# Subtyping multi-omics data Perform subtyping using multiple types of data ```r SubtypingOmicsData( dataList, kMin = 2, kMax = 5, k = NULL, agreementCutoff = 0.5, ncore = 1, verbose = T, sampledSetSize = 2000, knn.k = NULL, ... ) ``` ## Arguments - `dataList`: a list of data matrices. Each matrix represents a data type where the rows are items and the columns are features. The matrices must have the same set of items. - `kMin`: The minimum number of clusters used for automatically detecting the number of clusters in `PerturbationClustering`. This paramter is passed to `PerturbationClustering` and does not affect the final number of cluster in `SubtypingOmicsData`. Default value is `2`. - `kMax`: The maximum number of clusters used for automatically detecting the number of clusters in `PerturbationClustering`. This paramter is passed to `PerturbationClustering` and does not affect the final number of cluster in `SubtypingOmicsData`. Default value is `5`. - `k`: The number of clusters. If k is set then kMin and kMax will be ignored. - `agreementCutoff`: agreement threshold to be considered consistent. Default value is `0.5`. - `ncore`: Number of cores that the algorithm should use. Default value is `1`. - `verbose`: set it to `TRUE` of `FALSE` to get more or less details respectively. - `sampledSetSize`: The number of sample size used for the sampling process when dataset is big. Default value is `2000`. - `knn.k`: The value of k of the k-nearest neighbors algorithm. If knn.k is not set then it will be used elbow method to calculate the k. - `...`: these arguments will be passed to `PerturbationClustering` algorithm. See details for more information ## Returns `SubtypingOmicsData` returns a list with at least the following components: - **cluster1**: A vector of labels indicating the cluster to which each sample is allocated in Stage I - **cluster2**: A vector of labels indicating the cluster to which each sample is allocated in Stage II - **dataTypeResult**: A list of results for individual data type. Each element of the list is the result of the `PerturbationClustering` for the corresponding data matrix provided in dataList. ## Details `SubtypingOmicsData` implements the Subtyping multi-omic data that are based on Perturbaion clustering algorithm of Nguyen et al (2017), Nguyen et al (2019) and Nguyen, et al. (2021). The input is a list of data matrices where each matrix represents the molecular measurements of a data type. The input matrices must have the same number of rows. `SubtypingOmicsData` aims to find the optimum number of subtypes and location of each sample in the clusters from integrated input data `dataList` through two processing stages: 1. Stage I: The algorithm first partitions each data type using the function `PerturbationClustering`. It then merges the connectivities across data types into similarity matrices. Both kmeans and similarity-based clustering algorithms - partitioning around medoids `pam` are used to partition the built similarity. The algorithm returns the partitioning that agrees the most with individual data types. 2. Stage II: The algorithm attempts to split each discovered group if there is a strong agreement between data types, or if the subtyping in Stage I is very unbalanced. When clustering a large number of samples, this function uses a subsampling technique to reduce the computational complexity with the two parameters `sampledSetSize` and `knn.k`. Please consult Nguyen et al. (2021) for details. ## Examples ```r # Load the kidney cancer carcinoma data data(KIRC) # Perform subtyping on the multi-omics data dataList <- list (as.matrix(KIRC$GE), as.matrix(KIRC$ME), as.matrix(KIRC$MI)) names(dataList) <- c("GE", "ME", "MI") result <- SubtypingOmicsData(dataList = dataList) # Change Pertubation clustering algorithm's arguments result <- SubtypingOmicsData( dataList = dataList, clusteringMethod = "kmeans", clusteringOptions = list(nstart = 50) ) # Plot the Kaplan-Meier curves and calculate Cox p-value library(survival) cluster1=result$cluster1;cluster2=result$cluster2 a <- intersect(unique(cluster2), unique(cluster1)) names(a) <- intersect(unique(cluster2), unique(cluster1)) a[setdiff(unique(cluster2), unique(cluster1))] <- seq(setdiff(unique(cluster2), unique(cluster1))) + max(cluster1) colors <- a[levels(factor(cluster2))] coxFit <- coxph( Surv(time = Survival, event = Death) ~ as.factor(cluster2), data = KIRC$survival, ties = "exact" ) mfit <- survfit(Surv(Survival, Death == 1) ~ as.factor(cluster2), data = KIRC$survival) plot( mfit, col = colors, main = "Survival curves for KIRC, level 2", xlab = "Days", ylab = "Survival",lwd = 2 ) legend("bottomright", legend = paste( "Cox p-value:", round(summary(coxFit)$sctest[3], digits = 5), sep = "" ) ) legend( "bottomleft", fill = colors, legend = paste( "Group ", levels(factor(cluster2)),": ", table(cluster2)[levels(factor(cluster2))], sep ="" ) ) ``` ## References 1. H Nguyen, S Shrestha, S Draghici, & T Nguyen. PINSPlus: a tool for tumor subtype discovery in integrated genomic data. Bioinformatics, 35(16), 2843-2846, (2019). 2. T Nguyen, R Tagett, D Diaz, S Draghici. A novel method for data integration and disease subtyping. Genome Research, 27(12):2025-2039, 2017. 3. T. Nguyen, "Horizontal and vertical integration of bio-molecular data", PhD thesis, Wayne State University, 2017. 4. H Nguyen, D Tran, B Tran, M Roy, A Cassell, S Dascalu, S Draghici & T Nguyen. SMRT: Randomized Data Transformation for Cancer Subtyping and Big Data Analysis. Frontiers in oncology. 2021. ## See Also `PerturbationClustering`

SubtypingOmicsData function