Question: on the meaning and value of $stdev.unscaled in a limma's lmFitted MArrayLM object
gravatar for Massimo Pinto
10.0 years ago by
Massimo Pinto390
Massimo Pinto390 wrote:
Greetings all, I am writing to enquire about the meaning of some elements that are provided in an limma::MArray-LM object resulting from a call to limma::lmFit(); To avoid a verbose output, I tried to minimize the number of genes to be fitted, by selecting just two genes from my expression set: >eset.2167 <- eset.more[c(848,849)] >fit.2167 <- lmFit(eset.2167, designo)? # where 'designo' is my design matrix I did try fitting a selection from my expression set containing just one gene, but I understand that lmFit() does not like this, as it yields: "Error in? fit$effects[(fit$rank + 1):narrays, , drop = FALSE] : incorrect number of dimensions" My concern, specifically, is that I am not sure how the standard errors on the lmFit's coefficients are calculated, as they are returned with a value that differs from what I get from a separate run in MSExcel, using the built-in function linest(), with the same design matrix and vector of y kept the same. Convincingly, the output of best values of the fitted coefficients is identical in lmFit and MSExcel's linest(). Take for example $stdev.unscaled which is defined (from ?MArrayLM) as "$stdev.unscaled matrix containing unscaled standard deviations of the coefficients or contrasts" > fit.2167$stdev.unscaled ?????????? (Intercept)?? Dose1Gy Ageing6mo Dose1Gy:Ageing6mo Ageing6mo:LabLNGS Dose1Gy:Ageing6mo:LabLNGS A_23_P8812???????? 0.5 0.7071068 0.7071068???????????????? 1 0.7071068???????????????????????? 1 A_24_P7642???????? 0.5 0.7071068 0.7071068???????????????? 1 0.7071068???????????????????????? 1 Now to MSExcel, focusing on the first of the two genes (Gene ID 2167 mapped by probe A_23_P8812). Here are the standard errors on the fitted coefficients, as results from a call to linest() Dose1Gy:Ageing6mo:LabLNGS Ageing6mo:LabLNGS Dose1Gy:Ageing6mo Ageing6mo Dose1Gy (Intercept) best estimates 0,50034025 -1,75687825 -0,3867455 3,055038 0,21681525 8,00081275 std errors 0,262705064 0,185760532 0,262705064 0,185760532 0,185760532 0,131352532 As one can see, the stderr on the (intercept), for example, is 0,131352532, whereas limma:lmFit() gave my a 0.5. What is the reason for such apparent discrepancy? Is there any a-priori mistake made here, since Excel fitted just one gene while lmFit() focused on two genes in this specific example? Thanking you in advance, here is my sessionInfo() Yours, Massimo > sessionInfo() R version 2.10.0 (2009-10-26) x86_64-apple-darwin9.8.0 locale: [1] C attached base packages: ?[1] grid????? tcltk???? tools???? stats???? graphics? grDevices utils ??? datasets? methods?? base other attached packages: ?[1] GOstats_2.12.0????????? graph_1.22.2??????????? Category_2.12.0 ????? affy_1.24.2???????????? gplots_2.7.3??????????? caTools_1.10 ?[7] bitops_1.0-4.1????????? gdata_2.6.1???????????? gtools_2.6.1 ????? hgug4112a.db_2.3.5????? RSQLite_0.7-3 [13] DBI_0.2-4?????????????? Agi4x44PreProcess_1.6.0 genefilter_1.28.0 ????? annotate_1.24.0???????? AnnotationDbi_1.7.20??? limma_3.2.1 [19] Biobase_2.6.0?????????? svGUI_0.9-46??????????? svSocket_0.9-48 ????? svMisc_0.9-56 loaded via a namespace (and not attached): [1] GO.db_2.3.5????????? GSEABase_1.8.0?????? RBGL_1.20.0 XML_2.6-0??????????? affyio_1.13.5??????? preprocessCore_1.7.9 splines_2.10.0 [8] survival_2.35-7????? xtable_1.5-6 > Massimo Pinto Post Doctoral Research Fellow Enrico Fermi Centre and Italian Public Health Research Institute (ISS), Rome
go hgug4112a probe • 798 views
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