The Python implementation for Principal Coordinate Analysis. For distance metric, one of Jaccard, Bray-Curtis, or Jensen-Shannon divergence can be used.
usage: pcoa.py [-h] [-f DATA_FILE] [-d {Jaccard,BrayCurtis,JSD}] [-b]
[-n N_ARROWS] [-g GROUP_FILE]
optional arguments:
-h, --help show this help message and exit
-f DATA_FILE, --file DATA_FILE
tab-separated text file. rows are variables, columns
are samples.
-d {Jaccard,BrayCurtis,JSD}, --distance_metric {Jaccard,BrayCurtis,JSD}
choose distance metric used for PCoA.
-b, --biplot output biplot (with calculating factor loadings).
-n N_ARROWS, --number_of_arrows N_ARROWS
how many top-contributing arrows should be drawed.
-g GROUP_FILE, --grouping_file GROUP_FILE
plot samples by same colors and markers when they
belong to the same group. Please indicate Tab-
separated 'Samples vs. Group file' ( first columns are
sample names, second columns are group names ).