Cartography

Reduced dimension embeddings for pathogen sequences

Cartography is an open-source software for scientists, epidemiologists, etc. to run reduced dimension embeddings (PCA, MDS, t-SNE, and UMAP) on viral populations. This is the source code from the paper Cartography written by Sravani Nanduri and John Huddleston.

Documentation
Source Code
Bug reports

Installing the package

Simply install the package using pip.

pip install pathogen-embed

src.embed module

Command line interface

The full Documentation.

The below documentation does not detail the named and positional arguments.

Reduced dimension embeddings for pathogen sequences

usage: embed [-h] [--distance-matrix DISTANCE_MATRIX] [--separator SEPARATOR]
             [--alignment ALIGNMENT] [--cluster-data CLUSTER_DATA]
             [--cluster-threshold CLUSTER_THRESHOLD]
             [--random-seed RANDOM_SEED] [--output-dataframe OUTPUT_DATAFRAME]
             [--output-figure OUTPUT_FIGURE]
             {pca,t-sne,umap,mds} ...

Sub-commands:

pca

Principal Component Analysis

embed pca [-h] [--components COMPONENTS]
          [--explained-variance EXPLAINED_VARIANCE]

t-sne

t-distributed Stochastic Neighborhood Embedding

embed t-sne [-h] [--perplexity PERPLEXITY] [--learning-rate LEARNING_RATE]

umap

Uniform Manifold Approximation and Projection

embed umap [-h] [--nearest-neighbors NEAREST_NEIGHBORS] [--min-dist MIN_DIST]

mds

Multidimensional Scaling

embed mds [-h] [--components COMPONENTS]

API

src.embed.get_hamming_distances(genomes)

Calculate pairwise Hamming distances between the given list of genomes and return the nonredundant array of values for use with scipy’s squareform function. Bases other than standard nucleotides (A, T, C, G) are ignored.

• Parameters

  genomes (list) – a list of strings corresponding to genomes that should be compared

• Returns

  a list of distinct Hamming distances as a vector-form distance vector

• Return type

  list

>>> genomes = ["ATGCT", "ATGCT", "ACGCT"]
>>> get_hamming_distances(genomes)
[0, 1, 1]
>>> genomes = ["AT-GCT", "AT--CT", "AC--CT"]
>>> get_hamming_distances(genomes)
[0, 1, 1]