fast_dbscan

A lightweight, fast dbscan implementation for use on peptide strings. It uses pure C for the distance calculations and clustering. This code is then wrapped in python.

Note: as implemented, the software assumes all sequences have the same length.

Installation

pip

pip3 install fast_dbscan

Development version

git clone https://github.com/harmslab/fast_dbscan
cd fast_dbscan
sudo python3 setup.py install

Usage

Stand-alone

This will install a convenience program called fast_dbscan in the path. This can be invoked on the command line:

fast_dbscan filename epsilon [dl]

where filename is a file that contains sequences of identical length, with one per line, epsilon is the neighborhood distance cutoff (see below), and the optional argument dl says to use the Damerau-Levenshtein distance function rather than the simple distance function.

As library

import fast_dbscan

d = fast_dbscan.DBScanWrapper(distance_function='dl')
d.read_file(file_with_sequences)
d.run(epsilon=1,min_neighbors=12)

# Dictionary keying cluster id to sequences
clusters = d.results

Distance functions

• simple: add up entries in a distance matrix based on the identies of letters at each column in the alignment. Currently, the software uses hamming distance. This could be easily modified to use other matricies, provided distances can be calculated as integers. The matrix is populated in DBScanWrapper.__init__.
• dl: Damerau-Levenshtein distance, allowing deletion, insertion, substitution, and transposition.

Other parameters

• epsilon: the maximum distance between two samples for them to be considered within the same neighborhood.
• min_neighbors: the minimum number of sequence neighbors required to define a cluster