runTPCA {Rtpca} | R Documentation |
Run the TPCA analysis
runTPCA( objList, complexAnno = NULL, ppiAnno = NULL, rownameCol = NULL, summaryMethodStr = "median", distMethodStr = "euclidean", doRocAnalysis = TRUE, minCount = 3, nSamp = 10000, p_adj_method = "BH" )
objList |
inout list of objects, e.g. ExpressionSets retrieved after TPP data import or matrices or data frames |
complexAnno |
data frame annotating known protein complexes of interest to test |
ppiAnno |
data frame annotation known protein-protein interactions (PPI) to test |
rownameCol |
in case the input objects are tibbles this parameter takes in the name (character) of the column specifying protein names or ids |
summaryMethodStr |
character string indicating a method to use to summarize measurements across replicates, default is "median", other options are c("mean", "rbind") |
distMethodStr |
method to use within dist function, default is 'euclidean' |
doRocAnalysis |
logical indicating whether a ROC analysis should be performed which can be used to assess the predictive power of the dataset for protein-protein interactions / protein complexes based on distanc between melting curves of protein interactions partners |
minCount |
integer indicating how many subunits of a complex should be qunatified to inlucde it into the analysis, default is 3 |
nSamp |
integer indicating the number of random samples which should be performed to estimate empirical null distributions, default is 10000 |
p_adj_method |
character string indicating a valid method to be used for multiple testing adjusment, default is "BH" which makes p.adjust use benjamini-hochberg, for additional options check ?p.adjust |
an object of class tpcaResult with the following slots: 1) ObjList: containing the supplied list of objects
m1 <- matrix(1:12, ncol = 4) m2 <- matrix(2:13, ncol = 4) m3 <- matrix(c(2:10, 1:7), ncol = 4) rownames(m1) <- 1:3 rownames(m2) <- 2:4 rownames(m3) <- 2:5 colnames(m1) <- paste0("X", 1:4) colnames(m2) <- paste0("X", 1:4) colnames(m3) <- paste0("X", 1:4) mat_list <- list( m1, m2, m3 ) ppi_anno <- tibble( x = "2", y = "3", combined_score = 700, pair = "2:3") runTPCA( objList = mat_list, complexAnno = NULL, ppiAnno = ppi_anno )