Entering edit mode
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}}
