Question: Subsetting expression sets for mass spec data - second ask
0
gravatar for McGee, Monnie
6.5 years ago by
McGee, Monnie300
McGee, Monnie300 wrote:
Dear BioC Users, I would like to be able to subset a mass spectrometry data set by the biomarkers that were chosen as important biomarkers. I followed the code in the PROcess vignette to obtain the biomarkers as follows: testNorm is a normalized matrix of m/z values from 253 samples > bmkfile <- paste(getwd(), "testbiomarker.csv", sep = "/") > testBio = pk2bmkr(peakfile, testNorm, bmkfile) > mzs = as.numeric(rownames(testNorm)) > bks = getMzs(testBio) ## Should be "important" biomarkers for the Mass Spec data > bks [1] 308.497 350.487 378.092 396.084 676.031 3994.780 4597.540 7046.840 7965.760 8128.160 8351.810 9184.330 I created the expression set in the following way > treat = ifelse(colnames(testNorm) < 300,"Control","Cancer") > treatdf = as.data.frame(treat) > rownames(treatdf)=colnames(testNorm) > pdt = new("AnnotatedDataFrame",treatdf) > mzdf = as.data.frame(rownames(testNorm)) > rownames(mzdf)=rownames(testNorm) > mzfeat = new("AnnotatedDataFrame",mzdf) > testES = new("ExpressionSet",exprs=testNorm,phenoData=pdt,featureData=mzfeat) > varLabels(testES) [1] "treat" > table(pData(testES)) Cancer Control 162 91 > featureData(testES) An object of class "AnnotatedDataFrame" featureNames: 300.033 300.356 ... 19995.5 (13297 total) varLabels: V1 varMetadata: labelDescription Figuring out how to obtain the eSet took at least an hour. By the way, the purpose of the eSet is to obtain an object that is an input into an MLearn function for classification purposes, such as: dldFS = MLearn(treat ~.,testES2,dldaI,)), where testES2 is the eset containing only the information for the important biomarkers. Clearly, I can't run MLearn (especially with CV) with all 13K features in testES. Therefore, I would like to run MLearn using the biomarkers to determine whether these biomarkers actually discriminate between the cancer and control samples. And, yes, this is the Petricoin ovarian cancer data set, for those of you who know your Mass Spec data. Now I have an eSet with the rows labeled by the mass to charge ratios and the columns labeled by the samples I would like to obtain a subset of testES using the 10 biomarkers (bks) found above. Ideally, the following would work: >testES2 = testES[featureData(testES) == bks,] But I get the following error: Error in testES[featureData(testES) == bks, ] : error in evaluating the argument 'i' in selecting a method for function '[': Error in featureData(testES) == bks : comparison (1) is possible only for atomic and list types I tried making bks a character vector, but to no avail. I also tried the following: > testES2 = testES[featureData(testES) %in% bks,] ##(where bks is a character vector or not) Error in testES[featureData(testES) %in% bks, ] : error in evaluating the argument 'i' in selecting a method for function '[': Error in match(x, table, nomatch = 0L) : 'match' requires vector arguments Part of the problem is (probably) that I am not using the correct syntax for subsetting an eSet on the basis of featureData. Another part is that the biomarkers do not have exact matches in featureData(testES) because they were obtained using a peak finding algorithm that is supposed to align peaks across all 253 samples. So, how do I obtain the m/z ratios for the important features (the biomarkers) from this eSet? Is there another (saner) way to use the biomarkers in a classification algorithm in order to determine the misclassification rate with this particular set of biomarkers? And, finally, the session Info: > sessionInfo() R version 2.15.1 (2012-06-22) Platform: i386-apple-darwin9.8.0/i386 (32-bit) locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 attached base packages: [1] tools grid splines stats graphics grDevices utils datasets methods base other attached packages: [1] PROcess_1.32.0 Icens_1.28.0 survival_2.36-14 flowStats_1.14.0 flowWorkspace_1.2.0 [6] hexbin_1.26.0 IDPmisc_1.1.16 flowViz_1.20.0 XML_3.95-0 RBGL_1.32.1 [11] graph_1.34.0 Cairo_1.5-2 cluster_1.14.2 mvoutlier_1.9.8 sgeostat_1.0-24 [16] robCompositions_1.6.0 car_2.0-15 nnet_7.3-4 compositions_1.20-1 energy_1.4-0 [21] MASS_7.3-21 boot_1.3-5 tensorA_0.36 rgl_0.92.892 fda_2.3.2 [26] RCurl_1.95-0.1.2 bitops_1.0-4.1 Matrix_1.0-9 lattice_0.20-10 zoo_1.7-9 [31] flowCore_1.22.3 rrcov_1.3-02 pcaPP_1.9-48 mvtnorm_0.9-9992 robustbase_0.9-4 [36] Biobase_2.16.0 BiocGenerics_0.2.0 loaded via a namespace (and not attached): [1] feature_1.2.8 KernSmooth_2.23-8 ks_1.8.10 latticeExtra_0.6-24 RColorBrewer_1.0-5 [6] stats4_2.15.1 Thank you! Monnie Monnie McGee, PhD Associate Professor Statistical Science Southern Methodist University Office: 214-768-2462 Fax: 214-768-4035 Website: http://faculty.smu.edu/mmcgee
classification cancer process • 1.3k views
ADD COMMENTlink modified 6.5 years ago by Sean Davis21k • written 6.5 years ago by McGee, Monnie300
Answer: Subsetting expression sets for mass spec data - second ask
0
gravatar for Sean Davis
6.5 years ago by
Sean Davis21k
United States
Sean Davis21k wrote:
On Mon, Nov 26, 2012 at 10:55 PM, McGee, Monnie <mmcgee@mail.smu.edu> wrote: > > Dear BioC Users, > > I would like to be able to subset a mass spectrometry data set by the > biomarkers that were chosen as > important biomarkers. I followed the code in the PROcess vignette to > obtain the biomarkers as follows: > > testNorm is a normalized matrix of m/z values from 253 samples > > bmkfile <- paste(getwd(), "testbiomarker.csv", sep = "/") > > testBio = pk2bmkr(peakfile, testNorm, bmkfile) > > mzs = as.numeric(rownames(testNorm)) > > bks = getMzs(testBio) ## Should be "important" biomarkers for the Mass > Spec data > > bks > [1] 308.497 350.487 378.092 396.084 676.031 3994.780 4597.540 > 7046.840 7965.760 8128.160 8351.810 9184.330 > > I created the expression set in the following way > > treat = ifelse(colnames(testNorm) < 300,"Control","Cancer") > > treatdf = as.data.frame(treat) > > rownames(treatdf)=colnames(testNorm) > > pdt = new("AnnotatedDataFrame",treatdf) > > mzdf = as.data.frame(rownames(testNorm)) > > rownames(mzdf)=rownames(testNorm) > > mzfeat = new("AnnotatedDataFrame",mzdf) > > testES = > new("ExpressionSet",exprs=testNorm,phenoData=pdt,featureData=mzfeat) > > varLabels(testES) > [1] "treat" > > table(pData(testES)) > Cancer Control > 162 91 > > featureData(testES) > An object of class "AnnotatedDataFrame" > featureNames: 300.033 300.356 ... 19995.5 (13297 total) > varLabels: V1 > varMetadata: labelDescription > > Figuring out how to obtain the eSet took at least an hour. By the way, the > purpose of the eSet is to obtain an object > that is an input into an MLearn function for classification purposes, such > as: > dldFS = MLearn(treat ~.,testES2,dldaI,)), where testES2 is the eset > containing only the information for the > important biomarkers. Clearly, I can't run MLearn (especially with CV) > with all 13K features in testES. Therefore, > I would like to run MLearn using the biomarkers to determine whether these > biomarkers actually discriminate between > the cancer and control samples. And, yes, this is the Petricoin ovarian > cancer data set, for those of you who know > your Mass Spec data. > > Now I have an eSet with the rows labeled by the mass to charge ratios and > the columns labeled by the samples > I would like to obtain a subset of testES using the 10 biomarkers (bks) > found above. Ideally, the following > would work: > >testES2 = testES[featureData(testES) == bks,] > > Hi, Monnie. Try using featureNames() instead of featureData(). The featureData() method returns an AnnotatedDataFrame. You just want a vector of names, it appears, so featureNames() is the method you should use. Sean > But I get the following error: > Error in testES[featureData(testES) == bks, ] : > error in evaluating the argument 'i' in selecting a method for function > '[': Error in featureData(testES) == bks : > comparison (1) is possible only for atomic and list types > > I tried making bks a character vector, but to no avail. I also tried the > following: > > testES2 = testES[featureData(testES) %in% bks,] ##(where bks is a > character vector or not) > Error in testES[featureData(testES) %in% bks, ] : > error in evaluating the argument 'i' in selecting a method for function > '[': Error in match(x, table, nomatch = 0L) : > 'match' requires vector arguments > > Part of the problem is (probably) that I am not using the correct syntax > for subsetting an eSet on the basis of featureData. Another part is that the > biomarkers do not have exact matches in featureData(testES) because they > were obtained using a peak finding > algorithm that is supposed to align peaks across all 253 samples. So, how > do I obtain the m/z ratios for the important features (the biomarkers) from > this eSet? > Is there another (saner) way to use the biomarkers in a classification > algorithm in order to determine the misclassification rate with this > particular > set of biomarkers? > > And, finally, the session Info: > > sessionInfo() > R version 2.15.1 (2012-06-22) > Platform: i386-apple-darwin9.8.0/i386 (32-bit) > > locale: > [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 > > attached base packages: > [1] tools grid splines stats graphics grDevices utils > datasets methods base > > other attached packages: > [1] PROcess_1.32.0 Icens_1.28.0 survival_2.36-14 > flowStats_1.14.0 flowWorkspace_1.2.0 > [6] hexbin_1.26.0 IDPmisc_1.1.16 flowViz_1.20.0 > XML_3.95-0 RBGL_1.32.1 > [11] graph_1.34.0 Cairo_1.5-2 cluster_1.14.2 > mvoutlier_1.9.8 sgeostat_1.0-24 > [16] robCompositions_1.6.0 car_2.0-15 nnet_7.3-4 > compositions_1.20-1 energy_1.4-0 > [21] MASS_7.3-21 boot_1.3-5 tensorA_0.36 > rgl_0.92.892 fda_2.3.2 > [26] RCurl_1.95-0.1.2 bitops_1.0-4.1 Matrix_1.0-9 > lattice_0.20-10 zoo_1.7-9 > [31] flowCore_1.22.3 rrcov_1.3-02 pcaPP_1.9-48 > mvtnorm_0.9-9992 robustbase_0.9-4 > [36] Biobase_2.16.0 BiocGenerics_0.2.0 > > loaded via a namespace (and not attached): > [1] feature_1.2.8 KernSmooth_2.23-8 ks_1.8.10 > latticeExtra_0.6-24 RColorBrewer_1.0-5 > [6] stats4_2.15.1 > > > Thank you! > Monnie > > Monnie McGee, PhD > Associate Professor > Statistical Science > Southern Methodist University > Office: 214-768-2462 > Fax: 214-768-4035 > Website: http://faculty.smu.edu/mmcgee > _______________________________________________ > 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 COMMENTlink written 6.5 years ago by Sean Davis21k
would it be the worst thing in the world to add rownames() and colnames() support for eSet-derived objects in Biobase? setMethod("rownames", signature=signature(x="eSet"), function(x) featureNames(x)) setMethod("colnames", signature=signature(x="eSet"), function(x) sampleNames(x)) SummarizedExperiments already have these sort of "do what I mean" semantics... one reason I like using them. On Tue, Nov 27, 2012 at 3:08 AM, Sean Davis <sdavis2@mail.nih.gov> wrote: > On Mon, Nov 26, 2012 at 10:55 PM, McGee, Monnie <mmcgee@mail.smu.edu> > wrote: > > > > > Dear BioC Users, > > > > I would like to be able to subset a mass spectrometry data set by the > > biomarkers that were chosen as > > important biomarkers. I followed the code in the PROcess vignette to > > obtain the biomarkers as follows: > > > > testNorm is a normalized matrix of m/z values from 253 samples > > > bmkfile <- paste(getwd(), "testbiomarker.csv", sep = "/") > > > testBio = pk2bmkr(peakfile, testNorm, bmkfile) > > > mzs = as.numeric(rownames(testNorm)) > > > bks = getMzs(testBio) ## Should be "important" biomarkers for the Mass > > Spec data > > > bks > > [1] 308.497 350.487 378.092 396.084 676.031 3994.780 4597.540 > > 7046.840 7965.760 8128.160 8351.810 9184.330 > > > > I created the expression set in the following way > > > treat = ifelse(colnames(testNorm) < 300,"Control","Cancer") > > > treatdf = as.data.frame(treat) > > > rownames(treatdf)=colnames(testNorm) > > > pdt = new("AnnotatedDataFrame",treatdf) > > > mzdf = as.data.frame(rownames(testNorm)) > > > rownames(mzdf)=rownames(testNorm) > > > mzfeat = new("AnnotatedDataFrame",mzdf) > > > testES = > > new("ExpressionSet",exprs=testNorm,phenoData=pdt,featureData=mzfeat) > > > varLabels(testES) > > [1] "treat" > > > table(pData(testES)) > > Cancer Control > > 162 91 > > > featureData(testES) > > An object of class "AnnotatedDataFrame" > > featureNames: 300.033 300.356 ... 19995.5 (13297 total) > > varLabels: V1 > > varMetadata: labelDescription > > > > Figuring out how to obtain the eSet took at least an hour. By the way, > the > > purpose of the eSet is to obtain an object > > that is an input into an MLearn function for classification purposes, > such > > as: > > dldFS = MLearn(treat ~.,testES2,dldaI,)), where testES2 is the eset > > containing only the information for the > > important biomarkers. Clearly, I can't run MLearn (especially with CV) > > with all 13K features in testES. Therefore, > > I would like to run MLearn using the biomarkers to determine whether > these > > biomarkers actually discriminate between > > the cancer and control samples. And, yes, this is the Petricoin ovarian > > cancer data set, for those of you who know > > your Mass Spec data. > > > > Now I have an eSet with the rows labeled by the mass to charge ratios and > > the columns labeled by the samples > > I would like to obtain a subset of testES using the 10 biomarkers (bks) > > found above. Ideally, the following > > would work: > > >testES2 = testES[featureData(testES) == bks,] > > > > > Hi, Monnie. > > Try using featureNames() instead of featureData(). The featureData() > method returns an AnnotatedDataFrame. You just want a vector of names, it > appears, so featureNames() is the method you should use. > > Sean > > > > > But I get the following error: > > Error in testES[featureData(testES) == bks, ] : > > error in evaluating the argument 'i' in selecting a method for function > > '[': Error in featureData(testES) == bks : > > comparison (1) is possible only for atomic and list types > > > > I tried making bks a character vector, but to no avail. I also tried the > > following: > > > testES2 = testES[featureData(testES) %in% bks,] ##(where bks is a > > character vector or not) > > Error in testES[featureData(testES) %in% bks, ] : > > error in evaluating the argument 'i' in selecting a method for function > > '[': Error in match(x, table, nomatch = 0L) : > > 'match' requires vector arguments > > > > Part of the problem is (probably) that I am not using the correct syntax > > for subsetting an eSet on the basis of featureData. Another part is that > the > > biomarkers do not have exact matches in featureData(testES) because they > > were obtained using a peak finding > > algorithm that is supposed to align peaks across all 253 samples. So, how > > do I obtain the m/z ratios for the important features (the biomarkers) > from > > this eSet? > > Is there another (saner) way to use the biomarkers in a classification > > algorithm in order to determine the misclassification rate with this > > particular > > set of biomarkers? > > > > And, finally, the session Info: > > > sessionInfo() > > R version 2.15.1 (2012-06-22) > > Platform: i386-apple-darwin9.8.0/i386 (32-bit) > > > > locale: > > [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 > > > > attached base packages: > > [1] tools grid splines stats graphics grDevices utils > > datasets methods base > > > > other attached packages: > > [1] PROcess_1.32.0 Icens_1.28.0 survival_2.36-14 > > flowStats_1.14.0 flowWorkspace_1.2.0 > > [6] hexbin_1.26.0 IDPmisc_1.1.16 flowViz_1.20.0 > > XML_3.95-0 RBGL_1.32.1 > > [11] graph_1.34.0 Cairo_1.5-2 cluster_1.14.2 > > mvoutlier_1.9.8 sgeostat_1.0-24 > > [16] robCompositions_1.6.0 car_2.0-15 nnet_7.3-4 > > compositions_1.20-1 energy_1.4-0 > > [21] MASS_7.3-21 boot_1.3-5 tensorA_0.36 > > rgl_0.92.892 fda_2.3.2 > > [26] RCurl_1.95-0.1.2 bitops_1.0-4.1 Matrix_1.0-9 > > lattice_0.20-10 zoo_1.7-9 > > [31] flowCore_1.22.3 rrcov_1.3-02 pcaPP_1.9-48 > > mvtnorm_0.9-9992 robustbase_0.9-4 > > [36] Biobase_2.16.0 BiocGenerics_0.2.0 > > > > loaded via a namespace (and not attached): > > [1] feature_1.2.8 KernSmooth_2.23-8 ks_1.8.10 > > latticeExtra_0.6-24 RColorBrewer_1.0-5 > > [6] stats4_2.15.1 > > > > > > Thank you! > > Monnie > > > > Monnie McGee, PhD > > Associate Professor > > Statistical Science > > Southern Methodist University > > Office: 214-768-2462 > > Fax: 214-768-4035 > > Website: http://faculty.smu.edu/mmcgee > > _______________________________________________ > > 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]] > > _______________________________________________ > 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 > -- *A model is a lie that helps you see the truth.* * * Howard Skipper<http: cancerres.aacrjournals.org="" content="" 31="" 9="" 1173.full.pdf=""> [[alternative HTML version deleted]]
ADD REPLYlink written 6.5 years ago by Tim Triche4.2k
On 11/27/2012 09:32 AM, Tim Triche, Jr. wrote: > would it be the worst thing in the world to add rownames() and colnames() > support for eSet-derived objects in Biobase? > > setMethod("rownames", > signature=signature(x="eSet"), > function(x) featureNames(x)) > > setMethod("colnames", > signature=signature(x="eSet"), > function(x) sampleNames(x)) getters and setters, and for dimnames are now in devel 2.19.1. Martin > SummarizedExperiments already have these sort of "do what I mean" > semantics... one reason I like using them. > > > > On Tue, Nov 27, 2012 at 3:08 AM, Sean Davis <sdavis2 at="" mail.nih.gov=""> wrote: > >> On Mon, Nov 26, 2012 at 10:55 PM, McGee, Monnie <mmcgee at="" mail.smu.edu=""> >> wrote: >> >>> >>> Dear BioC Users, >>> >>> I would like to be able to subset a mass spectrometry data set by the >>> biomarkers that were chosen as >>> important biomarkers. I followed the code in the PROcess vignette to >>> obtain the biomarkers as follows: >>> >>> testNorm is a normalized matrix of m/z values from 253 samples >>>> bmkfile <- paste(getwd(), "testbiomarker.csv", sep = "/") >>>> testBio = pk2bmkr(peakfile, testNorm, bmkfile) >>>> mzs = as.numeric(rownames(testNorm)) >>>> bks = getMzs(testBio) ## Should be "important" biomarkers for the Mass >>> Spec data >>>> bks >>> [1] 308.497 350.487 378.092 396.084 676.031 3994.780 4597.540 >>> 7046.840 7965.760 8128.160 8351.810 9184.330 >>> >>> I created the expression set in the following way >>>> treat = ifelse(colnames(testNorm) < 300,"Control","Cancer") >>>> treatdf = as.data.frame(treat) >>>> rownames(treatdf)=colnames(testNorm) >>>> pdt = new("AnnotatedDataFrame",treatdf) >>>> mzdf = as.data.frame(rownames(testNorm)) >>>> rownames(mzdf)=rownames(testNorm) >>>> mzfeat = new("AnnotatedDataFrame",mzdf) >>>> testES = >>> new("ExpressionSet",exprs=testNorm,phenoData=pdt,featureData=mzfeat) >>>> varLabels(testES) >>> [1] "treat" >>>> table(pData(testES)) >>> Cancer Control >>> 162 91 >>>> featureData(testES) >>> An object of class "AnnotatedDataFrame" >>> featureNames: 300.033 300.356 ... 19995.5 (13297 total) >>> varLabels: V1 >>> varMetadata: labelDescription >>> >>> Figuring out how to obtain the eSet took at least an hour. By the way, >> the >>> purpose of the eSet is to obtain an object >>> that is an input into an MLearn function for classification purposes, >> such >>> as: >>> dldFS = MLearn(treat ~.,testES2,dldaI,)), where testES2 is the eset >>> containing only the information for the >>> important biomarkers. Clearly, I can't run MLearn (especially with CV) >>> with all 13K features in testES. Therefore, >>> I would like to run MLearn using the biomarkers to determine whether >> these >>> biomarkers actually discriminate between >>> the cancer and control samples. And, yes, this is the Petricoin ovarian >>> cancer data set, for those of you who know >>> your Mass Spec data. >>> >>> Now I have an eSet with the rows labeled by the mass to charge ratios and >>> the columns labeled by the samples >>> I would like to obtain a subset of testES using the 10 biomarkers (bks) >>> found above. Ideally, the following >>> would work: >>>> testES2 = testES[featureData(testES) == bks,] >>> >>> >> Hi, Monnie. >> >> Try using featureNames() instead of featureData(). The featureData() >> method returns an AnnotatedDataFrame. You just want a vector of names, it >> appears, so featureNames() is the method you should use. >> >> Sean >> >> >> >>> But I get the following error: >>> Error in testES[featureData(testES) == bks, ] : >>> error in evaluating the argument 'i' in selecting a method for function >>> '[': Error in featureData(testES) == bks : >>> comparison (1) is possible only for atomic and list types >>> >>> I tried making bks a character vector, but to no avail. I also tried the >>> following: >>>> testES2 = testES[featureData(testES) %in% bks,] ##(where bks is a >>> character vector or not) >>> Error in testES[featureData(testES) %in% bks, ] : >>> error in evaluating the argument 'i' in selecting a method for function >>> '[': Error in match(x, table, nomatch = 0L) : >>> 'match' requires vector arguments >>> >>> Part of the problem is (probably) that I am not using the correct syntax >>> for subsetting an eSet on the basis of featureData. Another part is that >> the >>> biomarkers do not have exact matches in featureData(testES) because they >>> were obtained using a peak finding >>> algorithm that is supposed to align peaks across all 253 samples. So, how >>> do I obtain the m/z ratios for the important features (the biomarkers) >> from >>> this eSet? >>> Is there another (saner) way to use the biomarkers in a classification >>> algorithm in order to determine the misclassification rate with this >>> particular >>> set of biomarkers? >>> >>> And, finally, the session Info: >>>> sessionInfo() >>> R version 2.15.1 (2012-06-22) >>> Platform: i386-apple-darwin9.8.0/i386 (32-bit) >>> >>> locale: >>> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 >>> >>> attached base packages: >>> [1] tools grid splines stats graphics grDevices utils >>> datasets methods base >>> >>> other attached packages: >>> [1] PROcess_1.32.0 Icens_1.28.0 survival_2.36-14 >>> flowStats_1.14.0 flowWorkspace_1.2.0 >>> [6] hexbin_1.26.0 IDPmisc_1.1.16 flowViz_1.20.0 >>> XML_3.95-0 RBGL_1.32.1 >>> [11] graph_1.34.0 Cairo_1.5-2 cluster_1.14.2 >>> mvoutlier_1.9.8 sgeostat_1.0-24 >>> [16] robCompositions_1.6.0 car_2.0-15 nnet_7.3-4 >>> compositions_1.20-1 energy_1.4-0 >>> [21] MASS_7.3-21 boot_1.3-5 tensorA_0.36 >>> rgl_0.92.892 fda_2.3.2 >>> [26] RCurl_1.95-0.1.2 bitops_1.0-4.1 Matrix_1.0-9 >>> lattice_0.20-10 zoo_1.7-9 >>> [31] flowCore_1.22.3 rrcov_1.3-02 pcaPP_1.9-48 >>> mvtnorm_0.9-9992 robustbase_0.9-4 >>> [36] Biobase_2.16.0 BiocGenerics_0.2.0 >>> >>> loaded via a namespace (and not attached): >>> [1] feature_1.2.8 KernSmooth_2.23-8 ks_1.8.10 >>> latticeExtra_0.6-24 RColorBrewer_1.0-5 >>> [6] stats4_2.15.1 >>> >>> >>> Thank you! >>> Monnie >>> >>> Monnie McGee, PhD >>> Associate Professor >>> Statistical Science >>> Southern Methodist University >>> Office: 214-768-2462 >>> Fax: 214-768-4035 >>> Website: http://faculty.smu.edu/mmcgee >>> _______________________________________________ >>> 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 >>> >> >> [[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 >> > > > -- Computational Biology / Fred Hutchinson Cancer Research Center 1100 Fairview Ave. N. PO Box 19024 Seattle, WA 98109 Location: Arnold Building M1 B861 Phone: (206) 667-2793
ADD REPLYlink written 6.5 years ago by Martin Morgan ♦♦ 23k
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