Hi,

I want to use singular value decompositions (SVD) to remove some artifacts
in my microarray data.
what i do is replacing the first eigenvalue to zero:

library(MASS)
data <- as.matrix(read.table("data.txt", header=TRUE,row.names=1, sep =
"\t", as.is = TRUE))
a.svd <- svd(data)
length(a.svd$d)
186
a.svd$d[1]<-0
ds <- diag(1/a.svd$d)
u <- a.svd$u
v <- a.svd$v
us <- as.matrix(u)
vs <- as.matrix(v)
a.remove <- vs%*%(ds)%*%t(us)

does my code above remove the first eigenvalue successfully? Thanks



--
Chen, Chao
Psychiatry
University of Chicago
924 E 57th St, Chicago, IL 60637
U. S. A.
MOE Key Laboratory of Contemporary Anthropology and Center for
Evolutionary Biology,
School of Life Sciences and Institutes of Biomedical Sciences,
Fudan University
220# Handan Road, Shanghai (200433)
P.R.China

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