flowType (version 2.10.0)

flowType-package: flowType: Phenotyping Flow Cytometry Assays

Description

flowType uses a simple threshold, Kmeans, flowMeans, or flowClust to partition every channel to a positive and a negative cell population. These partitions are then combined to generate a set of multi-dimensional phenotypes.

Details

Package:

flowType

Type:

Package

Version:

0.0.1

Date:

2011-04-27

License:

Artistic-2.0

LazyLoad:

yes

Depends:

methods

For a given FCS file, the flowType function extracts a the phenotypes and reports their cell frequencies (number of cells) and mean fluorescence intensity (MFI)s.

References

Please cite the following for the current version of flowType: O'Neill K, Jalali A, Aghaeepour N, Hoos H, Brinkman RR. Enhanced flowType/RchyOptimyx: a BioConductor pipeline for discovery in high-dimensional cytometry data. Bioinformatics. 2014 May 1;30(9):1329-30. doi: 10.1093/bioinformatics/btt770 The original paper and description can be found at: Nima Aghaeepour, Pratip K. Chattopadhyay, Anuradha Ganesan, Kieran O'Neill, Habil Zare, Adrin Jalali, Holger H. Hoos, Mario Roederer, and Ryan R. Brinkman. Early Immunologic Correlates of HIV Protection can be Identified from Computational Analysis of Complex Multivariate T-cell Flow Cytometry Assays. Bioinformatics, 2011.

Examples

#Load the library
library(flowType)
data(DLBCLExample)
MarkerNames <- c('Time', 'FSC-A','FSC-H','SSC-A','IgG','CD38','CD19','CD3','CD27','CD20', 'NA', 'NA')

#These markers will be analyzed
PropMarkers <- 3:5
MFIMarkers <- PropMarkers
MarkerNames <- c('FS', 'SS','CD3','CD5','CD19')

#Run flowType
Res <- flowType(DLBCLExample, PropMarkers, MFIMarkers, 'kmeans', MarkerNames);

MFIs=Res@MFIs;
Proportions=Res@CellFreqs;
Proportions <- Proportions / max(Proportions)
names(Proportions) <- unlist(lapply(Res@PhenoCodes, 
                      function(x){return(decodePhenotype(
                      x,Res@MarkerNames[PropMarkers],
                      Res@PartitionsPerMarker))}))

#Select the 30 largest phenotypes
index=order(Proportions,decreasing=TRUE)[1:30]
bp=barplot(Proportions[index], axes=FALSE, names.arg=FALSE)
text(bp+0.2, par("usr")[3]+0.02, srt = 90, adj = 0, labels = names(Proportions[index]), xpd = TRUE, cex=0.8)
axis(2);
axis(1, at=bp, labels=FALSE);
title(xlab='Phenotype Names', ylab='Cell Proportion')

#These phenotype can be analyzed using a predictive model (e.g., classification or regression)