Dear BioConductor mailing list!
I am using the R-2.7.2, the limma package and its interface limmaGUI.
I have a rather small number of slides (6) and probes spotted in 4
replicates (776x4=3104 spots).
I perform background correction (normexp, cutoff=10), within (global loess)
and between (Scale) array normalization.
To obtain the statistics for differential expression I choose the "least
squares" linear model fit and the calculation of Duplicate correlation, the
adjust method for p-value is "BH". I get the following toptable:
       *Name* *P.Value* *logFC* *AveExpr* *t* *P.Value* *adj.P.Val* *B*
hsa-miR-503 2.34E-06 1.945387328 8.136697759 5.884386792 2.34E-06
0.001343352 4.836590302 hsa-miR-921 3.46E-06 1.174433413 8.035845865
5.740377619 3.46E-06 0.001343352 4.471051142 hsa-miR-30c-2* 7.77E-06
3.117701794 8.27088038 5.445618379 7.77E-06 0.001550508 3.718681784
hsa-miR-198 7.99E-06 1.967637234 6.072614871 5.435302824 7.99E-06
0.001550508 3.692268736 miRPlus_42526 1.98E-05 3.697849577 9.172222106
5.105807836 1.98E-05 0.00307328 2.846724551 hsa-miR-665 2.55E-05 2.066123655
8.328358693 5.014730934 2.55E-05 0.003292112 2.612641297 hsa-miR-371-5p
6.28E-05 3.733502603 9.49054759 4.686906149 6.28E-05 0.006817575 1.770685081
hsa-miR-187* 7.03E-05 2.265561547 5.963832035 4.646081978 7.03E-05
0.006817575 1.666045526 hsa-miR-183* 9.64E-05 1.416875368 6.088668377
4.531017428 9.64E-05 0.008313829 1.371557327 hsa-miR-483-5p 0.000111163
1.789850517 6.054104314 4.479175715 0.000111163 0.008626216 1.239133367
hsa-miR-30b* 0.000123606 2.526268106 8.483475515 4.440467862 0.000123606
0.008719852 1.140379763 hsa-miR-620 0.00028076 1.84948372 8.448990163
4.139779212 0.00028076 0.018155808 0.377815095 miRPlus_17952 0.000337227
4.962342262 8.633804566 4.07218339 0.000337227 0.020129836 0.20777931
hsa-miR-675 0.000578682 1.991150452 5.45522505 3.871788436 0.000578682
0.028873358 -0.292416594 miRPlus_17869 0.000587344 1.280048919 8.085959229
3.866246126 0.000587344 0.028873358 -0.306158062 miRPlus_42793 0.000612763
1.315186076 6.393831483 3.850431131 0.000612763 0.028873358 -0.345339636
hsa-miR-193a-5p 0.000632535 1.418128062 7.751168126 3.838568191 0.000632535
0.028873358 -0.374700751 miRPlus_42487 0.00079838 5.437939056 10.51144667
3.751332648 0.00079838 0.033174042 -0.589807882 hsa-miR-637 0.000812251
1.586253029 5.38574605 3.744861076 0.000812251 0.033174042 -0.605707122

 According to the book, p-values and B-statistics should rank genes in the
same order.  As possible treshhold for adjusted p-values <0.05, B-value of 0
expresses a 50:50 chance that its really differentially expressed, a
negative B-value expresses a very very unlikey probability of differential
expression.

What makes me worry is that in my statistics I have low adj.p-value 0.03
together with negative B-values.
How do I have to handle this discrepance? Is this a hint that something is
wrong with my normalization?

 I performed validation by real-time PCR some time ago, at that time I
considered only the p-value (and not adjusted .p or the B), using a
treshhold of p<0.001.
Now I checked these old results once again, to understand if positive B and
adjusted p-values<0.05 in my case indiciated a high probability for
modulated expression.
It was true only for some mirRs (adjusted p-value < 0.05 an B positive)
where modulation was confirmedby real time PCR.
In contrast to other mirs (with adj.p > 0.05 and negative B) which showed
modulation in real-time PCR.
And yet other mir showed very good adjusted p and the B (adjp= 0.014,
B=1.71)- but no modulation real-time.

Are adjusted p-value and B-statistics too stringent or do I have to
reconsider normlization and linear model fit?
Do I expect too much from the Statistics?

Grazie!

Christine










Dr. Christine Völlenkle, Ph.D.
Research Laboratories-Molecular Cardiology
I.R.C.C.S. Policlinico San Donato
Via R. Morandi, 30
20097  S. Donato M.se (MI) Italy
Phone: +39 02 52774 683 (lab)
          +39 02 52774 533 (office)
Fax:    +39 02 52774 666
email: christine.voellenkle@gmail.com

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