Package 'PPLasso'

Prognostic Predictive Lasso for Biomarker Selection

Description

We provide new tools for the identification of prognostic and predictive biomarkers. For further details we refer the reader to the paper: Zhu et al. Identification of prognostic and predictive biomarkers in high-dimensional data with PPLasso. BMC Bioinformatics. 2023 Jan 23;24(1):25.

Details

The DESCRIPTION file:

Package:	PPLasso
Type:	Package
Title:	Prognostic Predictive Lasso for Biomarker Selection
Version:	2.0
Date:	2023-02-26
Authors@R:	c(person("Wencan", "Zhu", email = "[email protected]", role = c("aut", "cre")), person("Celine","Levy-Leduc", email="[email protected]", role = "ctb"), person("Nils", "Ternes", email="[email protected]", role = "ctb"))
Author:	Wencan Zhu [aut, cre], Celine Levy-Leduc [ctb], Nils Ternes [ctb]
Maintainer:	Wencan Zhu <[email protected]>
Description:	We provide new tools for the identification of prognostic and predictive biomarkers. For further details we refer the reader to the paper: Zhu et al. Identification of prognostic and predictive biomarkers in high-dimensional data with PPLasso. BMC Bioinformatics. 2023 Jan 23;24(1):25.
License:	GPL-2
Imports:	genlasso, ggplot2, cvCovEst, glmnet, MASS
VignetteBuilder:	knitr
Suggests:	knitr, rmarkdown
NeedsCompilation:	no
Packaged:	2023-02-26 15:54:29 UTC; mmip
Depends:	R (>= 3.5.0)
Repository:	CRAN
Date/Publication:	2023-02-27 09:12:35 UTC

Index of help topics:

Correction1Vect         Correction on two vectors
Correction2Vect         Correction on two vectors
PPLasso-package         Prognostic Predictive Lasso for Biomarker
                        Selection
ProgPredLasso           Identification of prognostic and predictive
                        biomarkers
top                     Thresholding to 0
top_thresh              Thresholding to a given threshold of the
                        smallest values

Further information is available in the following vignettes:

Vignettes	WLasso package (source, pdf)

This package provide usufull tool for the identification of prognostics and predictive biomarkers.

Author(s)

Wencan Zhu [aut, cre], Celine Levy-Leduc [ctb], Nils Ternes [ctb]

Maintainer: Wencan Zhu <[email protected]>

References

W. Zhu, C. Levy-Leduc, N. Ternes. "A variable selection approach for highly correlated predictors in high-dimensional genomic data". (2020)

---

Correction on two vectors

Description

For the estimation of $\beta$ in Zhu et al. (2022), this function keeps only the M largest values coefficientss set the others to 0.

Usage

Correction1Vect(X, Y, te = NULL, vector, top_grill. = c(1:length(vector)), delta = 0.95)

Arguments

X	Design matrix
Y	Response vector
te	treatment effects
vector	The vector on which we performe the thresholding
top_grill.	grill of the thresholding
delta	parameter $\delta$ in the thresholding

Value

This function returns the thresholded vector.

Author(s)

Wencan Zhu, Celine Levy-Leduc, Nils Ternes

Examples

vect_sample=sample(1:20,20)
X=t(sapply(c(1:10),FUN=function(x) rnorm(20)))
Y=rnorm(10)

Correction1Vect(X=X, Y=Y, vector=vect_sample)

## The function is currently defined as
function(X, Y, te=NULL, vector, top_grill.=c(1:length(vector)), delta=0.95){
  
  beta_interm <- sapply(top_grill., top, vect = vector)
  beta_te <- rbind(rep(te[1],length(top_grill.)), rep(te[2],length(top_grill.)), beta_interm)
  yhat <- as.matrix(X %*% beta_te)
  residuals <- sweep(yhat, 1, Y)
  mse_final_top <- colMeans(residuals^2)
  ratio_mse <- c()
  for (k in 1:(length(top_grill.) - 1)) {
    ratio_mse[k] <- round(mse_final_top[k + 1]/mse_final_top[k],6)
  }
  top_ratio <- min(which(ratio_mse >= delta))
  if (is.infinite(top_ratio)) {
    opt_final_top <- length(vector)
  }
  else {
    opt_final_top <- top_grill.[top_ratio]
  }
  
  return(round(top(vect = vector, thresh = opt_final_top), 6))
  
}

---

Correction on two vectors

Description

For the estimation of $\tilde{\beta}$ in Zhu et al. (2022), this function keeps only the K1 largest values of prognostic biomarkers coefficients and the k2 largest value of the presictive biomarkers coefficients and set the others to the smallest value among the k1 (k2) largest of prognostic (predictive part).

Usage

Correction2Vect(X, Y, te=NULL, vector_prog, vector_pred, 
top_grill.=c(1:length(vector_prog)), delta=0.95, toZero=FALSE)

Arguments

X	Design matrix
Y	Response vector
te	treatment effects
vector_prog	Vector of prognostic biomarkers
vector_pred	Vector of predictive biomarkers
top_grill.	grill of the thresholding
delta	parameter $\delta$ in the thresholding
toZero	should the threshold to 0 or not

Value

This function returns the thresholded vector.

Author(s)

Wencan Zhu, Celine Levy-Leduc, Nils Ternes

Examples

x1=sample(1:10,10)
x2=sample(1:10,10)

X=t(sapply(c(1:10),FUN=function(x) rnorm(20)))
Y=rnorm(10)

Correction2Vect(X=X, Y=Y, vector_prog=x1, vector_pred=x2)

## The function is currently defined as
function(X, Y, te=NULL, vector_prog, vector_pred, 
top_grill.=c(1:length(vector_prog)), delta=0.95, toZero=FALSE){
  
    if(toZero){
      matrix_top_fix <- sapply(top_grill., top, vect=vector_prog)
      matrix_top_opt <- sapply(top_grill., top, vect=vector_pred)
    } else {
      matrix_top_fix <- sapply(top_grill., top_thresh, vect=vector_prog)
      matrix_top_opt <- sapply(top_grill., top_thresh, vect=vector_pred)
    }
    
    
    opt_top_opt <- mse_fix <- c()
    for(j in 1:length(top_grill.)){
      fix_temp <- matrix_top_fix[,j]
      mse_temp <- c()
      yhat <- X%*%c(te, fix_temp, matrix_top_opt[,1])

      mse_temp[1] <- sum((Y-yhat)^2)
      for(m in 2:length(top_grill.)){
        opt_temp <- matrix_top_opt[,m]
        threshed_vect <- c(te, fix_temp, opt_temp)
        yhat <- X%*%threshed_vect
        mse_temp[m] <- sum((Y-yhat)^2)
        ratio_mse <- round(mse_temp[m]/mse_temp[m-1], 6)
        if(ratio_mse >= delta){
          opt_top_opt[j] <- top_grill.[m]
          mse_fix[j] <- mse_temp[m]
          break
        }
      }
      if(m==length(top_grill.)){
        opt_top_opt[j] <- top_grill.[m]
        mse_fix[j] <- mse_temp[m]
      }
      if(j==1){
        ratio_final <- 0
      } else {
        ratio_final <- mse_fix[j]/mse_fix[j-1]
      }
      if(ratio_final >= delta){
        opt_fix <- j
        opt_opt <- m
        break
      }
    }
    
    if(exists("opt_fix")==FALSE){
      opt_fix <- ncol(matrix_top_fix)
      opt_opt <- ncol(matrix_top_opt)
    }
    

    return(c(matrix_top_fix[,opt_fix], matrix_top_opt[,opt_opt]))

}

---

Identification of prognostic and predictive biomarkers

Description

The computes the regularization path of the Prognostic Predictive Lasso described in the paper Zhu et al. (2022) given in the references.

Usage

ProgPredLasso(X1, X2, Y=Y, cor_matrix=NULL, gamma=0.99, maxsteps=500, lambda='single')

Arguments

X1	Design matrix of patients characteristics with treatment 1
X2	Design matrix of patients characteristics with treatment 2
Y	Response variable
cor_matrix	Correlation matrix of biomarkers. If not specified, the function cvCovEst from package cvCovEst will be used to estimate this matrix.
gamma	Parameter $\gamma$ defined in the paper Zhu et al. (2020) given in the references. Its default value is 0.99.
maxsteps	Integer specifying the maximum number of steps for the generalized Lasso algorithm. Its default value is 500.
lambda	Using single tuning parameter or both.

Value

Returns a list with the following components

lambda	different values of the parameter $\lambda$ considered.
beta	matrix of the estimations of $\beta$ for all the $\lambda$ considered.
beta.min	estimation of $\beta$ which minimize the MSE.
bic	BIC for all the $\lambda$ considered.
mse	MSE for all the $\lambda$ considered.

Author(s)

Wencan Zhu, Celine Levy-Leduc, Nils Ternes

---

Thresholding to 0

Description

This function keeps only the K largest values of the vector and sets the others to 0.

Usage

top(vect, thresh)

Arguments

vect	vector to threshold
thresh	threshold

Value

This function returns the thresholded vector.

Author(s)

Wencan Zhu, Celine Levy-Leduc, Nils Ternes

Examples

x=sample(1:10,10)
thresh=3
top(x,thresh)

## The function is currently defined as
function(vect, thresh){
  sorted_vect <- sort(abs(vect),decreasing=TRUE)
  v<-sorted_vect[thresh]
  ifelse(abs(vect)>=v,vect,0)
}

---

Thresholding to a given threshold of the smallest values

Description

This function keeps only the K largest values of the vector and sets the others to the smallest value among the K largest.

Usage

top_thresh(vect,thresh)

Arguments

vect	vector to threshold
thresh	threshold

Value

This function returns the thresholded vector.

Author(s)

Wencan Zhu, Celine Levy-Leduc, Nils Ternes

Examples

x=sample(1:10,10)
thresh=3
top_thresh(x,thresh)

## The function is currently defined as
function (vect, thresh) 
{
    sorted_vect <- sort(abs(vect),decreasing=TRUE)
    v = sorted_vect[thresh]
    ifelse(abs(vect) >= v, vect, v)
  }

---