mocluster: Integrative clustering using multiple
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1 mocluster: Integrative clustering using multiple omics data Chen Meng Modified: June 03, Compiled: October 30, Contents 1 mocluster overview Run mocluster Quick start Session info mocluster overview Introduction to mocluter algorithm. 2 Run mocluster 2.1 Quick start In this vignette, we will define subtypes the NCI-60 cell lines using four transcriptomic data from different microarray platforms. First we load the package and data # loading package and gene expression data library(mogsa) data(nci604arrays) NCI604arrays is a list of data.frame. The list consists of microarray data for NCI-60 cell lines from different platforms. In each of the data.frame, columns are the 60 cell lines and rows are genes. The data was downloaded from [1], but only a small subset of genes were selected. Therefore, the result in this vignette is not intended for biological interpretation. Check the dimension of each matrix: sapply(nci604arrays, dim) # check dimensions of expression data
2 ## agilent hgu133 hgu133p2 hgu95 ## [1,] ## [2,] Before performing the mocluster analysis, we define some auxiliary variable to indicate the tissue of origin of cell lines and the color for each: tumortype <- sapply(strsplit(colnames(nci604arrays$agilent), split="\\."), "[", 1) colcode <- as.factor(tumortype) levels(colcode) <- c("red", "green", "blue", "cyan", "orange", colcode <- as.character(colcode) "gray25", "brown", "gray75", "pink") Then we can apply the mocluster algorithm to clustering the 60 cell lines. The mocluster employs consensus PCA (CPCA) approach to integrate multiple omics data. CPCA approach is particularly suitable for the integrative clustering algorithm. The First, we call mbpca to perform the consensus PCA: moa <- mbpca(nci604arrays, ncomp = 10, k = "all", method = "globalscore", option = "lambda1", center=true, scale=false, moa = TRUE, svd.solver = "fast", maxiter = 1000) ## calculating component 1... ## calculating component 2... ## calculating component 3... ## calculating component 4... ## calculating component 5... ## calculating component 6... ## calculating component 7... ## calculating component 8... ## calculating component 9... ## calculating component plot(moa, value="eig", type=2) hgu95 hgu133p2 hgu133 agilent Figure 1: The variance associated with each latent variable Colors distinguishes the contributions from different data sets. 2
3 In the above commands, the argument ncomp = 10 specifies that 10 latent variables should be calculated. The argument k = "all" suggest that no sparsity should be introduced to the loading factor. Sparsity in loading factor is a preferred property in multiple omics data analysis since only a subset of the variables would associated with the latent variables. Therefore, the interpret ability is increased. However, we do not introduce the sparsity for two reasons. First, we want to evaluate the relative contribution of variance as show in the figure. Second, we will use the permutation test to evaluate the latent variables that are representing the concordant structures in different data sets. This can be done with a permutation test, that is, permute the samples in each of the data and do the multi-block PCA: r <- bootmbpca(moa, mc.cores = 1, B=20, replace = FALSE, resample = "sample") ## method is set to 'globalscore' Figure 2: permutation test The result suggested that the top 3 latent variables account for concordance structures across data data. The elbow test also shows that the top 3 variables are significant. Therefore, we will use 3 latent variable in the subsequent analysis. Next, we are ready to calculate the latent variables with the parse loadings: moas <- mbpca(nci604arrays, ncomp = 3, k = 0.1, method = "globalscore", option = "lambda1", center=true, scale=false, moa = TRUE, svd.solver = "fast", maxiter = 1000) ## calculating component 1... ## calculating component 2... ## calculating component 3... k = 0.1 indicates we only keep 10% variables with non-zero coefficients in the result. The cluster will be perform using the latent variable, which could be extract by moascore. We first extract the latent variables for both the sparse and non-sparse loading results. Then compare their correlation. scr <- moascore(moa) scrs <- moascore(moas) diag(cor(scr[, 1:3], scrs)) ## PC1 PC3 ##
4 They have a very high correlation so we assume the should account for the same variance or biological effects. Visualize the plot in the first three dimensions. 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) BR CNS CO LE ME LC OV PR RE PC PC Using gap statistic to evaluate the optimal number of clusters gap <- mogap(moas, K.max = 12, cluster = "hcl") clusgap(x = sr, FUNcluster = fhclust, K.max = K.max, B = B, dist.method = dist.method, dist.diag = dist.diag, dist.upper = dist.upper, dist.p = dist.p, Gap k k Figure 3: gap statistic plot layout(matrix(1, 1, 1)) gap$nclust ## firstsemax Tibs2001SEmax globalsemax firstmax globalmax ## Using hierarchical cluster and plot hcl <- hclust(dist(scrs)) cls <- cutree(hcl, k=4) clscolor <- as.factor(cls) levels(clscolor) <- c("red", "blue", "orange", "pink") clscolor <- as.character((clscolor)) 4
5 heatmap(t(scrs[hcl$order, ]), ColSideColors = colcode[hcl$order], Rowv = NA, Colv=NA) PC3 PC1 BR.T47D CO.HCT116 CO.SW620 BR.MCF7 CO.COLO205 CO.HCT15 CO.KM12 CO.HCC2998 LC.NCIH322M CO.HT29 PR.DU145 LC.A549 LC.EKVX BR.MDAMB231 PR.PC3 OV.OVCAR3 OV.OVCAR4 OV.OVCAR5 LC.NCIH460 LC.NCIH23 LE.CCRFCEM OV.IGROV1 LE.MOLT4 LE.HL60 LC.NCIH522 LE.K562 LE.SR LE.RPMI8226 ME.SKMEL5 ME.UACC62 ME.M14 ME.SKMEL28 ME.SKMEL2 ME.MDAMB435 ME.MALME3M ME.UACC257 ME.MDAN CNS.SNB19 CNS.U251 CNS.SNB75 CNS.SF295 CNS.SF539 BR.HS578T CNS.SF268 RE.RXF393 ME.LOXIMVI LC.HOP62 RE.ACHN RE.A498 LC.NCIH226 RE.CAKI1 BR.BT549 RE.UO31 LC.HOP92 RE.7860 OV.SKOV3 RE.SN12C OV.OVCAR8 RE.TK10 OV.NCIADRRES heatmap(t(scrs[hcl$order, ]), ColSideColors = clscolor[hcl$order], Rowv = NA, Colv=NA) PC3 PC1 BR.T47D CO.HCT116 CO.SW620 BR.MCF7 CO.COLO205 CO.HCT15 CO.KM12 CO.HCC2998 LC.NCIH322M CO.HT29 PR.DU145 LC.A549 LC.EKVX BR.MDAMB231 PR.PC3 OV.OVCAR3 OV.OVCAR4 OV.OVCAR5 LC.NCIH460 LC.NCIH23 LE.CCRFCEM OV.IGROV1 LE.MOLT4 LE.HL60 LC.NCIH522 LE.K562 LE.SR LE.RPMI8226 ME.SKMEL5 ME.UACC62 ME.M14 ME.SKMEL28 ME.SKMEL2 ME.MDAMB435 ME.MALME3M ME.UACC257 ME.MDAN CNS.SNB19 CNS.U251 CNS.SNB75 CNS.SF295 CNS.SF539 BR.HS578T CNS.SF268 RE.RXF393 ME.LOXIMVI LC.HOP62 RE.ACHN RE.A498 LC.NCIH226 RE.CAKI1 BR.BT549 RE.UO31 LC.HOP92 RE.7860 OV.SKOV3 RE.SN12C OV.OVCAR8 RE.TK10 OV.NCIADRRES In order to interpret the result, extract the variable with non-zero coefficients genes <- moacoef(moas) genes$nonzerocoef$agilent.v1.neg ## id coef ## FGD3agilent FGD3agilent ## TMC6agilent TMC6agilent ## GMFGagilent GMFGagilent ## IQGAP2agilent IQGAP2agilent
6 3 Session info tolatex(sessioninfo()) R version Patched ( r74967), x8664-pc-linux-gnu Locale: LCCTYPE=enUS.UTF-8, LCNUMERIC=C, LCTIME=enUS.UTF-8, LCCOLLATE=C, LCMONETARY=enUS.UTF-8, LCMESSAGES=enUS.UTF-8, LCPAPER=enUS.UTF-8, LCNAME=C, LCADDRESS=C, LCTELEPHONE=C, LCMEASUREMENT=enUS.UTF-8, LCIDENTIFICATION=C Running under: Ubuntu LTS Matrix products: default BLAS: /home/biocbuild/bbs-3.8-bioc/r/lib/librblas.so LAPACK: /home/biocbuild/bbs-3.8-bioc/r/lib/librlapack.so Base packages: base, datasets, grdevices, graphics, methods, stats, utils Other packages: knitr 1.20, mogsa Loaded via a namespace (and not attached): AnnotationDbi , Biobase , BiocGenerics , BiocManager , BiocStyle , DBI 1.0.0, GSEABase , IRanges , KernSmooth , Matrix , R , RCurl , RSQLite 2.1.1, Rcpp , S4Vectors , XML , annotate , backports 1.1.2, bit , bit , bitops 1.0-6, blob 1.1.1, catools , checkmate 1.8.5, cluster , codetools , compiler 3.5.1, corpcor 1.6.9, digest , evaluate 0.12, gdata , genefilter , gplots 3.0.1, graph , graphite , grid 3.5.1, gtools 3.8.1, highr 0.7, htmltools 0.3.6, httr 1.3.1, lattice , magrittr 1.5, memoise 1.1.0, parallel 3.5.1, rappdirs 0.3.1, rmarkdown 1.10, rprojroot 1.3-2, splines 3.5.1, stats , stringi 1.2.4, stringr 1.3.1, survival , svd 0.4.1, tools 3.5.1, xtable 1.8-3, yaml References [1] Reinhold WC, Sunshine M, Liu H, Varma S, Kohn KW, Morris J, Doroshow J, and Pommier Y. Cellminer: A web-based suite of genomic and pharmacologic tools to explore transcript and drug patterns in the nci-60 cell line set. Cancer Research, 72(14): ,
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