dyebias.monotonicity {dyebias} | R Documentation

## Calculate the degree of monotonicity of the dye bias across the slides. DEPRACATED

### Description

If you order genes by their iGSDB, and hybridizations by slide bias, the graphs of each gene should form a 'fan' out of the origin (see also dyebias.trendplot). This function gives measure of the extent to which this is true.

This function has been depracated, as it is of limited use and takes too long to compute.

### Usage

      dyebias.monotonicity(data,
                           iGSDBs, dyebias.percentile = 5,
                           order = NULL)

### Arguments

`data` | The `marrayNorm` to determine the degree of monotonicity for
`iGSDBs` | A data frame with intrinsic gene-specific dye biases, the same as that used in `dyebias.apply.correction`, probably returned by `dyebias.estimate.iGSDBs`; see there for documentation.
`dyebias.percentile` | The percentile of intrinsic gene specific dye biases (iGSDBs) for which to highlight the reporters. Default should suffice in almost all cases.
`order` | If `order==NULL`, the slides are sorted by increasing slide bias prior to boxplotting. This is typically done for data that is not yet dye bias corrected. (This order is also returned in the object returned). If `order!=NULL`, the slides are put into this order first. This is typically done for a dye bias-corrected data set, using the order of the uncorrected set.

### Details

The total dye bias appears to be the product of iGSDB and slide bias. In other words, it is monotonous (always increasing or always decreasing), both with respect to the intrinsic gene specific dye bias and with respect to the slide bias. This function orders genes by their iGSDB and the slides by slide bias. Subsequently a linear regression of each gene is done, with x being the slide bias rank, (not the slide bias itself), and y being the M. The slopes of each linear regression line should be an increasing array of values, representing the 'fan' of lines. The degree to which this array is increasing is tested using the Mann-Kendall test, and is returned. In the case of uncorrected data, tau is generally larger than 0.3. After correction, tau should be close to zero.

### Value

A `dyebias.monotonicity` uses `cor.test`, which returns `htest` object. To this list an extra element, `order`, is added, which indicates the ordering of the data set by slide bias. The degree of monotonicity is indicated by the `estimate` element; its signficance by the `p.value` element.

### Note

This function takes very long to compute, since it calculates regressions for each gene.

### Author(s)

Philip Lijnzaad p.lijnzaad@umcutrecht.nl

### References

Margaritis, T., Lijnzaad, P., van Leenen, D., Bouwmeester, D., Kemmeren, P., van Hooff, S.R and Holstege, F.C.P. (2009). Adaptable gene-specific dye bias correction for two-channel DNA microarrays. Molecular Systems Biology, 5:266, 2009. doi:10.1038/msb.2009.21.

### See Also

`dyebias.trendplot`, `dyebias.monotonicityplot`

### Examples

      ## Not run:
         options(stringsAsFactors = FALSE)

         library(dyebias)
         library(dyebiasexamples)
         data(data.raw)
         data(data.norm)

         ### obtain estimate for the iGSDBs:
         iGSDBs.estimated <- dyebias.estimate.iGSDBs(data.norm,
                                                     is.balanced=TRUE,
                                                     verbose=FALSE)

         ### choose the estimators and which spots to correct:
         estimator.subset <- dyebias.umcu.proper.estimators(maInfo(maGnames(data.norm)))

         application.subset <- maW(data.norm) == 1 &
                       dyebias.application.subset(data.raw=data.raw, use.background=TRUE)

         ### do the correction:
         correction <- dyebias.apply.correction(data.norm=data.norm,
                                                iGSDBs = iGSDBs.estimated,
                                                estimator.subset=estimator.subset,
                                                application.subset = application.subset,
                                                verbose=FALSE)

      cat("monotonicity before correction")
      monotonicity <- dyebias.monotonicity(data=data.norm,
                                           iGSDBs=iGSDBs.estimated,
                                           order=NULL)
      monotonicity

      cat("monotonicity after correction")

      dyebias.monotonicity(data=correction$data.corrected,
                                           iGSDBs=iGSDBs.estimated,
                                           order= monotonicity$order)

    ## End(Not run)

[Package dyebias version 1.52.0 Index]