scale of mas5, rma, dchip, and plier results
1
0
Entering edit mode
@he-yiwen-nihcit-1177
Last seen 10.3 years ago
An embedded and charset-unspecified text was scrubbed... Name: not available Url: https://stat.ethz.ch/pipermail/bioconductor/attachments/20060824/ 4b6ea220/attachment.pl
• 1.4k views
ADD COMMENT
0
Entering edit mode
@rafael-a-irizarry-205
Last seen 10.3 years ago
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 COMMENT
0
Entering edit mode
An embedded and charset-unspecified text was scrubbed... Name: not available Url: https://stat.ethz.ch/pipermail/bioconductor/attachments/20060825/ 1c4141f9/attachment.pl
ADD REPLY

Login before adding your answer.

Traffic: 672 users visited in the last hour
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