Description Usage Arguments Details Value Author(s) References See Also Examples

Perform ISA on the (normalized) input matrix.

1 2 | ```
## S4 method for signature 'list'
isa.iterate(normed.data, ...)
``` |

`normed.data` |
The normalized data. A list of two matrices,
usually coming from |

`...` |
Additional arguments, see details below. |

`isa.iterate`

performs the ISA iteration on the specified input
seeds. It can be called as

1 2 3 4 5 6 7 | ```
isa.iterate(normed.data, row.seeds, col.seeds,
thr.row, thr.col = thr.row,
direction = c("updown", "updown"),
convergence = c("corx", "cor", "eps"),
cor.limit = 0.99, eps = 1e-04, corx=3,
oscillation = FALSE, maxiter = 100)
``` |

where the arguments are:

- normed.data
The normalized data. A list of two matrices, usually coming from

`isa.normalize`

.- row.seeds
The row seed vectors to start the ISA runs from. Every column is a seed vector. (If this argument and

`col.seeds`

are both present, then both of them are used.)- col.seeds
The column seed vectors to start the ISA runs from, every column is a seed vector. (If this argument and

`row.seeds`

are both present, then both of them are used.)- thr.row
Numeric scalar or vector giving the threshold parameter for the rows. Higher values indicate a more stringent threshold and the result biclusters will contain less rows on average. The threshold is measured by the number of standard deviations from the mean, over the values of the row vector. If it is a vector then it must contain an entry for each seed.

- thr.col
Numeric scalar or vector giving the threshold parameter for the columns. The analogue of

`thr.row`

.- direction
Character vector of length two, one for the rows, one for the columns. It specifies whether we are interested in rows/columns that are higher (‘

`up`

’) than average, lower than average (‘`down`

’), or both (‘`updown`

’).- convergence
Character scalar, the convergence criteria for the ISA iteration. If it is ‘

`cor`

’, then convergence is measured based on high Pearson correlation (see the`cor.limit`

argument below) of the subsequent row and column vectors. If it is ‘`eps`

’, then all entries of the subsequent row and column vectors must be close to each other, see the`eps`

argument below.‘

`corx`

’ is similar to ‘`cor`

’, but the current row/column vectors are compared to the ones`corx`

steps ago, instead of the ones in the previous step. See the`corx`

argument below, that gives the size of the shift.- cor.limit
The correlation limit for convergence if the ‘

`cor`

’ method is used.- eps
Limit for convergence if the ‘

`eps`

’ method is used.- corx
The number of iterations to use as a shift, for checking convergence with the ‘

`corx`

’ method.- oscillation
Logical scalar, whether to look for oscillating seeds. Usually there are not too many oscillating seeds, so it is safe to leave this on

`FALSE`

.- maxiter
The maximum number of iterations allowed.

A named list with many components. Please see the manual page of
`isa`

for a complete description.

Gabor Csardi [email protected]

Bergmann S, Ihmels J, Barkai N: Iterative signature algorithm for the
analysis of large-scale gene expression data *Phys Rev E Stat
Nonlin Soft Matter Phys.* 2003 Mar;67(3 Pt 1):031902. Epub 2003 Mar 11.

Ihmels J, Friedlander G, Bergmann S, Sarig O, Ziv Y, Barkai N:
Revealing modular organization in the yeast transcriptional network
*Nat Genet.* 2002 Aug;31(4):370-7. Epub 2002 Jul 22

Ihmels J, Bergmann S, Barkai N:
Defining transcription modules using large-scale gene expression data
*Bioinformatics* 2004 Sep 1;20(13):1993-2003. Epub 2004 Mar 25.

isa2-package for a short introduction on the Iterative
Signature Algorithm. See `isa`

for an easy way of running
ISA.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | ```
## A basic ISA work flow for a single threshold combination
## In-silico data
set.seed(1)
insili <- isa.in.silico()
## Random seeds
seeds <- generate.seeds(length=nrow(insili[[1]]), count=100)
## Normalize input matrix
nm <- isa.normalize(insili[[1]])
## Do ISA
isares <- isa.iterate(nm, row.seeds=seeds, thr.row=2, thr.col=1)
## Eliminate duplicates
isares <- isa.unique(nm, isares)
## Filter out not robust ones
isares <- isa.filter.robust(insili[[1]], nm, isares)
## Print the sizes of the modules
cbind( colSums(isares$rows!=0), colSums(isares$columns!=0) )
## Plot the original data and the modules found
if (interactive()) {
layout(rbind(1:2))
image(insili[[1]], main="In silico data")
image(outer(isares$rows[,1],isares$columns[,1])+
outer(isares$rows[,2],isares$columns[,2])+
outer(isares$rows[,3],isares$columns[,3]), main="ISA modules")
}
``` |

isa2 documentation built on May 29, 2017, 6:44 p.m.

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