Dear Matt,

Thank you very much for your answer. I'm glad the function works with
 biological replicates. Concerning the other points, you said that one
group is more consistent than the other one. When this appears, can
arrayWeights introduce a bias (more than if not using the function)? If
yes, what can I do?
Moreover, if I use logFC to make heatmap or anything else, should I use
logFC as estimated when using arrayWeights or should I use the regular fold
change?

My quality controls and arrayWeights  showed that some arrays have poor
quality. ArrayWeights allow me to keep them when detecting differentially
expressed genes. Is there a way to take into account the quality when doing
other tests as clustering, correlations... ?

Best,

Amos


2012/8/4 Matthew Ritchie <mritchie@wehi.edu.au>:
> Dear Amos,
>
>> I'm using the limma package and the arrayWeights function to make some
>> transcriptomic analysis: One group of samples versus an another group.
>> arrayWeights allow me to get very interesting results but in
>> documentation it says you need replicates. Are they technical or/ and
>> biological replicates? I couldn't find clearly the information.
>
> Ideally biological replicates.  Technical replicates are handled by other
> functions in limma, such as duplicateCorrelation().
>
>> My second question is: can arrayWeights introduce some bias? for
>> example in one of my analysis with 2 groups and 5 samples by group,
>> the weights found by arrayWeights are:
>> for the 1st group : 2.91 0.99 0.43 2.18 0.89
>> for the 2nd group : 1.40 1.24 0.29 1 .40 0.60
>> It seems that the weights are in general higher for the first group.
>> Is that a problem?
>
> This would indicate that the first group of arrays are on average more
> consistent than the second group of arrays.
>
>> Next in limma, when Weights calculated by arrayWeights are implemented
>> to the the lmFit function how are calculated the log2 fold change
>> (coefficients ) ?
>> I tried to calculate them by myself applying the weights to the
>> samples but I couldn't get the same results.
>> I observed that the average expression of each probe with or without
>> applying the weights is equal with the function topTable. Is that
>> normal?
>
> The weights are used in gene-wise weighted least squares regression to
> estimate the coefficients in the linear model.  So they will affect the
> logFC values.  They are not used in the calculation of average expression,
> so these value won't be affected by the use of weights.
>
> I hope this helps.  Best wishes,
>
> Matt
>
>
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