Question: Estimating fold change from limma 'log2FC' using lumi
0
gravatar for Gordon Smyth
7.5 years ago by
Gordon Smyth38k
Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
Gordon Smyth38k wrote:
Dear Ross, Short answer: The vst transformation is asymptotically equivalent to the log2, and 2^logFC is the correct transformation. Longer answer: See http://www.ncbi.nlm.nih.gov/pubmed/20929874 for why vst gives small fold changes, and what you should do about it. BTW, there is nothing "linear" about raw scale intensities for fold-changes. The logFC are far more linear in their statistical behaviour. Best wishes Gordon > Date: Thu, 16 Feb 2012 14:58:02 +1100 > From: Ross <ross.lazarus at="" gmail.com=""> > To: bioconductor at r-project.org > Subject: [BioC] Estimating fold change from limma 'log2FC' using lumi > vst instead of log2 transformation? > > Dear Bioconductors, > > I have a question related to estimates of microarray expression fold change > after using vst in lumi (as the authors recommend) rather than a simple > log2 transformation. I apologise if this is a foolish question or if my > google-fu was too weak to find the answer when I searched. > > I am working with illumina sentrix expression data, using lumi's vst and > quantile normalization, then limma to model differential expression. > > One of my collaborators has asked for the fold changes reported by limma > (as log2FC) on a linear scale so he can check the top ones with qPCR. > > Initially, I was going to make the obvious suggest that he take 2^log2FC to > transform what lumi reported as log2FC back to a linear scale - but this > probably not the right transformation - because vst is not log2! > > To confirm this, I reran the analysis replacing the lumi vst option with a > log2 transformation to see what differences there were. > > As can be seen from the small sample below, there were variations in gene > ranking, differences in p values (vst gave smaller p values) and the values > reported as 'log2FC' were vastly different. > > I understand that limma estimates "log2FC" as the difference between > transformed intensity values - but how can they be interpreted if the data > were transformed with vst rather than log2? I imagine that the vst results > are 'better' because it's a better transformation than log2 but I am not > sure how to explain this to my colleague - advice appreciated. > > > Using vst (apologies for the formatting): > > ID geneSymbol logFC CI.025 CI.975 AveExpr > t P.Value adj.P.Val B > 5447 BJnZRnS5W.xQwHiYVQ EEF1A2 6.737470 6.577077 6.897863 > 10.133500 137.41304 2.694315e-70 7.260639e-66 147.7207 > 23109 rQlXje3RRE1fQVEa3k SLN 7.534754 7.345115 7.724392 > 10.666689 129.97466 5.361216e-69 7.223702e-65 145.1452 > 18091 leKDHioqXpx7d3kd54 MYBPC1 7.524289 7.322845 7.725733 > 10.609381 122.18770 1.481136e-67 1.330455e-63 142.2374 > 18134 K_W3elXdKe58XuEknc MYL3 7.076920 6.862857 7.290984 > 10.309408 108.14785 1.038158e-64 6.994069e-61 136.3602 > 18123 llE343nSV6K5SQJVOc MYH7 7.296190 7.070833 7.521547 > 10.494978 105.91110 3.185741e-64 1.716987e-60 135.3381 > > > > Using log2: > > ID geneSymbol logFC CI.025 CI.975 AveExpr > t P.Value adj.P.Val B > 5447 BJnZRnS5W.xQwHiYVQ EEF1A2 8.833916 8.552823 9.115010 > 9.164795 102.80600 8.505465e-67 2.292053e-62 139.7942 > 18134 K_W3elXdKe58XuEknc MYL3 9.145482 8.812582 9.478383 > 9.337462 89.86878 1.832162e-63 2.468655e-59 133.0097 > 18179 9X8dQtVHUZNfXRQdIk MYOZ1 8.509667 8.194560 8.824774 > 9.452997 88.34276 4.864153e-63 4.369306e-59 132.1303 > 23659 6Ij1VCKLt6Cje5ehLo SRL 6.949980 6.685096 7.214864 > 8.398941 85.83111 2.517144e-62 1.695800e-58 130.6422 > 24961 3IlVNOVFFR3pHp9Nfw TPM2 8.845876 8.502794 9.188958 > 8.828952 84.34499 6.808768e-62 3.669654e-58 129.7369 > > >> sessionInfo() > R version 2.14.1 (2011-12-22) > Platform: x86_64-unknown-linux-gnu (64-bit) > > locale: > [1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C > [3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8 > [5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8 > [7] LC_PAPER=C LC_NAME=C > [9] LC_ADDRESS=C LC_TELEPHONE=C > [11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C > > attached base packages: > [1] stats graphics grDevices utils datasets methods base > > other attached packages: > [1] lumi_2.6.0 nleqslv_1.9.2 methylumi_2.0.4 Biobase_2.14.0 > [5] limma_3.10.2 > > loaded via a namespace (and not attached): > [1] affy_1.32.1 affyio_1.22.0 annotate_1.32.1 > [4] AnnotationDbi_1.16.11 BiocInstaller_1.2.1 DBI_0.2-5 > [7] grid_2.14.1 hdrcde_2.15 IRanges_1.12.5 > [10] KernSmooth_2.23-7 lattice_0.20-0 MASS_7.3-16 > [13] Matrix_1.0-3 mgcv_1.7-13 nlme_3.1-103 > [16] preprocessCore_1.16.0 RSQLite_0.11.1 xtable_1.6-0 > [19] zlibbioc_1.0.0 > ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}
ADD COMMENTlink modified 7.5 years ago by Ross30 • written 7.5 years ago by Gordon Smyth38k
Answer: Estimating fold change from limma 'log2FC' using lumi
0
gravatar for Ross
7.5 years ago by
Ross30
Ross30 wrote:
Thanks Gordon - a very helpful response and reference. (In future I shall endeavour use 'raw' for untransformed rather than 'linear' which is the way I always thought about the non-log scale on the other face of my old slide rule) On Fri, Feb 17, 2012 at 8:54 AM, Gordon K Smyth <smyth@wehi.edu.au> wrote: > Dear Ross, > > Short answer: > > The vst transformation is asymptotically equivalent to the log2, and > 2^logFC is the correct transformation. > > Longer answer: > > See > > http://www.ncbi.nlm.nih.gov/**pubmed/20929874<http: www.ncbi.nlm.n="" ih.gov="" pubmed="" 20929874=""> > > for why vst gives small fold changes, and what you should do about it. > > BTW, there is nothing "linear" about raw scale intensities for > fold-changes. The logFC are far more linear in their statistical behaviour. > > Best wishes > Gordon > > Date: Thu, 16 Feb 2012 14:58:02 +1100 >> From: Ross <ross.lazarus@gmail.com> >> To: bioconductor@r-project.org >> Subject: [BioC] Estimating fold change from limma 'log2FC' using lumi >> vst instead of log2 transformation? >> >> Dear Bioconductors, >> >> I have a question related to estimates of microarray expression fold >> change >> after using vst in lumi (as the authors recommend) rather than a simple >> log2 transformation. I apologise if this is a foolish question or if my >> google-fu was too weak to find the answer when I searched. >> >> I am working with illumina sentrix expression data, using lumi's vst and >> quantile normalization, then limma to model differential expression. >> >> One of my collaborators has asked for the fold changes reported by limma >> (as log2FC) on a linear scale so he can check the top ones with qPCR. >> >> Initially, I was going to make the obvious suggest that he take 2^log2FC >> to >> transform what lumi reported as log2FC back to a linear scale - but this >> probably not the right transformation - because vst is not log2! >> >> To confirm this, I reran the analysis replacing the lumi vst option with a >> log2 transformation to see what differences there were. >> ...stuff deleted for brevity > > [[alternative HTML version deleted]]
ADD COMMENTlink written 7.5 years ago by Ross30
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