dyebias.estimate.iGSDBs {dyebias} | R Documentation |
Obtain estimates for the instrinsic gene-specific dye bias (iGSDB) using a set of normalized data, as part of the GASSCO method.
data.norm |
A marrayNorm object containing the data for estimating the
dye bias. This object is supposed to be complete. In particular, maLabels(maGnames(data.norm)) must be set and must indicate the
identities of the reporter sequence (i.e., oligo or cDNA
sequence) of each spot. This helps identify replicate spots, which
are averaged as part of the estimation.
If the data is unbalanced (so is.balanced is FALSE ), maInfo(maTargets(data.norm)) is also required, and should
contain at least two attributes: Cy5 and Cy3 . Both
should indicate the factor value for the respective channel. |
is.balanced |
Logical indicating whether the data set represents a balanced design
(which is in fact the most common case). A design is balanced if all
factor values are present an equal number of times in both the
forward and reverse dye orientations. A self-self design is by
definition balanced (even if the number of slides is uneven). If
is.balanced is TRUE , the iGSDB estimate is
obtained by simply averaging, per reporter, all M values
(and the value of the reference argument is ignored).
If is.balanced==FALSE , the design is inferred from the
reference argument, and subsequently the limma
package is used to model the dye effect. This is typically done for
an unbalanced data set, but there is no harm in setting
is.balanced=FALSE for a design that by itself is already
balanced. If there are no missing values in the data, the results of
using the simple average and the limma procedure are identical
(although LIMMA takes longer to compute the iGSDBs). If the data set
contains many missing data points (NA's), the limma estimates differ
slightly from the simple averaged estimates (although it is not
clear which ones are better).
|
reference |
If the design is a single common reference,
reference should be this common reference. If the design
consists of a set of common reference designs, reference
should be a vector listing all the common references, and the name
of the factor value that is not the common reference should have its
own common reference as a prefix. E.g., if two mutant strains
mutA and mutB were assaysed, each against a different
reference ref1 and ref2 , the reference -argument
would be c("ref1", "ref2") , and the Cy3 and Cy5
attributes of maInfo(maTargets(data.norm)) must contain
values from "ref1:mutA", "ref2:mutA", "ref1:mutB",
"ref2:mutB" . (The colon is not important; the prefix is).
|
verbose |
Logical, indicating wether or not to be verbose |
This function implements the first step of the GASSCO method: estimating the so-called intrinsic gene specific dye biases, or briefly iGSDB. They can be estimated from a (preferably large) data set containing either self-self experiments, or dye-swapped slides.
The assumption underlying this approach is that with self-selfs, or with pairs of dye swaps, the only effect that can lead to systematic changes between Cy5 and Cy3, is in fact the dye effect.
There are two cases to distinguish, the balanced case, and the unbalanced case. In the balanced case, the iGSDB estimate is simply the average M (M = log_2(R/G) = log_2(Cy5/Cy3)) over all slides. A set of slides is balanced if all factor values are present in as many dye-swapped as non-dye-swapped slides. A set of self-self slides is in fact a degenerate form of this, and is therefore also balanced.
In the unbalanced case, one could omit slides until the data set is balanced. However, this is wasteful as we can use linear modelling to obtain estimates. We use the limma package for this (Smyth, 2005). The only unbalanced designs currently supported are a common reference design, and a set of common reference designs.
There are is no weights or subset argument to this function; the estimation is done for all reporters found. If there are replicate spots, they are averaged prior to the estimation (the reason being that we are not interested in p-values for the estimate)
Having obtained the iGSDB estimates, the corrections can be applied
to either to the hybridizations given by the data.norm
argument,
or to a different set of slides that is thought to have very similar
iGSDBs. Applying the corrections is done with dyebias.apply.correction
.
A data frame is returned with as many rows as there are reporters
(replicate spots have been averaged), and the following columns:
reporterId |
The name of the reporter |
dyebias |
The intrinsic gene-specific dye bias (iGSDB) of this reporter |
A |
The average expression level of this reporter in the given data set |
This data frame is typically used as input to dyebias.apply.correction
.
Note that the input data should be normalized, and that the dye swaps should not have been swapped back. After all, we're interested in the difference of Cy5 over Cy3, not the difference of experiment over reference.
Philip Lijnzaad p.lijnzaad@umcutrecht.nl
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, submitted
Dudoit, S. and Yang, Y.H. (2002) Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data. In: Parmigiani, G., Garrett, E.S. , Irizarry, R.A., and Zeger, S.L. (eds.) The Analysis of Gene Expression Data: Methods and Software, Springer, New~York.
Smyth, G.K. (2005) Limma: linear models for microarray data. In: Gentleman, R., Carey, V., Dudoit, S., Irizarry, R. and Huber, W. (eds). Bioinformatics and Computational Biology Solutions using R and Bioconductor, Springer, New~York.
dyebias.apply.correction
iGSDBs.estimated <- dyebias.estimate.iGSDBs(data.norm, is.balanced=TRUE, verbose=FALSE) summary(iGSDBs.estimated) ## Not run: hist(iGSDBs.estimated$dyebias, breaks=50) ## End(Not run)