| Title: | Thermal proximity co-aggregation with R |
|---|---|
| Description: | R package for performing thermal proximity co-aggregation analysis with thermal proteome profiling datasets to analyse protein complex assembly and (differential) protein-protein interactions across conditions. |
| Authors: | Nils Kurzawa [aut, cre], André Mateus [aut], Mikhail M. Savitski [aut] |
| Maintainer: | Nils Kurzawa <[email protected]> |
| License: | GPL-3 |
| Version: | 1.17.0 |
| Built: | 2024-11-30 04:03:53 UTC |
| Source: | https://github.com/bioc/Rtpca |
Extract CommonFeatures
## S4 method for signature 'tpcaResult' CommonFeatures(object)## S4 method for signature 'tpcaResult' CommonFeatures(object)
object |
and object of class tpcaResult |
a vector of common features across replicates
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 mat_list <- list( m1, m2, m3 ) ppi_anno <- tibble( x = "2", y = "3", combined_score = 700, pair = "2:3") tpcaObj <- runTPCA( objList = mat_list, complexAnno = NULL, ppiAnno = ppi_anno ) CommonFeatures(tpcaObj)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 mat_list <- list( m1, m2, m3 ) ppi_anno <- tibble( x = "2", y = "3", combined_score = 700, pair = "2:3") tpcaObj <- runTPCA( objList = mat_list, complexAnno = NULL, ppiAnno = ppi_anno ) CommonFeatures(tpcaObj)
Extract ComplexAnnotation
## S4 method for signature 'tpcaResult' ComplexAnnotation(object)## S4 method for signature 'tpcaResult' ComplexAnnotation(object)
object |
and object of class tpcaResult |
a data frame containing the complex annotation
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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) ComplexAnnotation(tpcaObj)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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) ComplexAnnotation(tpcaObj)
Extract ComplexBackgroundDistributionList
## S4 method for signature 'tpcaResult' ComplexBackgroundDistributionList(object)## S4 method for signature 'tpcaResult' ComplexBackgroundDistributionList(object)
object |
and object of class tpcaResult |
a list of data frames containing distances of random complexes with different number of members
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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) ComplexBackgroundDistributionList(tpcaObj)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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) ComplexBackgroundDistributionList(tpcaObj)
Extract ComplexRocTable
## S4 method for signature 'tpcaResult' ComplexRocTable(object)## S4 method for signature 'tpcaResult' ComplexRocTable(object)
object |
and object of class tpcaResult |
a data frame containing a complex analysis roc table
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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) ComplexRocTable(tpcaObj)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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) ComplexRocTable(tpcaObj)
Extract ContrastCondName
## S4 method for signature 'tpcaResult' ContrastCondName(object)## S4 method for signature 'tpcaResult' ContrastCondName(object)
object |
and object of class tpcaResult |
a character string describing the contrast condition
tpcaObj <- new("tpcaResult") ContrastCondName(tpcaObj)tpcaObj <- new("tpcaResult") ContrastCondName(tpcaObj)
Extract ContrastDistMat
## S4 method for signature 'tpcaResult' ContrastDistMat(object)## S4 method for signature 'tpcaResult' ContrastDistMat(object)
object |
an object of class tpcaResult |
a matrix containing the constrast distance matrix of all pariwise protein-protein melting curve distances computed from a TPP experiment
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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) ContrastDistMat(tpcaObj)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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) ContrastDistMat(tpcaObj)
Extract ContrastList
## S4 method for signature 'tpcaResult' ContrastList(object)## S4 method for signature 'tpcaResult' ContrastList(object)
object |
an object of class tpcaResult |
an object list containing TPP data
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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) ContrastList(tpcaObj)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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) ContrastList(tpcaObj)
Create distance matrix of all vs all protein melting profiles
createDistMat( objList, rownameCol = NULL, summaryMethodStr = "median", distMethodStr = "euclidean" )createDistMat( objList, rownameCol = NULL, summaryMethodStr = "median", distMethodStr = "euclidean" )
objList |
list of objects suitable for the analysis, currently allowed classes of objects are: matrices, data.frames, tibbles and ExpressionSets |
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' |
a distance matrix of all pairwise protein melting profiles
library(Biobase) 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 ) createDistMat(mat_list) expr1 <- ExpressionSet(m1) expr2 <- ExpressionSet(m2) expr3 <- ExpressionSet(m3) exprSet_list <- list( expr1, expr2, expr3 ) createDistMat(exprSet_list)library(Biobase) 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 ) createDistMat(mat_list) expr1 <- ExpressionSet(m1) expr2 <- ExpressionSet(m2) expr3 <- ExpressionSet(m3) exprSet_list <- list( expr1, expr2, expr3 ) createDistMat(exprSet_list)
Extract CtrlCondName
## S4 method for signature 'tpcaResult' CtrlCondName(object)## S4 method for signature 'tpcaResult' CtrlCondName(object)
object |
and object of class tpcaResult |
a character string describing the control condition
tpcaObj <- new("tpcaResult") CtrlCondName(tpcaObj)tpcaObj <- new("tpcaResult") CtrlCondName(tpcaObj)
Extract diffTpcaResultTable
## S4 method for signature 'tpcaResult' diffTpcaResultTable(object)## S4 method for signature 'tpcaResult' diffTpcaResultTable(object)
object |
an object of class tpcaResult |
a data frame containing the results from a diffTpca analysis
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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) diffTpcaResultTable(tpcaObj)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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) diffTpcaResultTable(tpcaObj)
Extract DistMat
## S4 method for signature 'tpcaResult' DistMat(object)## S4 method for signature 'tpcaResult' DistMat(object)
object |
an object of class tpcaResult |
a matrix containing the distance matrix of all pariwise protein-protein melting curve distances computed from a TPP experiment
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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) DistMat(tpcaObj)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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) DistMat(tpcaObj)
Extract DistMethod
## S4 method for signature 'tpcaResult' DistMethod(object)## S4 method for signature 'tpcaResult' DistMethod(object)
object |
and object of class tpcaResult |
a character string of the dist method
tpcaObj <- new("tpcaResult") DistMethod(tpcaObj)tpcaObj <- new("tpcaResult") DistMethod(tpcaObj)
Extract ObjList
## S4 method for signature 'tpcaResult' ObjList(object)## S4 method for signature 'tpcaResult' ObjList(object)
object |
an object of class tpcaResult |
an object list containing TPP data
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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) ObjList(tpcaObj)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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) ObjList(tpcaObj)
data frame assigning proteins to (in)directly interacting proteins within protein complexes
data("ori_et_al_complex_ppis")data("ori_et_al_complex_ppis")
data frame with columns complex_name, x, y, pair (unique pair id)
Ori et al. (2016), Genome Biology, 17, 47; Jensen et al. (2009), Nucleic Acids Research, 37, D412–D416
data("ori_et_al_complex_ppis")data("ori_et_al_complex_ppis")
data frame assigning proteins to annotated protein complexes
data("ori_et_al_complexes_df")data("ori_et_al_complexes_df")
data frame with columns ensembl_id, protein and id (complex identifier)
Ori et al. (2016), Genome Biology, 17, 47
data("ori_et_al_complexes_df")data("ori_et_al_complexes_df")
Plots a ROC curve representing how well a given TPP dataset recovers annotated proteins complexes. The ROC curve is generated based on the supplied protein complex annotation specificity is assessed by comparing the given complex annotation to random permutations of that table, i.e. proteins randomly grouped together.
plotComplexRoc(tpcaObj, computeAUC = FALSE)plotComplexRoc(tpcaObj, computeAUC = FALSE)
tpcaObj |
tpcaResult object |
computeAUC |
logical parameter indicating whether area under the ROC should be computed and indicated in the lower right corner of the plot |
ggplot object of a receiver operating curve (ROC)
rocTab = data.frame( TPR = c(0, 0.1, 0.2, 0.4, 0.5, 0.7, 0.9, 1), FPR = c(0, 0.05, 0.1, 0.2, 0.5, 0.7, 0.9, 1) ) tpcaTest <- new( "tpcaResult", ComplexRocTable = rocTab) plotComplexRoc(tpcaTest)rocTab = data.frame( TPR = c(0, 0.1, 0.2, 0.4, 0.5, 0.7, 0.9, 1), FPR = c(0, 0.05, 0.1, 0.2, 0.5, 0.7, 0.9, 1) ) tpcaTest <- new( "tpcaResult", ComplexRocTable = rocTab) plotComplexRoc(tpcaTest)
Plot differential TPCA analysis results
plotDiffTpcaVolcano( tpcaObj, alpha = 0.1, setXLim = FALSE, xlimit = c(-0.75, 0.75) )plotDiffTpcaVolcano( tpcaObj, alpha = 0.1, setXLim = FALSE, xlimit = c(-0.75, 0.75) )
tpcaObj |
a tpcaObj after having performed
a differential analysis, see |
alpha |
significance level / FDR at which null hypothesis should be rejected |
setXLim |
logical determining whether x-axis limits should be set according to xlimit |
xlimit |
numeric vector with two elements determing the x-axis limits, only is implemented if setXLim is set to TRUE |
ggplot displaying a volcano plot
library(dplyr) library(Biobase) m1 <- matrix(1:28, ncol = 4) m2 <- matrix(2:25, ncol = 4) m3 <- matrix(c(2:10, 1:19), ncol = 4) rownames(m1) <- 1:7 rownames(m2) <- 3:8 rownames(m3) <- 2:8 mat_list <- list( m1, m2, m3 ) c1 <- matrix(29:2, ncol = 4) c2 <- matrix(26:3, ncol = 4) c3 <- matrix(c(11:3, 20:2), ncol = 4) rownames(c1) <- 1:7 rownames(c2) <- 3:8 rownames(c3) <- 2:8 contrast_list <- list( c1, c2, c3 ) ppi_anno <- tibble( x = c("3", "3"), y = c("5", "7"), pair = c("3:5", "3:7")) ref_df <- tibble( pair = c("3:5", "3:7"), valueC2 = c(4, 8) ) diff_tpca <- runDiffTPCA( mat_list, contrast_list, ppiAnno = ppi_anno) plotDiffTpcaVolcano(diff_tpca)library(dplyr) library(Biobase) m1 <- matrix(1:28, ncol = 4) m2 <- matrix(2:25, ncol = 4) m3 <- matrix(c(2:10, 1:19), ncol = 4) rownames(m1) <- 1:7 rownames(m2) <- 3:8 rownames(m3) <- 2:8 mat_list <- list( m1, m2, m3 ) c1 <- matrix(29:2, ncol = 4) c2 <- matrix(26:3, ncol = 4) c3 <- matrix(c(11:3, 20:2), ncol = 4) rownames(c1) <- 1:7 rownames(c2) <- 3:8 rownames(c3) <- 2:8 contrast_list <- list( c1, c2, c3 ) ppi_anno <- tibble( x = c("3", "3"), y = c("5", "7"), pair = c("3:5", "3:7")) ref_df <- tibble( pair = c("3:5", "3:7"), valueC2 = c(4, 8) ) diff_tpca <- runDiffTPCA( mat_list, contrast_list, ppiAnno = ppi_anno) plotDiffTpcaVolcano(diff_tpca)
Plot thermal profile of protein pairs
plotPPiProfiles(tpcaObj, pair, splinesDf = 4)plotPPiProfiles(tpcaObj, pair, splinesDf = 4)
tpcaObj |
a tpcaObj after having performed
a differential analysis, see |
pair |
character vector of one or more protein names |
splinesDf |
numeric, degree of freedom of the spline fit to the melting curves |
ggplot displaying the thermal profile of a protein pair across conditions
library(Biobase) set.seed(12) m1 <- matrix(rnorm(50), ncol = 10) m2 <- matrix(rnorm(50), ncol = 10) rownames(m1) <- letters[1:5] rownames(m2) <- letters[1:5] colnames(m1) <- paste("fc", 1:10, sep = "_") colnames(m2) <- paste("fc", 1:10, sep = "_") pheno <- data.frame( temperature = seq(37, 67, length.out = 10)) rownames(pheno) <- paste("fc", 1:10, sep = "_") eset1 <- ExpressionSet( assayData = m1, phenoData = AnnotatedDataFrame(pheno) ) eset2 <- ExpressionSet( assayData = m2, phenoData = AnnotatedDataFrame(pheno) ) tpcaObj <- new("tpcaResult", ObjList = list(eset1), ContrastList = list(eset2), CtrlCondName = "control", ContrastCondName = "treatment") plotPPiProfiles(tpcaObj, pair = c("b", "d"))library(Biobase) set.seed(12) m1 <- matrix(rnorm(50), ncol = 10) m2 <- matrix(rnorm(50), ncol = 10) rownames(m1) <- letters[1:5] rownames(m2) <- letters[1:5] colnames(m1) <- paste("fc", 1:10, sep = "_") colnames(m2) <- paste("fc", 1:10, sep = "_") pheno <- data.frame( temperature = seq(37, 67, length.out = 10)) rownames(pheno) <- paste("fc", 1:10, sep = "_") eset1 <- ExpressionSet( assayData = m1, phenoData = AnnotatedDataFrame(pheno) ) eset2 <- ExpressionSet( assayData = m2, phenoData = AnnotatedDataFrame(pheno) ) tpcaObj <- new("tpcaResult", ObjList = list(eset1), ContrastList = list(eset2), CtrlCondName = "control", ContrastCondName = "treatment") plotPPiProfiles(tpcaObj, pair = c("b", "d"))
Plot PPI ROC curve
plotPPiRoc(tpcaObj, computeAUC = FALSE)plotPPiRoc(tpcaObj, computeAUC = FALSE)
tpcaObj |
tpcaResult object |
computeAUC |
logical parameter indicating whether area under the ROC should be computed and indicated in the lower right corner of the plot |
ggplot object of a receiver operating curve (ROC)
rocTab = data.frame( TPR = c(0, 0.1, 0.2, 0.4, 0.5, 0.7, 0.9, 1), FPR = c(0, 0.05, 0.1, 0.2, 0.5, 0.7, 0.9, 1) ) tpcaTest <- new( "tpcaResult", PPiRocTable = rocTab) plotPPiRoc(tpcaTest)rocTab = data.frame( TPR = c(0, 0.1, 0.2, 0.4, 0.5, 0.7, 0.9, 1), FPR = c(0, 0.05, 0.1, 0.2, 0.5, 0.7, 0.9, 1) ) tpcaTest <- new( "tpcaResult", PPiRocTable = rocTab) plotPPiRoc(tpcaTest)
Plot TPCA analysis results
plotTpcaVolcano(tpcaObj, alpha = 0.1)plotTpcaVolcano(tpcaObj, alpha = 0.1)
tpcaObj |
a tpcaObj after having performed
a differential analysis, see |
alpha |
significance level / FDR at which null hypothesis should be rejected |
ggplot displaying a volcano plot
library(dplyr) library(Biobase) m1 <- matrix(1:28, ncol = 4) m2 <- matrix(2:25, ncol = 4) m3 <- matrix(c(2:10, 1:19), ncol = 4) rownames(m1) <- 1:7 rownames(m2) <- 3:8 rownames(m3) <- 2:8 mat_list <- list( m1, m2, m3 ) complex_anno <- tibble( protein = c("3", "4", "5", "4", "5", "6", "7"), id = c(rep("1", 3), rep("2", 4)), count = c(rep(3, 3), rep(4, 4))) tpca_result <- runTPCA( mat_list, complexAnno = complex_anno) plotTpcaVolcano(tpca_result)library(dplyr) library(Biobase) m1 <- matrix(1:28, ncol = 4) m2 <- matrix(2:25, ncol = 4) m3 <- matrix(c(2:10, 1:19), ncol = 4) rownames(m1) <- 1:7 rownames(m2) <- 3:8 rownames(m3) <- 2:8 mat_list <- list( m1, m2, m3 ) complex_anno <- tibble( protein = c("3", "4", "5", "4", "5", "6", "7"), id = c(rep("1", 3), rep("2", 4)), count = c(rep(3, 3), rep(4, 4))) tpca_result <- runTPCA( mat_list, complexAnno = complex_anno) plotTpcaVolcano(tpca_result)
Extract PPiAnnotation
## S4 method for signature 'tpcaResult' PPiAnnotation(object)## S4 method for signature 'tpcaResult' PPiAnnotation(object)
object |
and object of class tpcaResult |
a data frame containing the results from a tpca analysis
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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) PPiAnnotation(tpcaObj)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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) PPiAnnotation(tpcaObj)
Extract PPiRocTable
## S4 method for signature 'tpcaResult' PPiRocTable(object)## S4 method for signature 'tpcaResult' PPiRocTable(object)
object |
an object of class tpcaResult |
a data frame containing the results from a tpca analysis
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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) PPiRocTable(tpcaObj)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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) PPiRocTable(tpcaObj)
Extract PPiRocTableAnno
## S4 method for signature 'tpcaResult' PPiRocTableAnno(object)## S4 method for signature 'tpcaResult' PPiRocTableAnno(object)
object |
an object of class tpcaResult |
a data frame containing annotation information for PPiRocTable
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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) PPiRocTableAnno(tpcaObj)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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) PPiRocTableAnno(tpcaObj)
Run differential TPCA analysis
runDiffTPCA( objList, contrastList, ctrlCondName = "control", contrastCondName = "treatment", ppiAnno = NULL, complexAnno = NULL, rownameCol = NULL, summaryMethodStr = "median", distMethodStr = "euclidean", n = 10000, p_adj_method = "BH" )runDiffTPCA( objList, contrastList, ctrlCondName = "control", contrastCondName = "treatment", ppiAnno = NULL, complexAnno = NULL, rownameCol = NULL, summaryMethodStr = "median", distMethodStr = "euclidean", n = 10000, p_adj_method = "BH" )
objList |
input list of objects, e.g. ExpressionSets retrieved after TPP data import or matrices or data frames |
contrastList |
input list of objects for comparison at e.g. different treatment condtion, same file formats work as for objList |
ctrlCondName |
character string indicating the name of the control condition, default is "control" |
contrastCondName |
character string indicating the name of the contrast condition, default is "treatment" |
ppiAnno |
data frame annotation known protein-protein interactions (PPI) to test |
complexAnno |
data frame annotating known protein complexes of interest 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' |
n |
number of random protein pair draws to obtain empirical p-value, default is 10000 |
p_adj_method |
method to be used for multiple testing adjusment, default is "BH" |
an object of class tpcaResult with the following slots: 1) ObjList: containing the supplied list of objects
library(dplyr) library(Biobase) m1 <- matrix(1:28, ncol = 4) m2 <- matrix(2:25, ncol = 4) m3 <- matrix(c(2:10, 1:19), ncol = 4) rownames(m1) <- 1:7 rownames(m2) <- 3:8 rownames(m3) <- 2:8 mat_list <- list( m1, m2, m3 ) c1 <- matrix(29:2, ncol = 4) c2 <- matrix(26:3, ncol = 4) c3 <- matrix(c(11:3, 20:2), ncol = 4) rownames(c1) <- 1:7 rownames(c2) <- 3:8 rownames(c3) <- 2:8 contrast_list <- list( c1, c2, c3 ) ppi_anno <- tibble( x = c("3", "3"), y = c("5", "7"), pair = c("3:5", "3:7")) ref_df <- tibble( pair = c("3:5", "3:7"), valueC2 = c(4, 8) ) diff_tpca <- Rtpca:::runDiffTPCA( mat_list, contrast_list, ppiAnno = ppi_anno)library(dplyr) library(Biobase) m1 <- matrix(1:28, ncol = 4) m2 <- matrix(2:25, ncol = 4) m3 <- matrix(c(2:10, 1:19), ncol = 4) rownames(m1) <- 1:7 rownames(m2) <- 3:8 rownames(m3) <- 2:8 mat_list <- list( m1, m2, m3 ) c1 <- matrix(29:2, ncol = 4) c2 <- matrix(26:3, ncol = 4) c3 <- matrix(c(11:3, 20:2), ncol = 4) rownames(c1) <- 1:7 rownames(c2) <- 3:8 rownames(c3) <- 2:8 contrast_list <- list( c1, c2, c3 ) ppi_anno <- tibble( x = c("3", "3"), y = c("5", "7"), pair = c("3:5", "3:7")) ref_df <- tibble( pair = c("3:5", "3:7"), valueC2 = c(4, 8) ) diff_tpca <- Rtpca:::runDiffTPCA( mat_list, contrast_list, ppiAnno = ppi_anno)
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" )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 )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 )
Set CommonFeatures
## S4 method for signature 'tpcaResult' SetCommonFeatures(object, commonFeatures)## S4 method for signature 'tpcaResult' SetCommonFeatures(object, commonFeatures)
object |
and object of class tpcaResult |
commonFeatures |
a vector of characters indicating the common features across replicates |
a vector of common features across replicates
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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) SetCommonFeatures(tpcaObj, c("2", "3"))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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) SetCommonFeatures(tpcaObj, c("2", "3"))
Set ComplexAnnotation
## S4 method for signature 'tpcaResult' SetComplexAnnotation(object, df)## S4 method for signature 'tpcaResult' SetComplexAnnotation(object, df)
object |
an object of class tpcaResult |
df |
data frame containing complex annotation |
an object of class tpcaResult
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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) SetComplexAnnotation(tpcaObj, data.frame(id = "complex1"))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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) SetComplexAnnotation(tpcaObj, data.frame(id = "complex1"))
Set ComplexBackgroundDistributionList
## S4 method for signature 'tpcaResult' SetComplexBackgroundDistributionList(object, lt)## S4 method for signature 'tpcaResult' SetComplexBackgroundDistributionList(object, lt)
object |
an object of class tpcaResult |
lt |
a list of data frames containing distances of random complexes with different number of members |
an object of class tpcaResult
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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) SetComplexBackgroundDistributionList(tpcaObj, list('3' = data.frame(pair = "A:B")))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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) SetComplexBackgroundDistributionList(tpcaObj, list('3' = data.frame(pair = "A:B")))
Set ComplexRocTable
## S4 method for signature 'tpcaResult' SetComplexRocTable(object, df)## S4 method for signature 'tpcaResult' SetComplexRocTable(object, df)
object |
and object of class tpcaResult |
df |
data.frame containg ComplexRocTable to set |
a data frame containing the results from a tpca analysis
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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) SetComplexRocTable(tpcaObj, data.frame(FPR = 1, TPR = 1))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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) SetComplexRocTable(tpcaObj, data.frame(FPR = 1, TPR = 1))
Set ContrastCondName
## S4 method for signature 'tpcaResult' SetContrastCondName(object, name)## S4 method for signature 'tpcaResult' SetContrastCondName(object, name)
object |
an object of class tpcaResult |
name |
a character string describing the contrast condition |
an object of class tpcaResult
tpcaObj <- new("tpcaResult") SetContrastCondName(tpcaObj, "DMSO")tpcaObj <- new("tpcaResult") SetContrastCondName(tpcaObj, "DMSO")
Set ContrastDistMat
## S4 method for signature 'tpcaResult' SetContrastDistMat(object, mat)## S4 method for signature 'tpcaResult' SetContrastDistMat(object, mat)
object |
and object of class tpcaResult |
mat |
matrix containg contrast distance matrix to set |
a data frame containing the results from a tpca analysis
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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) SetContrastDistMat(tpcaObj, matrix(c(0, 1, 0, 1), ncol = 2))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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) SetContrastDistMat(tpcaObj, matrix(c(0, 1, 0, 1), ncol = 2))
Set CtrlCondName
## S4 method for signature 'tpcaResult' SetCtrlCondName(object, name)## S4 method for signature 'tpcaResult' SetCtrlCondName(object, name)
object |
an object of class tpcaResult |
name |
a character string describing the control condition |
an object of class tpcaResult
tpcaObj <- new("tpcaResult") SetCtrlCondName(tpcaObj, "DMSO")tpcaObj <- new("tpcaResult") SetCtrlCondName(tpcaObj, "DMSO")
Set diffTpcaResultTable
## S4 method for signature 'tpcaResult' SetDiffTpcaResultTable(object, df)## S4 method for signature 'tpcaResult' SetDiffTpcaResultTable(object, df)
object |
an object of class tpcaResult |
df |
a data frame containing the results from a differential tpca analysis |
an object of class tpcaResult
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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) SetDiffTpcaResultTable(tpcaObj, data.frame(pair = "A:B"))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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) SetDiffTpcaResultTable(tpcaObj, data.frame(pair = "A:B"))
Set DistMat
## S4 method for signature 'tpcaResult' SetDistMat(object, mat)## S4 method for signature 'tpcaResult' SetDistMat(object, mat)
object |
an object of class tpcaResult |
mat |
matrix containg distance matrix to set |
a data frame containing the results from a tpca analysis
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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) SetDistMat(tpcaObj, matrix(c(0, 1, 0, 1), ncol = 2))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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) SetDistMat(tpcaObj, matrix(c(0, 1, 0, 1), ncol = 2))
Set distMethod
## S4 method for signature 'tpcaResult' SetDistMethod(object, method)## S4 method for signature 'tpcaResult' SetDistMethod(object, method)
object |
an object of class tpcaResult |
method |
character string of dist method |
an object of class tpcaResult
tpcaObj <- new("tpcaResult") SetDistMethod(tpcaObj, "euclidean")tpcaObj <- new("tpcaResult") SetDistMethod(tpcaObj, "euclidean")
Set PPiRocTable
## S4 method for signature 'tpcaResult' SetPPiRocTable(object, df)## S4 method for signature 'tpcaResult' SetPPiRocTable(object, df)
object |
an object of class tpcaResult |
df |
data.frame containg PPiRocTable to set |
an object of class tpcaResult
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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) SetPPiRocTable(tpcaObj, data.frame(FPR = 1, TPR = 1))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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) SetPPiRocTable(tpcaObj, data.frame(FPR = 1, TPR = 1))
Set PPiRocTableAnno
## S4 method for signature 'tpcaResult' SetPPiRocTableAnno(object, df)## S4 method for signature 'tpcaResult' SetPPiRocTableAnno(object, df)
object |
an object of class tpcaResult |
df |
data.frame containg PPiRocTable annotation to set |
an object of class tpcaResult
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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) SetPPiRocTableAnno(tpcaObj, data.frame(pair = "A:B"))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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) SetPPiRocTableAnno(tpcaObj, data.frame(pair = "A:B"))
Set summaryMethod
## S4 method for signature 'tpcaResult' SetSummaryMethod(object, method)## S4 method for signature 'tpcaResult' SetSummaryMethod(object, method)
object |
an object of class tpcaResult |
method |
character string of summary method |
an object of class tpcaResult
tpcaObj <- new("tpcaResult") SetSummaryMethod(tpcaObj, "median")tpcaObj <- new("tpcaResult") SetSummaryMethod(tpcaObj, "median")
Set TpcaResultTable
## S4 method for signature 'tpcaResult' SetTpcaResultTable(object, df)## S4 method for signature 'tpcaResult' SetTpcaResultTable(object, df)
object |
an object of class tpcaResult |
df |
a data frame containing the results from a tpca analysis |
an object of class tpcaResult
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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) SetTpcaResultTable(tpcaObj, data.frame(pair = "A:B"))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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) SetTpcaResultTable(tpcaObj, data.frame(pair = "A:B"))
data frame assigning proteins to interacting proteins
data("string_ppi_df")data("string_ppi_df")
data frame with columns x, y (gene symbol of interactors), combined_score, pair (unique pair id)
Jensen et al. (2009), Nucleic Acids Research, 37, D412–D416
data("string_ppi_df")data("string_ppi_df")
Extract SummaryMethod
## S4 method for signature 'tpcaResult' SummaryMethod(object)## S4 method for signature 'tpcaResult' SummaryMethod(object)
object |
and object of class tpcaResult |
a character string of the summary method
tpcaObj <- new("tpcaResult") SummaryMethod(tpcaObj)tpcaObj <- new("tpcaResult") SummaryMethod(tpcaObj)
S4 TPCA Result Class
an object of class tpcaResult with the following slots: 1) ObjList: containing the supplied list of objects (e.g. a list of Expression Sets summarizing a TPP experiment) 2) ContrastList: containing the supplied list of constrast objects (if supplied for performance of a differential Rtpca analysis) 3) CtrlCondName: character string indicating the control condition, e.g. "control" 4) ContrastCondName: character string indicating the contrast condition, e.g. "drug treatment" 5) DistMat: a matrix containing all pairwise protein-protein distances obtained from comparing their melting curves in the control condition 6) ContrastDistMat: a matrix containing all pairwise protein-protein distances obtained from comparing their melting curves in the contrast condition 7) CommonFeatures: a vector containing the features (proteins) found in common between control and contrast condition 8) ComplexAnnotation: a data frame supplied by the user annotating protein to protein complexes 9) ComplexBackgroundDistributionList: a list of distances drawn for random groups of proteins with different number of members 10) PPiAnnotation: a data frame supplied by the user annotating protein-protein interactions 11) PPiRocTable: data frame containing false positive rate and true positive rate based on ranking the TPCA analysis results by euclidean distance of melting curves of protein pairs, annotated PPIs are considered true positives 12) PPiRocTableAnno: annotation to PPiRocTable 13) ComplexRocTable: data frame containing false positive rate and true positive rate based on ranking the TPCA analysis results by euclidean distance of melting curves of proteins within annotated complexes, annotated complexes are considered true positives, proteins in randomly permuted complex annotations are considered false positives 14) summaryMethod: character string of summarization method used to summarize data across replicates 15) distMethod: character string of distance method used to compare melting curves of proteins 16) tpcaResultTable: data frame containing the results from a tpca analysis 17) diffTpcaResultTable: data frame containing the results from a differential tpca analysis
ObjListlist.
ContrastListlist.
CtrlCondNamecharacter.
ContrastCondNamecharacter.
DistMatmatrix.
ContrastDistMatmatrix
CommonFeaturesvector.
ComplexAnnotationdata.frame.
ComplexBackgroundDistributionListlist.
PPiAnnotationdata.frame.
PPiRocTabledata.frame
PPiRocTableAnnodata.frame
ComplexRocTabledata.frame
summaryMethodcharacter.
distMethodcharacter.
tpcaResultTabledata.frame.
diffTpcaResultTabledata.frame.
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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list)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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list)
Extract tpcaResultTable
## S4 method for signature 'tpcaResult' tpcaResultTable(object)## S4 method for signature 'tpcaResult' tpcaResultTable(object)
object |
an object of class tpcaResult |
a data frame containing the results from a tpca analysis
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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) tpcaResultTable(tpcaObj)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 mat_list <- list( m1, m2, m3 ) tpcaObj <- new("tpcaResult", ObjList = mat_list) tpcaResultTable(tpcaObj)