gkmsvm_kernel {gkmSVM}

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