# isa: Iterative Signature Algorithm In isa2: The Iterative Signature Algorithm

## Description

Run ISA with the default parameters

## Usage

 ```1 2``` ```## S4 method for signature 'matrix' isa(data, ...) ```

## Arguments

 `data` The input. It must be a numeric matrix. It may contain `NA` and/or `NaN` values, but then the algorithm might be a bit slower, as R matrix multiplication might be slower for these matrices, depending on your platform. `...` Additional arguments, see details below.

## Details

This function can be called as

 ```1 2 3 4``` ``` isa(data, thr.row=seq(1,3,by=0.5), thr.col=seq(1,3,by=0.5), no.seeds=100, direction=c("updown", "updown")) ```

where the arguments are:

data

The input. It must be a numeric matrix. It may contain `NA` and/or `NaN` values, but then the algorithm might be a bit slower, as R matrix multiplication might be slower for these matrices, depending on your platform.

thr.row

Numeric vector. The row threshold parameters for which the ISA will be run. We use all possible combinations of `thr.row` and `thr.col`.

thr.col

Numeric vector. The column threshold parameters for which the ISA will be run. We use all possible combinations of `thr.row` and `thr.col`.

no.seeds

Integer scalar, the number of seeds to use.

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`’).

The `isa` function provides an easy to use interface to the ISA. It runs all steps of a typical ISA work flow with their default parameters.

This involves:

1. Normalizing the data by calling `isa.normalize`.

2. Generating random input seeds via `generate.seeds`.

3. Running ISA with all combinations of given row and column thresholds, (by default 1, 1.5, 2, 2.5, 3); by calling `isa.iterate`.

4. Merging similar modules, separately for each threshold combination, by calling `isa.unique`.

5. Filtering the modules separately for each threshold combination, by calling `isa.filter.robust`.

6. Putting all modules from the runs with different thresholds into a single object.

7. Merging similar modules, across all threshold combinations, if two modules are similar, then the larger one, the one with the milder thresholds is kept.

Please see the manual pages of these functions for the details or if you want to change their default parameters.

## Value

A named list is returned with the following elements:

 `rows` The row components in the biclusters, a numeric matrix. Every column in it corresponds to a bicluster, if an element (the score of the row) is non-zero, that means that the row is included in the bicluster, otherwise it is not. Scores are between -1 and 1. If the scores of two rows have the same (nonzero) sign, that means that the two corresponding rows “behave” the same way. If they have opposite sign, that means that they behave the opposite way. If the corresponding seed has not converged during the allowed number of iterations, then that column of `rows` contains `NA` values. `columns` The column components of the biclusters, in the same format as the rows. If the corresponding seed has not converged during the allowed number of iterations, then that column of `columns` contains `NA` values. `seeddata` A data frame containing information about the biclusters. There is one row for each bicluster. The data frame has the following columns: `iterations`The number of iterations needed to converge to the bicluster. `oscillation`The oscillation period for oscillating biclusters. It is zero for non-oscillating ones. `thr.row`The row threshold that was used for find the bicluster. `thr.col`The column threshold that was used for finding the bicluster. `freq`The number of times the bicluster was found. `rob`The robustness score of the bicluster, see `robustness` for details. `rob.limit`The robustness limit that was used to filter the module. See `isa.filter.robust` for details. `rundata` A named list with information about the ISA runs. It has the following entries: `direction`Character vector of length two. Specifies which side(s) of the score distribution were kept in each ISA step. See the `direction` argument of `isa.iterate` for details. `convergence`Character scalar. The convergence criteria for the iteration. See the `convergence` argument of `isa.iterate` for details. `eps`Numeric scalar. The threshold for convergence, if the ‘eps’ convergence criteria was used. `cor.limit`Numeric scalar. The threshold for convergence, if the ‘cor’ convergence criteria was used. `corx`Numeric scalar, the shift in number of iterations, to check convergence. See the `convergence` argument of `isa.iterate` for details. `maxiter`Numeric scalar. The maximum number of iterations that were allowed for an input seed. `N`Numeric scalar. The total number of seeds that were used for all the thresholds. `prenormalize`Logical scalar. Whether the data was pre-normalized. `hasNA`Logical scalar. Whether the (normalized) data had `NA` or `NaN` values. `unique`Logical scalar. Whether the similar biclusters were merged by calling `isa.unique`. `oscillation`Logical scalar. Whether the algorithm looked for oscillating modules as well. `rob.perms`Numeric scalar, the number of permutations that were used to calculate the baseline robustness for filtering. See the `perms` argument of the `isa.filter.robust` function for details.

## Author(s)

Gabor Csardi [email protected]

## References

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.

 ``` 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``` ```## Not run: ## We generate some noisy in-silico data with modules and try to find ## them with the ISA. This might take one or two minutes. data <- isa.in.silico(noise=0.1) isa.result <- isa(data[[1]]) ## Find the best bicluster for each block in the input best <- apply(cor(isa.result\$rows, data[[2]]), 2, which.max) ## Check correlation sapply(seq_along(best), function(x) cor(isa.result\$rows[,best[x]], data[[2]][,x])) ## The same for the columns sapply(seq_along(best), function(x) cor(isa.result\$columns[,best[x]], data[[3]][,x])) ## Plot the data and the modules found if (interactive()) { layout(rbind(1:2,3:4)) image(data[[1]], main="In-silico data") sapply(best, function(b) image(outer(isa.result\$rows[,b], isa.result\$columns[,b]), main=paste("Module", b))) } ## End(Not run) ```