Kpax3 - Bayesian bi-clustering of categorical data
Kpax3 is a Julia package for inferring the group structure of genetic sequences. In general, any multivariate categorical dataset (such as presence/absence data) can be analyzed by Kpax3. Output consists of a clustering of both the rows (statistical units) and columns (statistical variables) of the provided data matrix. It is an improved version of K-Pax2, providing an MCMC algorithm for a proper Bayesian approach and a genetic algorithm for MAP estimation.
To know more about the underlying statistical model, refer to the following publications (the first is the primary citation if you use the package):
Pessia, A. and Corander, J. (2018). Kpax3: Bayesian bi-clustering of large sequence datasets. Bioinformatics, 34(12): 2132–2133. doi: 10.1093/bioinformatics/bty056
Pessia, A., Grad, Y., Cobey, S., Puranen, J. S., and Corander, J. (2015) K-Pax2: Bayesian identification of cluster-defining amino acid positions in large sequence datasets. Microbial Genomics, 1(1). doi: 10.1099/mgen.0.000025
Kpax3 can be easily installed from within Julia:
- Enter the Pkg REPL-mode by pressing
]in the Julia REPL
- Issue the command
- Press the Backspace key to return to the Julia REPL
The best way to learn how to use Kpax3 is by following the instructions in the fasta tutorial (for genetic sequences) or in the csv tutorial (for general categorical data).
It is also possible to run Kpax3 directly from the command line by using the script available on GitHub Gist.