Dna#

missionbio.mosaic.dna.Dna

class Dna(*args, **kwargs)#

Container for DNA data.

Inherits most methods from _Assay. See that for the documentation on other methods and visualizations.

Filtering#

`filter_variants`([min_dp, min_gq, vaf_ref, ...])	Find informative variants.
`filter_somatic_variants`([whitelist, blacklist])	Find pathogenic somatic variants.
`filter_variants_consecutive`([proximity])	Remove nearby variants.
`filter_barcodes`([completeness])	Remove cells with missing information.
`merge_similar_clusters`(layer[, similarity])	Merge similar clusters while ignoring missing data.

`find_relevant_variants`([...])	Variants that vary across clusters.
`get_annotations`([variants, genome, ...])	Annotate DNA variants.
`get_annotated_ids`([add_clinvar])	Get human-readable names for the variants
`set_annotated_ids`([add_clinvar])	Name the id column attribute with gene and protein change instead of variant coordinates
`truth`([filter_variants, drop_identical, ...])	Get the truth for the clones in the assay.
`mutated_cells`(labels[, variants, kind, layer])	The count/fraction of cells that are mutated for each clone.
`get_variant_bulk_metrics`([variants])	Metrics for each variant.
`genome`	Get the genome version of the assay from the metadata

assign_from_truth(truth[, add_doublets, ...])

find_clones([similarity])

Identify clones based on VAF.

group_by_genotype(features[, layer, ...])

Clusters cells into clones based on the provided variants and returns a dataframe of per-clone and per-variant statistics.

`plot_variant_correlation`()	The correlation of variants across all cells.
`update_coloraxis`(fig, layer[, coloraxis])	Sets the colorscale for DNA.

`run_umap`(attribute[, output_label, jitter])	Extends `run_umap()`
`heatmap`(attribute[, splitby, features, ...])	Extends `heatmap()`
`feature_scatter`(layer, ids, **kwargs)	Extends `feature_scatter()`
`scatterplot`(attribute[, colorby, features, ...])	Extends `scatterplot()`

Parameters: