In ksong4/NetPhorce: Phosphoproteomics Network Analysis (NetPhorce)

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
library(NetPhorce)

Loading library

library(NetPhorce)

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)

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

## 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)

2.0 Diganostic/Statistcal Plots

2.1 Distribution Plot {.tabset}

2.1.1 Static Version

plotDistribution(netPhorceData = netPhorceData,
                 condition = "Col0",
                 plotly = FALSE)

2.1.2 Interactive Version

plotDistribution(netPhorceData = netPhorceData,
                 condition = "Col0",
                 plotly = TRUE)

2.2 Histogram and Boxplot {.tabset}

2.2.1 Histogram and Boxplot Side-by-Side Plot

plotHistBox(netPhorceData = netPhorceData,
             condition = "tot3",
             histogram = TRUE,
             boxplot = TRUE)

2.2.2 Boxplot Only

plotHistBox(netPhorceData = netPhorceData,
             condition = "tot3",
             histogram = FALSE,
             boxplot = TRUE)

2.2.3 Histogram Only

plotHistBox(netPhorceData = netPhorceData,
             condition = "tot3",
             histogram = TRUE,
             boxplot = FALSE)

2.3 PCA Plot {.tabset}

2.3.1 Static Version

plotPCA(netPhorceData = netPhorceData,
        condition = "Col0",
        normalized = FALSE,
        plotly = FALSE)

2.3.2 Interactive Version

plotPCA(netPhorceData = netPhorceData,
        condition = "tot3",
        normalized = TRUE,
        plotly = TRUE)

2.4 Heatmap based on Unique IDs

2.4.1 Identify Unique IDs

uniqueIDs <- 
  findUniqueIDs(netPhorceData = netPhorceData,
                uniqueIDList = c(
                  # Significant Set Examples: 
                  "AT1G13030.1", "AT1G13360.3", "AT1G42550.1", 
                  # Unique/Abscence Set Examples 
                  "AT1G17280.9", "AT1G22310.2", "AT1G23890.2" 
                ),
                verbose = TRUE)

2.4.2 Heatmap Based on Found Unique IDs - Significant Set Interactive Version

plotUniqueIDsHeatmaps(netPhorceData = netPhorceData,
                      foundUniqueIDs = uniqueIDs,
                      heatmapType = "Significant",
                      minQVal = 0.05,
                      plotly = TRUE)

2.4.2 Heatmap Based on Found Unique IDs - Absence/Presence Set Static Version

plotUniqueIDsHeatmaps(netPhorceData = netPhorceData,
                      foundUniqueIDs = uniqueIDs,
                      heatmapType = "AbsencePresence",
                      plotly = FALSE)

2.4 Heatmap based on Cluster IDs

2.4.1 Identify Cluster IDs from Significant Set

clusterIDs_Sig <- 
  findClusters(netPhorceData = netPhorceData,
               clusterIDs = c(0),
               heatmapType = "Significant",
               minQVal = 0.1,
               verbose = TRUE)

2.4.2 Identify Cluster IDs from AbsencePresence Set

Note, you do not need to supply minQVal for Abscence Presence Heatmap

clusterIDs_AbsPrs <- 
  findClusters(netPhorceData = netPhorceData, 
               clusterIDs = c(1),
               minQVal = 0.1,
               heatmapType = "AbsencePresence",
               verbose = TRUE)

2.4.3 Heatmap Based on Found Unique IDs - Significant Set Interactive Version

plotClustersHeatmap(netPhorceData = netPhorceData,
                    foundClusterIDs = clusterIDs_Sig,
                    plotly = TRUE)

2.4.4 Heatmap Based on Found Unique IDs - Significant Set Static Version

plotClustersHeatmap(netPhorceData = netPhorceData,
                    foundClusterIDs = clusterIDs_AbsPrs,
                    plotly = FALSE)

2.6 Single or Multiple Peptides Plot

2.6.1 Extract Peptide ID

peptideIDs <- 
  findPeptideIDs(netPhorceData = netPhorceData, 
                 peptideIDList = c("AT1G01320.2_S1349_1", "AT1G05560.1_S153_1", "AT1G01320.2_S149_1"))

2.6.2 Single Peptide Plot - Interactive Version

plotSinglePeptide(netPhorceData = netPhorceData,
                   foundPepetidesIDs = peptideIDs,
                   plotAll = FALSE,
                   plotly = TRUE)

2.6.3 Single Peptide Plot - Static Version

plotSinglePeptide(netPhorceData = netPhorceData,
                   foundPepetidesIDs = peptideIDs,
                   plotAll = FALSE,
                   plotly = FALSE)

2.6.4 Multiple Peptides Plot - Interactive Version

plotMultiPeptides(netPhorceData = netPhorceData,
                  foundPepetidesIDs = peptideIDs,
                  condition = "Col0",
                  plotly = TRUE)

2.6.5 Multiple Peptides Plot - Static Version

plotMultiPeptides(netPhorceData = netPhorceData,
                  foundPepetidesIDs = peptideIDs,
                  condition = "tot3",
                  plotly = FALSE)

3.0 Regulation Plot

3.1 Regulation Plot - Interactive Version

plotRegulation(netPhorceData = netPhorceData ,
               condition = "Col0",
               plotly = TRUE)

3.2 Regulation Plot - Static Version

plotRegulation(netPhorceData = netPhorceData ,
               condition = "tot3",
               plotly = FALSE)

4.0 Network

plotNetPhorce(netPhorceData = netPhorceData,
              condition = "tot3",
              FASTADescription = TRUE)

ksong4/NetPhorce documentation built on March 24, 2023, 12:51 p.m.