PDMByFactorAnalysis: Calculate phenotypic distance matrix by factor analysis
In phenoDist: Phenotypic distance measures

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

View source: R/PDMatrix.R

This function transforms the cell features by factor analysis and computes the phenotypic distance matrix.

1	PDMByFactorAnalysis(x, unames, selectedCellFtrs, distMethod=c('manhattan','euclidean', 'correlation','mahalanobis'), nFactors, scores=c('regression','Bartlett'), ...)

`x`	An `imageHTS` object.
`unames`	A character vector, containing the well names from where to collect the cell features. See `getUnames` for details.
`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'.
`nFactors`	An integer scalar for the number of factors.
`scores`	A character string indicating the type of scores to be reported by factor analysis. This must be (an abbreviation of) one of the strings 'regression' or 'Bartlett'.
`...`	Additional arguments to be passed to the `factanal` function of the `stats` pacakge.

This function first collects individual cell features in all wells (which could be time and memory consuming), performs factor analysis on cell features and transforms cell features into a certain number of factors, and then the factors are averaged by well and passed to PDMByWellAvg to calculate the phenotypic distance matrix.

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

Xian Zhang

factanal, PDMByWellAvg

  library('phenoDist')

  ## load the imageHTS object
  load(system.file('kimorph', 'kimorph.rda', package='phenoDist'))
  x@localPath <- file.path(tempdir(), 'kimorph')
  
  ## segmentation and feature extraction
  unames <- setdiff(getUnames(x), getUnames(x, content='empty'))

  ## calculate pair-wise svm distance matrix
  load(system.file('kimorph', 'selectedFtrs.rda', package='phenoDist'))
  pdm <- PDMByFactorAnalysis(x, unames=getUnames(x, plate=1, row=2:3, col=3), selectedCellFtrs, distMethod='euclidean', nFactors=10, scores='regression')
  pdm