profileBic {BcDiag}

R Documentation

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').

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.

Value

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

Author(s)

Mengsteab Aregay mycs.zab@gmail.com

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

# 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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