Package 'o2plsda' reference manual

Title:	Multiomics Data Integration
Description:	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.
Authors:	Kai Guo [aut, cre], Junguk Hur [aut], Eva Feldman [aut]
Maintainer:	Kai Guo <[email protected]>
License:	GPL-3
Version:	0.0.25
Built:	2024-09-17 02:11:32 UTC
Source:	https://github.com/guokai8/o2plsda

Extract the loadings from an O2PLS fit

Description

This function extracts loading parameters from an O2PLS fit

Usage

loadings(x, ...)

## S3 method for class 'O2pls'
loadings(x, loading = c("Xjoint", "Yjoint", "Xorth", "Yorth"), ...)
loadings(x, ...)

## S3 method for class 'O2pls'
loadings(x, loading = c("Xjoint", "Yjoint", "Xorth", "Yorth"), ...)

Arguments

`x`	Object of class `O2pls`
`...`	For consistency
`loading`	the loadings for one of "Xjoint", "Yjoint", "Xorth", "Yorth"

Value

Loading matrix

extract the loading value from the O2PLSDA analysis

Description

extract the loading value from the O2PLSDA analysis

Usage

## S3 method for class 'o2plsda'
loadings(x, loading = "Xloading", ...)
## S3 method for class 'o2plsda'
loadings(x, loading = "Xloading", ...)

Arguments

`x`	Object of class `o2plsda`
`loading`	the loadings for one of "Xjoint", "Yjoint", "Xorth", "Yorth"
`...`	For consistency

extract the loading value from the PLSDA analysis

Description

extract the loading value from the PLSDA analysis

Usage

## S3 method for class 'plsda'
loadings(x, ...)
## S3 method for class 'plsda'
loadings(x, ...)

Arguments

`x`	Object of class `plsda`
`...`	For consistency

Cross validation for O2PLS

Description

Cross validation for O2PLS

Usage

o2cv(
  X,
  Y,
  nc,
  nx,
  ny,
  group = NULL,
  nr_folds = 5,
  ncores = 1,
  scale = FALSE,
  center = FALSE
)
o2cv(
  X,
  Y,
  nc,
  nx,
  ny,
  group = NULL,
  nr_folds = 5,
  ncores = 1,
  scale = FALSE,
  center = FALSE
)

Arguments

`X`	a Numeric matrix (input)
`Y`	a Numeric matrix (input)
`nc`	Integer. Number of joint PLS components.
`nx`	Integer. Number of orthogonal components in X
`ny`	Integer. Number of orthogonal components in Y
`group`	a vector to indicate the group for Y
`nr_folds`	Integer to indicate the folds for cross validation
`ncores`	Integer. Number of CPUs to use for cross validation
`scale`	boolean values determining if data should be scaled or not
`center`	boolean values determining if data should be centered or not

Value

a data frame with the Q and RMSE values

Author(s)

Kai Guo

Examples

set.seed(123)
X = matrix(rnorm(500),50,10)
Y = matrix(rnorm(500),50,10)
X = scale(X, scale = TRUE)
Y = scale(Y, scale = TRUE)
# group factor could be omitted if you don't have any group 
group <- rep(c("Ctrl","Treat"), each = 25)
cv <- o2cv(X, Y, 1:2, 1:2, 1:2, group=group, nr_folds = 2, ncores=1)
set.seed(123)
X = matrix(rnorm(500),50,10)
Y = matrix(rnorm(500),50,10)
X = scale(X, scale = TRUE)
Y = scale(Y, scale = TRUE)
# group factor could be omitted if you don't have any group 
group <- rep(c("Ctrl","Treat"), each = 25)
cv <- o2cv(X, Y, 1:2, 1:2, 1:2, group=group, nr_folds = 2, ncores=1)

fit O2PLS model with best nc, nx, ny

Description

fit O2PLS model with best nc, nx, ny

Usage

o2pls(X, Y, nc, nx, ny, scale = FALSE, center = FALSE)
o2pls(X, Y, nc, nx, ny, scale = FALSE, center = FALSE)

Arguments

`X`	a Numeric matrix (input)
`Y`	a Numeric matrix (input)
`nc`	Integer. Number of joint PLS components.
`nx`	Integer. Number of orthogonal components in X
`ny`	Integer. Number of orthogonal components in Y
`scale`	boolean values determining if data should be scaled or not
`center`	boolean values determining if data should be centered or not

Value

An object containing

`Xscore`	Joint $X$ scores
`Xloading`	Joint $X$ loadings
`Yscore`	Joint $Y$ scores
`Yloading`	Joint $Y$ loadings
`TYosc`	Orthogonal $X$ scores
`PYosc`	Orthogonal $X$ loadings
`WYosc`	Orthogonal $X$ weights
`UXosc`	Orthogonal $Y$ scores
`PXosc`	Orthogonal $Y$ loadings
`CXosc`	Orthogonal $Y$ weights
`BU`	Regression coefficient in `Tt` ~ `U`
`BT`	Regression coefficient in `U` ~ `Tt`
`Xhat`	Prediction of $X$ with $Y$
`Yhat`	Prediction of $Y$ with $X$
`R2Xhat`	Variation of the predicted $X$ as proportion of variation in $X$
`R2Yhat`	Variation of the predicted $Y$ as proportion of variation in $Y$
`R2X`	Variation of the modeled part in $X$ (defined by Joint + Orthogonal variation) as proportion of total variation in $X$
`R2Y`	Variation of the modeled part in $Y$ (defined by Joint + Orthogonal variation) as proportion of total variation in $Y$
`R2Xcorr`	Variation of the joint part in $X$
`R2Ycorr`	Variation of the joint part in $Y$
`R2Xo`	Variation of the orthogonal part in $X$ as proportion of variation in $X$
`R2Yo`	Variation of the orthogonal part in $Y$ as proportion of variation in $Y$
`R2Xp`	Variation in $X$ joint part predicted by $Y$ Joint part
`R2Yp`	Variation in $Y$ joint part predicted by $X$ Joint part
`varXj`	Variation in each Latent Variable (LV) in $X$ Joint part
`varYj`	Variation in each Latent Variable (LV) in $Y$ Joint part
`varXorth`	Variation in each Latent Variable (LV) in $X$ Orthogonal part
`varYorth`	Variation in each Latent Variable (LV) in $Y$ Orthogonal part
`Exy`	Residuals in $X$
`Fxy`	Residuals in $Y$

Author(s)

Kai Guo

Examples

set.seed(123)
X = matrix(rnorm(500),50,10)
Y = matrix(rnorm(500),50,10)
X = scale(X, scale = TRUE)
Y = scale(Y, scale = TRUE)
fit <- o2pls(X, Y, 1, 2, 2)
summary(fit)
set.seed(123)
X = matrix(rnorm(500),50,10)
Y = matrix(rnorm(500),50,10)
X = scale(X, scale = TRUE)
Y = scale(Y, scale = TRUE)
fit <- o2pls(X, Y, 1, 2, 2)
summary(fit)

Class "O2pls" This class represents the Annotation information

Description

Class "O2pls" This class represents the Annotation information

Slots

X: a Numeric matrix (input)
Y: a Numeric matrix (input)
params: paramaters ysed in o2pls analysis
results: list of o2pls results

Author(s)

Kai Guo

Orthogonal partial least squares discriminant analysis

Description

Computes orthogonal scores partial least squares regressions with the NIPALS algorithm. It return a comprehensive set of pls outputs (e.g. scores and vip).

Usage

oplsda(X, Y, nc, scale = FALSE, center = TRUE, maxiter = 100, tol = 1e-05)
oplsda(X, Y, nc, scale = FALSE, center = TRUE, maxiter = 100, tol = 1e-05)

Arguments

`X`	a O2pls object or a matrix of predictor variables.
`Y`	a single vector indicate the group
`nc`	the number of pls components (the one joint components + number of orthogonal components ).
`scale`	logical indicating whether `X` must be scaled (suggest TRUE).
`center`	boolean values determining if data should be centered or not
`maxiter`	maximum number of iterations.
`tol`	limit for convergence of the algorithm in the nipals algorithm.

Value

a list containing the following elements:

nc the number of components used(one joint components + number of orthogonal components
scores a matrix of scores corresponding to the observations in X, The components retrieved correspond to the ones optimized or specified.
Xloadings a matrix of loadings corresponding to the explanatory variables. The components retrieved correspond to the ones optimized or specified.
Yloadings a matrix of partial least squares loadings corresponding to Y
vip the VIP matrix.
xvar a matrix indicating the standard deviation of each component (sd), the variance explained by each single component (explained_var) and the cumulative explained variance (cumulative_explained_var). These values are computed based on the data used to create the projection matrices.
projection_matrix the matrix of projection matrix
weight a matrix of partial least squares ("pls") weights.

Author(s)

Kai Guo

Examples

X <- matrix(rnorm(50),10,5)
Y <- matrix(rnorm(50),10,5)
fit <- o2pls(X,Y,2,1,1)
yy <- rep(c(0,1),5)
fit0 <- oplsda(fit,yy,2)
X <- matrix(rnorm(50),10,5)
Y <- matrix(rnorm(50),10,5)
fit <- o2pls(X,Y,2,1,1)
yy <- rep(c(0,1),5)
fit0 <- oplsda(fit,yy,2)

Score or loading plot for the O2PLS results

Description

Score or loading plot for the O2PLS results

Usage

## S3 method for class 'O2pls'
plot(
  x,
  type = "score",
  var = "Xjoint",
  group = NULL,
  ind = c(1, 2),
  color = NULL,
  top = 20,
  ellipse = TRUE,
  order = FALSE,
  pt.size = 3,
  label = TRUE,
  label.size = 4,
  repel = TRUE,
  rotation = FALSE,
  ...
)
## S3 method for class 'O2pls'
plot(
  x,
  type = "score",
  var = "Xjoint",
  group = NULL,
  ind = c(1, 2),
  color = NULL,
  top = 20,
  ellipse = TRUE,
  order = FALSE,
  pt.size = 3,
  label = TRUE,
  label.size = 4,
  repel = TRUE,
  rotation = FALSE,
  ...
)

Arguments

`x`	an O2pls object
`type`	score or loading
`var`	specify Xjoint
`group`	color used for score plot
`ind`	which components to be used for score plot or loading plot
`color`	color used for score or loading plot
`top`	the number of largest loading value to plot
`ellipse`	TRUE/FALSE
`order`	order by the value or not
`pt.size`	point size
`label`	plot label or not (TRUE/FALSE)
`label.size`	label size
`repel`	use ggrepel to show the label or not
`rotation`	flip the figure or not (TRUE/FALSE)
`...`	For consistency

Value

a ggplot2 object

Author(s)

Kai Guo

Examples

X <- matrix(rnorm(50),10,5)
Y <- matrix(rnorm(50),10,5)
fit <- o2pls(X,Y,2,1,1)
plot(fit, type="score")
X <- matrix(rnorm(50),10,5)
Y <- matrix(rnorm(50),10,5)
fit <- o2pls(X,Y,2,1,1)
plot(fit, type="score")

Score, VIP or loading plot for the O2PLS results

Description

Score, VIP or loading plot for the O2PLS results

Usage

## S3 method for class 'o2plsda'
plot(
  x,
  type = "score",
  group = NULL,
  ind = c(1, 2),
  color = NULL,
  top = 20,
  ellipse = TRUE,
  order = FALSE,
  pt.size = 3,
  label = TRUE,
  label.size = 4,
  repel = FALSE,
  rotation = FALSE,
  ...
)
## S3 method for class 'o2plsda'
plot(
  x,
  type = "score",
  group = NULL,
  ind = c(1, 2),
  color = NULL,
  top = 20,
  ellipse = TRUE,
  order = FALSE,
  pt.size = 3,
  label = TRUE,
  label.size = 4,
  repel = FALSE,
  rotation = FALSE,
  ...
)

Arguments

`x`	an o2plsda object
`type`	score, vip or loading
`group`	color used for score plot
`ind`	which components to be used for score plot or loading plot
`color`	color used for score or loading plot
`top`	the number of largest loading value to plot
`ellipse`	TRUE/FALSE
`order`	order by the value or not
`pt.size`	point size
`label`	plot label or not (TRUE/FALSE)
`label.size`	label size
`repel`	use ggrepel to show the label or not
`rotation`	flip the figure or not (TRUE/FALSE)
`...`	For consistency

Value

a ggplot2 object

Author(s)

Kai Guo

Examples

X <- matrix(rnorm(50),10,5)
Y <- matrix(rnorm(50),10,5)
fit <- o2pls(X,Y,2,1,1)
yy <- rep(c(0,1),5)
fit0 <- oplsda(fit,yy,2)
plot(fit0, type="score", group = factor(yy))
X <- matrix(rnorm(50),10,5)
Y <- matrix(rnorm(50),10,5)
fit <- o2pls(X,Y,2,1,1)
yy <- rep(c(0,1),5)
fit0 <- oplsda(fit,yy,2)
plot(fit0, type="score", group = factor(yy))

Score, VIP or loading plot for the plsda results

Description

Score, VIP or loading plot for the plsda results

Usage

## S3 method for class 'plsda'
plot(
  x,
  type = "score",
  group = NULL,
  ind = c(1, 2),
  color = NULL,
  top = 20,
  ellipse = TRUE,
  order = FALSE,
  pt.size = 3,
  label = TRUE,
  label.size = 4,
  repel = FALSE,
  rotation = FALSE,
  ...
)
## S3 method for class 'plsda'
plot(
  x,
  type = "score",
  group = NULL,
  ind = c(1, 2),
  color = NULL,
  top = 20,
  ellipse = TRUE,
  order = FALSE,
  pt.size = 3,
  label = TRUE,
  label.size = 4,
  repel = FALSE,
  rotation = FALSE,
  ...
)

Arguments

`x`	an plsda object
`type`	score, vip or loading
`group`	color used for score plot
`ind`	which components to be used for score plot or loading plot
`color`	color used for score or loading plot
`top`	the number of largest loading value to plot
`ellipse`	TRUE/FALSE
`order`	order by the value or not
`pt.size`	point size
`label`	plot label or not (TRUE/FALSE)
`label.size`	label size
`repel`	use ggrepel to show the label or not
`rotation`	flip the figure or not (TRUE/FALSE)
`...`	For consistency

Value

a ggplot2 object

Author(s)

Kai Guo

Examples

X <- matrix(rnorm(500),10,50)
Y <- rep(c("a","b"),each=5)
fit0 <- plsda(X,Y,2)
plot(fit0, type = "score", group = factor(Y))
X <- matrix(rnorm(500),10,50)
Y <- rep(c("a","b"),each=5)
fit0 <- plsda(X,Y,2)
plot(fit0, type = "score", group = factor(Y))

Partial least squares discriminant analysis

Description

Perform a PLS discriminant analysis

Usage

plsda(X, Y, nc, scale = TRUE, center = TRUE, cv = TRUE, nr_folds = 5)
plsda(X, Y, nc, scale = TRUE, center = TRUE, cv = TRUE, nr_folds = 5)

Arguments

`X`	a matrix of predictor variables.
`Y`	a single vector indicate the group
`nc`	the number of pls components (the one joint components + number of orthogonal components ).
`scale`	logical indicating whether `X` must be scaled (suggest TRUE).
`center`	logical indicating whether `X` must be centered (suggest TRUE).
`cv`	logical indicating whether cross-validation will be performed or not (suggest TRUE).
`nr_folds`	nr_folds Integer to indicate the folds for cross validation.

Value

a list containing the following elements:

nc the number of components used(one joint components + number of orthogonal components
scores a matrix of scores corresponding to the observations in X, The components retrieved correspond to the ones optimized or specified.
Xloadings a matrix of loadings corresponding to the explanatory variables. The components retrieved correspond to the ones optimized or specified.
vip the VIP matrix.
xvar variance explained of X by each single component.
R2Y variance explained of Y by each single component.
PRESS The residual sum of squares for the samples which were not used to fit the model
Q2 quality of cross-validation

Author(s)

Kai Guo

Examples

X <- matrix(rnorm(500),10,50)
Y <- rep(c("a","b"),each=5)
fit <- plsda(X,Y,2)
X <- matrix(rnorm(500),10,50)
Y <- rep(c("a","b"),each=5)
fit <- plsda(X,Y,2)

Print the summary of O2PLS results.

Description

Print the summary of O2PLS results.

Usage

## S3 method for class 'O2pls'
print(x, ...)
## S3 method for class 'O2pls'
print(x, ...)

Arguments

`x`	An O2pls object
`...`	For consistency

Author(s)

Kai Guo

Examples

X <- matrix(rnorm(50),10,5)
Y <- matrix(rnorm(50),10,5)
object <- o2pls(X,Y,1,1,1)
print(object)
X <- matrix(rnorm(50),10,5)
Y <- matrix(rnorm(50),10,5)
object <- o2pls(X,Y,1,1,1)
print(object)

Print the summary of plsda results.

Description

Print the summary of plsda results.

Usage

## S3 method for class 'plsda'
print(x, ...)
## S3 method for class 'plsda'
print(x, ...)

Arguments

`x`	An plsda object
`...`	For consistency

Author(s)

Kai Guo

Examples

X <- matrix(rnorm(500),10,50)
Y <- rep(c("a","b"),each=5)
fit <- plsda(X,Y,2)
print(fit)
X <- matrix(rnorm(500),10,50)
Y <- rep(c("a","b"),each=5)
fit <- plsda(X,Y,2)
print(fit)

Extract the scores from an O2PLS fit

Description

This function extracts score matrices from an O2PLS fit

Usage

scores(x, ...)
scores(x, ...)

Arguments

`x`	Object of class `O2pls`
`...`	For consistency

Value

Scores matrix

Extract the scores from an O2PLS fit

Description

This function extracts scores parameters from an O2PLS fit

Usage

## S3 method for class 'O2pls'
scores(x, score = c("Xjoint", "Yjoint", "Xorth", "Yorth"), ...)
## S3 method for class 'O2pls'
scores(x, score = c("Xjoint", "Yjoint", "Xorth", "Yorth"), ...)

Arguments

`x`	Object of class `O2pls`
`score`	the scores matrix for one of "Xjoint", "Yjoint", "Xorth", "Yorth"
`...`	Other arguments

Value

score matrix

Extract the scores from an O2PLS DA analysis

Description

Extract the scores from an O2PLS DA analysis

Usage

## S3 method for class 'o2plsda'
scores(x, ...)
## S3 method for class 'o2plsda'
scores(x, ...)

Arguments

`x`	Object of class `o2plsda`
`...`	Other arguments

Value

score matrix

Author(s)

Kai Guo

Extract the scores PLSDA analysis

Description

Extract the scores PLSDA analysis

Usage

## S3 method for class 'plsda'
scores(x, ...)
## S3 method for class 'plsda'
scores(x, ...)

Arguments

`x`	Object of class `plsda`
`...`	Other arguments

Value

score matrix

Author(s)

Kai Guo

Summary of an O2PLS object

Description

Summary of an O2PLS object

Usage

## S3 method for class 'O2pls'
summary(object, ...)
## S3 method for class 'O2pls'
summary(object, ...)

Arguments

`object`	a O2pls object
`...`	For consistency

Value

Detail of O2PLS results

Author(s)

Kai Guo

Examples

X <- matrix(rnorm(50),10,5)
Y <- matrix(rnorm(50),10,5)
object <- o2pls(X,Y,1,1,1)
summary(object)
X <- matrix(rnorm(50),10,5)
Y <- matrix(rnorm(50),10,5)
object <- o2pls(X,Y,1,1,1)
summary(object)

Summary of an plsda object

Description

Summary of an plsda object

Usage

## S3 method for class 'plsda'
summary(object, ...)
## S3 method for class 'plsda'
summary(object, ...)

Arguments

`object`	a plsda object
`...`	For consistency

Value

Detail of plsda results

Author(s)

Kai Guo

Examples

X <- matrix(rnorm(500),10,50)
Y <- rep(c("a","b"),each=5)
fit <- plsda(X,Y,2)
summary(fit)
X <- matrix(rnorm(500),10,50)
Y <- rep(c("a","b"),each=5)
fit <- plsda(X,Y,2)
summary(fit)

Extract the VIP values from the O2PLS-DA object

Description

Extract the VIP values from the O2PLS-DA object

Usage

vip(x)
vip(x)

Arguments

`x`	the o2plsda object or plsda object

Value

a data frame

Package 'o2plsda'

Help Index

Extract the loadings from an O2PLS fit

Description

Usage

Arguments

Value

extract the loading value from the O2PLSDA analysis

Description

Usage

Arguments

extract the loading value from the PLSDA analysis

Description

Usage

Arguments

Cross validation for O2PLS

Description

Usage

Arguments

Value

Author(s)

Examples

fit O2PLS model with best nc, nx, ny

Description

Usage

Arguments

Value

Author(s)

Examples

Class "O2pls" This class represents the Annotation information

Description

Slots

Author(s)

Orthogonal partial least squares discriminant analysis

Description

Usage

Arguments

Value

Author(s)

Examples

Score or loading plot for the O2PLS results

Description

Usage

Arguments

Value

Author(s)

Examples

Score, VIP or loading plot for the O2PLS results

Description

Usage

Arguments

Value

Author(s)

Examples

Score, VIP or loading plot for the plsda results

Description

Usage

Arguments

Value

Author(s)

Examples

Partial least squares discriminant analysis

Description

Usage

Arguments

Value

Author(s)

Examples

Print the summary of O2PLS results.

Description

Usage

Arguments

Author(s)

Examples

Print the summary of plsda results.

Description

Usage

Arguments

Author(s)

Examples