runTPCA {Rtpca} | R Documentation

## Run the TPCA analysis

### Description

Run the TPCA analysis

### Usage

    runTPCA(
      objList,
      complexAnno = NULL,
      ppiAnno = NULL,
      rownameCol = NULL,
      summaryMethodStr = "median",
      distMethodStr = "euclidean",
      doRocAnalysis = TRUE,
      minCount = 3,
      nSamp = 10000,
      p_adj_method = "BH"
    )

### Arguments

`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

### Value

an object of class tpcaResult with the following slots: 1) ObjList: containing the supplied list of objects

### Examples

    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
    )

[Package Rtpca version 1.0.0 Index]