scirpy.ir_dist.metrics.HammingDistanceCalculator

class scirpy.ir_dist.metrics.HammingDistanceCalculator(cutoff=None, **kwargs)

Calculates the Hamming distance between sequences of identical length.

The edit distance is the total number of substitution events. Sequences with different lengths will be treated as though they exceeded the distance-cutoff, i.e. they receive a distance of 0 in the sparse distance matrix and will not be connected by an edge in the graph.

This class relies on Python-levenshtein to calculate the distances.

Choosing a cutoff:

Each modification stands for a substitution event. While lacking empirical data, it seems unlikely that CDR3 sequences with more than two modifications still recognize the same antigen.

Parameters:

• cutoff (Optional[int] (default: None)) – Will eleminate distances > cutoff to make efficient use of sparse matrices. The default cutoff is 2.

• n_jobs – Number of jobs to use for the pairwise distance calculation. If None, use all jobs (only for ParallelDistanceCalculators).

• block_size – The width of a block of the matrix that will be delegated to a worker process. The block contains block_size ** 2 elements.

Attributes table

DTYPE

The sparse matrix dtype.

Methods table

calc_dist_mat(seqs[, seqs2])	Calculate the distance matrix.
squarify(triangular_matrix)	Mirror a triangular matrix at the diagonal to make it a square matrix.

Attributes

HammingDistanceCalculator.DTYPE = 'uint8'

The sparse matrix dtype. Defaults to uint8, constraining the max distance to 255.

Methods

HammingDistanceCalculator.calc_dist_mat(seqs, seqs2=None)

Calculate the distance matrix.

See DistanceCalculator.calc_dist_mat().

Return type:

csr_matrix

static HammingDistanceCalculator.squarify(triangular_matrix)

Mirror a triangular matrix at the diagonal to make it a square matrix.

The input matrix must be upper triangular to begin with, otherwise the results will be incorrect. No guard rails!

Return type:

csr_matrix