ppa.unique: Filter co-modules that are very similar to each other

In isa2: The Iterative Signature Algorithm

Filter co-modules that are very similar to each other

Description

From a potentially non-unique set of PPA co-modules, create a unique set by removing all co-modules that are similar to others.

Usage

## S4 method for signature 'list,list'
 ppa.unique(normed.data, pparesult, ...)

Arguments

normed.data	The normalized input data, a list of four matrices, usually the output of the ppa.normalize function.
pparesult	The result of a PPA run, a set of co-modules.
...	Additional arguments, see details below.

Details

This function can be called as

    ppa.unique(normed.data, pparesult, method = c("cor"),
               ignore.div = TRUE, cor.limit = 0.9,
	       neg.cor = TRUE, drop.zero = TRUE)
  

where the arguments are:

normed.data

The normalized input data, a list of four matrices, usually the output of ppa.normalize.

pparesult

The results of a PPA run, a set of co-modules.

method

Character scalar giving the method to be used to determine if two co-modules are similar. Right now only ‘cor’ is implemented, this keeps both co-modules if their Pearson correlation is less than cor.limit, for their row1, row2 and column scores. See also the neg.cor argument.

ignore.div

Logical scalar, if TRUE, then the divergent co-modules will be removed.

cor.limit

Numeric scalar, giving the correlation limit for the ‘cor’ method.

neg.cor

Logical scalar, if TRUE, then the ‘cor’ method considers the absolute value of the correlation.

drop.zero

Logical scalar, whether to drop co-modules that have all zero scores.

Value

A named list, the filtered pparesult. See the return value of ppa.iterate for the details.

Author(s)

Gabor Csardi [e-mail]

References

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 Also

See ppa for an easy way of running the PPA

Examples

## Create an PPA module set
set.seed(1)
insili <- ppa.in.silico(noise=0.01)

## Random seeds
seeds <- generate.seeds(length=nrow(insili[[1]]), count=20)

## Normalize input matrix
nm <- ppa.normalize(insili[1:2])

## Do PPA
ppares <- ppa.iterate(nm, row1.seeds=seeds,
                      thr.row1=2, thr.row2=2, thr.col=1)

## Check correlation among modules
cc <- cor(ppares$rows1)
if (interactive()) { hist(cc[lower.tri(cc)],10) }

## Some of them are quite high, how many?
undiag <- function(x) { diag(x) <- 0; x }
sum(undiag(cc) > 0.99, na.rm=TRUE)

## Eliminate duplicated modules
ppares.unique <- ppa.unique(nm, ppares)

## How many modules left?
ncol(ppares.unique$rows1)

## Double check
cc2 <- cor(ppares.unique$rows1)
if (interactive()) { hist(cc2[lower.tri(cc2)],10) }

## High correlation?
sum(undiag(cc2) > 0.99, na.rm=TRUE)

isa2 documentation built on March 7, 2023, 8:16 p.m.