| Title: | Diagnostics Plots for Bicluster Data |
|---|---|
| Description: | Diagnostic tools based on two-way anova and median-polish residual plots for Bicluster output obtained from packages; "biclust" by Kaiser et al.(2008),"isa2" by Csardi et al. (2010) and "fabia" by Hochreiter et al. (2010). Moreover, It provides visualization tools for bicluster output and corresponding non-bicluster rows- or columns outcomes. It has also extended the idea of Kaiser et al.(2008) which is, extracting bicluster output in a text format, by adding two bicluster methods from the fabia and isa2 R packages. |
| Authors: | Aregay Mengsteab, Martin Otava, Tatsiana Khamiakova, Ewoud De Troyer |
| Maintainer: | Ewoud De Troyer <[email protected]> |
| License: | GPL-3 |
| Version: | 1.0.10 |
| Built: | 2025-03-08 03:15:19 UTC |
| Source: | https://github.com/cran/BcDiag |
Bicluster Diagnostics plots
The Bicluster Diagnostics plots(BcDiag) package is a visualization technique, for profiling and summarizing Bicluster data, particularly for gene expression level data. Target data matrix are bicluster genes(rows) and conditions(columns) versus clustered genes or conditions.
A BicDiag is a package of visualaization bicluster data, which is a subset matrix that have similar characterstics in terms of row(genes) and columns(conditions).
It has used three different types of bicluster algorithms to extract the biculsterd data; 'biclust','isa2' and 'fabia'. plots such as boxplot,histogram, line plot,3D plot are some of the plots that have used to visualize the data.
Major taskes of the package can be categorized in to three sections;
profiling and summarizing the biclustered vs. the clustered simultaneously
profiling and summarizing only the biclusterd data.
exploring the biclusterd data using anova and median polish techniques.
Mengsteab Aregay [email protected]
Hochreiter, S., Bodenhofer, U., Heusel, M.et al. (2010).FABIA: factor analysis for bicluster acquisition. Bioinformatices, 26, 1520-1527.
Kaiser S. and Leisch F. (2008). A Toolbox for Bicluster Analysis in R. Ludwigstrasse. 33.
Csardi G., Kutalik Z., and Bergmann S.(2010). Modular analysis of gene expression data with R. Bioinformatics, 26, 1376-7
The Bicluster algorithms in the packages biclust,fabia and isa2.
Provides ANOVA and median polish residual plots for biclustered data.
anomedOnlybic(dset, bres, fit="boxplot", mname="biclust", bnum=1, fabia.thresZ=0.5,fabia.thresL=NULL)anomedOnlybic(dset, bres, fit="boxplot", mname="biclust", bnum=1, fabia.thresZ=0.5,fabia.thresL=NULL)
dset |
data matrix. |
bres |
bicluster result. |
fit |
a string value to fit a plot; 'aplot','mplot','anovbplot','mpolishbplot','boxplot'. |
mname |
method name; 'biclust', 'isa2', 'fabia' or 'bicare'. |
bnum |
existing biclusters; '1','2'... |
fabia.thresZ |
Bicluster threshold for |
fabia.thresL |
Bicluster threshold for |
A function provides residuals plots for biclustered data based on ANOVA and median polish.
The function checks the required parameter values and fit the plot according to the user requirements.
Note that the "biclust" option for mname will also accept results from the packages iBBiG and rqubic.
Residual plots or residual box plots.
Mengsteab Aregay [email protected]
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.
data(breastc) library(biclust) # find bicluster using one of biclust algorithms bic <- biclust(breastc, method=BCPlaid()) # fit residual boxplot from ANOVA anomedOnlybic(dset=breastc,bres=bic,fit="boxplot",mname="biclust")data(breastc) library(biclust) # find bicluster using one of biclust algorithms bic <- biclust(breastc, method=BCPlaid()) # fit residual boxplot from ANOVA anomedOnlybic(dset=breastc,bres=bic,fit="boxplot",mname="biclust")
Microarray data set of van't Veer breast cancer.
data(breastc)data(breastc)
A data matrix with 1213 genes and 97 samples.
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.
data(breastc) head(breastc)data(breastc) head(breastc)
Log transformed Microarray data set of Rosenwald diffuse large-B-cell lymphoma.
data(dlbcl)data(dlbcl)
A data matrix with 661 genes and 141 samples.
Rosenwald, A., Wright, G., Chan, W.C., Connors, J.M., Campo, E. et al. (2002). The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma, New Engl. J. Med., 346. 1937-1947.
data(dlbcl) head(dlbcl)data(dlbcl) head(dlbcl)
Provides exploratory plots for biclustered and clustered data.
exploreBic(dset, bres, gby ="genes", pfor ="mean", mname ="biclust", bnum =1, fabia.thresZ=0.5,fabia.thresL=NULL)exploreBic(dset, bres, gby ="genes", pfor ="mean", mname ="biclust", bnum =1, fabia.thresZ=0.5,fabia.thresL=NULL)
dset |
data matrix. |
bres |
bicluster result. |
gby |
dimension to plot; 'genes' or 'conditions'. |
pfor |
plot for 'mean', 'median', 'variance', 'mad', 'all', or 'quant' (quantile). |
mname |
method name; 'biclust', 'isa2', 'fabia' or 'bicare' |
bnum |
existing biclusters; '1','2'... |
fabia.thresZ |
Bicluster threshold for |
fabia.thresL |
Bicluster threshold for |
The exploreBic function is mainly used for exploratory data analysis. It provides summary plots for mean, median, variance, MAD and quantile plot.
The exploreBic function checks if the parameters are appropriately submitted and then identifies the biclusters submatrix and calculates its summary statistics. Finally, the results are displayed on the required plot.
Note that the "biclust" option for mname will also accept results from the packages iBBiG and rqubic.
Summary plot will display according to the user specification.
Mengsteab Aregay [email protected]
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.
Hochreiter, S., Bodenhofer, U., Heusel, M.et al. (2010).FABIA: factor analysis for bicluster acquisition. Bioinformatices, 26, 1520-1527.
data(breastc) # find bicluster using biclust package library(biclust) bic <- biclust(breastc,method=BCPlaid()) # Plot the mean of biclusterd and clustered genes parallely. exploreBic(dset=breastc,bres=bic,gby="conditions",pfor="mean",mname="biclust")data(breastc) # find bicluster using biclust package library(biclust) bic <- biclust(breastc,method=BCPlaid()) # Plot the mean of biclusterd and clustered genes parallely. exploreBic(dset=breastc,bres=bic,gby="conditions",pfor="mean",mname="biclust")
Provides exploratory plots only for biclustering results.
exploreOnlybic(dset, bres, pfor= "all", gby= "genes", mname="biclust",bnum=1, fabia.thresZ=0.5,fabia.thresL=NULL)exploreOnlybic(dset, bres, pfor= "all", gby= "genes", mname="biclust",bnum=1, fabia.thresZ=0.5,fabia.thresL=NULL)
dset |
data matrix. |
bres |
biclustering result. |
gby |
group bicluster; 'genes' or 'conditions'. |
pfor |
fit a plot for 'mean', 'median', 'variance', 'mad', 'all', or 'quant' (quantile). |
mname |
method name; 'biclust', 'isa2', 'fabia' or 'bicare'. |
bnum |
existing biclusters; '1','2'... |
fabia.thresZ |
Bicluster threshold for |
fabia.thresL |
Bicluster threshold for |
The exploreOnlybic function has similar function with exploreBic. The only difference is that it provides exploratory plots only for biclustered data.
Summary plot will display only for biclustered data.
Note that the "biclust" option for mname will also accept results from the packages iBBiG and rqubic.
Mengsteab Aregay [email protected]
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.
Hochreiter, S., Bodenhofer, U., Heusel, M.et al. (2010).FABIA: factor analysis for bicluster acquisition. Bioinformatices, 26, 1520-1527.
data(breastc) # find bicluster using biclust algorithm library(biclust) bic <- biclust(breastc,method=BCPlaid()) # Plot the median of biclusterd data. exploreOnlybic(dset=breastc, bres=bic, pfor="all", gby="genes", mname="biclust", bnum=1)data(breastc) # find bicluster using biclust algorithm library(biclust) bic <- biclust(breastc,method=BCPlaid()) # Plot the median of biclusterd data. exploreOnlybic(dset=breastc, bres=bic, pfor="all", gby="genes", mname="biclust", bnum=1)
Provides profile plots for biclustered and clustered data.
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)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)
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 |
fabia.thresL |
Bicluster threshold for |
BClabel |
|
gene.lines |
Vector of indices or names of genes inside of Bicluster |
condition.lines |
Vector of indices or names of conditions inside of Bicluster |
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.
profile.bic(dset, bres, mname="biclust", bplot="all", gby="genes", bnum=1, teta=120, ph=30)
Mengsteab Aregay [email protected]
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.
# 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)# 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)
Provides a summary output in a text format, extracted from 'biclust','isa2' and 'fabia' bicluster algorithms.
writeBic(dset, fileName, bicResult, bicname, mname = c("fabia", "isa2", "biclust","bicare"), append = TRUE, delimiter = " ", fabia.thresZ=0.5,fabia.thresL=NULL)writeBic(dset, fileName, bicResult, bicname, mname = c("fabia", "isa2", "biclust","bicare"), append = TRUE, delimiter = " ", fabia.thresZ=0.5,fabia.thresL=NULL)
dset |
data matrix |
fileName |
the name of the bicluster file to be saved. |
bicResult |
bicluster result obtained from 'biclust','isa2' or 'fabia' |
bicname |
the title to be given for the biclustered data. |
mname |
method name; 'biclust', 'isa2', 'fabia' or 'bicare' |
append |
logical value; TRUE as default |
delimiter |
delimiter in created output file; default value is " ". |
fabia.thresZ |
Bicluster threshold for |
fabia.thresL |
Bicluster threshold for |
The original function was developed in 'biclust' package by Kaiser et.al (2008). We extend the function to be used for further bicluster algorithms, such as; 'isa2', 'fabia' and 'bicare'.
Note that the "biclust" option for mname will also accept results from the packages iBBiG and rqubic.
Biclustered text file with title, total number of biclustered, dimension and name of the biclustered genes(rows) or conditions(columns).
Mengsteab Aregay
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.
Csardi G., Kutalik Z., and Bergmann S.(2010). Modular analysis of gene expression data with R. Bioinformatics, 26, 1376-7
biclust
# create the biclustering result data(breastc) library(fabia) fab<- fabia(breastc) # write the biclustering result into a text file writeBic(dset=breastc,fileName="fabiabreast.txt", bicResult=fab, bicname="Biclust results for fabia", mname="fabia")# create the biclustering result data(breastc) library(fabia) fab<- fabia(breastc) # write the biclustering result into a text file writeBic(dset=breastc,fileName="fabiabreast.txt", bicResult=fab, bicname="Biclust results for fabia", mname="fabia")