PCA Plot

The principle component analysis (PCA) output before and after variance stabilizing normalization (vsn).

plotPCA(
  netPhorceData = netPhorceData,
  condition = NULL,
  normalized = FALSE,
  plotly = FALSE
)

Arguments

netPhorceData

(Required). Processed NetPhorce Object

condition

(Required). Select a specific condition from your experiment.

normalized

(Required). Use raw or normalized intensity data

plotly

(Required). If TRUE, output an interactive plotly version, else output a static ggplot2 version.

Value

A ggplot/plotly object

Examples

if (FALSE) {
## Loading Two Conditions Example
data("twoConditionsExample")
## 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)
## Generate Distribution Plot – GGPLOT version (Static)
plotPCA(netPhorceData = netPhorceData,
        condition = "tot3",
        plotly = FALSE)
## Generate Distribution Plot – PLOTLY version (Interactive)
plotPCA(netPhorceData = netPhorceData,
        condition = "Col0",
        plotly = TRUE)
}