runTPCA: Run the TPCA analysis
In Rtpca: Thermal proximity co-aggregation with R

Description Usage Arguments Value Examples

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
)

Loading required package: dplyr

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

Loading required package: tidyr
Checking input arguments. 


Creating distance matrices. 


Testing for complex co-aggregation. 


Performing PPi ROC analysis. 

class: tpcaResult 
Slot "ObjList": of class list and length 3 
Slot "ContrastList": of class list and length 0 
Slot "DistMat" with dimension: 2 2 
Slot "ContrastDistMat" with dimension: 0 0 
Slot "ComplexAnnotation" of class: tbl_df tbl data.frame with dim: 2 3 
Slot "ComplexBackgroundDistributionList" of class: list with length: 1 
Slot "PPiAnnotation" of class: tbl_df tbl data.frame with dim: 1 4 
Slot "PPiRocTable" of class: tbl_df tbl data.frame with dim: 1 3 
Slot "PPiRocTableAnno" of class: tbl_df tbl data.frame with dim: 1 2 
Slot "ComplexRocTable" of class: data.frame with dim: 0 0 
Slot "summaryMethod": median 
Slot "distMethod": euclidean 
Slot "tpcaResultTable" of class: tbl_df tbl data.frame with dim: 1 5 
Slot "diffTpcaResultTable" of class: data.frame with dim: 0 0