ppa.normalize | R Documentation |
Normalize the two input matrices and store them in a form that can be used effectively to perform the Ping-Pong Algorithm
## S4 method for signature 'list' ppa.normalize(data, ...)
data |
A list of two numeric matrices, with matching number of
columns. They might contain |
... |
Additional arguments, see details below. |
This function can be called as
isa.normalize(data, prenormalize = FALSE)
where the arguments are:
A list of two numeric matrices, with matching number of
columns. They might contain Na
and/or NaN
values.
Logical scalar, see details below.
It was observed that the PPA works best if the input matrices are scaled to have mean zero and standard deviation one.
A PPA step consist of four matrix-multiplications and this requires four different matrices, each of the two input matrices scaled row-wise and column-wise.
If the prenormalized
argument is set to TRUE
, then
row-wise scaling is calculated on the column-wise scaled matrices and
not on the raw input.
A list of four matrices, the first two corresponds to the first input matrix, the second two to the second matrix.
Gabor Csardi Gabor.Csardi@unil.ch
Kutalik Z, Bergmann S, Beckmann, J: A modular approach for integrative analysis of large-scale gene-expression and drug-response data Nat Biotechnol 2008 May; 26(5) 531-9.
See ppa
for an easy way of running the PPA
## In-silico data set.seed(1) insili <- ppa.in.silico() nm <- ppa.normalize(insili[1:2]) ## Check it max(abs(rowSums(nm[[1]]))) max(abs(rowSums(nm[[2]]))) max(abs(rowSums(nm[[3]]))) max(abs(rowSums(nm[[4]]))) max(abs(rowSums(nm[[1]]))) max(abs(rowSums(nm[[2]]))) max(abs(rowSums(nm[[3]]))) max(abs(rowSums(nm[[4]]))) ## Plot them if (interactive()) { layout(rbind(1:3,4:6)) image(insili[[1]], main="Original data 1") image(t(nm[[1]]), main="Row normalized 1") image(nm[[2]], main="Column normalized 1") image(insili[[2]], main="Original data 2") image(t(nm[[3]]), main="Row normalized 2") image(nm[[4]], main="Column normalized 2") }
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