Overlapping boxplots with transparency
1
0
Entering edit mode
@fong-chun-chan-4166
Last seen 10.3 years ago
Hi, I am pretty stumped on how I can approach this problem. Basically, I have two groups of boxplots which I would like to overlap on top of each other. Each boxplot in a group represents a probeset's expression values. So when I overlap them, I would like to see which probesets differ a great deal between the two groups. I know that the boxplot function has a parameter add that when you set to TRUE it will just add the entire boxplot to the current graph which is what I want. But sometimes the boxplots will just completely cover another boxplot. What would be great is if I could set transparency, but there doesn't appear to be any parameters to set. What I would like to do is demonstrated in the Affymetrix technical note: "Identifying and Validating Alternative Splicing Events" figure 15. Does anyone have any idea how I could do this? Any help would be greatly appreciated. Fong [[alternative HTML version deleted]]
graph graph • 3.7k views
ADD COMMENT •
0
Entering edit mode
Heidi Dvinge ★ 2.0k
@heidi-dvinge-2195
Last seen 10.3 years ago
Hi Fong, > Hi, > > I am pretty stumped on how I can approach this problem. Basically, I have > two groups of boxplots which I would like to overlap on top of each other. > Each boxplot in a group represents a probeset's expression values. So > when > I overlap them, I would like to see which probesets differ a great deal > between the two groups. I know that the boxplot function has a parameter > add that when you set to TRUE it will just add the entire boxplot to the > current graph which is what I want. But sometimes the boxplots will just > completely cover another boxplot. What would be great is if I could set > transparency, but there doesn't appear to be any parameters to set. > I'm not really sure why overlapping the boxes would be better than just showing them side by side. However, you can define transparent colours using e.g. adjustcolor() or the alpha parameter in rgb(). For example: > boxplot(count ~ spray, data = InsectSprays, col=rgb(1,0.5,0.3, alpha=0.5)) > boxplot(count*1.1 ~ spray, data = InsectSprays, col=rgb(0.2,0.5,0.3, alpha=0.5), add=TRUE) HTH \Heidi > What I would like to do is demonstrated in the Affymetrix technical note: > "Identifying and Validating Alternative Splicing Events" figure 15. > > Does anyone have any idea how I could do this? Any help would be greatly > appreciated. > > Fong > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor >
ADD COMMENT •
0
Entering edit mode
Thanks Heidi. That did the trick. Fong <fongchun@interchange.ubc.ca> On Wed, Mar 2, 2011 at 2:25 PM, Heidi Dvinge <heidi@ebi.ac.uk> wrote: > Hi Fong, > > > Hi, > > > > I am pretty stumped on how I can approach this problem. Basically, I > have > > two groups of boxplots which I would like to overlap on top of each > other. > > Each boxplot in a group represents a probeset's expression values. So > > when > > I overlap them, I would like to see which probesets differ a great deal > > between the two groups. I know that the boxplot function has a parameter > > add that when you set to TRUE it will just add the entire boxplot to the > > current graph which is what I want. But sometimes the boxplots will just > > completely cover another boxplot. What would be great is if I could set > > transparency, but there doesn't appear to be any parameters to set. > > > I'm not really sure why overlapping the boxes would be better than just > showing them side by side. However, you can define transparent colours > using e.g. adjustcolor() or the alpha parameter in rgb(). > > For example: > > boxplot(count ~ spray, data = InsectSprays, col=rgb(1,0.5,0.3, > alpha=0.5)) > > boxplot(count*1.1 ~ spray, data = InsectSprays, col=rgb(0.2,0.5,0.3, > alpha=0.5), add=TRUE) > > HTH > \Heidi > > > What I would like to do is demonstrated in the Affymetrix technical note: > > "Identifying and Validating Alternative Splicing Events" figure 15. > > > > Does anyone have any idea how I could do this? Any help would be greatly > > appreciated. > > > > Fong > > > > [[alternative HTML version deleted]] > > > > _______________________________________________ > > Bioconductor mailing list > > Bioconductor@r-project.org > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > Search the archives: > > http://news.gmane.org/gmane.science.biology.informatics.conductor > > > > > [[alternative HTML version deleted]]
ADD REPLY •
0
Entering edit mode
Dear List, I'd like to use maanova to analyze a set of 64 affy arrays (U219). The samples are from 8 donors (4x male and female each), which where taken in a short timecourse (T0-T3) both treated and untreated. I'm interested transcripts that are affected by the treatment and especially in those that react differently in male and female samples. The code I used to fit the anova model was: > anova.in = read.madata(exprs(data.filt), designfile=designfile)> fit = fitmaanovaanova.in, + formula = ~time+treat+gender+donor+time*treat+time*gender+treat*gender,+ random = ~donor) However, when I like to extract transcripts, that are e.g. changing over time I have following error: > ftest.time = matestanova.in, fit, term="time", test.method= c(0,1), + test.type="ftest", shuffle.method="sample", n.perm=500)Error: No degree of freedom to do the test When I repeat the analysis with a fixed model (skipping the donor in the formula) I have the same problem. So I'd would appreciate any help on how to correctly build the formula to be able to extract the DE genes. Thank you very much in advance, Mike Session Info: R version 2.12.0 (2010-10-15)Platform: i386-pc-mingw32/i386 (32-bit) locale:[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 [3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C [5] LC_TIME=German_Germany.1252 attached base packages:[1] stats graphics grDevices utils datasets methods base other attached packages:[1] affy_1.28.0 Biobase_2.10.0 maanova_1.20.0 loaded via a namespace (and not attached):[1] affyio_1.18.0 preprocessCore_1.12.0 tools_2.12.0
ADD REPLY •
0
Entering edit mode
Hi all, In case it is important: when fitting the model I get some warnings: Warnmeldungen:1: In any(result$random[idx.mainterm]) :wandle Argument des Typs 'double' nach boolesch2: In any(result$random[idx.mainterm]) :wandle Argument des Typs 'double' nach boolesch3: In any(result$random[idx.mainterm]) :wandle Argument des Typs 'double' nach boolesch4: In any(parsed.formula$random) :wandle Argument des Typs 'double' nach boolesch5: In any(result$random[idx.mainterm]) :wandle Argument des Typs 'double' nach boolesch6: In any(result$random[idx.mainterm]) :wandle Argument des Typs 'double' nach boolesch7: In any(result$random[idx.mainterm]) :wandle Argument des Typs 'double' nach boolesch8: In any(parsed.formula$random) :wandle Argument des Typs 'double' nach boolesch Kind regards, Mike -----Urspr?ngliche Nachricht-----Von: "Mike Walter" Gesendet: 17.03.2011 11:02:49An: bioconductor at r-project.org Betreff: [BioC] r/maanova error in matest()>>Dear List,> >I'd like to use maanova to analyze a set of 64 affy arrays (U219). The samples are from 8 donors (4x male and female each), which where taken in a short timecourse (T0-T3) both treated and untreated. I'm interested transcripts that are affected by the treatment and especially in those that react differently in male and female samples. The code I used to fit the anova model was: > >> anova.in = read.madata(exprs(data.filt), designfile=designfile)> fit = fitmaanovaanova.in, + formula = ~time+treat+gender+donor+time*treat+time*gender+treat*gender,+ random = ~donor) > >However, when I like to extract transcripts, that are e.g. changing over time I have following error: > >> ftest.time = matestanova.in, fit, term="time", test.method= c(0,1), + test.type="ftest", shuffle.method="sample", n.perm=500)Error: No degree of freedom to do the test > >When I repeat the analysis with a fixed model (skipping the donor in the formula) I have the same problem. >So I'd would appreciate any help on how to correctly build the formula to be able to extract the DE genes. >>Thank you very much in advance,>>Mike>>>>Session Info:> >R version 2.12.0 (2010-10-15)Platform: i386-pc-mingw32/i386 (32-bit) locale:[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 [3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C [5] LC_TIME=German_Germany.1252 attached base packages:[1] stats graphics grDevices utils datasets methods base other attached packages:[1] affy_1.28.0 Biobase_2.10.0 maanova_1.20.0 loaded via a namespace (and not attached):[1] affyio_1.18.0 preprocessCore_1.12.0 tools_2.12.0 >>_______________________________________________>Bioconductor mailing list >Bioconductor at r-project.org>https://stat.ethz.ch/mailman/listinfo/bioconductor >Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor
ADD REPLY •

Login before adding your answer.

Similar Posts
Loading Similar Posts
Traffic: 469 users visited in the last hour
Content Search
Users
Tags
Badges
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6