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BcDiag (version 1.0.10)

profileBic: The profileBic function.

Description

Provides profile plots for biclustered and clustered data.

Usage

profileBic(dset, bres, mname = c("fabia", "isa2", "biclust","bicare"), bplot = "all", gby = "genes", bnum = 1, teta = 120, ph = 30, fabia.thresZ=0.5,fabia.thresL=NULL, BClabel=TRUE,gene.lines=NULL,condition.lines=NULL)

Arguments

dset
data matrix.
bres
biclustering result.
mname
method name; 'biclust', 'isa2', 'fabia' or 'bicare'.
bplot
types of plots; 'all','lines', 'boxplot', 'histogram' or '3D'.
gby
grouped by; 'genes', or 'conditions'.
bnum
Existing biclusters; '1','2',...
teta
numerical value to rotate the 3D; 0, 90, 180,...
ph
numerical value to rotate the 3D; 0, 90, 180,...
fabia.thresZ
Bicluster threshold for mname="fabia". Threshold for sample belonging to bicluster; default 0.5.
fabia.thresL
Bicluster threshold for mname="fabia". Threshold for loading belonging to bicluster (if not given it is estimated).
BClabel
TRUE/FALSE to show BC labels on the lines plot.
gene.lines
Vector of indices or names of genes inside of Bicluster bnum. These gene profiles will be highlighted in the line plot (bplot='lines').
condition.lines
Vector of indices or names of conditions inside of Bicluster bnum. These condition profiles will be highlighted in the line plot (bplot='lines').

Value

profile.bic(dset, bres, mname="biclust", bplot="all", gby="genes", bnum=1, teta=120, ph=30)

Details

The profile.bic function checks if all parameters are correctly submitted and then identifies the biclustered and clustered data.

Note that the "biclust" option for mname will also accept results from the packages iBBiG and rqubic.

References

Van't Veer, L.J., Dai, H., van de Vijver, M.J., He, Y.D., Hart, A.A. et al. (2002). Gene expression profiling predicts clinical outcome of breast cancer,Nature, 415, 530-536.

Kaiser S. and Leisch F. (2008). A Toolbox for Bicluster Analysis in R. Ludwigstrasse. 33.

Examples

Run this code
# create the biclustering result
data(breastc)
library(biclust)
bic<- biclust(breastc, method=BCPlaid())
# 3 biclusters found

# 3D profile plot for biclustered and clustered data.
profileBic(dset=breastc,bres=bic,mname="biclust",
bplot="3D",gby="genes",teta=-30,ph=50,bnum=1)

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