Package 'mineCitrus'

Title: Extract and Analyze Median Molecule Intensity from 'citrus' Output
Description: Citrus is a computational technique developed for the analysis of high dimensional cytometry data sets. This package extracts, statistically analyzes, and visualizes marker expression from 'citrus' data. This code was used to generate data for Figures 3 and 4 in the forthcoming manuscript: Throm et al. “Identification of Enhanced Interferon-Gamma Signaling in Polyarticular Juvenile Idiopathic Arthritis with Mass Cytometry”, JCI-Insight. For more information on Citrus, please see: Bruggner et al. (2014) <doi:10.1073/pnas.1408792111>. To download the 'citrus' package, please see <https://github.com/nolanlab/citrus>.
Authors: Allison Throm
Maintainer: Allison Throm <[email protected]>
License: GPL-2
Version: 1.0.0
Built: 2024-12-15 07:31:38 UTC
Source: CRAN

Help Index

Gets matrices of medians for each individual sample for all measured parameters for all clusters

Description

Gets matrices of medians for each individual sample for all measured parameters for all clusters

Usage

allmeds(citrus.combinedFCSSet, citrus.foldClustering, citrus.foldFeatureSet)

Arguments

citrus.combinedFCSSet

loaded from citrusClustering.RData file generated by Citrus run
citrus.foldClustering

loaded from citrusClustering.RData file generated by Citrus run
citrus.foldFeatureSet

computed from first two variables using citrus.calculateFoldFeatureSet function from citrus package

Value

Returns a list with each element corresponding to a matrix (rows as samples, columns as measured parameters) for a different cluster (for the minimum threshold specified)

Examples

library(mineCitrus)
data("citrus.combinedFCSSet")
data("citrus.foldClustering")
data("citrus.foldFeatureSet")
meds<-allmeds(citrus.combinedFCSSet=citrus.combinedFCSSet,
              citrus.foldClustering=citrus.foldClustering,
              citrus.foldFeatureSet=citrus.foldFeatureSet)

Cytometry data set for example of Citrus data set from nolanlab/citrus

Description

A dataset containing the a simple example of cytometry data

Usage

citrus.combinedFCSSet

Format

A large citrus.combinedFCSSet object with 5 elements:

data

Toy data set for cytometry

fileChannelNames

Names of channels for measured parameters included in toy cytmetry data set

fileIds

ID numbers for each file included in toy cytometry data set

fileNames

Names of files included in toy cytmetry data set

fileReagentNames

Names of measured channels in toy cytmetry data set

...

Source

https://github.com/nolanlab/citrus

Clustering data for example of Citrus data set from nolanlab/citrus

Description

A dataset containing the clustering of different cell groups

Usage

citrus.foldClustering

Format

A large citrus.foldClustering object with 5 elements:

allClustering

A list describing which events belong to which clusters

foldClustering

A list describing which events belong to which clusters for each fold

foldMappingAssignments

A list describing assignments with fold clustering

folds

Descriptions of each data clustering

nFolds

The number of times data is clustered

...

Source

https://github.com/nolanlab/citrus

Correlation data for example of Citrus data set from nolanlab/citrus

Description

A dataset containing the association of red and blue in clusters with different sample groups

Usage

citrus.foldFeatureSet

Format

A list with 8 elements:

allFeatures

Data set for each sample for all markers and clusters

allLargeEnoughClusters

Vector of clusters meeting size threshold

foldFeatures

Data for each fold clustering

foldLargeEnoughClusters

Clusters meeting size threshold for each fold clustering

folds

Descriptions of each data clustering

leftoutFeatures

Data omitted from analyses

minimumClusterSizePercent

Minimum size threshold to retain clusters in analysis

nFolds

The number of times data is clustered

...

Source

https://github.com/nolanlab/citrus

Gets matrices of medians for each individual sample for all measured parameters for all clusters

Description

Gets matrices of medians for each individual sample for all measured parameters for all clusters

Usage

classclustermeds(citrus.foldFeatureSet, citrus.foldClustering,
  citrus.combinedFCSSet, groupsizes, meds)

Arguments

citrus.foldFeatureSet

computed from first two variables using citrus.calculateFoldFeatureSet function from citrus package
citrus.foldClustering

loaded from citrusClustering.RData file generated by Citrus run
citrus.combinedFCSSet

loaded from citrusClustering.RData file generated by Citrus run
groupsizes

list of sizeso f the groups run in Citrus, in order of the selection for citrus run
meds

The names of the columns from citrus.combinedFCSSet$data of interest to extract medians for

Value

Returns a list of matrices with columns corresponding to selected features and rows corresponding to sample groups; each list element corresponds to data for a different cluster

Examples

library(mineCitrus)
data("citrus.combinedFCSSet")
data("citrus.foldClustering")
data("citrus.foldFeatureSet")
meds<-allmeds(citrus.combinedFCSSet=citrus.combinedFCSSet,
              citrus.foldClustering=citrus.foldClustering,
              citrus.foldFeatureSet=citrus.foldFeatureSet)
medians<-classclustermeds(citrus.foldFeatureSet,citrus.foldClustering,
                          citrus.combinedFCSSet,groupsizes=c(10,10),meds=meds)

Gets matrix of medians for desired measured features for all clusters meeting threshold requirements specified in Citrus

Description

Gets matrix of medians for desired measured features for all clusters meeting threshold requirements specified in Citrus

Usage

clustermeds(citrus.foldFeatureSet, citrus.foldClustering, medsofinterest,
  citrus.combinedFCSSet)

Arguments

citrus.foldFeatureSet

computed from first two variables using citrus.calculateFoldFeatureSet function from citrus package
citrus.foldClustering

loaded from citrusClustering.RData file generated by Citrus run
medsofinterest

The names of the columns from citrus.combinedFCSSet$data of interest to extract medians for
citrus.combinedFCSSet

loaded from citrusClustering.RData file generated by Citrus run

Value

Returns a matrix with columns corresponding to selected features and rows corresponding to samples

Examples

library(mineCitrus)
data("citrus.combinedFCSSet")
data("citrus.foldClustering")
data("citrus.foldFeatureSet")
medians<-clustermeds(citrus.foldFeatureSet=citrus.foldFeatureSet,
                     citrus.foldClustering=citrus.foldClustering,
                     medsofinterest=c("Red","Blue"),
                     citrus.combinedFCSSet=citrus.combinedFCSSet)

Plot dot plots of features where both clusters are significantly different from the reference cluster without processing data before hand

Description

Plot dot plots of features where both clusters are significantly different from the reference cluster without processing data before hand

Usage

difMarkerPlots(data, clusters, markers, diffclust, strat)

Arguments

data

output from call to allmeds function
clusters

clusterIDs of the desired clusters to compare and plot
markers

indices of the columns of the data matrix for features to be analyse
diffclust

clusterID of for cluster to statisticaly compare others to
strat

clusterIDs for stratifying clusters as indicated by Citrus results

Value

Dot plots for all features where both clusters are significantly different from the reference cluster

Examples

library(mineCitrus)
data("citrus.combinedFCSSet")
data("citrus.foldClustering")
data("citrus.foldFeatureSet")
meds<-allmeds(citrus.combinedFCSSet=citrus.combinedFCSSet,
              citrus.foldClustering=citrus.foldClustering,
              citrus.foldFeatureSet=citrus.foldFeatureSet)
graphs<-difMarkerPlots(data=meds,clusters=c(19999,19972,19988),
                       markers=c(2,3),diffclust=19999,strat=19999)

Plot dot plots of features where one cluster is significantly different from the reference cluster without processing data before hand

Description

Plot dot plots of features where one cluster is significantly different from the reference cluster without processing data before hand

Usage

difMarkerPlots2(data, clusters, markers, diffclust, strat)

Arguments

data

output from call to allmeds function
clusters

clusterIDs of the desired clusters to compare and plot
markers

indices of the columns of the data matrix for features to be analyse
diffclust

clusterID of for cluster to statisticaly compare others to
strat

clusterIDs for stratifying clusters as indicated by Citrus results

Value

Dot plots for all features where one cluster is significantly different from the reference cluster

Examples

library(mineCitrus)
data("citrus.combinedFCSSet")
data("citrus.foldClustering")
data("citrus.foldFeatureSet")
meds<-allmeds(citrus.combinedFCSSet=citrus.combinedFCSSet,
              citrus.foldClustering=citrus.foldClustering,
              citrus.foldFeatureSet=citrus.foldFeatureSet)
graphs<-difMarkerPlots2(data=meds,clusters=c(19999,19972,19988),markers=c(2,3),
                        diffclust=19999,strat=19999)

Filters list of data matrices with columns corresponding to the measured parameters of interest

Description

Filters list of data matrices with columns corresponding to the measured parameters of interest

Usage

filterMarker(clustdat, markers)

Arguments

clustdat

a list of data matrices with list elements corresponding to clusters and matrices of intensities of measured parameters
markers

Indices of the columns of parmeters to keep

Value

A list of data matrices with columns of data matrices only corresponding to measured parameters of interest

Examples

library(mineCitrus)
data("citrus.combinedFCSSet")
data("citrus.foldClustering")
data("citrus.foldFeatureSet")
meds<-allmeds(citrus.combinedFCSSet=citrus.combinedFCSSet,
              citrus.foldClustering=citrus.foldClustering,
              citrus.foldFeatureSet=citrus.foldFeatureSet)
meds2<-filterMarker(clustdat=meds,markers=c(2,3))

Filters list to contain only desired clusters

Description

Filters list to contain only desired clusters

Usage

findclust(data, clusters)

Arguments

data

a list of data matrices with list elements corresponding to clusters and matrices of intensities of measured parameters
clusters

indices of the clusters to retain

Value

A list of data matrices for the desired clusters

Examples

library(mineCitrus)
data("citrus.combinedFCSSet")
data("citrus.foldClustering")
data("citrus.foldFeatureSet")
meds<-allmeds(citrus.combinedFCSSet=citrus.combinedFCSSet,
              citrus.foldClustering=citrus.foldClustering,
              citrus.foldFeatureSet=citrus.foldFeatureSet)
filteredmeds<-findclust(data=meds,clusters=c(19999,19972,19988))

Assesses significance of ANOVA and t-test results

Description

Assesses significance of ANOVA and t-test results

Usage

findSig(posHocRes)

Arguments

posHocRes

results from a call to the posthoc function

Value

A dataframe indicating the significances of results

Examples

library(mineCitrus)
data("citrus.combinedFCSSet")
data("citrus.foldClustering")
data("citrus.foldFeatureSet")
meds<-allmeds(citrus.combinedFCSSet=citrus.combinedFCSSet,
              citrus.foldClustering=citrus.foldClustering,
              citrus.foldFeatureSet=citrus.foldFeatureSet)
filteredmeds<-findclust(data=meds,clusters=c(19999,19972,19988))
meds2<-filterMarker(clustdat=filteredmeds,markers=c(2,3))
foranova<-processforanova(filtereddata=meds2)
ttests<-posthoc(processedDat=foranova,clustIDdif=19999)
sig<-findSig(posHocRes=ttests)

Plot dot plots of features where both clusters are significantly different from the reference cluster

Description

Plot dot plots of features where both clusters are significantly different from the reference cluster

Usage

plotdif(BJHdf, anovadata, strat)

Arguments

BJHdf

results of a call to findsig
anovadata

results of call to processforanova
strat

clusterIDs for clusters that are stratifying

Value

Dot plots for all features where both clusters are significantly different from the reference cluster

Examples

library(mineCitrus)
data("citrus.combinedFCSSet")
data("citrus.foldClustering")
data("citrus.foldFeatureSet")
meds<-allmeds(citrus.combinedFCSSet=citrus.combinedFCSSet,
              citrus.foldClustering=citrus.foldClustering,
              citrus.foldFeatureSet=citrus.foldFeatureSet)
filteredmeds<-findclust(data=meds,clusters=c(19999,19972,19988))
meds2<-filterMarker(clustdat=filteredmeds,markers=c(2,3))
foranova<-processforanova(filtereddata=meds2)
ttests<-posthoc(processedDat=foranova,clustIDdif=19999)
sig<-findSig(posHocRes=ttests)
graphs<-plotdif(BJHdf=sig,anovadata=foranova,strat=19999)

Plot dot plots of features where one cluster is significantly different from the reference cluster

Description

Plot dot plots of features where one cluster is significantly different from the reference cluster

Usage

plotdif2(BJHdf, anovadata, strat)

Arguments

BJHdf

results of a call to findsig
anovadata

results of call to processforanova
strat

clusterIDs for clusters that are stratifying

Value

Dot plots for all features where one cluster is significantly different from the reference cluster

Examples

library(mineCitrus)
data("citrus.combinedFCSSet")
data("citrus.foldClustering")
data("citrus.foldFeatureSet")
meds<-allmeds(citrus.combinedFCSSet=citrus.combinedFCSSet,
              citrus.foldClustering=citrus.foldClustering,
              citrus.foldFeatureSet=citrus.foldFeatureSet)
filteredmeds<-findclust(data=meds,clusters=c(19999,19972,19988))
meds2<-filterMarker(clustdat=filteredmeds,markers=c(2,3))
foranova<-processforanova(filtereddata=meds2)
ttests<-posthoc(processedDat=foranova,clustIDdif=19999)
sig<-findSig(posHocRes=ttests)
graphs<-plotdif2(BJHdf=sig,anovadata=foranova,strat=19999)

Runs ANOVA and t-tests comparing clusters and markers in clusters

Description

Runs ANOVA and t-tests comparing clusters and markers in clusters

Usage

posthoc(processedDat, clustIDdif)

Arguments

processedDat

data that has been processed using the processforanova function
clustIDdif

ID number of the cluster to compare the others to

Value

A list of t-test results for each of the comparisons

Examples

library(mineCitrus)
data("citrus.combinedFCSSet")
data("citrus.foldClustering")
data("citrus.foldFeatureSet")
meds<-allmeds(citrus.combinedFCSSet=citrus.combinedFCSSet,
              citrus.foldClustering=citrus.foldClustering,
              citrus.foldFeatureSet=citrus.foldFeatureSet)
filteredmeds<-findclust(data=meds,clusters=c(19999,19972,19988))
meds2<-filterMarker(clustdat=filteredmeds,markers=c(2,3))
foranova<-processforanova(filtereddata=meds2)
ttests<-posthoc(processedDat=foranova,clustIDdif=19999)

Processes cluster signaling data in form for statistical analysis

Description

Processes cluster signaling data in form for statistical analysis

Usage

processforanova(filtereddata)

Arguments

filtereddata

a list with each element corresonding to a cluster of interest and matrices containing individual sample data for desired markers

Value

A dataframe sufficient for using the posthoc function to compute statistics

Examples

library(mineCitrus)
data("citrus.combinedFCSSet")
data("citrus.foldClustering")
data("citrus.foldFeatureSet")
meds<-allmeds(citrus.combinedFCSSet=citrus.combinedFCSSet,
              citrus.foldClustering=citrus.foldClustering,
              citrus.foldFeatureSet=citrus.foldFeatureSet)
filteredmeds<-findclust(data=meds,clusters=c(19999,19972,19988))
foranova<-processforanova(filtereddata=filteredmeds)

Reorders to rows (corresponding to different clusters) of a matrix of medians to a desired order

Description

Reorders to rows (corresponding to different clusters) of a matrix of medians to a desired order

Usage

sortmat(mat, desiredorder)

Arguments

mat

matrix of median data
desiredorder

row labels from matrix in desired order

Value

Returns a matrix with rows rearranged in desired order

Examples

library(mineCitrus)
data("citrus.combinedFCSSet")
data("citrus.foldClustering")
data("citrus.foldFeatureSet")
medians<-clustermeds(citrus.foldFeatureSet=citrus.foldFeatureSet,
                     citrus.foldClustering=citrus.foldClustering,
                     medsofinterest=c("Red","Blue"),
                     citrus.combinedFCSSet=citrus.combinedFCSSet)
names<-rownames(medians)
names<-names[c(31,1:30)]
sortedmedians<-sortmat(mat=medians,desiredorder=names)