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

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 |

`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

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
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
``` |

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