## ----setup, class.source='fold-hide', warning=FALSE--------------------------- #install.packages("knitr") library(knitr) opts_chunk$set(echo = TRUE) # To ensure reproducibility set.seed(12) ## ----packages_installation, eval=FALSE, class.source='fold-hide'-------------- # pkgs <- c("rchemo") # sapply(pkgs, function(x) { # if (!requireNamespace(x, quietly = TRUE)) { # install.packages(x) # } # }) ## ----packages_load, warning=FALSE, message=FALSE, class.source='fold-hide'---- library(rchemo) # to load rchemo ## ----import_Zhang2023--------------------------------------------------------- data(Zhang2023, package = "rchemo") ## ----check_dims, class.source='fold-hide'------------------------------------- # Check dimension BlockNames <- names(Zhang2023) nbrBlocs <- length(BlockNames) dims <- lapply(X=Zhang2023[BlockNames], FUN=dim) names(dims) <- BlockNames dims # Remove unuseful object for the next steps rm(nbrBlocs, dims) ## ----check_orders_and_names, class.source='fold-hide'------------------------- # Check rows names in any order row_names <- lapply(X=Zhang2023[BlockNames], FUN=rownames) rns <- do.call(cbind, row_names) rns.unique <- apply(rns, 1, function(x) length(unique(x))) if (max(rns.unique) > 1) { stop("Rows names are not identical between blocks.") } # Check order of samples check_row_names <- all(sapply(X=row_names, FUN=identical, y = row_names[[1]])) if (!check_row_names && max(rns.unique) == 1) { print("Rows names are not in the same order for all blocks.") } # Remove unuseful object for the next steps rm(row_names, rns, rns.unique, check_row_names) ## ----change_data_format------------------------------------------------------- GCTOF <- Zhang2023$GCTOF HILICPOS <- Zhang2023$HILICPOS sample_metadata <- Zhang2023$metadata rm(Zhang2023) ## ----PCA_separate_datablocks, class.source='fold-hide'------------------------ # GCTOF with unit variance scaling pcaGCTOF <- pcanipalsna(X = scale(GCTOF[,1:dim(GCTOF)[2]]), nlv = nrow(GCTOF), gs = TRUE, tol = .Machine$double.eps^0.5, maxit = 200) ## diagram of explained variance barplot(summary(pcaGCTOF,X = scale(GCTOF[,1:dim(GCTOF)[2]]))$explvar$pvar * 100, names.arg = 1:nrow(GCTOF), main = "diagram of explained variance - PCA GCTOF") ## score plot plotxy(X= pcaGCTOF$T, group = sample_metadata$Gender, asp = 0, col = 3:4, alpha.f = .8, zeroes = TRUE, circle = FALSE, ellipse = FALSE, labels = FALSE, legend = TRUE, main = "components - PCA GCTOF", ncol = 1, pch=16) ## loading plot plotxy(X= pcaGCTOF$P, group = NULL, asp = 0, col = NULL, alpha.f = .8, zeroes = TRUE, circle = FALSE, ellipse = FALSE, labels = TRUE, legend = FALSE, main = "loadings - PCA GCTOF", ncol = 1, cex=0.8) #HILICPOS with unit variance scaling pcaHILICPOS <- pcanipalsna(X = scale(HILICPOS[,1:dim(HILICPOS)[2]]), nlv = nrow(HILICPOS), gs = TRUE, tol = .Machine$double.eps^0.5, maxit = 200) ## diagram of explained variance barplot(summary(pcaHILICPOS,X = scale(HILICPOS[,1:dim(HILICPOS)[2]]))$explvar$pvar * 100, names.arg = 1:nrow(HILICPOS), main = "diagram of explained variance - PCA HILIC POS") ## score plot plotxy(X= pcaHILICPOS$T, group = sample_metadata$Gender, asp = 0, col = 3:4, alpha.f = .8, zeroes = TRUE, circle = FALSE, ellipse = FALSE, labels = FALSE, legend = TRUE, main = "components - PCA HILIC POS", ncol = 1, pch=16) ## loading plot plotxy(X= pcaHILICPOS$P, group = NULL, asp = 0, col = NULL, alpha.f = .8, zeroes = TRUE, circle = FALSE, ellipse = FALSE, labels = TRUE, legend = FALSE, main = "loadings - PCA HILIC POS", ncol = 1, cex=0.8) # Remove unuseful object for the next steps rm(pcaGCTOF, pcaHILICPOS) ## ----run_nlvtest-------------------------------------------------------------- nlvtestmbplsda <- mbplsr_mbplsda_allsteps(Xlist = list(GCTOF = GCTOF[,1:dim(GCTOF)[2]], HILICPOS = HILICPOS[,1:dim(HILICPOS)[2]]), Xnames = c("GCTOF", "HILICPOS"), Xscaling = c("none","pareto","sd")[3], Y = sample_metadata[,"Gender", drop=FALSE], Yscaling = c("none","pareto","sd")[1], weights = NULL, newXlist = NULL, newXnames = NULL, method = c("mbplsrda","mbplslda","mbplsqda")[2], prior = c("unif", "prop")[1], step = c("nlvtest","permutation","model","prediction")[1], nlv = 4, #modeloutput = c("scores","loadings","coef","vip"), cvmethod = c("kfolds","loo")[1], nbrep = 10, seed = 123, samplingk = NULL, nfolds = 3, #npermut = 30, criterion = c("err","rmse")[1], selection = c("localmin","globalmin","1std")[1], outputfilename = NULL) nlvtestmbplsda nlvoptmbplsda <- nlvtestmbplsda[nlvtestmbplsda$optimum==1,"nblv"] # to obtain the optimal number of LV. # to plot the results of the cross-validation plot(nlvtestmbplsda$nblv, nlvtestmbplsda$err_mean, xlab = "number of LV", ylab = "CV classification error rate", pch = 16, ylim = c(0,0.6)) segments(nlvtestmbplsda$nblv,nlvtestmbplsda$err_mean-nlvtestmbplsda$err_sd,nlvtestmbplsda$nblv,nlvtestmbplsda$err_mean+nlvtestmbplsda$err_sd) segments(nlvtestmbplsda$nblv-0.1,nlvtestmbplsda$err_mean-nlvtestmbplsda$err_sd,nlvtestmbplsda$nblv+0.1,nlvtestmbplsda$err_mean-nlvtestmbplsda$err_sd) segments(nlvtestmbplsda$nblv-0.1,nlvtestmbplsda$err_mean+nlvtestmbplsda$err_sd,nlvtestmbplsda$nblv+0.1,nlvtestmbplsda$err_mean+nlvtestmbplsda$err_sd) # Remove unuseful object for the next steps rm(nlvtestmbplsda) ## ----run_permutation---------------------------------------------------------- permutmbplsda <- mbplsr_mbplsda_allsteps(Xlist = list(GCTOF = GCTOF[,1:dim(GCTOF)[2]], HILICPOS = HILICPOS[,1:dim(HILICPOS)[2]]), Xnames = c("GCTOF", "HILICPOS"), Xscaling = c("none","pareto","sd")[3], Y = sample_metadata[,"Gender",drop=FALSE], Yscaling = c("none","pareto","sd")[1], weights = NULL, newXlist = NULL, newXnames = NULL, method = c("mbplsrda","mbplslda","mbplsqda")[2], prior = c("unif", "prop")[1], step = c("nlvtest","permutation","model","prediction")[2], nlv = nlvoptmbplsda, modeloutput = c("scores","loadings","coef","vip"), cvmethod = c("kfolds","loo")[1], nbrep = 10, seed = 123, samplingk = NULL, nfolds = 3, npermut = 10, criterion = c("err","rmse")[1], # selection = c("localmin","globalmin","1std")[1], import = c("R","ChemFlow","W4M")[1], outputfilename = NULL) #plot of the results plot(permutmbplsda, pch = 16, ylab = "CV classification error rate", xlab = "dyssimilarity Y-Ypermuted") # Remove unuseful object for the next steps rm(permutmbplsda) ## ----run_model---------------------------------------------------------------- modelmbplsda <- mbplsr_mbplsda_allsteps(Xlist = list(GCTOF = GCTOF[,1:dim(GCTOF)[2]], HILICPOS = HILICPOS[,1:dim(HILICPOS)[2]]), Xnames = c("GCTOF", "HILICPOS"), Xscaling = c("none","pareto","sd")[3], Y = sample_metadata[,"Gender",drop=FALSE], Yscaling = c("none","pareto","sd")[1], weights = NULL, newXlist = NULL, newXnames = NULL, method = c("mbplsrda","mbplslda","mbplsqda")[2], prior = c("unif", "prop")[1], step = c("nlvtest","permutation","model","prediction")[3], nlv = nlvoptmbplsda, modeloutput = c("scores","loadings","coef","vip"), cvmethod = c("kfolds","loo")[1], # nbrep = 30, # seed = 123, # samplingk = NULL, # nfolds = 5, # npermut = 30, # criterion = c("err","rmse")[1], # selection = c("localmin","globalmin","1std")[1], import = c("R","ChemFlow","W4M")[1], outputfilename = NULL) # score plot plotxy(X= modelmbplsda$scores, group = sample_metadata$Gender, asp = 0, col = 3:4, alpha.f = .8, zeroes = TRUE, circle = FALSE, ellipse = FALSE, labels = FALSE, legend = TRUE, main = "scores - MB PLS DA", ncol = 1, pch=16) # loading plot plotxy(X= modelmbplsda$loadings, group = substr(rownames(modelmbplsda$loadings),1,6), asp = 0, col = NULL, alpha.f = .8, zeroes = TRUE, circle = FALSE, ellipse = FALSE, labels = FALSE, legend = TRUE, main = "loadings - MB PLS DA", ncol = 1, cex=0.8, pch = 16) # VIP curve plot(modelmbplsda$vip[order(modelmbplsda$vip[,nlvoptmbplsda], decreasing = TRUE),nlvoptmbplsda], pch = 16,cex = 0.8, col = as.numeric(as.factor(substr(rownames(modelmbplsda$vip[order(modelmbplsda$vip[,nlvoptmbplsda], decreasing = TRUE),nlvoptmbplsda, drop=FALSE]),1,6))), ylab = "VIP value", main = "VIP curve MB PLS DA") legend("topright", legend = c("GCTOF", "HILICPOS"), pch = 16, col = 1:2) # Remove unuseful object for the next steps rm(modelmbplsda) ## ----run_prediction----------------------------------------------------------- predmbplsda <- mbplsr_mbplsda_allsteps(Xlist = list(GCTOF = GCTOF[,1:dim(GCTOF)[2]], HILICPOS = HILICPOS[,1:dim(HILICPOS)[2]]), Xnames = c("GCTOF", "HILICPOS"), Xscaling = c("none","pareto","sd")[3], Y = sample_metadata[,"Gender",drop=FALSE], Yscaling = c("none","pareto","sd")[1], weights = NULL, newXlist = NULL, newXnames = NULL, method = c("mbplsrda","mbplslda","mbplsqda")[2], prior = c("unif", "prop")[1], step = c("nlvtest","permutation","model","prediction")[4], nlv = nlvoptmbplsda, # modeloutput = c("scores","loadings","coef","vip"), # # cvmethod = c("kfolds","loo")[1], # nbrep = 30, # seed = 123, # samplingk = NULL, # nfolds = 5, # npermut = 30, # # criterion = c("err","rmse")[1], # selection = c("localmin","globalmin","1std")[1], import = c("R","ChemFlow","W4M")[1], outputfilename = NULL) predmbplsda # Remove unuseful object for the next steps rm(predmbplsda, nlvoptmbplsda) ## ----reproducibility---------------------------------------------------------- sessionInfo()