usage: kmers.py
Count k-mer frequencies of given `fasta`
optional arguments:
-h, --help show this help message and exit
--fasta filepath Metagenomic assembly fasta file (default: None)
--kmers filepath K-mers frequency tab-delimited table (will skip if
file exists) (default: None)
--size int k-mer size in bp (default: 5)
--norm-output filepath
Path to normalized kmers table (will skip if file
exists) (default: None)
--norm-method {ilr,clr,am_clr}
Normalization method to transform kmer counts prior to
PCA and embedding. ilr: isometric log-ratio transform
(scikit-bio implementation). clr: center log-ratio
transform (scikit-bio implementation). am_clr: center
log-ratio transform (Autometa implementation).
(default: am_clr)
--pca-dimensions int Number of dimensions to reduce to PCA feature space
after normalization and prior to embedding (NOTE:
Setting to zero will skip PCA step) (default: 50)
--embedding-output filepath
Path to write embedded kmers table (will skip if file
exists) (default: None)
--embedding-method {sksne,bhsne,umap,densmap,trimap}
embedding method [sk,bh]sne are corresponding
implementations from scikit-learn and tsne,
respectively. (default: bhsne)
--embedding-dimensions int
Number of dimensions of which to reduce k-mer
frequencies (default: 2)
--force Whether to overwrite existing annotations (default:
False)
--cpus int num. processors to use. (default: 2)
--seed int Seed to set random state for dimension reduction
determinism. (default: 42)