Provided by: phast_1.7+dfsg-1_amd64 
NAME
pbsTrain - Estimate a discrete encoding scheme for probabilistic biological
DESCRIPTION
Estimate a discrete encoding scheme for probabilistic biological sequences (PBSs) based on
training data. Input file should be a table of probability vectors, with a row for each
distinct vector, and a column of counts (positive integers) followed by d columns for the
elements of the d-dimensional probability vectors (see example below). It may be produced
with 'prequel' using the --suff-stats option. Output is a code file that can be used with
pbsEncode, pbsDecode, etc. By default, a code of size 255 is created, so that encoded
PBSs can be represented with one byte per position (the 256th letter in the code is
reserved for gaps). The --nbytes option allows larger codes to be created, if desired.
The code is estimated by a two-part procedure designed to minimize the "training error"
(defined as the total KL divergence) of the encoded training data with respect to the
original training data. First, a "grid" is defined for the probability simplex,
partitioning it into regions that intersect "cells" (hypercubes) in a matrix in
d-dimensional space. This grid has n "rows" per dimension. By default, n is given the
largest possible value such that the number of simplex regions is no larger than the
target code size, but smaller values of n can be specified using --nrows. Each simplex
region is assigned a letter in the code, and the representative point for that letter is
set equal to the mean (weighted by the counts) of all vectors in the training data that
fall in that region. This can be shown to minimize the training error for this initial
encoding scheme. (If no vectors fall in a region, then the representative point is set
equal to the centroid of the region, which can be shown to minimize the expected KL
divergence of points uniformly distributed in the region.)
In the second part of the estimation procedure, the remaining letters in the code are
defined by a greedy algorithm, which attempts to further minimize the training error.
Briefly, on each step, the simplex region with the largest contribution to the total error
is identified, and the next letter in the code is assigned to that region. In this new
encoding, there are multiple letters, hence multiple representative points, per region;
the representative point for a given vector is taken to be the closest, in terms of KL
divergence, of the representative points associated with the simplex region in which that
vector falls. When a new representative point is added to a region, all representative
points for that region are reoptimized using a k-means type algorithm. This procedure is
repeated, letter by letter, until the number of code letters equals the target code size.
EXAMPLE
Generate training data using prequel:
prequel --suff-stats mammals.fa mytree.mod training
A file called "training.stats" will be generated.
It will look
something like this:
#count
p(A) p(C) p(G) p(T)
170085
0.043485 0.797886 0.029534 0.129096
158006
0.191119 0.046081 0.695205 0.067595
221937
0.047309 0.122834 0.043852 0.786004
221585
0.781156 0.044520 0.126179 0.048146
159472
0.067254 0.697947 0.045959 0.188840
...
Now estimate a code from the training data:
pbsTrain training.stats > mammals.code
The code file contains some metadata followed by a list of code indices and representative
points, e.g.,
##NROWS = 7
##DIMENSION = 4
##NBYTES = 1
##CODESIZE = 255
# Code generated by pbsTrain, with argument(s) "training.stats"
# acs, Mon Jul 18 23:29:07 2005
# Average training error = 0.001298 bits
Each index of the code is shown below with its representative probability vector (p1, p2,
..., pd).
#code_index p1 p2 ... 0 0.107143 0.107143 0.107143
0.678571
1 0.033226 0.093854 0.031987 0.840933
2 0.000059 0.001645 0.000111 0.998185
3 0.139270 0.021059 0.278993 0.560678
...
The reported "average training error" is the training error divided by the number of data
points (the sum of the counts).
OPTIONS
--nrows, -n <n> Number of "rows" per dimension in the simplex grid. Default is maximum
possible for code size.
--nbytes, -b <b>
Number of bytes per encoded probabilistic base (default 1). The size of the code
will be 256^b - 1 (one letter in the code is reserved for gaps). Values as large
as 4 are allowed for b, but in the current implementation, performance
considerations effectively limit it to 2 or 3.
--no-greedy, -G Skip greedy optimization -- just assign a single representative point to
each region of the probability simplex, equal to the (weighted) mean of all vectors
from the training data that fall in that region.
--no-train, -x <dim>
Ignore the data entirely; just use the centroid of each simplex partition. The
dimension of the simplex must be given (<dim>) but no data file is required.
--log, -l <file>
write log of optimization procedure to specified file.
--help, -h
Print this help message.