[BioC] about limma linear models

[Naomi Altman]

If you have biological replicates, then using LIMMA is preferred with 
small sample sizes.  If you have only technical replicates, then you 
really cannot do a proper statistical analysis of the data.  Since 
you have a disconnected design, you might use separate channel 
analysis to simplify the comparisons you want to make.

Regards,
Naomi Altman


At 07:40 AM 5/16/2011, =?GBK?B?va241Q==?= wrote:
>Hello Everyone!
>
>
>I'm now working with my expression microarray data by limma to 
>detect differential expression probes. I have biology as my 
>knowledge background ,not statistics, so I'm confused with the 
>design matrix and contrast matrix in the limma usersguide.  now i 
>have read the target file
>as follows:
>SlideNumber   FileName Cy3 Cy5
>1           1       15_1_3.txt  BI  BM
>2           2       15_1_4.txt  BM  BI
>3           3       18_1_2.txt BI2 BM2
>4           4       18_1_3.txt BM2 BI2
>5           5       16_1_1.txt BE2 BM2
>6           6       16_1_2.txt BM2 BE2
>
>
>because there is no connection from BI(or BM) to the other samples, 
>dose that mean I have to contrast the differences(BI-BM, BI2-BM2, 
>BE2-BM2, BE2-BI2) separately?
>Though I read the linear models and Empirical Bayes Methods theory 
>carefully, I only know little.  I wonder It is proper to detect 
>differential expression by limma when there are only two replicates?
>
>
>
>
>
>
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>
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