Description Usage Arguments Details Value Author(s) References See Also Examples
Analysis multiple omics data using MFA or STATIS. The input multiple tables are in a form that columns are samples and rows are variables/features.
1 2 3 4 5 6 7 8 
data 
A list of 
proc.row 
Preprocessing of rows of datasets, should be one of

w.data 
The weights of each separate dataset, should be one of
or 
w.row 
If it is not null, it should be a list of positive numerical vectors, the length of which should be the same with the number of rows of each dataset to indicated the weight of rows of datasets. 
statis 
A logical indicates whether STATIS method should be used. See details. 
moa 
Logical; whether the output should be converted to an object of
class 
Different methods employs different precessing of row and datasets. For multipple factorial analysis (MFA), the rows of each dataset are first centered and scaled, then each dataset is weighted by the reverse of its first eigenvalue (proc.row=center_ssq1, w.data="lambda1"). This algorithm does not have a well defined criterion to be optimized (see reference).
If statis=TRUE, the statis algorithm will be used, that is, each dataset will be further weighted so that datasets closer to the overall structure will receive a higher weight.
An object of class moaclass
.
Chen Meng
Herve Abdi, Lynne J. Williams, Domininique Valentin and Mohammed BennaniDosse. STATIS and DISTATIS: optimum multitable principal component analysis and three way metric multidimensional scaling. WIREs Comput Stat 2012. Volume 4, Issue 2, pages 124167 Herve Abdi, Lynne J. Williams, Domininique Valentin. Multiple factor analysis: principal component analysis for multitable and multiblock data sets. WIREs Comput Stat 2013
sup.moa
, mogsa
. More about plot see
moaclass
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  # library(mogsa)
# loading data
data(NCI60_4arrays)
# run analysis
ana < moa(NCI60_4arrays, proc.row = "center_ssq1", w.data = "inertia", statis = TRUE)
# plot
# plot eigen value
plot(ana, value = "eig", type = 2)
# plot the normalized (percentage) eigen value
plot(ana, value = "tau", type = 2)
# ploting the observations
colcode < as.factor(sapply(strsplit(colnames(NCI60_4arrays$agilent), split="\\."), "[", 1))
plot(ana, type = 1, value = "obs", col=colcode)
plot(ana, type = 2, value = "obs", col=colcode, data.pch=1:4)
# plot variables/features in each data sets
plot(ana, value = "var", layout=matrix(1:4, 2, 2))
# plot the RV coefficients for the data sets
plot(ana, value = "RV")
# to extract the components representing significant concordance structures between datasets
bt < bootMoa(moa = ana, proc.row = "center_ssq1", w.data = "inertia", statis = TRUE, B = 20)

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