[BioC] Filtering is not recommended with LIMMA?

[Garcia Orellana,Miriam]

Dear Dr. Smyth.
Thanks for your explanation.
I have checked the example you provide in LIMMA user guide # 15.4. which is using agilent arrays. In there I found that you are using a "new" at least for me, normalization method (> y <- backgroundCorrect(x,method="normexp") and > y <- normalizeBetweenArrays(y,method="quantile"), followed by the filtering method using the trend = true, that you suggested in your first reply.

I have been using GCRMA as normalization method. So I am wonder if I could still use the true/false filtering method with GCRMA. Also I tried to look for some people that requested/published code when using affymetrix array instead of agilent to perform the same analysis as in # 15.4, and I couldn't find that, Does that mean that it do not work for affymetrix, I guess I am wrong.

Thank you very much indeed.

Miriam
********************************
Miriam Garcia, MS, PhD
Department of Animal Sciences
University of Florida


________________________________________
From: Gordon K Smyth [smyth at wehi.EDU.AU]
Sent: Wednesday, May 22, 2013 7:37 PM
To: Garcia Orellana,Miriam
Cc: Bioconductor mailing list
Subject: Filtering is not recommended with LIMMA?

Dear Miriam,

I don't know what I/NI filtering is and it isn't really my job to make a
running commentary on every filtering method that gets published.

However the limma algorithm analyses the spread of the genewise variances.
Any filtering method based on genewise variances will change the
distribution of variances, will interfere with the limma algorithm and
hence will give poor results.

Like most people, I recommend filtering out genes that don't appear to be
expressed in any sample.  See for example Case studies 15.3 or 15.4 in the
limma User's Guide.

However you will find if you use eBayes(fit,trend=TRUE) instead of the
usual eBayes(fit) that limma gives pretty good results regardless how much
filtering you do, provided of course that the filtering is on expression
and not on variance.

The literature tends to say that the reason for filtering is to reduce the
amount of multiple testing, but in truth the increase in power from this
is only slight.  The more important reason for filtering in most
applications is to remove highly variable genes at low intensities.  The
importance of filtering is highly dependent on how you pre-processed your
data.  Filtering is less important if you (i) use a good background
correction or normalising method that damps down variability at low
intensities and (ii) use eBayes(trend=TRUE) which accommodates a
mean-variance trend.

Best wishes
Gordon


> On 21 May 2013, at 03:06, "Garcia Orellana,Miriam" <mgarciao at ufl.edu> wrote:
>
>> Dear Dr. Smyth.
>>
>> Would you be that kind to help me on deciding whether yes or no to
>> filter my microarray data set with a filtering method correcting for
>> variance such as I/NI method from Talloen et al. (2007). Whereas many
>> researchers say that filtering should increase the power of the test,
>> then increasing the chance to get true deferentially expressed genes.
>> However when I analyzed my data set. I found the next: (meaning lower
>> number of DEG when filtering).
>>
>>
>> Ortoghonal contrasts                # of genes
>> (adjustedP >0.05 and   FC >1.4)
>> w/o filtering   I/NI filtering
>> FAT        195    118
>> FA         329    151
>> MR         169    103
>> FAT by MR          854    321
>> FA by MR          961     283
>>
>> Also, I found that Bourgon et al. (2010) do not recommend to combine
>> the use of limma t-statistic with filtering. So please, I will
>> appreciate your suggestion on whether filter or not filter my data set.
>>
>> Thanks in advance.
>> Miriam
>>
>>
>> ********************************
>> Miriam Garcia, MS, PhD
>> Department of Animal Sciences
>> University of Florida

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