| Type: | Package |
| Title: | Extract and Analyze Median Molecule Intensity from 'citrus' Output |
| Version: | 1.0.0 |
| Date: | 2018-07-05 |
| Author: | Allison Throm |
| Maintainer: | Allison Throm <throm@wustl.edu> |
| 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. |
| License: | GPL-2 |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | ggplot2 |
| Depends: | R (≥ 2.10) |
| RoxygenNote: | 6.0.1 |
| NeedsCompilation: | no |
| Packaged: | 2018-07-05 12:29:02 UTC; allisonthrom |
| Repository: | CRAN |
| Date/Publication: | 2018-07-05 13:40:03 UTC |
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))
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)
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))
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)