Af1

This page provides a curated list of functions and properties available in the malariagen_data API for data on mosquitoes from the Anopheles funestus subgroup.

To set up the API, use the following code:

import malariagen_data
af1 = malariagen_data.Af1()

All the functions below can then be accessed as methods on the af1 object. E.g., to call the sample_metadata() function, do:

df_samples = af1.sample_metadata()

For more information about the data and terms of use, please see the MalariaGEN Anopheles funestus genomic surveillance project home page.

Basic data access

releases	Currently available data releases.
sample_sets([release])	Access a dataframe of sample sets.
lookup_release(sample_set)	Find which release a sample set was included in.
lookup_study(sample_set)	Find which study a sample set belongs to.

Reference genome data access

contigs	Contigs in the reference genome.
genome_sequence(region[, inline_array, chunks])	Access the reference genome sequence.
genome_features([region, attributes])	Access genome feature annotations.
plot_transcript(transcript[, sizing_mode, ...])	Plot a transcript, using bokeh.
plot_genes(region[, sizing_mode, width, ...])	Plot a genes track, using bokeh.

Sample metadata access

sample_metadata([sample_sets, sample_query, ...])	Access sample metadata for one or more sample sets.
add_extra_metadata(data[, on])	Add extra sample metadata, e.g., including additional columns which you would like to use to query and group samples.
clear_extra_metadata()	Clear any extra metadata previously added.
lookup_sample(sample[, sample_set])	Get the metadata for a specific sample and sample set.
count_samples([sample_sets, sample_query, ...])	Create a pivot table showing numbers of samples available by space, time and taxon.
plot_samples_bar(x[, color, sort, ...])	Plot a bar chart showing the number of samples available, grouped by some variable such as country or year.
plot_samples_interactive_map([sample_sets, ...])	Plot an interactive map showing sampling locations using ipyleaflet.
plot_sample_location_mapbox(*, sample_sets)	Plot markers on a map showing sample locations as a Mapbox scatter plot.
plot_sample_location_geo(*, sample_sets[, ...])	Plot markers on a map showing sample locations as a geographic scatter plot.
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.
cohorts(cohort_set)	Read data for a specific cohort set, including cohort size, country code, taxon, administrative units name, ISO code, geoBoundaries shape ID and representative latitude and longitude points.

SNP data access

site_mask_ids	Identifiers for the different site masks that are available.
snp_calls(region[, sample_sets, ...])	Access SNP sites, site filters and genotype calls.
snp_allele_counts(region[, sample_sets, ...])	Compute SNP allele counts.
plot_snps(region[, sample_sets, ...])	Plot SNPs in a given genome region.
site_annotations(region[, site_mask, ...])	Load site annotations.
is_accessible(region[, site_mask, ...])	Compute genome accessibility array.
biallelic_snp_calls(region[, sample_sets, ...])	Access SNP calls at sites which are biallelic within the selected samples.
biallelic_diplotypes(region[, sample_sets, ...])	Load biallelic SNP genotypes.

Haplotype data access

phasing_analysis_ids	Identifiers for the different phasing analyses that are available.
haplotypes(region[, analysis, sample_sets, ...])	Access haplotype data.
haplotype_sites(region, field[, analysis, ...])	Access haplotype site data (positions or alleles).

CNV data access

coverage_calls_analysis_ids	Identifiers for the different coverage calls analyses that are available.
cnv_hmm(region[, sample_sets, sample_query, ...])	Access CNV HMM data from CNV calling.
cnv_coverage_calls(region, sample_set, analysis)	Access CNV HMM data from genome-wide CNV discovery and filtering.
plot_cnv_hmm_coverage(sample, region[, ...])	Plot CNV HMM data for a single sample, together with a genes track, using bokeh.
plot_cnv_hmm_heatmap(region[, sample_sets, ...])	Plot CNV HMM data for multiple samples as a heatmap, with a genes track, using bokeh.
gene_cnv(region[, sample_sets, ...])	Compute modal copy number by gene, from HMM data.

Note that CNV discordant read calls are not currently supported.

Integrative genomics viewer (IGV)

igv(region[, tracks, init])	Create an IGV browser and inject into the current notebook.
view_alignments(region, sample[, ...])	Launch IGV and view sequence read alignments and SNP genotypes from the given sample.

SNP and CNV frequency analysis

snp_allele_frequencies(transcript, cohorts)	Compute SNP allele frequencies for a gene transcript.
snp_allele_frequencies_advanced(transcript, ...)	Group samples by taxon, area (space) and period (time), then compute SNP allele frequencies.
aa_allele_frequencies(transcript, cohorts[, ...])	Compute amino acid substitution frequencies for a gene transcript.
aa_allele_frequencies_advanced(transcript, ...)	Group samples by taxon, area (space) and period (time), then compute amino acid change allele frequencies.
gene_cnv_frequencies(region, cohorts[, ...])	Compute modal copy number by gene, then compute the frequency of amplifications and deletions in one or more cohorts, from HMM data.
gene_cnv_frequencies_advanced(region, ...[, ...])	Group samples by taxon, area (space) and period (time), then compute gene CNV counts and frequencies.
plot_frequencies_heatmap(df[, index, ...])	Plot a heatmap from a pandas DataFrame of frequencies, e.g., output from snp_allele_frequencies() or gene_cnv_frequencies().
plot_frequencies_time_series(ds[, height, ...])	Create a time series plot of variant frequencies using plotly.
plot_frequencies_interactive_map(ds[, ...])	Create an interactive map with markers showing variant frequencies or cohorts grouped by area (space), period (time) and taxon.

Principal components analysis (PCA)

pca(region, n_snps[, n_components, ...])	Run a principal components analysis (PCA) using biallelic SNPs from the selected genome region and samples.
plot_pca_variance(evr[, width, height, ...])	Plot explained variance ratios from a principal components analysis (PCA) using a plotly bar plot.
plot_pca_coords(data[, x, y, color, symbol, ...])	Plot sample coordinates from a principal components analysis (PCA) as a plotly scatter plot.
plot_pca_coords_3d(data[, x, y, z, color, ...])	Plot sample coordinates from a principal components analysis (PCA) as a plotly 3D scatter plot.

Genetic distance and neighbour-joining trees (NJT)

plot_njt(region, n_snps[, color, symbol, ...])	Plot an unrooted neighbour-joining tree, computed from pairwise distances between samples using biallelic SNP genotypes.
njt(region, n_snps[, algorithm, metric, ...])	Construct a neighbour-joining tree between samples using biallelic SNP genotypes.
biallelic_diplotype_pairwise_distances(...)	Compute pairwise distances between samples using biallelic SNP genotypes.

Heterozygosity analysis

plot_heterozygosity(sample, region[, ...])	Plot windowed heterozygosity for a single sample over a genome region.
roh_hmm(sample, region[, window_size, ...])	Infer runs of homozygosity for a single sample over a genome region.
plot_roh(sample, region[, window_size, ...])	Plot windowed heterozygosity and inferred runs of homozygosity for a single sample over a genome region.

Diversity analysis

cohort_diversity_stats(cohort, cohort_size, ...)	Compute genetic diversity summary statistics for a cohort of individuals.
diversity_stats(cohorts, cohort_size, region)	Compute genetic diversity summary statistics for multiple cohorts.
plot_diversity_stats(df_stats[, color, ...])	Plot diversity summary statistics for multiple cohorts.

Genome-wide selection scans

h12_calibration(contig[, analysis, ...])	Generate h12 GWSS calibration data for different window sizes.
plot_h12_calibration(contig[, analysis, ...])	Plot h12 GWSS calibration data for different window sizes.
h12_gwss(contig, window_size[, analysis, ...])	Run h12 genome-wide selection scan.
plot_h12_gwss(contig, window_size[, ...])	Plot h12 GWSS data.
plot_h12_gwss_multi_panel(contig, cohorts, ...)	Plot h12 GWSS data with multiple tracks.
plot_h12_gwss_multi_overlay(contig, cohorts, ...)	Plot h12 GWSS data with multiple traces overlaid.
h1x_gwss(contig, window_size, cohort1_query, ...)	Run a H1X genome-wide scan to detect genome regions with shared selective sweeps between two cohorts.
plot_h1x_gwss(contig, window_size, ...[, ...])	Run and plot a H1X genome-wide scan to detect genome regions with shared selective sweeps between two cohorts.
g123_calibration(contig[, sites, site_mask, ...])	Generate G123 GWSS calibration data for different window sizes.
plot_g123_calibration(contig, sites[, ...])	Plot G123 GWSS calibration data for different window sizes.
g123_gwss(contig, window_size[, sites, ...])	Run a G123 genome-wide selection scan.
plot_g123_gwss(contig, window_size[, sites, ...])	Plot G123 GWSS data.
ihs_gwss(contig[, analysis, sample_sets, ...])	Run iHS GWSS.
plot_ihs_gwss(contig[, analysis, ...])	Run and plot iHS GWSS data.
xpehh_gwss(contig[, analysis, sample_sets, ...])	Run XP-EHH GWSS.
plot_xpehh_gwss(contig[, analysis, ...])	Run and plot XP-EHH GWSS data.

Haplotype clustering and network analysis

plot_haplotype_clustering(region[, ...])	Hierarchically cluster haplotypes in region and produce an interactive plot.
plot_haplotype_network(region[, analysis, ...])	Construct a median-joining haplotype network and display it using Cytoscape.
haplotype_pairwise_distances(region[, ...])	Compute pairwise distances between haplotypes.

Diplotype clustering

plot_diplotype_clustering(region[, ...])	Hierarchically cluster diplotypes in region and produce an interactive plot.
plot_diplotype_clustering_advanced(region[, ...])	Perform diplotype clustering, annotated with heterozygosity, gene copy number and amino acid variants.

Fst analysis

average_fst(region, cohort1_query, cohort2_query)	Compute average Hudson's Fst between two specified cohorts.
pairwise_average_fst(region, cohorts[, ...])	Compute pairwise average Hudson's Fst between a set of specified cohorts.
plot_pairwise_average_fst(fst_df[, ...])	Plot a heatmap of pairwise average Fst values.
fst_gwss(contig, window_size, cohort1_query, ...)	Run a Fst genome-wide scan to investigate genetic differentiation between two cohorts.
plot_fst_gwss(contig, window_size, ...[, ...])	Run and plot a Fst genome-wide scan to investigate genetic differentiation between two cohorts.