gkmsvm_classify: Classifying(/scoring) new sequences using the gkmSVM model

In gkmSVM: Gapped-Kmer Support Vector Machine

Classifying(/scoring) new sequences using the gkmSVM model

Description

Given support vectors SVs and corresponding coefficients alphas and a set of sequences, calculates the SVM scores for the sequences.

Usage

gkmsvm_classify(seqfile, svmfnprfx, outfile, L=10, K=6, maxnmm=3, 
maxseqlen=10000, maxnumseq=1000000, useTgkm=1, alg=0, addRC=TRUE, usePseudocnt=FALSE, 
batchSize=100000, wildcardLambda=1.0, wildcardMismatchM=2, alphabetFN="NULL", 
svseqfile=NA, alphafile=NA)

Arguments

seqfile	input sequences file name (FASTA format)
svmfnprfx	SVM model file name prefix
outfile	output file name
L	word length, default=10
K	number of informative columns, default=6
maxnmm	maximum number of mismatches to consider, default=3
maxseqlen	maximum sequence length in the sequence files, default=10000
maxnumseq	maximum number of sequences in the sequence files, default=1000000
useTgkm	filter type: 0(use full filter), 1(use truncated filter: this gaurantees non-negative counts for all L-mers), 2(use h[m], gkm count vector), 3(wildcard), 4(mismatch), default=1
alg	algorithm type: 0(auto), 1(XOR Hashtable), 2(tree), default=0
addRC	adds reverse complement sequences, default=TRUE
usePseudocnt	adds a constant to count estimates, default=FALSE
batchSize	number of sequences to compute scores for in batch, default=100000
wildcardLambda	lambda for wildcard kernel, defaul=0.9
wildcardMismatchM	max mismatch for Mismatch kernel or wildcard kernel, default=2
alphabetFN	alphabets file name, if not specified, it is assumed the inputs are DNA sequences
svseqfile	SVM support vectors sequence file name (not needed if svmfnprfx is provided)
alphafile	SVM support vectors weights file name (not needed if svmfnprfx is provided)

Details

classification using SVM: gkmsvm_classify can be used to score any set of sequences. Note that the same set of parameters (L, K, maxnmm) used in the gkmsvm_kernel should be specified for optimal classification.

gkmsvm_classify(testfn, svmfnprfx, outfn); #scores test sequences

Author(s)

Mahmoud Ghandi

Examples

  #Input file names:  
  posfn= 'test_positives.fa'   #positive set (FASTA format)
  negfn= 'test_negatives.fa'   #negative set (FASTA format)
  testfn= 'test_testset.fa'    #test set (FASTA format)
  
  #Output file names:  
  kernelfn= 'test_kernel.txt' #kernel matrix
  svmfnprfx= 'test_svmtrain'  #SVM files 
  outfn =   'output.txt'      #output scores for sequences in the test set       

#  gkmsvm_kernel(posfn, negfn, kernelfn);                #computes kernel 
#  gkmsvm_train(kernelfn,posfn, negfn, svmfnprfx);       #trains SVM
#  gkmsvm_classify(testfn, svmfnprfx, outfn);            #scores test sequences 

gkmSVM documentation built on Aug. 21, 2023, 1:06 a.m.