README.md

In angy89/MVDA_package: Multi-View Biological Data Analysis

MVDA_package

Multi-view clustering methodology in which the information from different data layers (views) is integrated at the levels of the results of each single view clustering iterations.

Reference Paper:

Serra, A., Fratello, M., Fortino, V., Raiconi, G., Tagliaferri, R., & Greco, D. (2015). MVDA: a multi-view genomic data integration methodology. BMC bioinformatics, 16(1), 261. ISO 690

More information a: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0680-3

```R

Installation instruction from GitHub

library(devtools) install_github("angy89/CorKohonen_package") install_github("angy89/MVDA_package")

Example of usage

library(MVDA) data(tcga_breast)

1 step miRNASeq Preprocessing

miRNA_km <- kmeans_preprocessing(DB = miRNA, nCenters = 10)

prep_res = best_preprocessing(DB = miRNA,nCentersRange = c(5,10,20,30,40), som_dim = list(c(1,5),c(2,5),c(4,5),c(6,5),c(5,8)), clustAlgos = c("KMeans","Pam","Ward","SOM")) plot(prep_res$gplt)

miRNA_km_summary <- clustering_summary(DB=miRNA, cluster=miRNA_km$clustering)

rf_rank_res = rf_ranking(prototype=miRNA_km$centers,patient_classes=info$x, MTRY=c(1,2,3),NTREE=c(100,200,500), NPERM = c(1,2,3), NODESIZE=c(1,5))

RNA_pamk <- pamk_preprocessing(DB = RNA,nCenters = 50) RNA_pamk_summary <- clustering_summary(DB=RNA, cluster=RNA_pamk$clustering)

2 step prototype ranking

sda_km_miRNA <- sda_ranking(prototype = miRNA_km$centers, classes = info$x,ranking.score = "avg") plot(sda_km_miRNA) sda_RNA <- sda_ranking(prototype = RNA_pamk$centers, classes = info$x,ranking.score = "avg") plot(sda_RNA)

3 step single view clustering

prototypes_miRNA <- miRNA_km$centers[rownames(sda_km_miRNA),] nCenter <- 4 miRNA_pat_km <- kmeans_sv(nCenters = nCenter,prototype = prototypes_miRNA) cm_km <- confusion_matrix(classes = info$x, clustering = miRNA_pat_km$clustering, patientsDB = t(prototypes_miRNA), nCluster = nCenter) miRNAerror <- CMsup_error(CM_sup = cm_km,nPat = dim(prototypes_miRNA)[2]) prototypes_RNA <- RNA_pamk$centers[rownames(sda_RNA),] nCenter <- 4 RNA_pat_km <- kmeans_sv(nCenters = nCenter,prototype = prototypes_RNA) cm_km <- confusion_matrix(classes = info$x, clustering = RNA_pat_km$clustering, patientsDB = t(prototypes_RNA), nCluster = nCenter) RNAerror <- CMsup_error(CM_sup = cm_km,nPat = dim(prototypes_RNA)[2])

4 step integration

nrows <- dim(info)[1] ncols <- length(table(miRNA_pat_km$clustering)) rows_id <- rownames(info) cols_id <- paste("Clustering",names(table(miRNA_pat_km$clustering)),sep=" ") X1 <- supervised_matrix(classes = miRNA_pat_km$clustering,nRow = nrows, nCol = ncols,row_id = rows_id,col_id = cols_id) X2 <- supervised_matrix(classes = RNA_pat_km$clustering,nRow = nrows, nCol = ncols,row_id = rows_id,col_id = cols_id) SUP <- supervised_matrix(classes = as.numeric(info$x),nRow = nrows, nCol = length(table(info$x)), row_id = rows_id,col_id = cols_id) X <- cbind(X1,X2,SUP) K <- dim(X)[2]

patientsDB <- t(cbind(t(prototypes_RNA),t(prototypes_miRNA))) mf_res <- MF(A = X,k = 10,eps = 0.000001,iter_max = 1000,nView = 3,V1.lc1 = 4, V1.lc2 = 4,V1.lc3 = 4,V1.lc4 = 0,V1.lc5 = 0, patients_classes = info$x,patientsDB = patientsDB)

plot_TMat(TMat = mf_res$T_mat,mv_clust = mf_res$mv_cluster,viewNames = c("miRNA","RNA","PriorKnowledge"),fisherRes = mf_res$fisher)

GLI_res <- general_late_integration(A = X,k = K,alfa = 1, eps = 0.001,patientsDB = patientsDB,patients_classes = info$x)

plotCM(mf_res$confMat)

feature_importance_for_clusters = OrderByCorrelationFeaturesPatients(DB=patientsDB,cluster.vector = mf_res$mv_cluster)#,n_cluster=nrow(mf_res$confMat)) stability_results_MF = mvclst(method = "MF",nPatients = 151,nClusters = K,A = X,eps = 0.000001,iter_max = 1000)

stability_results_GLI = mvclst(method = "GLI",A = X,nPatients = 151,nClusters = K,eps = 0.001,alfa =1)

var_col = plot_feature_variance_between_centroids(center=mf_res$center,v_limit = 12,h_limit=1)

center_correlation(centers = mf_res$center)

mv_clust_boxplot(centers = mf_res$center, clusterMV = mf_res$mv_cluster, cluster_class= mf_res$labels, var_col=var_col,nFeat = 6,patientsDB=patientsDB, patClasses=c("Basal","Her2","LumA","LumB","Normal"), patClassColors = c("purple","blue","green","red","orange"))

angy89/MVDA_package documentation built on May 7, 2019, 8:58 p.m.