| Title: | Example data for the dyebias package, which implements the GASSCO method. |
|---|---|
| Description: | Data for the dyebias package, consisting of 4 self-self hybrizations of self-spotted yeast slides, as well as data from Array Express accession E-MTAB-32 |
| Authors: | Philip Lijnzaad and Thanasis Margaritis |
| Maintainer: | Philip Lijnzaad <[email protected]> |
| License: | GPL-3 |
| Version: | 1.53.0 |
| Built: | 2026-10-10 10:29:39 UTC |
| Source: | https://github.com/bioc/dyebiasexamples |
The dyebias-package, described in Margaritis et al. (2009) can
be used to get rid of dye bias in two-colour microarrays. The
data.raw and data.norm objects are used in its examples.
The objects represent four hybridizations of identical mRNA, with increasing Cy3 and Cy5 labeling percentages (identical per slide) and differently spiked-in external controls to judge the process of dyebias correction.
data(data.raw) data(data.norm)data(data.raw) data(data.norm)
The data uses the marray-package by Dudoit and Yang (2002).
data.raw is a marrayRaw object, data.norm is a
marrayNorm object derived from it by print-tip LOESS
normalization. Neither is dyebias-corrected yet.
The column R.group of maInfo(maTargets(data.norm))
shows the details. Eg., 4%_2EC indicates that the labeling
(of both channels) was at 4%, and the external controls were spiked
in at a concentration twice that of the green channel. See Margaritis
et~al. (2009) for details.
The Tuteja data is also included in this package under the
(inst)/doc directory, as this data is not proper rda, tab or csv
data. For details, refer to the original publication and/or the
dyebias vignette.
Philip Lijnzaad
All accession numbers below refer to ArrayExpress (http://www.ebi.ac.uk/microarray).
This two-colour microarrray data was obtained from identical mRNA extracts (protocol P-UMCU-37), spiked with external controls, dUTP-labeled with Cy3 and Cy5 (protocol P-UMCU-38). This was hybridized (protocol P-UMCU-39) onto self-spotted slides containing 70-mer oligonucleotides (2 replicates per oligo, Operon "Array-Ready", and including 2838 control features; protocol P-UMCU-34). Scanning was done with an Agilent G2565AA scanner (protocol P-UMCU-40) and images were quantified with BioDiscovery's ImaGene 7.x (protocol P-UMCU-42)
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, New~York: Springer
data(data.raw) data(data.norm)data(data.raw) data(data.norm)
convenience function to convert GEO objects to marray objects
gse |
GSE data set |
slide.ids |
Return only the slides with these ids. If NULL, return all. |
type |
what to extract; must be either "norm" or "raw". |
gene.selector |
function(table) acting on Table(GPL) giving back an index with the rows considered to be genes. |
reporterid.name |
column containing the reporter.id, in Table(gpl). |
cy3.name |
The column name containing the factor value for the Cy3 (green) channel |
cy5.name |
The column name containing the factor value for the Cy5 (red) channel |
R.name |
column name for extracting the R data from Table(gsm) |
G.name |
column name for extracting the G data from Table(gsm) |
M.name |
column name for extracting the M data from Table(gsm) |
A.name |
column name for extracting the A data from Table(gsm) |
Rf.name |
column name for extracting the Rf data from Table(gsm) |
Gf.name |
column name for extracting the Gf data from Table(gsm) |
Rb.name |
column name for extracting the Rb data from Table(gsm) |
Gb.name |
column name for extracting the Gb data from Table(gsm) |
The XYZ.name mechanism is the same as that used in
read.marrayRaw; i.e. you specify the name of the
column that contains the desired data.
A full-fledged marrayRaw (if
type was "raw") or marrayNorm (if type was "norm") is returned.
At some point, this functionality should be merged into the convert package.
Philip Lijnzaad
Davis, S. and Meltzer, P.S (2007). GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics 23, 1846–1847 (doi:10.1093/bioinformatics/btm254).
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, New~York: Springer
Chen,S., de~Vries, M.A. and Bell, S.P. (2007) Genes Dev. 21, 2897–2907 "Orc6 is required for dynamic recruitment of Cdt1 during repeated Mcm2-7 loading" (doi:10.1101/gad.1596807)
## Not run: ## Running this example takes too much time; if you want that, see the ## second example in the vignette ## End(Not run)## Not run: ## Running this example takes too much time; if you want that, see the ## second example in the vignette ## End(Not run)
Some spots (reporters/probes) should not be used when estimating the slide bias. Typical examples are mitochondrial genes and spots known to cross-hybridize. This function finds the ones that are OK to use.
reporter.info |
A data.frame, one row per spot, with (at least) columns |
verbose |
Logical speficying whether to be verbose or not |
This function is particular to the slides and database set-up at the Holstege lab, but might serve as inspiration.
Returns and index vector that can be
used as the estimator.subset-argument to
dyebias.application.subset.
Philip Lijnzaad [email protected]
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
### choose the estimators and which spots to correct: estimator.subset <- dyebias.umcu.proper.estimators(maInfo(maGnames(data.norm))) summary(estimator.subset) ### do the correction ## Not run: correction <- dyebias.apply.correction(data.norm=data.norm, iGSDBs = iGSDBs.estimated, estimator.subset=estimator.subset, application.subset = TRUE, verbose=TRUE) ## End(Not run)### choose the estimators and which spots to correct: estimator.subset <- dyebias.umcu.proper.estimators(maInfo(maGnames(data.norm))) summary(estimator.subset) ### do the correction ## Not run: correction <- dyebias.apply.correction(data.norm=data.norm, iGSDBs = iGSDBs.estimated, estimator.subset=estimator.subset, application.subset = TRUE, verbose=TRUE) ## End(Not run)