Hi Katie,
Taylor, Katie wrote:
> Hello,
>
> I hope that someone can help me. I have done an experiment using
> Illumina DASL WG array. I have 48 samples split into 6 groups. I
have
> normalised them all together using lumi; vst followed by quantile
> normalisation. I have then gone on to use limma to get the topTables
> for each of the comparisons that I make. The script works well and
> produces everything that I wanted. However I have a problem with the
> topTables. When one group(NC) is compared to the others, every gene
> is shown to be significant. However, when the other groups are
> compared together they are more sensible with some genes showing
> differential expression and others not. Has anyone come across this
> problem before or know what I need to do?
This question as posted is unanswerable. Nobody knows what model you
fit, what comparison you are talking about, how you compared one
group(NC) to the others, etc.
When you post (see posting guide here, btw
http://www.bioconductor.org/docs/postingGuide.html), try to put
yourself
in the position of those trying to answer your question. Most people
on
this list are very busy doing their own stuff, so are not going to be
willing to play 20 questions with you, so you need to give all
relevant
information up front.
Giving a small example that people can run themselves is quite useful.
One way you can do this is to give all the commands you have run to
create e.g., the design matrices, etc, and then give the output from
dput() on a subset of your data, which can then be copied and pasted
into R to give people the same sort of data you are using.
As an example:
> dput(head(exprs(eset)))
structure(c(8.0399691481705, 7.7908394268982, 5.17556847589099,
5.91653223377504, 6.33519434042879, 6.39053279705292,
7.89152179582958,
7.66120938361982, 5.06702337275377, 5.75450019771177,
6.01043589707915,
6.14780794238939, 8.02232884268966, 7.71682660743587,
5.08267189703526,
5.81662823373788, 6.1982949231392, 6.3576798822155, 7.91067876763638,
7.62268842623152, 5.09180441725433, 5.74198962597234,
6.13399522016068,
6.26066335982967), .Dim = c(6L, 4L), .Dimnames = list(c("100_g_at",
"1000_at", "1001_at", "1002_f_at", "1003_s_at", "1004_at"), c("20A",
"20B", "10A", "10B")))
I can then paste this back into an R session:
> z <- structure(c(8.0399691481705, 7.7908394268982,
5.17556847589099,
5.91653223377504, 6.33519434042879, 6.39053279705292,
7.89152179582958,
7.66120938361982, 5.06702337275377, 5.75450019771177,
6.01043589707915,
6.14780794238939, 8.02232884268966, 7.71682660743587,
5.08267189703526,
5.81662823373788, 6.1982949231392, 6.3576798822155, 7.91067876763638,
7.62268842623152, 5.09180441725433, 5.74198962597234,
6.13399522016068,
6.26066335982967), .Dim = c(6L, 4L), .Dimnames = list(c("100_g_at",
"1000_at", "1001_at", "1002_f_at", "1003_s_at", "1004_at"), c("20A",
"20B", "10A", "10B")))
z <- structure(c(8.0399691481705, 7.7908394268982, 5.17556847589099,
+ 5.91653223377504, 6.33519434042879, 6.39053279705292,
7.89152179582958,
+ 7.66120938361982, 5.06702337275377, 5.75450019771177,
6.01043589707915,
+ 6.14780794238939, 8.02232884268966, 7.71682660743587,
5.08267189703526,
+ 5.81662823373788, 6.1982949231392, 6.3576798822155,
7.91067876763638,
+ 7.62268842623152, 5.09180441725433, 5.74198962597234,
6.13399522016068,
+ 6.26066335982967), .Dim = c(6L, 4L), .Dimnames = list(c("100_g_at",
+ "1000_at", "1001_at", "1002_f_at", "1003_s_at", "1004_at"), c("20A",
+ "20B", "10A", "10B")))
And then I have the same data, all reconstituted
> z
20A 20B 10A 10B
100_g_at 8.039969 7.891522 8.022329 7.910679
1000_at 7.790839 7.661209 7.716827 7.622688
1001_at 5.175568 5.067023 5.082672 5.091804
1002_f_at 5.916532 5.754500 5.816628 5.741990
1003_s_at 6.335194 6.010436 6.198295 6.133995
1004_at 6.390533 6.147808 6.357680 6.260663
Best,
Jim
>
> Thanks,
>
> Katie
>
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--
James W. MacDonald, M.S.
Biostatistician
Douglas Lab
University of Michigan
Department of Human Genetics
5912 Buhl
1241 E. Catherine St.
Ann Arbor MI 48109-5618
734-615-7826
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