## ----style, eval=TRUE, echo=FALSE, results='asis'-------------------------- BiocStyle::latex(bibstyle="unsrt") # BiocStyle::latex() ## ----include=FALSE--------------------------------------------------------- library(knitr) opts_chunk$set( concordance=TRUE ) ## ----include=FALSE--------------------------------------------------------- library(knitr) opts_chunk$set( concordance=TRUE, cache=TRUE, message=FALSE, out.width=".55\\textwidth", echo=TRUE, fig.width=6, fig.height=6, fig.align="center", result="markup", hold=TRUE ) ## ----loadLib--------------------------------------------------------------- # loading package and gene expression data library(mogsa) data(NCI60_4arrays) ## ----dataDim--------------------------------------------------------------- sapply(NCI60_4arrays, dim) # check dimensions of expression data ## ----auxiVar--------------------------------------------------------------- tumorType <- sapply(strsplit(colnames(NCI60_4arrays$agilent), split="\\."), "[", 1) colcode <- as.factor(tumorType) levels(colcode) <- c("red", "green", "blue", "cyan", "orange", "gray25", "brown", "gray75", "pink") colcode <- as.character(colcode) ## ----mbpca1, fig.cap="The variance associated with each latent variable. Colors distinguishes the contributions from different data sets."---- moa <- mbpca(NCI60_4arrays, ncomp = 10, k = "all", method = "globalScore", option = "lambda1", center=TRUE, scale=FALSE, moa = TRUE, svd.solver = "fast", maxiter = 1000) plot(moa, value="eig", type=2) ## ----boot, fig.cap="permutation test"-------------------------------------- r <- bootMbpca(moa, mc.cores = 1, B=20, replace = FALSE, resample = "sample") ## ----mpbca2---------------------------------------------------------------- moas <- mbpca(NCI60_4arrays, ncomp = 3, k = 0.1, method = "globalScore", option = "lambda1", center=TRUE, scale=FALSE, moa = TRUE, svd.solver = "fast", maxiter = 1000) ## ----scoreCor-------------------------------------------------------------- scr <- moaScore(moa) scrs <- moaScore(moas) diag(cor(scr[, 1:3], scrs)) ## ----plot1, fig.width=10, fig.height=6------------------------------------- layout(matrix(1:2, 1, 2)) plot(scrs[, 1:2], col=colcode, pch=20) legend("topright", legend = unique(tumorType), col=unique(colcode), pch=20) plot(scrs[, 2:3], col=colcode, pch=20) ## ----gap, fig.cap="gap statistic plot"------------------------------------- gap <- moGap(moas, K.max = 12, cluster = "hcl") layout(matrix(1, 1, 1)) gap$nClust ## ----cluster, fig.width=6, fig.height=3------------------------------------ hcl <- hclust(dist(scrs)) cls <- cutree(hcl, k=4) clsColor <- as.factor(cls) levels(clsColor) <- c("red", "blue", "orange", "pink") clsColor <- as.character((clsColor)) heatmap(t(scrs[hcl$order, ]), ColSideColors = colcode[hcl$order], Rowv = NA, Colv=NA) heatmap(t(scrs[hcl$order, ]), ColSideColors = clsColor[hcl$order], Rowv = NA, Colv=NA) ## ----coef------------------------------------------------------------------ genes <- moaCoef(moas) genes$nonZeroCoef$agilent.V1.neg ## ----sessionInfo, results = 'asis', eval = TRUE, echo = TRUE--------------- toLatex(sessionInfo())