You should really check the original data, not the ratio, and then decide, rather than blindly choosing to use or remove those extreme values. As Kasper said, some could well represent genes that show strong expresion on one condition only, either because they become silenced or activated, and these are potentially very interesting. Jose Quoting Weiwei Shi <helprhelp at="" gmail.com="">: > thanks for all of suggestions here. > > i will go w/o removing those "outliers" first and update some result > if necessary. > > On 9/19/06, Kasper Daniel Hansen <khansen at="" stat.berkeley.edu=""> wrote: >> >> On Sep 19, 2006, at 12:18 PM, Weiwei Shi wrote: >> >> > my current way is using mahalanobis() distance. >> > >> > to Sean: >> > do u think that example: -14k is ok? >> >> That example could be a case of the gene being expressed in one >> condition and not being expressed in another. I do not remember where >> the data are from (or if you have even described that) or platform >> or ..., but I would agree with Sean and say that you do not want to >> blindly remove the genes. Note that we are not advising that you >> shouldn't remove the gene, just that you should take a careful look >> at the data and try to decide what to do. >> >> As Fangxin clearly writes, it is hard to really know what is an outlier. >> >> Kasper >> >> >> > >> > On 9/19/06, fhong at salk.edu <fhong at="" salk.edu=""> wrote: >> >> Dear Weiwei, >> >> The definition of outlier is not clear, and no data point should be >> >> treated as outlier unless there is reason to believe so. The >> >> simple way to >> >> detect it is that 1.5IQR criteria, which you can write your own >> >> code (one >> >> or two lines). Update me if there are any other method to detect >> >> outliers. >> >> >> >> Fangxin >> >> >> >> >> >>> dear listers: >> >>> >> >>> I have a question on whether bioconductor has some tool-kit to >> >>> detect >> >>> outliers and remove them. >> >>> >> >>> my original dataset looks like this: >> >>> V1 V51 V53 V55 V57 >> >>> 1 -493249600 1.459459 -3.069444 -1.300000 1.935484 >> >>> 2 -1613096495 -1.139269 -5.525281 -16.592593 -1.831978 >> >>> 3 1626196571 -3.500000 -1.011662 2.223881 3.921053 >> >>> 4 -1397009217 -3.571429 1.685714 -1.180297 -6.807692 >> >>> 5 1428659728 -1.405405 -1.469004 -4.779754 -1.033708 >> >>> 6 459853658 -2.158879 -7.510823 -1.085581 -9.382979 >> >>> 7 530182506 -1.431677 -1.336343 -3.126437 4.878788 >> >>> 8 1173842263 1.215385 1.856410 -2.059794 -6.020833 >> >>> 9 28847 2.407895 -2.048889 -1.730337 -1.178947 >> >>> 10 -1961875610 2.864159 -2.301234 -4.733264 -1.172058 >> >>> >> >>> V1: internal probe id >> >>> the rests are different samples. the cells are fold-change of >> >>> disease/normal. >> >>> >> >>> summary of the sample columns( V51, ... V57) gives the following: >> >>> V51 V53 V55 V57 >> >>> Min. :-482.000 Min. : -55.7342 Min. :-122.074 Min. >> >>> :-14086.750 >> >>> 1st Qu.: -2.159 1st Qu.: -1.7312 1st Qu.: -2.125 1st Qu.: >> >>> -1.831 >> >>> Median : -1.199 Median : -1.0416 Median : -1.200 Median : >> >>> -1.080 >> >>> Mean : -0.918 Mean : 0.1662 Mean : -1.027 Mean : >> >>> -1.874 >> >>> 3rd Qu.: 1.441 3rd Qu.: 1.5721 3rd Qu.: 1.419 3rd Qu.: >> >>> 1.521 >> >>> Max. : 198.434 Max. :1478.1639 Max. : 95.768 Max. : >> >>> 683.519 >> >>> >> >>> >> >>> My question is, is there any package which can detect those outliers >> >>> (like -14086.750)and remove them and get an "average" for each gene >> >>> (instead of each probe)? >> >>> >> >>> Thank you. >> >>> >> >>> Weiwei >> >>> >> >>> -- >> >>> Weiwei Shi, Ph.D >> >>> Research Scientist >> >>> GeneGO, Inc. >> >>> >> >>> "Did you always know?" >> >>> "No, I did not. But I believed..." >> >>> ---Matrix III >> >>> >> >>> _______________________________________________ >> >>> 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 >> >>> >> >>> >> >> >> >> >> >> -------------------- >> >> Fangxin Hong Ph.D. >> >> Plant Biology Laboratory >> >> The Salk Institute >> >> 10010 N. Torrey Pines Rd. >> >> La Jolla, CA 92037 >> >> E-mail: fhong at salk.edu >> >> (Phone): 858-453-4100 ext 1105 >> >> >> >> >> > >> > >> > -- >> > Weiwei Shi, Ph.D >> > Research Scientist >> > GeneGO, Inc. >> > >> > "Did you always know?" >> > "No, I did not. But I believed..." >> > ---Matrix III >> > >> > _______________________________________________ >> > 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 >> >> > > > -- > Weiwei Shi, Ph.D > Research Scientist > GeneGO, Inc. > > "Did you always know?" > "No, I did not. But I believed..." > ---Matrix III > > _______________________________________________ > 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 > -- Dr. Jose I. de las Heras Email: J.delasHeras at ed.ac.uk The Wellcome Trust Centre for Cell Biology Phone: +44 (0)131 6513374 Institute for Cell & Molecular Biology Fax: +44 (0)131 6507360 Swann Building, Mayfield Road University of Edinburgh Edinburgh EH9 3JR UK