Hi BioCs,
I have a doubt about this function.
I'm working with a two-channel dye-swap microarray experiments. After
testing all normalization methods, I conclude that the robustspline
method
was the best for my data. After normalizing, I did the statistical
analysis
using the lmFit and eBayes functions.
My commands were:
fit <- lmFit(MA, design, ndups=2, spacing=12, cor=corfit$consensus)
fit2 <- eBayes(fit)
Everything worked just fine. However, I read the lmFit help and a
doubt came
up:
lmFit(object,design=NULL,ndups=1,spacing=1,block=NULL,correlation,weig
hts=NULL,
method="ls",...)
method: character string, "ls" for least squares or "robust" for
robust
regression
Then, I repeat the same commands, but changing the parameter "method",
since
I used the robustspline method for normalization. I got different top
100
genes most differentially expressed. And now I really confused. I
don't know
what to do with this "method" parameter.
I hope someone can explain to me!
Thanks,
Priscila
--
Priscila Grynberg, B.Sc., M.Sc.
Doutoranda em Bioinformática (Bioinformatics D.Sc student)
Laboratório de Genética Bioquímica
Universidade Federal de Minas Gerais
Tel: +55 31 3409-2628
CV: http://lattes.cnpq.br/8808643075395963
[[alternative HTML version deleted]]
Hi Priscila,
The robustspline method for normalization has
nothing to do with the lmFit(method="robust").
lmFit can either fit the model using a least
squares regression or a robust regression, which
down-weights replicates that are different from
the other replicates. Whether or not to use
lmFit(method="robust") doesn't depend on which
normalization method you use, but rather (IMO)
how many replicates you have. If you have a
relatively large number of replicates, say 6 or
more, then the robust fitting of the model may
help to remove true outliers from affecting the
data. However, if you only have 3 replicates, as
is usual for microarray experiments, using the
robust estimation may remove real variation in
your samples and lead to more false-positives.
That's my take on the situation...
Jenny
At 10:49 AM 1/8/2009, Priscila Grynberg wrote:
>Content-Type: text/plain
>Content-Disposition: inline
>Content-length: 1308
>
>Hi BioCs,
>I have a doubt about this function.
>
>I'm working with a two-channel dye-swap microarray experiments. After
>testing all normalization methods, I conclude that the robustspline
method
>was the best for my data. After normalizing, I did the statistical
analysis
>using the lmFit and eBayes functions.
>
>My commands were:
>
>fit <- lmFit(MA, design, ndups=2, spacing=12, cor=corfit$consensus)
>
>fit2 <- eBayes(fit)
>
>Everything worked just fine. However, I read the lmFit help and a
doubt came
>up:
>
>lmFit(object,design=NULL,ndups=1,spacing=1,block=NULL,correlation,wei
ghts=NULL,
>method="ls",...)
>
>method: character string, "ls" for least squares or "robust" for
robust
>regression
>
>Then, I repeat the same commands, but changing the parameter
"method", since
>I used the robustspline method for normalization. I got different top
100
>genes most differentially expressed. And now I really confused. I
don't know
>what to do with this "method" parameter.
>
>I hope someone can explain to me!
>
>Thanks,
>
>Priscila
>
>
>
>
>--
>Priscila Grynberg, B.Sc., M.Sc.
>Doutoranda em Bioinform?tica (Bioinformatics D.Sc student)
>Laborat?rio de Gen?tica Bioqu?mica
>Universidade Federal de Minas Gerais
>Tel: +55 31 3409-2628
>CV: http://lattes.cnpq.br/8808643075395963
>
> [[alternative HTML version deleted]]
>
>
>_______________________________________________
>Bioconductor mailing list
>Bioconductor at stat.math.ethz.ch
>https://stat.ethz.ch/mailman/listinfo/bioconductor
>Search the archives:
>http://news.gmane.org/gmane.science.biology.informatics.conductor
Jenny Drnevich, Ph.D.
Functional Genomics Bioinformatics Specialist
W.M. Keck Center for Comparative and Functional Genomics
Roy J. Carver Biotechnology Center
University of Illinois, Urbana-Champaign
330 ERML
1201 W. Gregory Dr.
Urbana, IL 61801
USA
ph: 217-244-7355
fax: 217-265-5066
e-mail: drnevich at illinois.edu
Thanks, it was really helpful.
Priscila
On Thu, Jan 8, 2009 at 3:58 PM, Jenny Drnevich
<drnevich@illinois.edu>wrote:
> Hi Priscila,
>
> The robustspline method for normalization has nothing to do with the
> lmFit(method="robust"). lmFit can either fit the model using a least
squares
> regression or a robust regression, which down-weights replicates
that are
> different from the other replicates. Whether or not to use
> lmFit(method="robust") doesn't depend on which normalization method
you use,
> but rather (IMO) how many replicates you have. If you have a
relatively
> large number of replicates, say 6 or more, then the robust fitting
of the
> model may help to remove true outliers from affecting the data.
However, if
> you only have 3 replicates, as is usual for microarray experiments,
using
> the robust estimation may remove real variation in your samples and
lead to
> more false-positives.
>
> That's my take on the situation...
> Jenny
>
> At 10:49 AM 1/8/2009, Priscila Grynberg wrote:
>
>> Content-Type: text/plain
>> Content-Disposition: inline
>> Content-length: 1308
>>
>>
>> Hi BioCs,
>> I have a doubt about this function.
>>
>> I'm working with a two-channel dye-swap microarray experiments.
After
>> testing all normalization methods, I conclude that the robustspline
method
>> was the best for my data. After normalizing, I did the statistical
>> analysis
>> using the lmFit and eBayes functions.
>>
>> My commands were:
>>
>> fit <- lmFit(MA, design, ndups=2, spacing=12, cor=corfit$consensus)
>>
>> fit2 <- eBayes(fit)
>>
>> Everything worked just fine. However, I read the lmFit help and a
doubt
>> came
>> up:
>>
>>
>> lmFit(object,design=NULL,ndups=1,spacing=1,block=NULL,correlation,w
eights=NULL,
>> method="ls",...)
>>
>> method: character string, "ls" for least squares or "robust" for
robust
>> regression
>>
>> Then, I repeat the same commands, but changing the parameter
"method",
>> since
>> I used the robustspline method for normalization. I got different
top 100
>> genes most differentially expressed. And now I really confused. I
don't
>> know
>> what to do with this "method" parameter.
>>
>> I hope someone can explain to me!
>>
>> Thanks,
>>
>> Priscila
>>
>>
>>
>>
>> --
>> Priscila Grynberg, B.Sc., M.Sc.
>> Doutoranda em Bioinformática (Bioinformatics D.Sc student)
>> Laboratório de Genética Bioquímica
>> Universidade Federal de Minas Gerais
>> Tel: +55 31 3409-2628
>> CV: http://lattes.cnpq.br/8808643075395963
>>
>> [[alternative HTML version deleted]]
>>
>>
>> _______________________________________________
>> Bioconductor mailing list
>> Bioconductor@stat.math.ethz.ch
>> https://stat.ethz.ch/mailman/listinfo/bioconductor
>> Search the archives:
>> http://news.gmane.org/gmane.science.biology.informatics.conductor
>>
>
> Jenny Drnevich, Ph.D.
>
> Functional Genomics Bioinformatics Specialist
> W.M. Keck Center for Comparative and Functional Genomics
> Roy J. Carver Biotechnology Center
> University of Illinois, Urbana-Champaign
>
> 330 ERML
> 1201 W. Gregory Dr.
> Urbana, IL 61801
> USA
>
> ph: 217-244-7355
> fax: 217-265-5066
> e-mail: drnevich@illinois.edu
>
--
Priscila Grynberg, B.Sc., M.Sc.
Doutoranda em Bioinformática (Bioinformatics D.Sc student)
Laboratório de Genética Bioquímica
Universidade Federal de Minas Gerais
Tel: +55 31 3409-2628
CV: http://lattes.cnpq.br/8808643075395963
[[alternative HTML version deleted]]