NetPhorce Data Extraction Tutorial

1.0 Process the Data

## Loading One Condition Data
data("twoConditionsExample", package = "NetPhorce")

## ## Identify the Key Columns
identifiedCols <- confirmColumnNames(rawMaxQuant = twoConditionsExample,
                                     positionCol = "Position",
                                     reverseCol = "Reverse",
                                     localizationProbCol = "Localization prob",
                                     potentialContaminationCol = "Potential contaminant",
                                     aminoAcidCol = "Amino acid",
                                     uniqueIDCol = "Protein",
                                     seqWindowIDCol = "Sequence window",
                                     fastaIDCol = "Fasta headers")

## Identify the Intensity Columns with Condition, Time Point and Replication Information
intensityCols <- confirmIntensityColumns(rawMaxQuant = twoConditionsExample,
                                         intensityPattern = "con_time_rep",
                                         verbose = TRUE)

## Process the data based on the identified columns
netPhorceData <- processData(rawMaxQuant = twoConditionsExample,
                             processedColNames = identifiedCols,
                             processedIntensity = intensityCols,
                             minReplication = 3,
                             minLocalProb = 0.75)
#> 
#> Number of Conditions Found: 2. Undergoing a time consuming step, please wait...
#> 
#> Complete.

## Validating the Kinase/Phosphatase Information
netPhorceData <- validateKinaseTable(netPhorceData = netPhorceData,
                                     defaultKinaseTable = TRUE,
                                     abbrev = "Ath")
#> Kinase and Phosphatase Matching Table:

## Regulation Validation based on user inputs
netPhorceData <- regulationCheck(netPhorceData = netPhorceData,
                                 upReg = 0.25,
                                 downReg = 0.25,
                                 absMinThreshold = 0.1,
                                 qValueCutOff = 0.05,
                                 verbose = TRUE)

## Network Analysis
netPhorceData <- networkAnalysis(netPhorceData = netPhorceData,
                               requestPlotData = TRUE)
#> 
#> Undergoing a time consuming step, please wait...

2.0 Extract Summary Table

summaryTable <- extractSummaryTable(netPhorceData = netPhorceData)
#> Joining with `by = join_by(UniqueID)`
DT::datatable(summaryTable,
              options = list(
                pageLength = 15, 
                scrollX = TRUE,
                rownames = FALSE))

3.0 Extract Network Results

networkResults <- extractNetworkResults(netPhorceData = netPhorceData)
DT::datatable(networkResults,
              options = list(
                pageLength = 15, 
                scrollX = TRUE,
                rownames = FALSE))