View source: R/citrus.featureFunctions.R
citrus.calculateFeatures | R Documentation |
Calculate descriptive properties for each cluster in each sample
citrus.calculateFeatures(citrus.combinedFCSSet, clusterAssignments, clusterIds,
featureType = "abundances", conditions = NULL, ...)
citrus.combinedFCSSet |
A |
clusterAssignments |
List of indicies of cluster assignments for each cluster. |
clusterIds |
Vector of cluster ID's for which to calculate features. |
featureType |
Type of feature to calculate. Valid options are: |
conditions |
Vector of conditions for which to calculate features. See details. |
... |
Other arguments passed to individual feature-calculation functions. |
If conditions=NULL
, citrus.calculateFeatures
constructs features for all samples
in the citrus.combinedFCSSet
. If conditions
is a single element, citrus.calculateFeatures
constructs features for samples in that condition. If conditions
contains two elements, the first
condition is used as a baseline condition, the second is used as a comparison condition, and citrus.calculateFeatures
returns the difference in feature values between the comparison and baseline conditions.
Matrix of cluster features
Robert Bruggner
citrus.calculateFeature.type
######################################################
# Calculate cluster abundances for single condition
######################################################
# 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")
# Cluster data
citrus.clustering = citrus.cluster(citrus.combinedFCSSet,clusteringColumns)
# Large enough clusters
largeEnoughClusters = citrus.selectClusters(citrus.clustering)
# Build features
abundanceFeatures = citrus.calculateFeatures(citrus.combinedFCSSet,clusterAssignments=citrus.clustering$clusterMembership,clusterIds=largeEnoughClusters)
######################################################
# Calculate median levels of functional markers in
# stimulated conditions relative to unstimluated
# condtion
######################################################
# Where the data Lives
dataDirectory = file.path(system.file(package = "citrus"),"extdata","example2")
# Create list of files to be analyzed
fileList = data.frame(unstim=list.files(dataDirectory,pattern="unstim"),stim1=list.files(dataDirectory,pattern="stim1"))
# Read the data
citrus.combinedFCSSet = citrus.readFCSSet(dataDirectory,fileList)
# Vector of parameters to be used for clustering
clusteringColumns = c("LineageMarker1","LineageMarker2")
# Vector of parameters to calculate medians for
functionalColumns = c("FunctionalMarker1","FunctionalMarker2")
# Cluster data
citrus.clustering = citrus.cluster(citrus.combinedFCSSet,clusteringColumns)
# Large enough clusters
largeEnoughClusters = citrus.selectClusters(citrus.clustering)
# Build features
medianDifferenceFeatures = citrus.calculateFeatures(citrus.combinedFCSSet,
clusterAssignments=citrus.clustering$clusterMembership,
clusterIds=largeEnoughClusters,
featureType="medians",
medianColumns=functionalColumns,
conditions=c("unstim","stim1"))
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