normRepLoess {maigesPack} | R Documentation |
This function normalises a microarray object re-doing the LOWESS fitting several times, selecting a pre-specified proportion of points aleatorily.
normRepLoess(raw, span=0.4, propLoess=0.5, nRep=50, func="none", bkgSub="none", ...)
raw |
an object of class maigesRaw to be normalised. |
span |
real number in (0,1) representing the proportion of points to use in the loess regression. |
propLoess |
real number in (0,1) representing the proportion of points (spots) to be used in each iteration of loess. |
nRep |
number of repetitions for loess procedure. |
func |
character string giving the function to estimate the final W value. You must use 'mean', 'median' or 'none' (default). |
bkgSub |
character with background subtraction method, using the
function backgroundcorrect from limma
package. |
... |
additional parameters for function
loessFit from limma package. |
The LOWESS fitting for normalising microarray data is a computational
intensive task, so pay attention to not specify a very large argument
in nRep
. If you do so, your process will take so much time to conclude.
The result of this function is an object of class maiges
.
Gustavo H. Esteves <gesteves@vision.ime.usp.br>
## Loading the dataset data(gastro) ## Doing the repetition loess with default parameters. Be carefull, this ## is very time consuming ## Not run: gastro.norm = normRepLoess(gastro.raw2) ## End(Not run) ## Do the same normalization selecting 60% dos spots with 10 ## repetitions and estimating the W by the mean value. ## Not run: gastro.norm = normRepLoess(gastro.raw2, propLoess=0.6, nRep=10, func="mean") ## End(Not run)