SquadSimResServiceImpl-class: Class "SquadSimResServiceImpl"

In SQUADD: Add-on of the SQUAD Software

Description

The Class SquadSimResServiceImpl contains the information needed to plot the simulation matrix, the prediction map and the correlation circle.

Slots

folder:

Object of class "character", path to the folder to find the SQUAD (1) data.

time:

Object of class "numeric", time point used to get the interpolated activate state value.

ncolor:

Object of class "numeric", number of color code to used in the prediction map. default = 5

legend:

Object of class "vector", contains the name of each node of the model (Must appear in the same order than the simulation).

indexDeno:

Object of class "numeric", the index of the file to used as denominator (the names of each file in the folder should contain an index, i.e 1_xxx.txt), default = 1

method:

Object of class "character", the fitting method to interpolate the curve ("linear" to get a straight line (2) or "lowess" to get a smoothed interpolation (3)).

selectNode:

Object of class "character", contains the name of the node to display.

conditionList:

Object of class "character", list of the perturbation condition names.

Constructor

simResService(folder, time, ncolor, legend, indexDeno, method, selectNode, conditionList): construct a instance of SquadSimResServiceImpl.

Methods

getFittedTable

signature(object = "SquadSimResServiceImpl"): Build and display the table of interpolated/fitted values (see getFittedTable for more details).

plotSimMatrix

signature(object = "SquadSimResServiceImpl"): plot the simulation matrix (see plotSimMatrix for more details).

plotPredMap

signature(object = "SquadSimResServiceImpl"): plot the prediction map (see plotPredMap for more details).

plotCC

signature(object = "SquadSimResServiceImpl"): plot the correlation circle from PCA analyis (see plotCC for more details).

Accessor methods

See examples Section.

Author(s)

Martial Sankar

References

(1) Di Cara A, Garg A, De Micheli G, Xenarios I, Mendoza L (2007) Dynamic simulation of regulatory networks using SQUAD. BMC Bioinformatics 8: 462. (2) Cleveland, W. S. (1979) Robust locally weighted regression and smoothing scatterplots. J. Amer. Statist. Assoc. 74, 829:836. (3) Chambers, J. M. (1992) Linear models. Chapter 4 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth And Brooks/Cole.

Examples

fpath <- system.file("extdata", package="SQUADD")
fileModel <- file.path(fpath,"data_IAA")
nm <- c("ARF(a)", "ARF(i)", "AR_Genes", "Aux/IAA", "BES1/BZR1", "BIN2", "BR", "BRI1-BAK1","BRR_Genes","BRX","BR_Biosynthesis","BZR1", "DO", "IAA", "IAA_Biosynthesis", "NGA1", "PIN", "SCFTir1","StimAux", "StimBR")
t <- 50

## call constructor
obj <- simResService(folder=fileModel, time=t, ncolor=5,legend=nm, indexDeno=1,	method="lowess")

## Apply method 
tab <- getFittedTable(obj)
obj["selectNode"] <-c("DO","IAA_Biosynthesis","BR_Biosynthesis", "IAA", "BR")

plotSimMatrix(obj)
plotPredMap(obj)

obj["conditionList"] <- c("Normal", "brxlof", "BRrescue","brxarfilof", "brxarfiBRrescue", "brxgof")
plotCC(obj)

 ## getter/setter test
obj["time"] <- 45

SQUADD documentation built on Nov. 8, 2020, 6:50 p.m.