Normalization for different amts of RNA in limma
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Helen Cattan ▴ 100
@helen-cattan-687
Last seen 7.1 years ago
Hi all, Firstly thanks for all the help in the past! Now, I have biological replicate arrays for which different amounts of RNA have been used (3 micrograms and 4 micrograms). So a fairly big difference of over 30%. I am performing normalizeWithinArrays and then normalizeBetweenArrays in limma. The between array normalization is necessary since the arrays were scanned at different PMT settings (both in the linear scale) but I also need to normalize for the different amounts of RNA if this is possible. I imagine that the relationship between the amount of RNA added to a slide and the amount that hybridizes to the array is not a linear relationship, probably sigmoidal but I haven't tested this. If this is so, would normalizeBetweenArrays account for this? Is there a different type of normalization that would? Or could I transform the data in some way so that it would work for both different RNA amounts and different scanning settings? Also is it possible to visualize the normalized R and G values (but not as M and A values)? Since when I look at my top table I'm seeing genes that appear yellowish on the arrays with similar values between arrays (ok so these are raw values) and not the ones that appear bright red or green since the raw values are so different between them but the ratios are similar (eg Cy5=1000, Cy3=500 on array1 and Cy5=2000, Cy3=1000 on array2). This makes me suspect this normalization is not enough for my data. I'm using R v1.8.1 and limma v1.6.1 Does anyone have any suggestions or comments please? Thanks, Helen [[alternative HTML version deleted]]
Normalization limma Normalization limma • 647 views
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Willy Wynant ▴ 90
@willy-wynant-672
Last seen 7.1 years ago
Hi, I have encountered a problem with the pamr.from.excel function. When I try to perform this function on my file it seems that I have 781 genes whereas my files has 1000 genes. When I compare the geneid obtained and those who are present in my file I make the remark that some packages of genes have benn removed. I mean that I have the 100 first genes and then the 20 following genes are removed and so on..... I really don't understand what happened ! For details I had : data<-pamr.from.excel("file.txt",90,sample.labels=TRUE,batch.labels=FA LSE) Read 70470 items Read in 781 genes Read in 88 samples Read in 88 sample labels Make sure these figures are correct!! Could you help me ? Thank you, Regards Willy
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@michael-watson-iah-c-378
Last seen 7.1 years ago
Helen I'll try and tackle these issues, but perhaps an e-mail to the microarray-norm@ebi.ac.uk mailing list would also help >but I also need to normalize for the different amounts of RNA if this is possible. Most normalisation procedures assume that most genes are not changing and therefore that the "average" log(ratio) is zero. If this assumption holds for your data, then any normalisation procedure which sets the average log(ratio) to zero (median, loess etc) *should* also be handling the different amounts of RNA (but not in a very sophisticated manner, see below) >I imagine that the relationship between the amount of RNA added to a slide and the >amount that hybridizes to the array is not a linear relationship, probably sigmoidal >but I haven't tested this. I teach on the Birmingham Microarray Technology Course and data from this suggests that there is a sigmoidal relationship between concentration of DNA on the spot and intensity. I imagine the same holds true for amounts of RNA >If this is so, would normalizeBetweenArrays account for this? Median certainly won't, as this undoubtedly assumes a linear relationship. However, I think Loess should account in some way for the sigmoidal relationship we assume is present between amount of RNA and intensity. >Is there a different type of normalization that would? Not unless you have done previous experiments to define the relationship between amount of RNA and intensity or ratio on your system. There may be some way of doing this if you have spiked in controls, but I am not sure how. >Also is it possible to visualize the normalized R and G values (but not as >M and A values)? Someone has definitely posted to this list before about accessing normalised R and G values, so it is possible, but I can't remember how I'm not helping am I? ;-) Mick
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@james-w-macdonald-5106
Last seen 2 days ago
United States
Here is a function to convert M and A values back to normalized R and G values. convert.back <- function(M, A){ #Here M = maM values and A = maA values G <- (2*A-M)/2 R <- (2*A+M)/2 return(cbind(R,G)) } HTH, Jim James W. MacDonald Affymetrix and cDNA Microarray Core University of Michigan Cancer Center 1500 E. Medical Center Drive 7410 CCGC Ann Arbor MI 48109 734-647-5623 >>> "michael watson (IAH-C)" <michael.watson@bbsrc.ac.uk> 05/11/04 06:38AM >>> Helen I'll try and tackle these issues, but perhaps an e-mail to the microarray-norm@ebi.ac.uk mailing list would also help >but I also need to normalize for the different amounts of RNA if this is possible. Most normalisation procedures assume that most genes are not changing and therefore that the "average" log(ratio) is zero. If this assumption holds for your data, then any normalisation procedure which sets the average log(ratio) to zero (median, loess etc) *should* also be handling the different amounts of RNA (but not in a very sophisticated manner, see below) >I imagine that the relationship between the amount of RNA added to a slide and the >amount that hybridizes to the array is not a linear relationship, probably sigmoidal >but I haven't tested this. I teach on the Birmingham Microarray Technology Course and data from this suggests that there is a sigmoidal relationship between concentration of DNA on the spot and intensity. I imagine the same holds true for amounts of RNA >If this is so, would normalizeBetweenArrays account for this? Median certainly won't, as this undoubtedly assumes a linear relationship. However, I think Loess should account in some way for the sigmoidal relationship we assume is present between amount of RNA and intensity. >Is there a different type of normalization that would? Not unless you have done previous experiments to define the relationship between amount of RNA and intensity or ratio on your system. There may be some way of doing this if you have spiked in controls, but I am not sure how. >Also is it possible to visualize the normalized R and G values (but not as >M and A values)? Someone has definitely posted to this list before about accessing normalised R and G values, so it is possible, but I can't remember how I'm not helping am I? ;-) Mick _______________________________________________ Bioconductor mailing list Bioconductor@stat.math.ethz.ch https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
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There is already a function in Limma to do this MA.RG() and RG.MA() works both ways MA -> RG and RG ->MA Natalie On Tue, 11 May 2004, James MacDonald wrote: > Here is a function to convert M and A values back to normalized R and G > values. > > convert.back <- function(M, A){ #Here M = maM values and A = maA > values > G <- (2*A-M)/2 > R <- (2*A+M)/2 > return(cbind(R,G)) > } > > HTH, > > Jim > > > > James W. MacDonald > Affymetrix and cDNA Microarray Core > University of Michigan Cancer Center > 1500 E. Medical Center Drive > 7410 CCGC > Ann Arbor MI 48109 > 734-647-5623 > > >>> "michael watson (IAH-C)" <michael.watson@bbsrc.ac.uk> 05/11/04 > 06:38AM >>> > Helen > > I'll try and tackle these issues, but perhaps an e-mail to the > microarray-norm@ebi.ac.uk mailing list would also help > > >but I also need to normalize for the different amounts of RNA if this > is possible. > > Most normalisation procedures assume that most genes are not changing > and therefore that the "average" log(ratio) is zero. If this assumption > holds for your data, then any normalisation procedure which sets the > average log(ratio) to zero (median, loess etc) *should* also be handling > the different amounts of RNA (but not in a very sophisticated manner, > see below) > > >I imagine that the relationship between the amount of RNA added to a > slide and the > >amount that hybridizes to the array is not a linear relationship, > probably sigmoidal > >but I haven't tested this. > > I teach on the Birmingham Microarray Technology Course and data from > this suggests that there is a sigmoidal relationship between > concentration of DNA on the spot and intensity. I imagine the same > holds true for amounts of RNA > > >If this is so, would normalizeBetweenArrays account for this? > > Median certainly won't, as this undoubtedly assumes a linear > relationship. However, I think Loess should account in some way for the > sigmoidal relationship we assume is present between amount of RNA and > intensity. > > >Is there a different type of normalization that would? > > Not unless you have done previous experiments to define the > relationship between amount of RNA and intensity or ratio on your > system. There may be some way of doing this if you have spiked in > controls, but I am not sure how. > > >Also is it possible to visualize the normalized R and G values (but > not as > >M and A values)? > > Someone has definitely posted to this list before about accessing > normalised R and G values, so it is possible, but I can't remember how > > I'm not helping am I? ;-) > > Mick > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor > -- ______________________________ Natalie Thorne Research Associate Computational Biology Group Hutchison/MRC Research Centre Department of Oncology University of Cambridge Hills Rd, Cambridge CB2 2XZ Email: npt22@cam.ac.uk Phone: +44 (0)1223 763389 Fax : +44 (0)1223 763262
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Naomi Altman ★ 6.0k
@naomi-altman-380
Last seen 6 months ago
United States
Here is my go at it: 1. The biggest worry is that in the more intense sample there will be more saturation. The level of differential expression cannot be determined from the saturated spots. 2. If differential expression is really a ratio, then the amount of RNA in the sample should not affect measures of differential expression - i.e. (10R/10G)=R/G. Clearly the ratio idea is not exact, but taking logarithms before doing the analysis is based on this idea. 3. If you are using ANOVA or limma to do the computations, if (2) is correct and if you have an array effect in your model, your measures of differential expression should be OK. Alternatively, you could put "RNA amount" as a factor in the model and you should be OK. 4. I agree that you should normalize between arrays. I am not very familiar with the limma routines - you will want to normalize both the mean and the variance. Normalizing the variance should help reduce effects due to the amount of RNA in the sample. 5. If you are using an analysis that does not involve taking the difference in logarithms, points 2-4 will not help you. In that case, you need to know the relationship between amount in the sample and hybridization intensity. --Naomi At 05:04 PM 5/10/2004, Helen Cattan wrote: > >Hi all, > >Firstly thanks for all the help in the past! > >Now, I have biological replicate arrays for which different amounts of >RNA have been used (3 micrograms and 4 micrograms). So a fairly big >difference of over 30%. > >I am performing normalizeWithinArrays and then normalizeBetweenArrays in >limma. The between array normalization is necessary since the arrays >were scanned at different PMT settings (both in the linear scale) but I >also need to normalize for the different amounts of RNA if this is >possible. I imagine that the relationship between the amount of RNA >added to a slide and the amount that hybridizes to the array is not a >linear relationship, probably sigmoidal but I haven't tested this. If >this is so, would normalizeBetweenArrays account for this? Is there a >different type of normalization that would? Or could I transform the >data in some way so that it would work for both different RNA amounts >and different scanning settings? > > > > > [[alternative HTML version deleted]] > >_______________________________________________ >Bioconductor mailing list >Bioconductor@stat.math.ethz.ch >https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor Naomi S. Altman 814-865-3791 (voice) Associate Professor Bioinformatics Consulting Center Dept. of Statistics 814-863-7114 (fax) Penn State University 814-865-1348 (Statistics) University Park, PA 16802-2111
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