Package survclip. November 22, 2017

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1 Type Package Title Survival Analysis for Pathways Version Author Package survclip November 22, 2017 Maintainer Survival analysis using pathway topology. Data reduction techniques with graphical models are used to identify pathways or modules that are associated to survival. Encoding UTF-8 LazyData true Depends Imports clipper, checkmate, graph, methods, qpgraph, corpcor, survival, rrcov, FactoMineR, elasticnet Suggests RUnit, BiocGenerics, graphite, curatedovariandata, MASS, BiocStyle, org.hs.eg.db License AGPL-3 NeedsCompilation no Repository CRAN Date/Publication :16:23 UTC R topics documented: cliquesurvivaltest computedays computepcs exp gettoploadgenes graph pathwaysurvivaltest survannot survcliques-class survpath-class

2 2 cliquesurvivaltest Index 11 cliquesurvivaltest Perform survival test on all the cliques of the graph. This function performs survival test on given pathway using a matrix with survival annotation. cliquesurvivaltest(expr, survannot, graph, pcssurvcoxmethod = c("regular", "sparse"), alwaysshrink = FALSE, maxpcs = 10, survformula = "Surv(days, status) ~") Arguments expr graph expression matrix a graphnel object survannot a data frame for survival annotations specified according to the survformuala. The data frame must contain days and status. pcssurvcoxmethod a method to perform PCA. A method to perform "regular", "sparse" for regular PCA and sparse PCA, respectively. alwaysshrink maxpcs survformula if TRUE, always shrink the covariance matrix. Deafult=FALSE maximum number of PCs used in the Cox formula "Surv(days, status) ~ PC1.." the formula to use in coxph analysis. Defaut="Surv(days, status) ~". Please note that the formula end with ~ meaning that PCs will be added. Details Survival test is made according to survformula. With regular method, a regular PCA analysis is used to compute PCs. With sparse method, a penalized regression is used for the estimation of PCs (as implemented in elasticnet). Value A survcliques object. See Also pathwaysurvivaltest gettoploadgenes

3 computedays 3 if (require(graphite)) { data(exp) data(survannot) data(graph) row.names(exp) <- paste0("entrezid:", row.names(exp)) genes <- intersect(graph::nodes(graph), row.names(exp)) graph <- graph::subgraph(genes, graph) expr <- exp[genes,, drop=false] cliquesurvivaltest(expr, survannot, graph, maxpcs=2) } computedays Function to compute days. A simple function to start from an interval of dates to days. computedays(timetable) Arguments timetable a table with starting date and ending date. Value the days computepcs For internal usage only. Compute principal components according to the chosen method. For internal usage. Compute the PCs according to 3 methods: regular, ropological, sparse. computepcs(exp, shrink, method = c("regular", "topological", "sparse"), cliques = NULL, maxpcs)

4 4 exp Arguments exp shrink method cliques maxpcs exp shrink boolean either regular, topological, sparse if NULL, no topology. mac pcs returned. Details For internal usage. Value x=scores, sdev=sd x sdev pcs standar deviation for pcs. exp Expression dataset. Toy example of an expression dataset for survival test. data("exp") Format A matrix with 246 genes (rows) measured across 73 patients (columns). data(exp)

5 gettoploadgenes 5 gettoploadgenes Extract the relevant genes associated with survival Given a survcliques object the function extracts those genes that are the most influent in the PCs identified as significant with a certain threshold. gettoploadgenes(scobj, thr=0.05, n=5, loadthr=0.6) Arguments scobj thr n loadthr an object survcliques threshold to consider a clique as significant. This threshold is used also for the significance of the zscores in zlist return up to n top relevant genes filter loadings according to loadthr absolute value Details Function to reveal those genes that are more relevant in the survival process. The relevance of a gene is based on PC loadings. Value a data.frame organized as follows: 1. featuregene names 2. clidclique id 3. geneloadgene loading 4. whichpcthe significant PC where the gene is relevant All significant cliques are represented. The importance of the genes is expressed by its loading. See Also cliquesurvivaltest

6 6 pathwaysurvivaltest if (require(graphite)) { data(exp) data(survannot) data(graph) row.names(exp) <- paste0("entrezid:", row.names(exp)) genes <- intersect(graph::nodes(graph), row.names(exp)) graph <- graph::subgraph(genes, graph) expr <- exp[genes,, drop=false] cliquetest <- cliquesurvivaltest(expr, survannot, graph, maxpcs=2) gettoploadgenes(cliquetest) } graph Expression dataset. Toy example of graphnel object for pathway the survival test. data("graph") Format A graphnel object of a pathway with 308 nodes. data(graph) pathwaysurvivaltest Survival analysis on the whole pathway This function performs survival analysis on pathways. The analysis can be either topological or classical. The analysis is based on data reduction based on Principal Component Analysis and a Cox proportional hazard model on the most influent PCs. pathwaysurvivaltest(expr, survannot, graph, pcssurvcoxmethod = c("regular", "topological", "sparse"), alwaysshrink = FALSE, maxpcs = 10, survformula = "Surv(days, status) ~")

7 pathwaysurvivaltest 7 Arguments expr survannot expression matrix. a data frame for survival annotations specified according to the survformula. The data frame must contain days and status. graph a graphnel object for a graph pcssurvcoxmethod a method to perform PCA. Can be "regular", "topological", "sparse" for regular PCA, topological based PCA and sparse PCA, respectively. The latter one (sparse) is particularly suited for cliques only. alwaysshrink maxpcs survformula if TRUE, always shrink the covariance matrix. Deafult=FALSE maximum number of PCs used in the cox formula "Surv(days, status) ~ PC1.." the formula used in Coxph analysis. Defaut="Surv(days, status) ~". Please note that the formula end with ~ meaning that PCs will be added. Details With regular method, a regular PCA analysis is used to compute PCs. With topological method, the covariance matrix is estimated using the topology of the pathway with IPS algorithm. With sparse method, a penalized regression is used for the estimation of PCs (as implemented in elasticnet). The max number of PCs used by the model is estimated by "estim_ncp" in FactoMineR. A maximum number of PCs can be fixed by the user. The minimum ot the two is chosen. Value A survpath object. See Also cliquesurvivaltest if (require(graphite)) { data(exp) data(survannot) data(graph) row.names(exp) <- paste0("entrezid:", row.names(exp)) genes <- intersect(graph::nodes(graph), row.names(exp)) graph <- graph::subgraph(genes, graph) expr <- exp[genes,, drop=false] pathwaysurvivaltest(expr, survannot, graph, maxpcs=2) }

8 8 survcliques-class survannot Annotations. Survival annotation for toy example dataset. data("survannot") Format A data frame with 73 observations on the following 2 variables. days a character vector, with the days to Death/last follow up after 1st surgical event. status dead = 1, alive = 0 data(survannot) survcliques-class Class "survcliques" Class for survival clique analysis Objects from the Class Slots Objects can be created by calls of the form new("survcliques",...). alphas, zlist, cliques, cliquesloadings..data: Object of class "list" alphas: Object of class "numeric". It contains pvalues of the cliques. zlist: Object of class "list". For each cliques, the list of pvalues for all the covariates. cliques: Object of class "list". The list of the genes in the cliques. coxobjs: Object of class "list". For each cliques, the object used for Coxph analysis. cliquesloadings: Object of class "list". For each cliques, the loadings as calculated by the PCA. cliquesexpr: Object of class "list". For each cliques, the cliques expression.

9 survpath-class 9 Extends Class "list", from data part. Class "vector", by class "list", distance 2. Class "Ulist", by class "list", distance 2. Class "Uvector", by class "list", distance 3. Methods No methods defined with class "survcliques" in the signature. showclass("survcliques") survpath-class Class "survpath" Class for survival pathway analysis. Objects from the Class Slots Objects can be created by calls of the form new("survpath",...). pvalue, zlist, coxobj, loadings and the method used. Extends.Data: Object of class "list" pvalue: Object of class "numeric". It contains the pvalue of the whole model generated by the analysis. zlist: Object of class "numeric". List of pvalues for all the covariates. coxobj: Object of class "data.frame". The object used for Coxph analysis. loadings: Object of class "matrix". The loadings as calculated by the PCA. method: Object of class "character". Type of method used. Class "list", from data part. Class "vector", by class "list", distance 2. Class "Ulist", by class "list", distance 2. Class "Uvector", by class "list", distance 3. Methods No methods defined with class "survpath" in the signature.

10 10 survpath-class showclass("survpath")

11 Index Topic classes survcliques-class, 8 survpath-class, 9 Topic datasets exp, 4 graph, 6 survannot, 8 cliquesurvivaltest, 2, 5, 7 comppcs (computepcs), 3 computedays, 3 computepcs, 3 exp, 4 gettoploadgenes, 2, 5 graph, 6 list, 9 pathwaysurvivaltest, 2, 6 sparsecomppcs (computepcs), 3 survannot, 8 survcliques (survcliques-class), 8 survcliques-class, 8 survpath (survpath-class), 9 survpath-class, 9 topocomppcs (computepcs), 3 Ulist, 9 Uvector, 9 vector, 9 11