row_height=4, colors='T10', xgap=0, ygap=0.5)#

Plot a heatmap of ancestry-informative marker (AIM) genotypes.

Parameters

aims{‘gamb_vs_colu’, ‘gambcolu_vs_arab’}: Which ancestry informative markers to use.
sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
sample_querystr, optional: A pandas query string which will be evaluated against the sample metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.
sortbool, optional: If true (default), sort the samples by the total fraction of AIM alleles for the second species in the comparison.
row_heightint, optional: Height per sample in px.
colorsstr, optional: Choose your favourite color palette.
xgapfloat, optional: Creates lines between columns (variants).
ygapfloat, optional: Creates lines between rows (samples).

Returns

figplotly.graph_objects.Figure

cohort_diversity_stats()#

Ag3.cohort_diversity_stats(cohort, cohort_size, region, site_mask, site_class, sample_sets=None, random_seed=42, n_jack=200, confidence_level=0.95)#

Compute genetic diversity summary statistics for a cohort of individuals.

Parameters

cohortstr or (str, str): Either a string giving one of the predefined cohort labels, or a pair of strings giving a custom cohort label and a sample query to select samples in the cohort.
cohort_sizeint: Number of individuals to use for computation of summary statistics. If the cohort is larger than this size, it will be randomly down-sampled.
regionstr: Chromosome arm (e.g., “2L”), gene name (e.g., “AGAP007280”) or genomic region defined with coordinates (e.g., “2L:44989425-44998059”).
site_mask{“gamb_colu_arab”, “gamb_colu”, “arab”}: Site filters mask to apply.
site_classstr, 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_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
random_seedint, optional: Seed for random number generator.
n_jackint, optional: 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: Confidence level to use for confidence interval calculation. 0.95 means 95% confidence interval.

Returns

statspandas.Series: A series with summary statistics and their confidence intervals.

diversity_stats()#

Ag3.diversity_stats(cohorts, cohort_size, region, site_mask, site_class, sample_query=None, sample_sets=None, random_seed=42, n_jack=200, confidence_level=0.95)#

Compute genetic diversity summary statistics for multiple cohorts.

Parameters

cohortsstr or dict: Either a string giving one of the predefined cohort columns, or a dictionary mapping cohort labels to sample queries.
cohort_sizeint: Number of individuals to use for computation of summary statistics. If the cohort is larger than this size, it will be randomly down-sampled.
regionstr: Chromosome arm (e.g., “2L”), gene name (e.g., “AGAP007280”) or genomic region defined with coordinates (e.g., “2L:44989425-44998059”).
site_mask{“gamb_colu_arab”, “gamb_colu”, “arab”}: Site filters mask to apply.
site_classstr, 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, optional: A pandas query string which will be evaluated against the sample metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.
sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
random_seedint, optional: Seed for random number generator.
n_jackint, optional: 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: Confidence level to use for confidence interval calculation. 0.95 means 95% confidence interval.

Returns

df_statspandas.DataFrame: A DataFrame where each row provides summary statistics and their confidence intervals for a single cohort.

plot_diversity_stats()#

Ag3.plot_diversity_stats(df_stats, color=None, bar_plot_height=450, bar_width=30, scatter_plot_height=500, scatter_plot_width=500, template='plotly_white', plot_kwargs=None)#

Plot diversity statistics.

Parameters

df_statspandas.DataFrame: Output from diversity_stats().
colorstr, optional: Column to color by.
bar_plot_heightint, optional: Height of bar plots in pixels (px).
bar_widthint, optional: Width per bar in pixels (px).
scatter_plot_heightint, optional: Height of scatter plot in pixels (px).
scatter_plot_widthint, optional: Width of scatter plot in pixels (px).
templatestr, optional: Plotly template.
plot_kwargsdict, optional: Extra plotting parameters

plot_heterozygosity()#

Ag3.plot_heterozygosity(sample, region, site_mask, window_size, sample_set=None, y_max=0.03, width=800, track_height=170, genes_height=120, circle_kwargs=None, show=True)#

Plot windowed heterozygosity for a single sample over a genome region.

Parameters

samplestr or int: Sample identifier or index within sample set.
regionstr: Contig name (e.g., “2L”), gene name (e.g., “AGAP007280”) or genomic region defined with coordinates (e.g., “2L:44989425-44998059”).
site_maskstr: Site filters mask to apply, e.g. “gamb_colu”
window_sizeint: Number of sites per window.
sample_setstr, optional: Sample set identifier. Not needed if sample parameter gives a sample identifier.
y_maxfloat, optional: Y axis limit.
widthint, optional: Plot width in pixels (px).
track_heightint, optional: Heterozygosity track height in pixels (px).
genes_heightint, optional: Genes track height in pixels (px).
circle_kwargsdict, optional: Passed through to bokeh circle() function.
showbool, optional: If true, show the plot.

Returns

figFigure: Bokeh figure.

roh_hmm()#

Ag3.roh_hmm(sample, region, site_mask, window_size, sample_set=None, phet_roh=0.001, phet_nonroh=(0.003, 0.01), transition=0.001)#

Infer runs of homozygosity for a single sample over a genome region.

Parameters

samplestr or int: Sample identifier or index within sample set.
regionstr: Contig name (e.g., “2L”), gene name (e.g., “AGAP007280”) or genomic region defined with coordinates (e.g., “2L:44989425-44998059”).
site_maskstr: Site filters mask to apply, e.g. “gamb_colu”
window_sizeint: Number of sites per window.
sample_setstr, optional: Sample set identifier. Not needed if sample parameter gives a sample identifier.
phet_roh: float, optional: Probability of observing a heterozygote in a ROH.
phet_nonroh: tuple of floats, optional: One or more probabilities of observing a heterozygote outside a ROH.
transition: float, optional: Probability of moving between states. A larger window size may call for a larger transitional probability.

Returns

df_rohpandas.DataFrame: A DataFrame where each row provides data about a single run of homozygosity.

plot_roh()#

Ag3.plot_roh(sample, region, site_mask, window_size, sample_set=None, phet_roh=0.001, phet_nonroh=(0.003, 0.01), transition=0.001, y_max=0.03, width=800, heterozygosity_height=170, roh_height=50, genes_height=120, circle_kwargs=None, show=True)#

Plot windowed heterozygosity and inferred runs of homozygosity for a single sample over a genome region.

Parameters

samplestr or int: Sample identifier or index within sample set.
regionstr: Contig name (e.g., “2L”), gene name (e.g., “AGAP007280”) or genomic region defined with coordinates (e.g., “2L:44989425-44998059”).
site_maskstr: Site filters mask to apply, e.g. “gamb_colu”
window_sizeint: Number of sites per window.
sample_setstr, optional: Sample set identifier. Not needed if sample parameter gives a sample identifier.
phet_roh: float, optional: Probability of observing a heterozygote in a ROH.
phet_nonroh: tuple of floats, optional: One or more probabilities of observing a heterozygote outside a ROH.
transition: float, optional: Probability of moving between states. A larger window size may call for a larger transitional probability.
y_maxfloat, optional: Y axis limit.
widthint, optional: Plot width in pixels (px).
heterozygosity_heightint, optional: Heterozygosity track height in pixels (px).
roh_heightint, optional: ROH track height in pixels (px).
genes_heightint, optional: Genes track height in pixels (px).
circle_kwargsdict, optional: Passed through to bokeh circle() function.
showbool, optional: If true, show the plot.

Returns

figFigure: Bokeh figure.

h12_calibration()#

Ag3.h12_calibration(contig, analysis, sample_query=None, sample_sets=None, cohort_size=30, window_sizes=(100, 200, 500, 1000, 2000, 5000, 10000, 20000), random_seed=42)#

Generate h12 GWSS calibration data for different window sizes.

Parameters

contig: str: Chromosome arm (e.g., “2L”)
analysis{“arab”, “gamb_colu”, “gamb_colu_arab”}: Which phasing analysis to use. If analysing only An. arabiensis, the “arab” analysis is best. If analysing only An. gambiae and An. coluzzii, the “gamb_colu” analysis is best. Otherwise, use the “gamb_colu_arab” analysis.
sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
sample_querystr, optional: A pandas query string which will be evaluated against the sample metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.
cohort_sizeint, optional: If provided, randomly down-sample to the given cohort size.
window_sizesint or list of int, optional: The sizes of windows used to calculate h12 over. Multiple window sizes should be used to calibrate the optimal size for h12 analysis.
random_seedint, optional: Random seed used for down-sampling.

Returns

calibration runslist of numpy.ndarrays: A list of h12 calibration run arrays for each window size, containing values and percentiles.

plot_h12_calibration()#

Ag3.plot_h12_calibration(contig, analysis, sample_query=None, sample_sets=None, cohort_size=30, window_sizes=(100, 200, 500, 1000, 2000, 5000, 10000, 20000), random_seed=42, title=None)#

Plot h12 GWSS calibration data for different window sizes.

Parameters

contig: str: Chromosome arm (e.g., “2L”)
analysis{“arab”, “gamb_colu”, “gamb_colu_arab”}: Which phasing analysis to use. If analysing only An. arabiensis, the “arab” analysis is best. If analysing only An. gambiae and An. coluzzii, the “gamb_colu” analysis is best. Otherwise, use the “gamb_colu_arab” analysis.
sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
sample_querystr, optional: A pandas query string which will be evaluated against the sample metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.
cohort_sizeint, optional: If provided, randomly down-sample to the given cohort size.
window_sizesint or list of int, optional: The sizes of windows used to calculate h12 over. Multiple window sizes should be used to calibrate the optimal size for h12 analysis.
random_seedint, optional: Random seed used for down-sampling.
titlestr, optional: If provided, title string is used to label plot.

Returns

figfigure: A plot showing h12 calibration run percentiles for different window sizes.

h12_gwss()#

Ag3.h12_gwss(contig, analysis, window_size, sample_sets=None, sample_query=None, cohort_size=30, random_seed=42)#

Run h12 GWSS.

Parameters

contig: str: Chromosome arm (e.g., “2L”)
analysis{“arab”, “gamb_colu”, “gamb_colu_arab”}: Which phasing analysis to use. If analysing only An. arabiensis, the “arab” analysis is best. If analysing only An. gambiae and An. coluzzii, the “gamb_colu” analysis is best. Otherwise, use the “gamb_colu_arab” analysis.
window_sizeint: The size of windows used to calculate h12 over.
sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
sample_querystr, optional: A pandas query string which will be evaluated against the sample metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.
cohort_sizeint, optional: If provided, randomly down-sample to the given cohort size.
random_seedint, optional: Random seed used for down-sampling.

Returns

xnumpy.ndarray: An array containing the window centre point genomic positions.
h12numpy.ndarray: An array with h12 statistic values for each window.

plot_h12_gwss()#

Ag3.plot_h12_gwss(contig, analysis, window_size, sample_sets=None, sample_query=None, cohort_size=30, random_seed=42, title=None, width=800, track_height=170, genes_height=100)#

Plot h12 GWSS data.

Parameters

contig: str: Chromosome arm (e.g., “2L”)
analysis{“arab”, “gamb_colu”, “gamb_colu_arab”}: Which phasing analysis to use. If analysing only An. arabiensis, the “arab” analysis is best. If analysing only An. gambiae and An. coluzzii, the “gamb_colu” analysis is best. Otherwise, use the “gamb_colu_arab” analysis.
window_sizeint: The size of windows used to calculate h12 over.
sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
sample_querystr, optional: A pandas query string which will be evaluated against the sample metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.
cohort_sizeint, optional: If provided, randomly down-sample to the given cohort size.
random_seedint, optional: Random seed used for down-sampling.
titlestr, optional: If provided, title string is used to label plot.
widthint, optional: Plot width in pixels (px).
track_heightint. optional: GWSS track height in pixels (px).
genes_heightint. optional: Gene track height in pixels (px).

Returns

figfigure: A plot showing windowed h12 statistic with gene track on x-axis.

h1x_gwss()#

Ag3.h1x_gwss(contig, analysis, window_size, cohort1_query, cohort2_query, sample_sets=None, cohort_size=30, random_seed=42)#

Run a H1X genome-wide scan to detect genome regions with shared selective sweeps between two cohorts.

Parameters

contig: str: Chromosome arm (e.g., “2L”)
analysis{“arab”, “gamb_colu”, “gamb_colu_arab”}: Which phasing analysis to use. If analysing only An. arabiensis, the “arab” analysis is best. If analysing only An. gambiae and An. coluzzii, the “gamb_colu” analysis is best. Otherwise, use the “gamb_colu_arab” analysis.
window_sizeint: The size of windows used to calculate h12 over.
cohort1_querystr: A pandas query string which will be evaluated against the sample metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.
cohort2_querystr: A pandas query string which will be evaluated against the sample metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.
sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
cohort_sizeint, optional: If provided, randomly down-sample to the given cohort size.
random_seedint, optional: Random seed used for down-sampling.

Returns

xnumpy.ndarray: An array containing the window centre point genomic positions.
h1xnumpy.ndarray: An array with H1X statistic values for each window.

plot_h1x_gwss()#

Ag3.plot_h1x_gwss(contig, analysis, window_size, cohort1_query, cohort2_query, sample_sets=None, cohort_size=30, random_seed=42, title=None, width=800, track_height=190, genes_height=100)#

Run and plot a H1X genome-wide scan to detect genome regions with shared selective sweeps between two cohorts.

Parameters

contig: str: Chromosome arm (e.g., “2L”)
analysis{“arab”, “gamb_colu”, “gamb_colu_arab”}: Which phasing analysis to use. If analysing only An. arabiensis, the “arab” analysis is best. If analysing only An. gambiae and An. coluzzii, the “gamb_colu” analysis is best. Otherwise, use the “gamb_colu_arab” analysis.
window_sizeint: The size of windows used to calculate h12 over.
cohort1_querystr: A pandas query string which will be evaluated against the sample metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.
cohort2_querystr: A pandas query string which will be evaluated against the sample metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.
sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
cohort_sizeint, optional: If provided, randomly down-sample to the given cohort size.
random_seedint, optional: Random seed used for down-sampling.
titlestr, optional: If provided, title string is used to label plot.
widthint, optional: Plot width in pixels (px).
track_heightint. optional: GWSS track height in pixels (px).
genes_heightint. optional: Gene track height in pixels (px).

Returns

figfigure: A plot showing windowed H1X statistic with gene track on x-axis.

plot_haplotype_clustering()#

Ag3.plot_haplotype_clustering(region, analysis, sample_sets=None, sample_query=None, color=None, symbol=None, linkage_method='single', count_sort=True, distance_sort=False, cohort_size=None, random_seed=42, width=1000, height=500, **kwargs)#

Hierarchically cluster haplotypes in region and produce an interactive plot.

Parameters

region: str or list of str or Region or list of Region: Chromosome arm (e.g., “2L”), gene name (e.g., “AGAP007280”), genomic region defined with coordinates (e.g., “2L:44989425-44998059”) or a named tuple with genomic location Region(contig, start, end). Multiple values can be provided as a list, in which case data will be concatenated, e.g., [“3R”, “3L”].
analysis{“arab”, “gamb_colu”, “gamb_colu_arab”}: Which phasing analysis to use. If analysing only An. arabiensis, the “arab” analysis is best. If analysing only An. gambiae and An. coluzzii, the “gamb_colu” analysis is best. Otherwise, use the “gamb_colu_arab” analysis.
sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
sample_querystr, optional: A pandas query string which will be evaluated against the sample metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.
colorstr, optional: Identifies a column in the sample metadata which determines the colour of dendrogram leaves (haplotypes).
symbolstr, optional: Identifies a column in the sample metadata which determines the shape of dendrogram leaves (haplotypes).
linkage_method: str, optional: The linkage algorithm to use, valid options are ‘single’, ‘complete’, ‘average’, ‘weighted’, ‘centroid’, ‘median’ and ‘ward’. See the Linkage Methods section of the scipy.cluster.hierarchy.linkage docs for full descriptions.
count_sort: bool, optional: For each node n, the order (visually, from left-to-right) n’s two descendant links are plotted is determined by this parameter. If True, the child with the minimum number of original objects in its cluster is plotted first. Note distance_sort and count_sort cannot both be True.
distance_sort: bool, optional: For each node n, the order (visually, from left-to-right) n’s two descendant links are plotted is determined by this parameter. If True, The child with the minimum distance between its direct descendants is plotted first.
cohort_sizeint, optional: If provided, randomly down-sample to the given cohort size.
random_seedint, optional: Random seed used for down-sampling.
widthint, optional: The figure width in pixels
height: int, optional: The figure height in pixels

Returns

figFigure: Plotly figure.

plot_haplotype_network()#

Ag3.plot_haplotype_network(region, analysis, sample_sets=None, sample_query=None, max_dist=2, color=None, color_discrete_sequence=None, color_discrete_map=None, category_orders=None, node_size_factor=50, server_mode='inline', height=650, width='100%', layout='cose', layout_params=None, server_port=None)#

Construct a median-joining haplotype network and display it using Cytoscape.

A haplotype network provides a visualisation of the genetic distance between haplotypes. Each node in the network represents a unique haplotype. The size (area) of the node is scaled by the number of times that unique haplotype was observed within the selected samples. A connection between two nodes represents a single SNP difference between the corresponding haplotypes.

Parameters

region: str or list of str or Region or list of Region: Chromosome arm (e.g., “2L”), gene name (e.g., “AGAP007280”), genomic region defined with coordinates (e.g., “2L:44989425-44998059”) or a named tuple with genomic location Region(contig, start, end). Multiple values can be provided as a list, in which case data will be concatenated, e.g., [“3R”, “3L”].
analysis{“arab”, “gamb_colu”, “gamb_colu_arab”}: Which phasing analysis to use. If analysing only An. arabiensis, the “arab” analysis is best. If analysing only An. gambiae and An. coluzzii, the “gamb_colu” analysis is best. Otherwise, use the “gamb_colu_arab” analysis.
sample_setsstr or list of str, optional: Can be a sample set identifier (e.g., “AG1000G-AO”) or a list of sample set identifiers (e.g., [“AG1000G-BF-A”, “AG1000G-BF-B”]) or a release identifier (e.g., “3.0”) or a list of release identifiers.
sample_querystr, optional: A pandas query string which will be evaluated against the sample metadata e.g., “taxon == ‘coluzzii’ and country == ‘Burkina Faso’”.
max_distint, optional: Join network components up to a maximum distance of 2 SNP differences.
colorstr, optional: Identifies a column in the sample metadata which determines the colour of pie chart segments within nodes.
color_discrete_sequencelist, optional: Provide a list of colours to use.
color_discrete_mapdict, optional: Provide an explicit mapping from values to colours.
category_orderslist, optional: Control the order in which values appear in the legend.
node_size_factorint, optional: Control the sizing of nodes.
server_mode{“inline”, “external”, “jupyterlab”}: Controls how the Jupyter Dash app will be launched. See https://medium.com/plotly/introducing-jupyterdash-811f1f57c02e for more information.
heightint, optional: Height of the plot.
widthint, optional: Width of the plot.
layoutstr: Name of the network layout to use to position nodes.
layout_params: Additional parameters to the layout algorithm.
server_port: Manually override the port on which the Dash app will run.

Returns

app: The running Dash app.

MalariaGEN vector data user guide

Ag3 API

Contents

Ag3 API#

Ag3()#

sample_sets()#

sample_metadata()#

sample_cohorts()#

count_samples()#

plot_samples_interactive_map()#

cross_metadata()#

species_calls()#

genome_sequence()#

geneset()#

snp_calls()#

snp_sites()#

snp_genotypes()#

site_filters()#

is_accessible()#

snp_effects()#

site_annotations()#

cnv_hmm()#

cnv_coverage_calls()#

cnv_discordant_read_calls()#

gene_cnv()#

haplotypes()#

snp_allele_frequencies()#

aa_allele_frequencies()#

gene_cnv_frequencies()#

plot_frequencies_heatmap()#

plot_frequencies_time_series()#

plot_frequencies_interactive_map()#

plot_frequencies_map_markers()#

snp_allele_frequencies_advanced()#

aa_allele_frequencies_advanced()#

gene_cnv_frequencies_advanced()#

plot_genes()#

plot_transcript()#

plot_cnv_hmm_coverage()#

plot_cnv_hmm_heatmap()#

resolve_region()#

igv()#

view_alignments()#

wgs_data_catalog()#

snp_allele_counts()#

pca()#

plot_pca_variance()#

plot_pca_coords()#

plot_pca_coords_3d()#

plot_snps()#

aim_variants()#

aim_calls()#

plot_aim_heatmap()#

cohort_diversity_stats()#

diversity_stats()#

plot_diversity_stats()#

plot_heterozygosity()#

roh_hmm()#

plot_roh()#

h12_calibration()#

plot_h12_calibration()#

h12_gwss()#

plot_h12_gwss()#

h1x_gwss()#

plot_h1x_gwss()#

plot_haplotype_clustering()#

plot_haplotype_network()#