profileBic {BcDiag}

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 [e-mail]

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)