[BioC] Do my Limma results look "normal"?

Thu Jun 5 16:06:46 CEST 2008

Hi thanks for the advice Robert, I'm new to this. Anyway here's my
list of commands:

library(limma)
Cy3 <- "F532 Median - B532"
Cy3b <- "B532 Mean"
targets <- readTargets("targets.csv")
RG <- read.maimages( targets$FileName,
source="genepix",columns=list(R=Cy3,G=Cy3, Rb=Cy3b, Gb=Cy3b))
# remove the extraneous values red channel values
RG$R <- NULL
RG$Rb <- NULL
pData <- data.frame(population = c('a', 'a', 'a', 'a', 'b', 'b', 'b'))
rownames(pData) <-  RG$targets$FileName
design <- model.matrix(~factor(pData$population))
library('vsn')
mat <- vsnMatrix(RG$G)
rownames(mat at hx) <- RG$genes$Name
mat at hx <- mat at hx[order(rownames(mat at hx)), ]
corfit <- duplicateCorrelation(mat, design, ndups=4)
fit <- lmFit(mat, design, ndups=4, correlation=corfit$consensus)
ebayes <- eBayes(fit)
topTable(ebayes, coef = 2, adjust = "BH", n = 399, lfc=1.5)

The full script with a whole load of comments included and other stuff
included can be viewed here:
http://article.gmane.org/gmane.science.biology.informatics.conductor/18032/match=miRNA

And here's my output of SessionInfo():

R version 2.6.2 (2008-02-08)
i686-pc-linux-gnu

locale:
LC_CTYPE=en_IE.UTF-8;LC_NUMERIC=C;LC_TIME=en_IE.UTF-8;LC_COLLATE=en_IE.UTF-8;LC_MONETARY=en_IE.UTF-8;LC_MESSAGES=en_IE.UTF-8;LC_PAPER=en_IE.UTF-8;LC_NAME=C;LC_ADDRESS=C;LC_TELEPHONE=C;LC_MEASUREMENT=en_IE.UTF-8;LC_IDENTIFICATION=C

attached base packages:
[1] tools     stats     graphics  grDevices utils     datasets  methods
[8] base

other attached packages:
[1] statmod_1.3.6        vsn_3.2.1            affy_1.16.0
[4] preprocessCore_1.0.0 affyio_1.6.1         Biobase_1.16.3
[7] limma_2.12.0

loaded via a namespace (and not attached):
[1] grid_2.6.2      lattice_0.17-6  rcompgen_0.1-17

Hope thats better,

Paul Geeleher
Department of Mathematics
National University of Ireland
Galway
Ireland

On Thu, Jun 5, 2008 at 2:41 PM, Robert Gentleman <rgentlem at fhcrc.org> wrote:
> Hi Paul,
>
>  Please check the posting guide and provide us with the information
> requested there (like sessionInfo and the commands you ran). And I typically
> don't give any advice (excpet to follow the posting guide) to people who
> don't use signatures that identify them.
>
>  Robrt
>
>
> Paul Geeleher wrote:
>>
>> Hi,
>>
>> This is the first time I've ever analyzed a microarray experiment
>> using Limma (or anything else for that matter) and I was hoping that
>> somebody could look at my results and tell me if they look normal.
>>
>> The experiment is measuring differential expression between miRNAs of
>> HER2+ and HER2- breast cancer tissue. There are 3 HER2+ arrays and 4
>> HER2- arrays and each of the 399 miRNAs is replicated 4 times in each
>> array.
>>
>> TopTable() reveals the following miRNAs with a fold change above 1.5,
>> which I thought was a reasonable cutoff:
>>
>>                  ID     logFC         t      P.Value    adj.P.Val
>>  B
>> 273      hsa-miR-451 -4.645060 -8.226854 4.510441e-09 9.246404e-07
>> 10.8484797
>> 128      hsa-miR-205  3.551495  7.574564 2.370061e-08 3.239083e-06
>>  9.2222865
>> 13       hsa-miR-101 -2.310652 -6.569497 3.374177e-07 2.567796e-05
>>  6.6146751
>> 282      hsa-miR-486 -2.686910 -6.542808 3.626060e-07 2.567796e-05
>>  6.5439656
>> 55       hsa-miR-144 -2.890719 -5.889594 2.152998e-06 1.261042e-04
>>  4.7952480
>> 387      mmu-miR-463 -2.609257 -5.764143 3.042120e-06 1.559086e-04
>>  4.4561920
>> 388      mmu-miR-464 -2.080402 -5.696976 3.662006e-06 1.668247e-04
>>  4.2743601
>> 151      hsa-miR-223 -1.722956 -5.637290 4.318942e-06 1.770766e-04
>>  4.1126276
>> 51    hsa-miR-142-3p -3.262824 -5.397809 8.386312e-06 3.125807e-04
>>  3.4626378
>> 14   hsa-miR-101_MM1 -1.922710 -5.224075 1.358743e-05 4.175776e-04
>>  2.9905370
>> 159  hsa-miR-26b_MM2 -2.221853 -5.206724 1.425875e-05 4.175776e-04
>>  2.9433849
>> 236 hsa-miR-376a_MM1 -1.633555 -4.653220 6.637043e-05 1.700742e-03
>>  1.4433277
>> 266     hsa-miR-432*  1.512622  4.627293 7.131510e-05 1.719952e-03
>>  1.3734422
>> 168      hsa-miR-29b -1.954087 -4.198854 2.323860e-04 4.763912e-03
>>  0.2280262
>> 31  hsa-miR-126*_MM2 -1.537988 -3.209957 3.233842e-03 5.099520e-02
>> -2.2888897
>> 52    hsa-miR-142-5p -1.881192 -2.831493 8.332384e-03 9.002153e-02
>> -3.1731794
>>
>>
>> Another person is sanity testing this data using GeneSpring and they
>> are getting much higher p-values compared to mine. They are also
>> taking the step of excluding quite a few of the miRNAs from the
>> experiment based on their standard deviation across the arrays of each
>> group. Should I be doing this also or is this taken into account by
>> the eBayes() function or lmFit()?
>>
>> If you are interested the script I wrote to do the analysis is here:
>>
>> http://article.gmane.org/gmane.science.biology.informatics.conductor/18032/match=miRNA
>>
>> Thanks for any advice,
>>
>> -Paul.
>>
>> _______________________________________________
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>>
>
> --
> Robert Gentleman, PhD
> Program in Computational Biology
> Division of Public Health Sciences
> Fred Hutchinson Cancer Research Center
> 1100 Fairview Ave. N, M2-B876
> PO Box 19024
> Seattle, Washington 98109-1024
> 206-667-7700
> rgentlem at fhcrc.org
>