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Question: scale of mas5, rma, dchip, and plier results
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gravatar for He, Yiwen NIH/CIT
11.2 years ago by
He, Yiwen NIH/CIT350 wrote:
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ADD COMMENTlink modified 11.2 years ago by Rafael A. Irizarry2.3k • written 11.2 years ago by He, Yiwen NIH/CIT350
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gravatar for Rafael A. Irizarry
11.2 years ago by
Rafael A. Irizarry2.3k wrote:
rma-log2 mas5-original dchip-original (i think) as a general rule if the scale is around 0 to 16 its log2 if its around 0 to 40000 its original scale. On Thu, 24 Aug 2006, He, Yiwen (NIH/CIT) [C] wrote: > Hi, > > > > I'm trying to compare the probe level data analysis tools including > mas5, rma, dchip, and plier. Using the affy package and plier package, I > was testing on the affybatch.example data. > > > > Here's my code: > > > >> sessionInfo() > > Version 2.3.0 (2006-04-24) > > i386-pc-mingw32 > > > > attached base packages: > > [1] "tools" "methods" "stats" "graphics" "grDevices" "utils" > "datasets" > > [8] "base" > > > > other attached packages: > > plier affy affyio Biobase > > "1.4.0" "1.10.0" "1.0.0" "1.10.0" > > > >> data(affybatch.example) > > > > ######################### > > # MAS5: > > ######################### > > > >> esetMAS <- mas5(affybatch.example) > > background correction: mas > > PM/MM correction : mas > > expression values: mas > > background correcting...done. > > 150 ids to be processed > > > >> dataMAS <- exprs(esetMAS) > > > >> dataMAS[1:10,] > > 20A 20B 10A > > A28102_at 15.46299 26.674671 12.68777 > > AB000114_at 14.95012 32.449716 15.00104 > > AB000115_at 14.86081 23.366739 12.84647 > > AB000220_at 13.06876 16.581650 14.25944 > > AB002314_at 13.25265 7.556996 13.39463 > > AB002315_at 12.84931 8.891005 12.54743 > > AB002318_at 15.81936 16.666234 16.58733 > > AB002365_at 24.55800 22.789565 14.79018 > > AB002366_at 318.06190 14.877511 19.96851 > > AC000099_at 13.77299 15.014392 13.15784 > > > > ######################### > > # RMA: > > ######################### > > > >> esetRMA <- rma(affybatch.example) > > Background correcting > > Normalizing > > Calculating Expression > > > >> dataRMA <- exprs(esetRMA) > > > >> dataRMA[1:10,] > > 20A 20B 10A > > A28102_at 4.619839 5.429170 4.456409 > > AB000114_at 4.529760 6.005186 4.673793 > > AB000115_at 4.518184 4.949890 4.654417 > > AB000220_at 4.422690 4.450132 4.422690 > > AB002314_at 4.335935 3.950547 4.488247 > > AB002315_at 4.235200 3.842533 4.312301 > > AB002318_at 4.494510 4.494510 4.494510 > > AB002365_at 4.679239 4.421983 4.421983 > > AB002366_at 4.526551 3.919298 4.243068 > > AC000099_at 4.308961 4.308961 4.221789 > > > > ######################### > > # dChip: > > ######################### > > > >> esetDchip <- expresso(affybatch.example, > normalize.method="invariantset", bg.correct=F, > pmcorrect.method="pmonly", summary.method="liwong") > > > >> dataDchip <- exprs(esetDchip) > > > >> dataDchip[1:10,] > > 20A 20B 10A > > A28102_at 101.81628 133.90870 99.43829 > > AB000114_at 100.92215 145.67048 106.54000 > > AB000115_at 94.79908 127.11273 95.28790 > > AB000220_at 97.58051 103.37127 100.09415 > > AB002314_at 99.53569 83.58326 104.83590 > > AB002315_at 97.15775 86.10468 101.93582 > > AB002318_at 101.65877 99.06988 100.42416 > > AB002365_at 107.30458 99.97733 97.87809 > > AB002366_at 105.38395 90.52626 98.17004 > > AC000099_at 112.49714 123.53361 106.67597 > > > > ######################### > > # PLIER: > > ######################### > >> esetPLIER <- justPlier(affybatch.example) > > > >> dim(exprs(esetPLIER)) > > [1] 150 3 > > > >> dataPLIER <- exprs(esetPLIER) > > > >> dataPLIER[1:10,] > > 20A 20B 10A > > A28102_at -11.486192 -11.479329 -11.784546 > > AB000114_at -11.460412 -11.496415 -11.444121 > > AB000115_at -11.772116 -11.576864 -11.598807 > > AB000220_at -10.877553 -11.411278 -10.707376 > > AB002314_at -11.243266 -10.853202 -10.849639 > > AB002315_at -11.240642 -10.892680 -10.857467 > > AB002318_at 3.352295 1.780319 1.079215 > > AB002365_at 3.533914 1.561504 2.719863 > > AB002366_at 3.972462 -7.970215 1.086926 > > AC000099_at 4.311271 4.325806 3.339701 > > > > It seems that the expression values from 4 methods are on very different > scales. Based on a previous message at > https://stat.ethz.ch/pipermail/bioconductor/2005-May/009146.html, > justPlier seems to have converted the data to log2. I'm wondering about > the other methods - mainly, are they in log space or linear space? Do > people typically see such big difference in the expression values, or is > it something specific to this sample data? > > > > Can anyone please help clarifying this? > > > > Thank you! > > > > Yiwen He > > Contractor, SRA International Inc. > > Bioinformatics and Molecular Analysis Section > > Center for Information Technology > > National Institute of Health > > -------- > > Bldg. 12A, Rm. 1018 > > 12 South Dr., Bethesda, MD 20892 > > Email: heyiwen at mail.nih.gov > Phone: 301-402-4636 > > Fax: 301-402-2867 > > > > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor >
ADD COMMENTlink written 11.2 years ago by Rafael A. Irizarry2.3k
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ADD REPLYlink written 11.2 years ago by Di Wu190
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