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Question: Can I input ordinal variables into a model in Limma?
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4.7 years ago by
Scott Robinson130 wrote:
Dear Gordon, I implemented this and am wondering how you interpret logFC from topTable with regards the linear trend? I have tried replicating it, but no joy. Thanks, Scott PS code below: design <- model.matrix(~copd + asthma + smoking + sex) > design (Intercept) copd.L copd.Q asthmaSEVERE smokingEX-SMOKER 1 1 -7.850462e-17 -0.8164966 0 0 2 1 -7.850462e-17 -0.8164966 0 0 3 1 -7.850462e-17 -0.8164966 0 0 4 1 -7.850462e-17 -0.8164966 0 0 5 1 -7.850462e-17 -0.8164966 0 0 6 1 -7.850462e-17 -0.8164966 0 0 7 1 -7.850462e-17 -0.8164966 0 0 8 1 -7.850462e-17 -0.8164966 0 0 9 1 7.071068e-01 0.4082483 0 0 10 1 7.071068e-01 0.4082483 0 0 11 1 -7.850462e-17 -0.8164966 0 0 12 1 7.071068e-01 0.4082483 0 0 13 1 7.071068e-01 0.4082483 0 0 14 1 -7.850462e-17 -0.8164966 0 0 15 1 7.071068e-01 0.4082483 0 0 16 1 -7.071068e-01 0.4082483 0 0 17 1 -7.071068e-01 0.4082483 0 0 18 1 -7.071068e-01 0.4082483 0 0 19 1 -7.071068e-01 0.4082483 0 0 20 1 -7.071068e-01 0.4082483 0 0 21 1 -7.071068e-01 0.4082483 0 0 22 1 -7.071068e-01 0.4082483 0 0 23 1 -7.071068e-01 0.4082483 0 0 24 1 -7.071068e-01 0.4082483 0 0 25 1 -7.071068e-01 0.4082483 0 0 26 1 -7.071068e-01 0.4082483 0 0 27 1 -7.071068e-01 0.4082483 0 0 28 1 -7.071068e-01 0.4082483 0 0 29 1 -7.071068e-01 0.4082483 0 0 30 1 -7.071068e-01 0.4082483 0 0 31 1 -7.071068e-01 0.4082483 1 0 32 1 -7.071068e-01 0.4082483 1 0 33 1 -7.071068e-01 0.4082483 1 0 34 1 -7.071068e-01 0.4082483 1 0 35 1 -7.071068e-01 0.4082483 1 0 36 1 -7.071068e-01 0.4082483 1 0 37 1 -7.071068e-01 0.4082483 1 0 38 1 -7.071068e-01 0.4082483 1 0 39 1 -7.071068e-01 0.4082483 1 0 40 1 -7.071068e-01 0.4082483 1 0 41 1 -7.071068e-01 0.4082483 1 0 42 1 -7.071068e-01 0.4082483 1 0 43 1 -7.071068e-01 0.4082483 1 0 44 1 -7.071068e-01 0.4082483 1 0 45 1 -7.071068e-01 0.4082483 1 0 46 1 -7.071068e-01 0.4082483 0 0 47 1 -7.071068e-01 0.4082483 0 0 48 1 -7.071068e-01 0.4082483 0 0 49 1 -7.071068e-01 0.4082483 0 0 50 1 -7.071068e-01 0.4082483 0 0 51 1 -7.071068e-01 0.4082483 0 0 52 1 -7.071068e-01 0.4082483 0 0 53 1 -7.071068e-01 0.4082483 0 0 54 1 -7.071068e-01 0.4082483 0 0 55 1 -7.071068e-01 0.4082483 0 0 56 1 -7.071068e-01 0.4082483 0 0 57 1 -7.071068e-01 0.4082483 0 0 58 1 -7.071068e-01 0.4082483 0 0 59 1 -7.071068e-01 0.4082483 0 0 60 1 -7.071068e-01 0.4082483 0 0 61 1 -7.071068e-01 0.4082483 1 0 62 1 -7.071068e-01 0.4082483 1 0 63 1 -7.071068e-01 0.4082483 1 0 64 1 -7.071068e-01 0.4082483 1 0 65 1 -7.071068e-01 0.4082483 1 0 66 1 -7.071068e-01 0.4082483 1 0 67 1 -7.071068e-01 0.4082483 1 0 68 1 -7.071068e-01 0.4082483 1 0 69 1 -7.071068e-01 0.4082483 1 0 70 1 -7.071068e-01 0.4082483 1 0 71 1 -7.071068e-01 0.4082483 1 0 72 1 -7.071068e-01 0.4082483 1 0 73 1 -7.071068e-01 0.4082483 1 0 74 1 -7.071068e-01 0.4082483 1 0 75 1 -7.071068e-01 0.4082483 1 0 76 1 7.071068e-01 0.4082483 0 1 77 1 -7.850462e-17 -0.8164966 0 1 78 1 -7.850462e-17 -0.8164966 0 1 79 1 7.071068e-01 0.4082483 0 1 80 1 7.071068e-01 0.4082483 0 1 81 1 7.071068e-01 0.4082483 0 1 82 1 -7.850462e-17 -0.8164966 0 1 83 1 7.071068e-01 0.4082483 0 1 84 1 7.071068e-01 0.4082483 0 1 85 1 -7.850462e-17 -0.8164966 0 1 86 1 7.071068e-01 0.4082483 0 1 87 1 -7.850462e-17 -0.8164966 0 1 88 1 7.071068e-01 0.4082483 0 1 89 1 -7.850462e-17 -0.8164966 0 1 90 1 -7.850462e-17 -0.8164966 0 1 91 1 -7.850462e-17 -0.8164966 0 0 92 1 -7.850462e-17 -0.8164966 0 0 93 1 7.071068e-01 0.4082483 0 0 94 1 7.071068e-01 0.4082483 0 0 95 1 -7.071068e-01 0.4082483 0 0 96 1 -7.071068e-01 0.4082483 0 0 97 1 -7.071068e-01 0.4082483 0 0 98 1 -7.071068e-01 0.4082483 0 0 99 1 -7.071068e-01 0.4082483 1 0 100 1 -7.071068e-01 0.4082483 1 0 101 1 -7.071068e-01 0.4082483 1 0 102 1 -7.071068e-01 0.4082483 1 0 103 1 -7.071068e-01 0.4082483 0 0 104 1 -7.071068e-01 0.4082483 0 0 105 1 -7.071068e-01 0.4082483 0 0 106 1 -7.071068e-01 0.4082483 0 0 smokingSMOKER sexM 1 1 1 2 1 0 3 1 0 4 1 1 5 1 0 6 1 0 7 1 1 8 1 0 9 1 1 10 1 1 11 1 1 12 1 1 13 1 0 14 1 1 15 1 0 16 0 0 17 0 1 18 0 0 19 0 0 20 0 1 21 0 0 22 0 0 23 0 0 24 0 0 25 0 1 26 0 1 27 0 1 28 0 0 29 0 0 30 0 0 31 1 0 32 1 1 33 1 0 34 1 0 35 1 1 36 1 0 37 1 1 38 1 1 39 1 0 40 1 0 41 1 1 42 1 0 43 1 0 44 1 1 45 1 1 46 1 0 47 1 0 48 1 1 49 1 0 50 1 1 51 1 1 52 1 1 53 1 0 54 1 0 55 1 1 56 1 0 57 1 1 58 1 0 59 1 1 60 1 0 61 0 0 62 0 0 63 0 0 64 0 1 65 0 1 66 0 1 67 0 1 68 0 0 69 0 1 70 0 1 71 0 1 72 0 1 73 0 1 74 0 0 75 0 0 76 0 1 77 0 0 78 0 1 79 0 1 80 0 0 81 0 1 82 0 0 83 0 1 84 0 0 85 0 1 86 0 0 87 0 0 88 0 0 89 0 1 90 0 1 91 1 0 92 1 1 93 1 1 94 1 0 95 0 0 96 0 1 97 0 1 98 0 0 99 1 1 100 1 0 101 1 0 102 1 0 103 1 1 104 1 1 105 1 1 106 1 0 attr(,"assign") [1] 0 1 1 2 3 3 4 attr(,"contrasts") attr(,"contrasts")$copd [1] "contr.poly" attr(,"contrasts")$asthma [1] "contr.treatment" attr(,"contrasts")$smoking [1] "contr.treatment" attr(,"contrasts")$sex [1] "contr.treatment" > > corfit <- duplicateCorrelation(data, design, ndups = 1, block = as.factor(techRep)) > fit <- lmFit(data, design, block = as.factor(techRep), cor = corfit$consensus) > fit <- eBayes(fit) > > topTable(fit, coef="copd.L", adjust="BH", number=1) ID logFC AveExpr t P.Value adj.P.Val B 14244 219133_at -1.06972 7.116668 -5.540948 2.366908e-07 0.006463079 6.574808 > > #all copd vs all non-copd > mean(data[14244,which(copd=="GOLD_III" | copd=="GOLD_II")]) - mean(data[14244,which(copd=="control")]) [1] -0.5029798 > #all only-copd vs all healthy > mean(data[14244,which(copd=="GOLD_III" | copd=="GOLD_II")]) - mean(data[14244,which(copd=="control" & asthma=="control")]) [1] -0.5752098 > #severe vs moderate > mean(data[14244,which(copd=="GOLD_III")]) - mean(data[14244,which(copd=="GOLD_II")]) [1] -0.3566912 > #severe vs normal > mean(data[14244,which(copd=="GOLD_III")]) - mean(data[14244,which(copd=="control" & asthma=="control")]) [1] -0.7745372 ________________________________________ From: Scott Robinson Sent: 01 September 2013 16:28 To: Gordon K Smyth Cc: Bioconductor mailing list Subject: RE: Can I input ordinal variables into a model in Limma? Thanks very much Gordon. ________________________________________ From: Gordon K Smyth [smyth@wehi.EDU.AU] Sent: 01 September 2013 01:51 To: Scott Robinson Cc: Bioconductor mailing list Subject: Can I input ordinal variables into a model in Limma? Dear Scott, An ordinal variable is just a special case of a categorical variable, and you include it in the limma design matrix just as you would any other categorical variable. For example: DiseaseState <- ordered(DiseaseState, levels=c("mild", "moderate", "severe")) design <- model.matrix(~DiseaseState) etc. By default, the first coefficient in this design will be the overall mean, the second will be linear trend (monotonic increase or decrease), and the third will be quadratic trend ("moderate" more extreme than the other two levels rather than intermediate between them). Best wishes Gordon > Date: Fri, 30 Aug 2013 07:36:58 -0700 (PDT) > From: "Scott Robinson [guest]" <guest at="" bioconductor.org=""> > To: bioconductor at r-project.org, scott.robinson at glasgow.ac.uk > Subject: [BioC] Can I input ordinal variables into a model in Limma? > > > I am working on some microarray data where the samples come from > patients with different severities of disease state - something like > "mild", "moderate", "severe". > > I suppose this is an 'ordinal' variable, but only know how to input > categorical and continuous variables into the model and searching the > Limma manual for the word 'ordinal' doesn't get me anywhere. > > Is it possible to work ordinal variables into my model? If not and I > still want to use Limma is it best to treat it as categorical or > continuous? Or is there an alternative package I could use which has > this functionality? > > Many thanks in advance, > > Scott > > -- output of sessionInfo(): > >> sessionInfo() > R version 3.0.1 (2013-05-16) > Platform: x86_64-w64-mingw32/x64 (64-bit) > > locale: > [1] LC_COLLATE=English_United Kingdom.1252 > [2] LC_CTYPE=English_United Kingdom.1252 > [3] LC_MONETARY=English_United Kingdom.1252 > [4] LC_NUMERIC=C > [5] LC_TIME=English_United Kingdom.1252 > > attached base packages: > [1] parallel stats graphics grDevices utils datasets methods > [8] base > > other attached packages: > [1] limma_3.16.7 sparcl_1.0.3 lattice_0.20-23 > [4] corrplot_0.71 affyPLM_1.36.0 preprocessCore_1.22.0 > [7] simpleaffy_2.36.1 gcrma_2.32.0 genefilter_1.42.0 > [10] affy_1.38.1 Biobase_2.20.1 BiocGenerics_0.6.0 > > loaded via a namespace (and not attached): > [1] affyio_1.28.0 annotate_1.38.0 AnnotationDbi_1.22.6 > [4] BiocInstaller_1.10.3 Biostrings_2.28.0 DBI_0.2-7 > [7] grid_3.0.1 IRanges_1.18.3 RSQLite_0.11.4 > [10] splines_3.0.1 stats4_3.0.1 survival_2.37-4 > [13] XML_3.98-1.1 xtable_1.7-1 zlibbioc_1.6.0 > ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:6}} ADD COMMENTlink modified 4.7 years ago by Gordon Smyth35k • written 4.7 years ago by Scott Robinson130 0 4.7 years ago by Gordon Smyth35k Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia Gordon Smyth35k wrote: Dear Scott, The linear term is interpreted as linear trend. It is on a standardized scale, so that the log2-fold-change between the severe and mild levels is sqrt(0.5) times the reported coefficient (logFC) for the linear term. Best wishes Gordon > Date: Wed, 12 Mar 2014 15:04:11 +0000 > From: Scott Robinson <scott.robinson at="" glasgow.ac.uk=""> > To: Gordon K Smyth <smyth at="" wehi.edu.au=""> > Cc: Bioconductor mailing list <bioconductor at="" r-project.org=""> > Subject: Re: [BioC] Can I input ordinal variables into a model in > Limma? > > Dear Gordon, > > I implemented this and am wondering how you interpret logFC from > topTable with regards the linear trend? I have tried replicating it, but > no joy. > > Thanks, > > Scott > > PS code below: > > design <- model.matrix(~copd + asthma + smoking + sex) >> design > (Intercept) copd.L copd.Q asthmaSEVERE smokingEX- SMOKER > 1 1 -7.850462e-17 -0.8164966 0 0 > 2 1 -7.850462e-17 -0.8164966 0 0 > 3 1 -7.850462e-17 -0.8164966 0 0 > 4 1 -7.850462e-17 -0.8164966 0 0 > 5 1 -7.850462e-17 -0.8164966 0 0 > 6 1 -7.850462e-17 -0.8164966 0 0 > 7 1 -7.850462e-17 -0.8164966 0 0 > 8 1 -7.850462e-17 -0.8164966 0 0 > 9 1 7.071068e-01 0.4082483 0 0 > 10 1 7.071068e-01 0.4082483 0 0 > 11 1 -7.850462e-17 -0.8164966 0 0 > 12 1 7.071068e-01 0.4082483 0 0 > 13 1 7.071068e-01 0.4082483 0 0 > 14 1 -7.850462e-17 -0.8164966 0 0 > 15 1 7.071068e-01 0.4082483 0 0 > 16 1 -7.071068e-01 0.4082483 0 0 > 17 1 -7.071068e-01 0.4082483 0 0 > 18 1 -7.071068e-01 0.4082483 0 0 > 19 1 -7.071068e-01 0.4082483 0 0 > 20 1 -7.071068e-01 0.4082483 0 0 > 21 1 -7.071068e-01 0.4082483 0 0 > 22 1 -7.071068e-01 0.4082483 0 0 > 23 1 -7.071068e-01 0.4082483 0 0 > 24 1 -7.071068e-01 0.4082483 0 0 > 25 1 -7.071068e-01 0.4082483 0 0 > 26 1 -7.071068e-01 0.4082483 0 0 > 27 1 -7.071068e-01 0.4082483 0 0 > 28 1 -7.071068e-01 0.4082483 0 0 > 29 1 -7.071068e-01 0.4082483 0 0 > 30 1 -7.071068e-01 0.4082483 0 0 > 31 1 -7.071068e-01 0.4082483 1 0 > 32 1 -7.071068e-01 0.4082483 1 0 > 33 1 -7.071068e-01 0.4082483 1 0 > 34 1 -7.071068e-01 0.4082483 1 0 > 35 1 -7.071068e-01 0.4082483 1 0 > 36 1 -7.071068e-01 0.4082483 1 0 > 37 1 -7.071068e-01 0.4082483 1 0 > 38 1 -7.071068e-01 0.4082483 1 0 > 39 1 -7.071068e-01 0.4082483 1 0 > 40 1 -7.071068e-01 0.4082483 1 0 > 41 1 -7.071068e-01 0.4082483 1 0 > 42 1 -7.071068e-01 0.4082483 1 0 > 43 1 -7.071068e-01 0.4082483 1 0 > 44 1 -7.071068e-01 0.4082483 1 0 > 45 1 -7.071068e-01 0.4082483 1 0 > 46 1 -7.071068e-01 0.4082483 0 0 > 47 1 -7.071068e-01 0.4082483 0 0 > 48 1 -7.071068e-01 0.4082483 0 0 > 49 1 -7.071068e-01 0.4082483 0 0 > 50 1 -7.071068e-01 0.4082483 0 0 > 51 1 -7.071068e-01 0.4082483 0 0 > 52 1 -7.071068e-01 0.4082483 0 0 > 53 1 -7.071068e-01 0.4082483 0 0 > 54 1 -7.071068e-01 0.4082483 0 0 > 55 1 -7.071068e-01 0.4082483 0 0 > 56 1 -7.071068e-01 0.4082483 0 0 > 57 1 -7.071068e-01 0.4082483 0 0 > 58 1 -7.071068e-01 0.4082483 0 0 > 59 1 -7.071068e-01 0.4082483 0 0 > 60 1 -7.071068e-01 0.4082483 0 0 > 61 1 -7.071068e-01 0.4082483 1 0 > 62 1 -7.071068e-01 0.4082483 1 0 > 63 1 -7.071068e-01 0.4082483 1 0 > 64 1 -7.071068e-01 0.4082483 1 0 > 65 1 -7.071068e-01 0.4082483 1 0 > 66 1 -7.071068e-01 0.4082483 1 0 > 67 1 -7.071068e-01 0.4082483 1 0 > 68 1 -7.071068e-01 0.4082483 1 0 > 69 1 -7.071068e-01 0.4082483 1 0 > 70 1 -7.071068e-01 0.4082483 1 0 > 71 1 -7.071068e-01 0.4082483 1 0 > 72 1 -7.071068e-01 0.4082483 1 0 > 73 1 -7.071068e-01 0.4082483 1 0 > 74 1 -7.071068e-01 0.4082483 1 0 > 75 1 -7.071068e-01 0.4082483 1 0 > 76 1 7.071068e-01 0.4082483 0 1 > 77 1 -7.850462e-17 -0.8164966 0 1 > 78 1 -7.850462e-17 -0.8164966 0 1 > 79 1 7.071068e-01 0.4082483 0 1 > 80 1 7.071068e-01 0.4082483 0 1 > 81 1 7.071068e-01 0.4082483 0 1 > 82 1 -7.850462e-17 -0.8164966 0 1 > 83 1 7.071068e-01 0.4082483 0 1 > 84 1 7.071068e-01 0.4082483 0 1 > 85 1 -7.850462e-17 -0.8164966 0 1 > 86 1 7.071068e-01 0.4082483 0 1 > 87 1 -7.850462e-17 -0.8164966 0 1 > 88 1 7.071068e-01 0.4082483 0 1 > 89 1 -7.850462e-17 -0.8164966 0 1 > 90 1 -7.850462e-17 -0.8164966 0 1 > 91 1 -7.850462e-17 -0.8164966 0 0 > 92 1 -7.850462e-17 -0.8164966 0 0 > 93 1 7.071068e-01 0.4082483 0 0 > 94 1 7.071068e-01 0.4082483 0 0 > 95 1 -7.071068e-01 0.4082483 0 0 > 96 1 -7.071068e-01 0.4082483 0 0 > 97 1 -7.071068e-01 0.4082483 0 0 > 98 1 -7.071068e-01 0.4082483 0 0 > 99 1 -7.071068e-01 0.4082483 1 0 > 100 1 -7.071068e-01 0.4082483 1 0 > 101 1 -7.071068e-01 0.4082483 1 0 > 102 1 -7.071068e-01 0.4082483 1 0 > 103 1 -7.071068e-01 0.4082483 0 0 > 104 1 -7.071068e-01 0.4082483 0 0 > 105 1 -7.071068e-01 0.4082483 0 0 > 106 1 -7.071068e-01 0.4082483 0 0 > smokingSMOKER sexM > 1 1 1 > 2 1 0 > 3 1 0 > 4 1 1 > 5 1 0 > 6 1 0 > 7 1 1 > 8 1 0 > 9 1 1 > 10 1 1 > 11 1 1 > 12 1 1 > 13 1 0 > 14 1 1 > 15 1 0 > 16 0 0 > 17 0 1 > 18 0 0 > 19 0 0 > 20 0 1 > 21 0 0 > 22 0 0 > 23 0 0 > 24 0 0 > 25 0 1 > 26 0 1 > 27 0 1 > 28 0 0 > 29 0 0 > 30 0 0 > 31 1 0 > 32 1 1 > 33 1 0 > 34 1 0 > 35 1 1 > 36 1 0 > 37 1 1 > 38 1 1 > 39 1 0 > 40 1 0 > 41 1 1 > 42 1 0 > 43 1 0 > 44 1 1 > 45 1 1 > 46 1 0 > 47 1 0 > 48 1 1 > 49 1 0 > 50 1 1 > 51 1 1 > 52 1 1 > 53 1 0 > 54 1 0 > 55 1 1 > 56 1 0 > 57 1 1 > 58 1 0 > 59 1 1 > 60 1 0 > 61 0 0 > 62 0 0 > 63 0 0 > 64 0 1 > 65 0 1 > 66 0 1 > 67 0 1 > 68 0 0 > 69 0 1 > 70 0 1 > 71 0 1 > 72 0 1 > 73 0 1 > 74 0 0 > 75 0 0 > 76 0 1 > 77 0 0 > 78 0 1 > 79 0 1 > 80 0 0 > 81 0 1 > 82 0 0 > 83 0 1 > 84 0 0 > 85 0 1 > 86 0 0 > 87 0 0 > 88 0 0 > 89 0 1 > 90 0 1 > 91 1 0 > 92 1 1 > 93 1 1 > 94 1 0 > 95 0 0 > 96 0 1 > 97 0 1 > 98 0 0 > 99 1 1 > 100 1 0 > 101 1 0 > 102 1 0 > 103 1 1 > 104 1 1 > 105 1 1 > 106 1 0 > attr(,"assign") > [1] 0 1 1 2 3 3 4 > attr(,"contrasts") > attr(,"contrasts")$copd > [1] "contr.poly" > > attr(,"contrasts")$asthma > [1] "contr.treatment" > > attr(,"contrasts")$smoking > [1] "contr.treatment" > > attr(,"contrasts")$sex > [1] "contr.treatment" > >> >> corfit <- duplicateCorrelation(data, design, ndups = 1, block = as.factor(techRep)) >> fit <- lmFit(data, design, block = as.factor(techRep), cor = corfit$consensus) >> fit <- eBayes(fit) >> >> topTable(fit, coef="copd.L", adjust="BH", number=1) > ID logFC AveExpr t P.Value adj.P.Val B > 14244 219133_at -1.06972 7.116668 -5.540948 2.366908e-07 0.006463079 6.574808 >> >> #all copd vs all non-copd >> mean(data[14244,which(copd=="GOLD_III" | copd=="GOLD_II")]) - mean(data[14244,which(copd=="control")]) > [1] -0.5029798 >> #all only-copd vs all healthy >> mean(data[14244,which(copd=="GOLD_III" | copd=="GOLD_II")]) - mean(data[14244,which(copd=="control" & asthma=="control")]) > [1] -0.5752098 >> #severe vs moderate >> mean(data[14244,which(copd=="GOLD_III")]) - mean(data[14244,which(copd=="GOLD_II")]) > [1] -0.3566912 >> #severe vs normal >> mean(data[14244,which(copd=="GOLD_III")]) - mean(data[14244,which(copd=="control" & asthma=="control")]) > [1] -0.7745372 > > > > > ________________________________________ > From: Scott Robinson > Sent: 01 September 2013 16:28 > To: Gordon K Smyth > Cc: Bioconductor mailing list > Subject: RE: Can I input ordinal variables into a model in Limma? > > Thanks very much Gordon. > > ________________________________________ > From: Gordon K Smyth [smyth at wehi.EDU.AU] > Sent: 01 September 2013 01:51 > To: Scott Robinson > Cc: Bioconductor mailing list > Subject: Can I input ordinal variables into a model in Limma? > > Dear Scott, > > An ordinal variable is just a special case of a categorical variable, and > you include it in the limma design matrix just as you would any other > categorical variable. For example: > > DiseaseState <- ordered(DiseaseState, > levels=c("mild", "moderate", "severe")) > design <- model.matrix(~DiseaseState) > > etc. By default, the first coefficient in this design will be the overall > mean, the second will be linear trend (monotonic increase or decrease), > and the third will be quadratic trend ("moderate" more extreme than the > other two levels rather than intermediate between them). > > Best wishes > Gordon > > >> Date: Fri, 30 Aug 2013 07:36:58 -0700 (PDT) >> From: "Scott Robinson [guest]" <guest at="" bioconductor.org=""> >> To: bioconductor at r-project.org, scott.robinson at glasgow.ac.uk >> Subject: [BioC] Can I input ordinal variables into a model in Limma? >> >> >> I am working on some microarray data where the samples come from >> patients with different severities of disease state - something like >> "mild", "moderate", "severe". >> >> I suppose this is an 'ordinal' variable, but only know how to input >> categorical and continuous variables into the model and searching the >> Limma manual for the word 'ordinal' doesn't get me anywhere. >> >> Is it possible to work ordinal variables into my model? If not and I >> still want to use Limma is it best to treat it as categorical or >> continuous? Or is there an alternative package I could use which has >> this functionality? >> >> Many thanks in advance, >> >> Scott >> >> -- output of sessionInfo(): >> >>> sessionInfo() >> R version 3.0.1 (2013-05-16) >> Platform: x86_64-w64-mingw32/x64 (64-bit) >> >> locale: >> [1] LC_COLLATE=English_United Kingdom.1252 >> [2] LC_CTYPE=English_United Kingdom.1252 >> [3] LC_MONETARY=English_United Kingdom.1252 >> [4] LC_NUMERIC=C >> [5] LC_TIME=English_United Kingdom.1252 >> >> attached base packages: >> [1] parallel stats graphics grDevices utils datasets methods >> [8] base >> >> other attached packages: >> [1] limma_3.16.7 sparcl_1.0.3 lattice_0.20-23 >> [4] corrplot_0.71 affyPLM_1.36.0 preprocessCore_1.22.0 >> [7] simpleaffy_2.36.1 gcrma_2.32.0 genefilter_1.42.0 >> [10] affy_1.38.1 Biobase_2.20.1 BiocGenerics_0.6.0 >> >> loaded via a namespace (and not attached): >> [1] affyio_1.28.0 annotate_1.38.0 AnnotationDbi_1.22.6 >> [4] BiocInstaller_1.10.3 Biostrings_2.28.0 DBI_0.2-7 >> [7] grid_3.0.1 IRanges_1.18.3 RSQLite_0.11.4 >> [10] splines_3.0.1 stats4_3.0.1 survival_2.37-4 >> [13] XML_3.98-1.1 xtable_1.7-1 zlibbioc_1.6.0 ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}