4-color microarrays
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hdvi ▴ 50
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Hi We're currently beginning to use microarrays with 4 colors, and I was wondering whether anyone out there had any experience analyzing those using Bioconductor? I've previously used limma for normal microarray analysis and also for arrays with just 1 dye. I've been quite happy with that, but it appears that if I want to continue with that I have to e.g. split the data from each array into two 'pseudo-arrays', which isn't exactly optimal. I won't be doing expression analysis, but I'll need functions for normalizing the data etc. Has anyone tried something similar? Thanks in advance \Heidi
Microarray limma Microarray limma • 1.4k views
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A.J. Rossini ▴ 210
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A.J. Rossini wrote: > Interesting -- does anyone know if you need to "compensate" for 4-color? (I > know that you _sometimes_ have to for 4-color flow cytometry) > > best, > -tony Most likely, yes! Use affine normalization to do it. It works for two or more channels. I've got all methods implemented in aroma (search google), but I'm about to release a lightweight version of this called aroma.light. For the moment see the below tech report (another version submitted) at http://www.maths.lth.se/bioinformatics/publications/: H. Bengtsson and O. H?ssjer, Methodological study of affine transformations of gene expression data with proposed normalization method, Preprints in Mathematical Sciences 2003:38, Mathematical Statistics, Lund University, 2003. Quantile normalization a la RMA (Affy) would also do, but has more degrees of freedom compared to the 2*nbrOfChannels-1 parameters for affine normalization. > On 9/9/05, hdvi <hdvi at="" well.ox.ac.uk=""> wrote: > >>Hi >> >>We're currently beginning to use microarrays with 4 colors, and I was >>wondering whether anyone out there had any experience analyzing those >>using Bioconductor? >> >>I've previously used limma for normal microarray analysis and also for >>arrays with just 1 dye. I've been quite happy with that, but it appears >>that if I want to continue with that I have to e.g. split the data from >>each array into two 'pseudo-arrays', which isn't exactly optimal. I >>won't be doing expression analysis, but I'll need functions for >>normalizing the data etc. Has anyone tried something similar? You suggests something like cyclic lowess (since curve-fit normalization operates on paired channels by definition). It has been implemented elsewhere, but I suggest to look into affine normalization for this, because it is much more natural/intuitive. Cheers Henrik PS. I'm going to MGED in Norway soon, so I'll be online less often. DS. >> >>Thanks in advance >>\Heidi >> >>_______________________________________________ >>Bioconductor mailing list >>Bioconductor at stat.math.ethz.ch >>https://stat.ethz.ch/mailman/listinfo/bioconductor >> > > > >
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Now after normalization, the question is how to handle the 4 colors per spot per array. I would do single channel analysis using the array as a block. This is described in the Limma Users Guide for 2 colors, and should work the same for any number of channels. Of course, if you have multiple spots per gene or a mix of biological and technical replicates you will need a more complex model than limma can handle. --Naomi At 01:51 PM 9/9/2005, Henrik Bengtsson wrote: >A.J. Rossini wrote: > > Interesting -- does anyone know if you need to "compensate" for > 4-color? (I > > know that you _sometimes_ have to for 4-color flow cytometry) > > > > best, > > -tony > >Most likely, yes! Use affine normalization to do it. It works for two >or more channels. I've got all methods implemented in aroma (search >google), but I'm about to release a lightweight version of this called >aroma.light. For the moment see the below tech report (another version >submitted) at http://www.maths.lth.se/bioinformatics/publications/: > >H. Bengtsson and O. H?ssjer, Methodological study of affine >transformations of gene expression data with proposed normalization >method, Preprints in Mathematical Sciences 2003:38, Mathematical >Statistics, Lund University, 2003. > >Quantile normalization a la RMA (Affy) would also do, but has more >degrees of freedom compared to the 2*nbrOfChannels-1 parameters for >affine normalization. > > > On 9/9/05, hdvi <hdvi at="" well.ox.ac.uk=""> wrote: > > > >>Hi > >> > >>We're currently beginning to use microarrays with 4 colors, and I was > >>wondering whether anyone out there had any experience analyzing those > >>using Bioconductor? > >> > >>I've previously used limma for normal microarray analysis and also for > >>arrays with just 1 dye. I've been quite happy with that, but it appears > >>that if I want to continue with that I have to e.g. split the data from > >>each array into two 'pseudo-arrays', which isn't exactly optimal. I > >>won't be doing expression analysis, but I'll need functions for > >>normalizing the data etc. Has anyone tried something similar? > >You suggests something like cyclic lowess (since curve-fit normalization >operates on paired channels by definition). It has been implemented >elsewhere, but I suggest to look into affine normalization for this, >because it is much more natural/intuitive. > >Cheers > >Henrik > >PS. I'm going to MGED in Norway soon, so I'll be online less often. DS. > > >> > >>Thanks in advance > >>\Heidi > >> > >>_______________________________________________ > >>Bioconductor mailing list > >>Bioconductor at stat.math.ethz.ch > >>https://stat.ethz.ch/mailman/listinfo/bioconductor > >> > > > > > > > > > >_______________________________________________ >Bioconductor mailing list >Bioconductor at stat.math.ethz.ch >https://stat.ethz.ch/mailman/listinfo/bioconductor Naomi S. Altman 814-865-3791 (voice) Associate Professor Dept. of Statistics 814-863-7114 (fax) Penn State University 814-865-1348 (Statistics) University Park, PA 16802-2111
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A.J. Rossini wrote: > > > On 9/9/05, *Henrik Bengtsson* <hb at="" maths.lth.se="" <mailto:hb="" at="" maths.lth.se="">> > wrote: > > A.J. Rossini wrote: > > Interesting -- does anyone know if you need to "compensate" for > 4-color? (I > > know that you _sometimes_ have to for 4-color flow cytometry) > > > > best, > > -tony > > Most likely, yes! Use affine normalization to do it. It works for two > or more channels. I've got all methods implemented in aroma (search > google), but I'm about to release a lightweight version of this called > aroma.light. For the moment see the below tech report (another version > submitted) at http://www.maths.lth.se/bioinformatics/publications/: > > H. Bengtsson and O. H?ssjer, Methodological study of affine > transformations of gene expression data with proposed normalization > method, Preprints in Mathematical Sciences 2003:38, Mathematical > Statistics, Lund University, 2003. > > Quantile normalization a la RMA (Affy) would also do, but has more > degrees of freedom compared to the 2*nbrOfChannels-1 parameters for > affine normalization. > > > > Henrik > > PS. I'm going to MGED in Norway soon, so I'll be online less often. DS. > > > Well, what I was suggesting was based on selection of dyes. With > 4-color flow cytometry data, if the colors are not well selected, you > have to worry about "bleed-over", so bad (or "forced by manufacturer") > selections require that you may have to compensate (down-grade) for > bleed over from the other channels if the wavelengths of the colors are > too close or the selectivity is too wide. > > It's definitely true for 8-color and higher flow data, borderline for > 4/6 color. Sorry not answering to your idea there; I was simply focusing on the "normalization" part. I definitely agree that crosstalk becomes an important issue the more dyes you have within a giving wavelength region. Even in Cy3/Cy5 data you do see a bit of crosstalk in the direction from the higher energy dye to the lower energy (emitted photons from one is absorbed by the other). You could use similar methods as for cytometry data to correct for this type of crosstalk. However, in gene expression data you have to add some external controls in order to estimate the correction factor(s). To just comment on the affine normalization: Doing an affine normalization (transformation) in (R,G) will not destroy your chances to correct for the crosstalk afterwards, but you could possibly incorporate in in one single estimation/correction step. It would be interesting the see some multi-channel data, if anyone has it available. Cheers Henrik > > -- > best, > -tony > > blindglobe at gmail.com <mailto:blindglobe at="" gmail.com=""> > Muttenz, Switzerland. > "Commit early,commit often, and commit in a repository from which we can > easily > roll-back your mistakes" (AJR, 4Jan05).
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