Package 'flowMeans'

flowMeans Package

Description

Non-parametric Flow Cytometry Data Gating

Details

Package:	flowMeans
Type:	Package
Version:	1.0
Date:	2010-03-02
License:	Artistic-2.0 or newer
LazyLoad:	yes

Author(s)

Nima Aghaeepour <[email protected]>

Examples

library(flowMeans)
data(x)
res <- flowMeans(x, c("FL1.H", "FL2.H", "FL3.H", "FL4.H"), MaxN=10)
plot(x[,c(3,4)], res, c("FL1.H", "FL2.H"))

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Change-Point Detection

Description

Fits a two-component piecewise linear regression to the minimum distance between merged clusters vs the number of clusters for a list of merged cluster solutions.

Usage

changepointDetection(vect, OrthagonalResiduals = FALSE, PlotFlag = FALSE)

Arguments

vect	A vector of minimum distances between clusters chosen to be merged at each iteration.
OrthagonalResiduals	Boolean value, indicates if the residuals must be transformed to orthagonal distance or not.
PlotFlag	Boolean value, indicating if the regression lines must be visualized.

Value

MinIndex	Index of the merging step that produced the final results.
l1	First regression line used for finding the changepoint for stopping the merging process.
l2	Second regression line used for finding the changepoint for stopping the merging process.

Author(s)

Nima Aghaeepour

Examples

library(flowMeans)
data(x)
res <- flowMeans(x, c("FL1.H", "FL2.H", "FL3.H", "FL4.H"), MaxN=10)
ft<-changepointDetection(res@Mins)
plot(res@Mins)
abline(ft$l1)
abline(ft$l2)

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flowMeans

Description

Finds a good fit to the data using k-means clustering algorithm. Then merges the adjacent dense spherical clusters to find non-spherical clusters.

Usage

flowMeans(x, varNames=NULL, MaxN = NA, NumC = NA, iter.max = 50, nstart = 10,
Mahalanobis = TRUE, Standardize = TRUE, Update = "Mahalanobis", OrthagonalResiduals=TRUE,
MaxCovN=NA, MaxKernN=NA, addNoise=TRUE)

Arguments

x	A matrix, data frame of observations, or object of class flowFrame. Rows correspond to observations and columns correspond to variables.
varNames	A character vector specifying the variables (columns) to be included in clustering. When it is left unspecified, all the variables will be used.
MaxN	Maximum number of clusters. If set to NA (default) the value will be estimated automatically.
NumC	Number of clusters. If set to NA (default) the value will be estimated automatically.
iter.max	The maximum number of iterations allowed.
nstart	The number of random sets used for initialization.
Mahalanobis	Boolean value. If TRUE (default) mahalanobis distance will be used. Otherwised, euclidean distance will be used.
Standardize	Boolean value. If TRUE (default) the data will be transformed to the [0,1] interval.
Update	String value. If set to "Mahalanobis" the distance function will be updated at each merging iteration with recalculating mahalanobis distances. If set to "Mean" the distance matrix will be updated after each merging step with averaging. If set to "None" the distance matrix will not be updated.
MaxCovN	Maximum number of points, used for calculating the covariance. If set to NA (default), all the points will be used.)
MaxKernN	Maximum number of points, used for counting the modes using kernel density estimation. If set to NA (default), all the points will be used.)
addNoise	Boolean value. Determines if uniform noise must be added to the data to prevent singularity issues or not.
OrthagonalResiduals	Boolean value, indicates if the residuals must be transformed to orthagonal distance or not.

Details

If Mahalanobis distance is not used (i.e., Mahalanobis=FALSE) then the Update value cannot be set to Mahalanobis (i.e., Update="Mahalanobis")

Value

Label	A vector of integers indicating the cluster to which each point is allocated.
Labels	A list of vectors of integers indicating the cluster to which each point is allocated at each merging iteration.
Mats	A list of distance matrixes between clusters at every merging iteration.
MaxN	Maximum number of clusters
Mins	A vector of integers indicating the distance between the two clusters chosen to be merged at every iteration.
MinIndex	Index of the merging step that produced the final results.
Line1	First regression line used for finding the changepoint for stopping the merging process.
Line2	Second regression line used for finding the changepoint for stopping the merging process.

Author(s)

Nima Aghaeepour

Examples

library(flowMeans)
data(x)
res <- flowMeans(x, c("FL1.H", "FL2.H", "FL3.H", "FL4.H"), MaxN=10)
plot(x[,c(3,4)], res, c("FL1.H", "FL2.H"))

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Scatterplot of Clustering Results

Description

This method generates scatterplot revealing the cluster assignment.

Usage

## S4 method for signature 'ANY,Populations'
plot(x, y, varNames=NULL, ...)
## S4 method for signature 'flowFrame,Populations'
plot(x, y, varNames=NULL, ...)

Arguments

x	A matrix, data frame of observations, or object of class flowFrame. This is the object on which flowClust was performed.
y	Object returned from flowMeans.
varNames	A character vector specifying the variables (columns) to be included in the plot. When it is left unspecified, all the variables will be used.
...	Extra parameters that will be passed to the generic plot function

Author(s)

Nima Aghaeepour <[email protected]>

See Also

flowMeans

Examples

library(flowMeans)
data(x)
plot(data.frame(x))

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Show Method for Populations Class

Description

This method lists out the slots contained in a Populations object.

Usage

## S4 method for signature 'Populations'
show(object)

Arguments

object

Object returned from flowMeans

Author(s)

Nima Aghaeepour <[email protected]>

See Also

flowMeans

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Summary Method for flowMeans Object

Description

This method prints out various characteristics of the populations found by flowMeans.

Usage

## S4 method for signature 'Populations'
summary(object,...)

Arguments

object	Object returned from flowMeans.
...	Object returned from flowMeans.

Details

This method prints out various characteristics of the populations found by flowMeans.

Author(s)

Nima Aghaeepour <[email protected]>

See Also

flowMeans

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xSample

Description

A flow cytometry sample produced for diagnosis of the Graft versus Host Disease (GvHD)

Usage

data(x)

Format

A matrix describing expression values of 6 markers and 14936 cells. Each column represents a marker and each row represents a cell.

Source

R.R. Brinkman, M. Gasparetto, S.J.J. Lee, A.J. Ribickas, J. Perkins, W. Janssen, R. Smiley, and C. Smith. High-content flow cytometry and temporal data analysis for defining a cellular signature of graft- versus-host disease. Biology of Blood and Marrow Transplantation, 13(6):691?700, 2007.

Examples

data(x)
## maybe str(x) ; plot(x) ...

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