Hidden state prediction

PICRUSt2 wraps the castor R package to run hidden-state prediction (hsp) to predict gene family abundances.

Hidden-state prediction for 16S copy number and E.C. numbers can be run with these commands:

hsp.py -i 16S -t OUTPUT.tre -o 16S_predicted -p 10

hsp.py -i EC -t OUTPUT.tre -o EC_predicted -p 10

The above inputs are:

• -t TREEFILE: Newick tree with study sequences placed amongst reference sequences.
• -i TRAIT_OPTION: Which default pre-calculated count table to use (one of '16S', 'COG', 'EC', 'KO', 'PFAM', 'TIGRFAM')
• -o PREFIX: Prefix for output files: RDS (R object containing state probabilities), predicted counts, and optionally a table of CIs.
• --observed_trait_table TRAIT_COUNTFILE: Trait file to use if a non-default file is needed (most users should use one of the default trait options above).
• -m METHOD: Hidden-state prediction method to use, which needs to be one of: maximum parsimony (mp), empirical probabilities (emp_prob), Markov model (mk_model), subtree averaging (subtree_average), phylogenetic independent contrast (pic), or squared-change parsimony (scq).
• -p INT: Number of processes to run in parallel.