Produces an object of class mpm that allows for exploratory
multivariate analysis of large data matrices, such as gene expression data
from microarray experiments.
mpm(
data,
logtrans = TRUE,
logrepl = 1e-09,
center = c("double", "row", "column", "global", "none"),
normal = c("global", "row", "column", "none"),
closure = c("none", "row", "column", "global", "double"),
row.weight = c("constant", "mean", "median", "max", "logmean", "RW"),
col.weight = c("constant", "mean", "median", "max", "logmean", "CW"),
CW = rep(1, ncol(data) - 1),
RW = rep(1, nrow(data)),
pos.row = rep(FALSE, nrow(data)),
pos.column = rep(FALSE, ncol(data) - 1)
)a data frame with the row descriptors in the first column. For microarray data rows indicate genes and columns biological samples.
an optional logical value. If TRUE, data are first
transformed to logarithms (base e) before the other operations.
Non-positive numbers are replaced by logrepl. If FALSE, data
are left unchanged. Defaults to TRUE.
an optional numeric value that replaces non-positive numbers
in log-transformations. Defaults to 1e-9.
optional character string specifying the centering operation that is carried out on the optionally log-transformed, closed data matrix. If "double" both row- and column-means are subtracted. If "row" row-means are subtracted. If "column" column-means are subtracted. If "none" the data are left uncentered. Defaults to "double".
optional character string specifying the normalization operation that is carried out on the optionally log-transformed, closed, and centered data matrix. If "global" the data are normalized using the global standard deviation. If "row" data are divided by the standard deviations of the respective row. If "column" data are divided by their respective column standard deviation. If "none" no normalization is carried out. Defaults to "global".
optional character string specifying the closure operation that is carried out on the optionally log-transformed data matrix. If "double", data are divided by row- and column-totals. If "row" data are divided by row-totals. If "column" data are divided by column-totals. If "none" no closure is carried out. Defaults to "none".
optional character string specifying the weights of the different rows in the analysis. This can be "constant", "mean", "median", "max", "logmean", or "RW". If "RW" is specified, weights must be supplied in the vector RW. In other cases weights are computed from the data. Defaults to "constant", i.e. constant weighting.
optional character string specifying the weights of the
different columns in the analysis. This can be "constant",
"mean", "median", "max", "logmean", or "CW".
If "CW" is specified, weights must be supplied in the vector
CW. In other cases weights are computed from the data. Defaults to
"constant", i.e. constant weighting.
optional numeric vector with external column weights. Defaults to 1 (constant weights).
optional numeric vector with external row weights. Defaults to 1 (constant weights).
logical vector indicating rows that are not to be included in
the analysis but must be positioned on the projection obtained with the
remaining rows. Defaults to FALSE.
logical vector indicating columns that are not to be
included in the analysis but must be positioned on the projection obtained
with the remaining columns. Defaults to FALSE.
An object of class mpm representing the projection of data
after the different operations of transformation, closure, centering, and
normalization in an orthogonal space. Generic functions plot and
summary have methods to show the results of the analysis in more
detail. The object consists of the following components:
matrix with the data after optional log-transformation, closure, centering and normalization.
character vector with names of the row elements as supplied in the first column of the original data matrix
character vector with the names of columns obtained from the column names from the original data matrix
closure operation as specified in the function call
centering operation as specified in the function call
normalization operation as specified in the function call
type of weighting used for rows as specified in the function call
type of weighting used for columns as specified in the function call
vector with calculated weights for rows
vector with calculated weights for columns
vector with row means of original data
vector with column means of original data
logical vector indicating positioned rows as specified in the function call
logical vector indicating positioned columns as specified in the function call
list with components returned
by La.svd
eigenvalues for each orthogonal factor from obtained from the weighted singular value decomposition
contributions of each factor to the total variance of the pre-processed data, i.e. the eigenvalues as a fraction of the total eigenvalue.
the matched call.
The function mpm presents a unified approach to exploratory
multivariate analysis encompassing principal component analysis,
correspondence factor analysis, and spectral map analysis. The algorithm
computes projections of high dimensional data in an orthogonal space. The
resulting object can subsequently be used in the construction of biplots
(i.e. plot.mpm).
The projection of the pre-processed data matrix in the orthogonal space is
calculated using the La.svd function.
Wouters, L., Goehlmann, H., Bijnens, L., Kass, S.U., Molenberghs, G., Lewi, P.J. (2003). Graphical exploration of gene expression data: a comparative study of three multivariate methods. Biometrics 59, 1131-1140.
# NOT RUN {
data(Golub)
# Principal component analysis
r.pca <- mpm(Golub[,1:39], center = "column", normal = "column")
# Correspondence factor analysis
r.cfa <- mpm(Golub[,1:39],logtrans = FALSE, row.weight = "mean",
col.weight = "mean", closure = "double")
# Weighted spectral map analysis
r.sma <- mpm(Golub[,1:39], row.weight = "mean", col.weight = "mean")
# }
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