Running Cocoatree with your own metric

It is possible to perform Cocoatree’s SCA pipeline by defining it’s own metric. In this example, we show how to use a callable to compute the coevolution metric.

import numpy as np

import cocoatree.datasets as c_data
import cocoatree
from sklearn.metrics import pairwise
from sklearn.preprocessing import OneHotEncoder

Load the dataset

We start by importing the dataset. In this case, we can directly load the S1 serine protease dataset provided in cocoatree. To work on your on dataset, you can use the cocoatree.io.load_msa function.

serine_dataset = c_data.load_S1A_serine_proteases()
loaded_seqs = serine_dataset["alignment"]
loaded_seqs_id = serine_dataset["sequence_ids"]
n_loaded_pos, n_loaded_seqs = len(loaded_seqs[0]), len(loaded_seqs)

Perform a SCA-like analysis using your own metrics

Here, as a coevolution_matrix, we take the example of a kernel matrix a provided by the scikit-learn library

def my_coevolution_matrix(sequences, seq_weights=None, freq_regul=0.08):
    seqs = np.array([[i for i in s] for s in sequences])
    X = OneHotEncoder().fit_transform(seqs.T)
    return pairwise.pairwise_kernels(X)


coevol_matrix, coevol_matrix_ngm, results = cocoatree.perform_sca(
    loaded_seqs_id, loaded_seqs, n_components=3,
    coevolution_metric=my_coevolution_matrix)