gkmsvm_kernel: Computing the kernel matrix

In gkmSVM: Gapped-Kmer Support Vector Machine

Computing the kernel matrix

Description

Generates a lower triangle of kernel matrix (i.e. pairwise similarities) between the sequences.

Usage

gkmsvm_kernel(posfile, negfile, outfile, L=10, K=6, maxnmm=3, maxseqlen=10000, 
maxnumseq=1000000, useTgkm=1, alg=0, addRC=TRUE, usePseudocnt=FALSE, wildcardLambda=1.0,
wildcardMismatchM=2, alphabetFN="NULL")

Arguments

posfile	positive sequences file name (FASTA format)
negfile	negative sequences file name (FASTA format)
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
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

Details

It calculates the full kernel matrix that can be then used to train an SVM classifier. gkmsvm_kernel(posfn, negfn, kernelfn);

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.