plot.citrus.regressionResult: Plot results of a Citrus regression analysis

In nolanlab/citrus: Citrus: Identification of stratifying subpopulations in Flow Cytometry Data.

Plot results of a Citrus regression analysis

Description

Makes many plots showing results of a Citrus analysis

Usage

plot.citrus.regressionResult(citrus.regressionResult, outputDirectory,
  citrus.foldClustering, citrus.foldFeatureSet, citrus.combinedFCSSet,
  plotTypes = c("errorRate", "stratifyingFeatures", "stratifyingClusters",
  "clusterGraph"), hierarchyGraph = NULL, ...)

Arguments

citrus.regressionResult	A citrus.regressionResult object.
outputDirectory	Full path to output directory for plots.
citrus.foldClustering	A citrus.foldClustering object.
citrus.foldFeatureSet	A citrus.foldFeatureSet object.
citrus.combinedFCSSet	A citrus.combinedFCSSet object.
plotTypes	Vector of plots types to make. Valid options are errorRate (Cross-validated error rates for predictive models), stratifyingFeatures (plots of non-zero model features),stratifyingClusters (plots of clustering marker distributions in stratifying clusters), and clusterGraph (Plots of clustering hierarchy graph).
hierarchyGraph	A hierarchy graph configuration created by citrus.createHierarchyGraph. If NULL, automatically generated.

Author(s)

Robert Bruggner

Examples

# Where the data lives
dataDirectory = file.path(system.file(package = "citrus"),"extdata","example1")

# Create list of files to be analyzed
fileList = data.frame("unstim"=list.files(dataDirectory,pattern=".fcs"))

# Read the data
citrus.combinedFCSSet = citrus.readFCSSet(dataDirectory,fileList)

# List of columns to be used for clustering
clusteringColumns = c("Red","Blue")

# List disease group of each sample
labels = factor(rep(c("Healthy","Diseased"),each=10))

# Cluster data
citrus.foldClustering = citrus.clusterAndMapFolds(citrus.combinedFCSSet,clusteringColumns,nFolds=1)

# Build abundance features
citrus.foldFeatureSet = citrus.calculateFoldFeatureSet(citrus.foldClustering,citrus.combinedFCSSet)

# Endpoint regress
citrus.regressionResult = citrus.endpointRegress(modelType="pamr",citrus.foldFeatureSet,labels,family="classification")

# Plot results
# plot(citrus.regressionResult,outputDirectory,"/path/to/output/directory/",citrus.foldClustering,citrus.foldFeatureSet,citrus.combinedFCSSet)

nolanlab/citrus documentation built on April 19, 2024, 6:49 p.m.