PDMByFactorAnalysis {phenoDist} | R Documentation |
This function transforms the cell features by factor analysis and computes the phenotypic distance matrix.
PDMByFactorAnalysis(x, unames, selectedCellFtrs, distMethod=c('manhattan','euclidean', 'correlation','mahalanobis'), nFactors, scores=c('regression','Bartlett'), ...)
x |
An |
unames |
A character vector, containing the well names from where to collect the cell features. See |
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 |
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