PCA and Dataset treatment
Hisham Abu Samra
Hiya,
Thank you very much for all the efforts put into ViSta!
I have two questions regarding Principal Components Analysis (PCA) and dataset treatment using the latest version: ViSta 7.9.2.5.
Q1: The data consists of 150 observations (samples with different chemical compositions) and 130 variables (spectroscopic chemical composition-related signal intensities).
The dataset was first pre-treated in such a way that the signal intensities are normalised for each sample. Importing the data to ViSta and carrying out PCA was successful.
However, PCA analysis failed on the same data after mean-centering (that is each single variable is subtracted of its mean value across all samples). Surely there are no issues with importing the data.
The following error messages appeared:
(Error: Floating point exception. Happened in: #<Subr-LINPACK-DSVDC: #1d5ff88>)
On the other hand, this problem does not seem to occur when the input data matrix size is reduced to 150 Obs. X 110 Var. Could this be related to the input matrix size which ViSta could process?
Q2: Is it possible to carry out specific data treatment prior to performing PCA using ViSta to obtain:
(i) mean-centering of the dataset.
(ii) variance scaling (each single variable is divided by its standard deviation in the input dataset).
Thank you very much in advance.
Hisham
