o2plsda provides functions to do O2PLS-DA analysis for mutiple omics integration. The package could use the group information when we select the best paramaters with cross-validation. The DA function is stills under development. 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. ## Installation
library(devtools)
install_github("guokai8/o2plsda")library(o2plsda)##
## Attaching package: 'o2plsda'## The following object is masked from 'package:stats':
##
## loadingsset.seed(123)
# sample * values
X = matrix(rnorm(5000),50,100)
# sample * values
Y = matrix(rnorm(5000),50,100)
rownames(X) <- paste("S",1:50,sep="")
rownames(Y) <- paste("S",1:50,sep="")
colnames(X) <- paste("Gene",1:100,sep="")
colnames(Y) <- paste("Lipid",1:100,sep="")
X = scale(X, scale=T)
Y = scale(Y, scale=T)
## group factor could be omitted if you don't have any group
group <- rep(c("Ctrl","Treat"),each = 25)Do cross validation with group information
set.seed(123)
## nr_folds : cross validation k-fold (suggest 10)
## ncores : parallel paramaters for large datasets
cv <- o2cv(X,Y,1:5,1:3,1:3,group=group,nr_folds = 10)## #####################################
## The best paramaters are nc = 5 , nx = 3 , ny = 3
## #####################################
## The Qxy is 0.08222935 and the RMSE is: 2.030108
## ##########################################################################
# The best paramaters are nc = 5 , nx = 3 , ny = 3
#####################################
# The Qxy is 0.08222935 and the RMSE is: 2.030108
#####################################Then we can do the O2PLS analysis with nc = 5, nx = 3, ny =3. You can also select the best paramaters by looking at the cross validation results.
fit <- o2pls(X,Y,5,3,3)
summary(fit)##
## ######### Summary of the O2PLS results #########
## ### Call o2pls(X, Y, nc= 5 , nx= 3 , ny= 3 ) ###
## ### Total variation
## ### X: 4900 ; Y: 4900 ###
## ### Total modeled variation ### X: 0.286 ; Y: 0.304 ###
## ### Joint, Orthogonal, Noise (proportions) ###
## X Y
## Joint 0.176 0.192
## Orthogonal 0.110 0.112
## Noise 0.714 0.696
## ### Variation in X joint part predicted by Y Joint part: 0.906
## ### Variation in Y joint part predicted by X Joint part: 0.908
## ### Variation in each Latent Variable (LV) in Joint part:
## LV1 LV2 LV3 LV4 LV5
## X 181.764 179.595 191.210 152.174 157.819
## Y 229.308 204.829 175.926 173.382 155.934
## ### Variation in each Latent Variable (LV) in X Orthogonal part:
## LV1 LV2 LV3
## X 227.856 166.718 143.602
## ### Variation in each Latent Variable (LV) in Y Orthogonal part:
## LV1 LV2 LV3
## Y 225.833 166.231 157.976
##
## ##################################################### Summary of the O2PLS results #########
### Call o2pls(X, Y, nc= 5 , nx= 3 , ny= 3 ) ###
### Total variation
### X: 4900 ; Y: 4900 ###
### Total modeled variation ### X: 0.286 ; Y: 0.304 ###
### Joint, Orthogonal, Noise (proportions) ###
# X Y
#Joint 0.176 0.192
#Orthogonal 0.110 0.112
#Noise 0.714 0.696
### Variation in X joint part predicted by Y Joint part: 0.906
### Variation in Y joint part predicted by X Joint part: 0.908
### Variation in each Latent Variable (LV) in Joint part:
# LV1 LV2 LV3 LV4 LV5
#X 181.764 179.595 191.210 152.174 157.819
#Y 229.308 204.829 175.926 173.382 155.934
### Variation in each Latent Variable (LV) in X Orthogonal part:
# LV1 LV2 LV3
#X 227.856 166.718 143.602
### Variation in each Latent Variable (LV) in Y Orthogonal part:
# LV1 LV2 LV3
#Y 225.833 166.231 157.976Extract the loadings and scores from the fit results
Xl <- loadings(fit,loading="Xjoint")
Xs <- scores(fit,score="Xjoint")The package is still under development.
For any questions please contact guokai8@gmail.com or https://github.com/guokai8/o2plsda/issues