Multiomics Data Integration
get the sd for a matrix
get the total values for a matrix
do cross-validation with group factors
Partial least squares discriminant analysis
two matrix mutiplication
three matrix mutiplication
MCCV sampling
extract the loading value from the O2PLSDA analysis
extract the loading value from the PLSDA analysis
Extract the loadings from an O2PLS fit
Cross validation for O2PLS
Class "O2pls" This class represents the Annotation information
fit O2PLS model with best nc, nx, ny
orthogonal scores algorithn of partial leat squares (opls) projection
Orthogonal partial least squares discriminant analysis
order a vector
order a vector of sting
Score or loading plot for the O2PLS results
split a vector
Summary of an O2PLS object
Summary of an plsda object
unlist a list
Extract the VIP values from the O2PLS-DA object
trans matrix * matrix
Score, VIP or loading plot for the O2PLS results
Score, VIP or loading plot for the plsda results
Partial least squares discriminant analysis
Print the summary of O2PLS results.
Print the summary of plsda results.
calcualte the Q value
calculate RMSE
sum square of a matrix
sampling a vector
lapply sampling
Extract the scores from an O2PLS fit
Extract the scores from an O2PLS DA analysis
Extract the scores PLSDA analysis
Extract the scores from an O2PLS fit
sort string
Provides functions to do 'O2PLS-DA' analysis for multiple omics data integration. The algorithm came from "O2-PLS, a two-block (X±Y) latent variable regression (LVR) method with an integral OSC filter" which published by Johan Trygg and Svante Wold at 2003 <doi:10.1002/cem.775>. 'O2PLS' is a bidirectional multivariate regression method that aims to separate the covariance between two data sets (it was recently extended to multiple data sets) (Löfstedt and Trygg, 2011 <doi:10.1002/cem.1388>; Löfstedt et al., 2012 <doi:10.1016/j.aca.2013.06.026>) from the systematic sources of variance being specific for each data set separately.