PDMBySvmWeightVector: Compute phenotypic distance matrix by SVM weight vector
In phenoDist: Phenotypic distance measures

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/PDMatrix.R

This function performs an SVM classification between a given sample and the negative control, calculates the weight vector, and then computes the phenotypic distance matrix based on the weight vectors.

1	PDMBySvmWeightVector(x, unames, neg='rluc', selectedCellFtrs, distMethod=c('manhattan','euclidean', 'correlation','mahalanobis'), verbose=FALSE, kernel='linear', ...)

`x`	An `imageHTS` object.
`unames`	A character vector, containing the well names from where to collect the cell features. See `getUnames` for details.
`neg`	A character string to identify the negative controls.
`selectedCellFtrs`	A character vector for cell features to be used in the calculation. If missing, all features are used.
`distMethod`	A character string indicating which distance method should be used. This must be (an abbreviation of) one of the strings 'manhattan', 'euclidean', 'correlation' or 'mahalanobis'.
`verbose`	A logical scalar indicating whether progress should be reported.
`kernel`	The kernel argument for the `svm` function of the `e1071` package.
`...`	Additional arguments to be passed to the `svm` function of the `e1071` package.

For each well, this function collects features of all cells from the well and all cells from the negative control wells, and performs a bi-class classification using Support Vector Machine (SVM). The classification weight vectors are calculated for all wells passed to PDMByWellAvg to compute the phenotypic distance matrix.

A symmetric distance matrix with dimensions equaling to the length of unames.

Xian Zhang

svm, PDMByWellAvg

  library('phenoDist')

  ## load the imageHTS object
  load(system.file('kimorph', 'kimorph.rda', package='phenoDist'))
  x@localPath <- file.path(tempdir(), 'kimorph')

  ## calculate pair-wise svm distance matrix
  load(system.file('kimorph', 'selectedFtrs.rda', package='phenoDist'))
  pdm <- PDMBySvmWeightVector(x, unames=getUnames(x,plate=1, row=2:3, col=3), neg='rluc', selectedCellFtrs=selectedCellFtrs, distMethod='euclidean', verbose=FALSE, cost=1, kernel='linear')