wgcna

[Raquel Martinez Garcia]

Hi,

I'm running a tutorial ("Meta-analyses of data from two (or more) microarray data sets"), which use wgcna package. I have an error in the function modulePreservation (it is below).
I'm using R2.13
Can you help me? Do you know, what is happens?


Thanks
Raquel


multiExpr = list(A = list(data=t(badea)),B = list(data=t(mayo)))
# two independent datasets (dim = 13447 x 36)
mp = modulePreservation(multiExpr,multiColor,referenceNetworks=1,verbose=3,networkType="signed",nPermutations=2,maxGoldModuleSize=100,maxModuleSize=400)
   ..checking data for excessive amounts of missing data..
      Flagging genes and samples with too many missing values...
       ..step 1
      Flagging genes and samples with too many missing values...
       ..step 1
   ..unassigned 'module' name: grey
   ..all network sample 'module' name: gold
   ..calculating observed preservation values
   ..calculating permutation Z scores
  ..Working with set 1 as reference set
  ....working with set 2 as test set
   ......working on permutation 1
   ......working on permutation 2
Error en zAll[, stat] = z :
   número de items para para sustituir no es un múltiplo de la longitud del reemplazo
   (number of items  to replace  is not a  multiple of&nbs
p;replacement  length)

--
********************************************

Raquel Martinez Garcia, Graduate Student
Gastrointestinal Cancer Clinical Research Unit
&  Structural Biology and BioComputing Programme
Spanish National Cancer Research Center (CNIO)
Melchor Fernandez Almagro, 3.
28029 Madrid, Spain.
Phone: [phone] #3015
[hidden email]
http://www.cnio.es

********************************************



**NOTA DE CONFIDENCIALIDAD** Este correo electrónico, y ...{{dropped:7}}


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[plangfelder]

On Wed, Jul 6, 2011 at 8:27 AM, Raquel Martinez Garcia
<[hidden email]> wrote:
> Hi,
>
> I'm running a tutorial ("Meta-analyses of data from two (or more) microarray data sets"), which use wgcna package. I have an error in the function modulePreservation (it is below).
> I'm using R2.13
> Can you help me? Do you know, what is happens?

Hi Raquel,

I'm the author of the function. I see you have already modified the
tutorial with your own input. The error you see may be a bug in the
function, but it may also be due to the fact that you use only 2
permutations. I suggest you try to increase the number of permutations
to at least 10, but for meaningful results you should use at least 50.

HTH,

Peter

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[mistrm]

Hi Peter and Raquel

I am following the same tutorial and seem to have the same error appear and I am using 30 permutations (code below). Is it a bug or something that I can easily fix? I'm not quite sure how to interpret the error.

multiExpr = list(A1=list(data=t(ctl)),A2=list(data=t(sz)))
multiColor = list(A1 = modulesCTL)
mp=modulePreservation(multiExpr, multiColor, referenceNetworks=1, verbose=3, networkType="signed",
        nPermutations=30, maxGoldModuleSize=100, maxModuleSize=400)

Error in zAll[, stat] = z :
  number of items to replace is not a multiple of replacement length

Any help would be great, thanks!
Meeta

[plangfelder]

Hi Meeta,

yes, there was a bug in the package. Please install the newest version
and try again.

Best,

Peter

On Tue, Jul 12, 2011 at 1:20 PM, mistrm <[hidden email]> wrote:

> Hi Peter and Raquel
>
> I am following the same tutorial and seem to have the same error appear and
> I am using 30 permutations (code below). Is it a bug or something that I can
> easily fix? I'm not quite sure how to interpret the error.
>
> multiExpr = list(A1=list(data=t(ctl)),A2=list(data=t(sz)))
> multiColor = list(A1 = modulesCTL)
> mp=modulePreservation(multiExpr, multiColor, referenceNetworks=1, verbose=3,
> networkType="signed",
>        nPermutations=30, maxGoldModuleSize=100, maxModuleSize=400)
>
> Error in zAll[, stat] = z :
>  number of items to replace is not a multiple of replacement length
>
> Any help would be great, thanks!
> Meeta
>
> --
> View this message in context: http://r.789695.n4.nabble.com/wgcna-tp3649354p3663425.html
> Sent from the R help mailing list archive at Nabble.com.
>
> ______________________________________________
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>

--
Sent from my Linux computer. Way better than iPad :)

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[Ingezz]

Dear Peter,

I am trying to apply the WGCNA meta-analysis for two (or more) microarray datasets-tutorial to my own data.

> mp=modulePreservation(multiExpr,multiColor,referenceNetworks=1,verbose=3,networkType="signed",  nPermutations=30,maxGoldModuleSize=100,maxModuleSize=400)

However, the error I am getting is:

Error in .checkExpr(multiData, verbose, indent) :
  The submitted 'multiExpr' data contain genes or samples
   with zero variance or excessive counts of missing entries.
   Please use the function goodSamplesGenes on each set to filter out the problematic
   genes and samples before running modulePreservation.

Seems pretty clear, but applying goodSamplesGenes function results in no exclusions for both arrays.

>  GM2 <- goodSamplesGenes(M2, minFraction = 1/2, minNSamples =8, minNGenes =20)

What could be the issue here? Should I increase the stringency? What stringency should be used with goodsamplegenes for modulepreservation to work?

Many thanks in advance!

Kind regards,
Inge

[plangfelder]

On Fri, Jun 15, 2012 at 8:04 AM, Ingezz <[hidden email]> wrote:

> Dear Peter,
>
> I am trying to apply the WGCNA meta-analysis for two (or more) microarray
> datasets-tutorial to my own data.
>
>> mp=modulePreservation(multiExpr,multiColor,referenceNetworks=1,verbose=3,networkType="signed",
>> nPermutations=30,maxGoldModuleSize=100,maxModuleSize=400)
>
> However, the error I am getting is:
>
> Error in .checkExpr(multiData, verbose, indent) :
>  The submitted 'multiExpr' data contain genes or samples
>   with zero variance or excessive counts of missing entries.
>   Please use the function goodSamplesGenes on each set to filter out the
> problematic
>   genes and samples before running modulePreservation.
>
> Seems pretty clear, but applying goodSamplesGenes function results in no
> exclusions for both arrays.
>
>>  GM2 <- goodSamplesGenes(M2, minFraction = 1/2, minNSamples =8, minNGenes
>> =20)
>
> What could be the issue here? Should I increase the stringency? What
> stringency should be used with goodsamplegenes for modulepreservation to
> work?

Hi Inge,

first, I suggest that in the future you contact me directly, since
others on this list probably have no idea what we're talking about.

It is difficult to diagnose the problem without the data. I suggest
you specify the verbose argument to both functions with a high value
(e.g., 5) which should produce more informative output.

I assume you are aware of the fact that the function goodSamplesGenes
returns a list whose component allOK determines whether any genes or
samples should be removed. Other components in the list specify which
genes and/or samples should be removed. The actual removal needs to be
done by you (I haven't written a function to do that yet but may do so
in the future to make it more convenient). In this sense the error
message is a bit misleading and I will change that as well.

HTH,

Peter

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[Fabrice Tourre]

Peter,

Thank you. In fact, I am also very interesting to WGCNA.

On Sat, Jun 16, 2012 at 3:29 AM, Peter Langfelder
<[hidden email]> wrote:

> On Fri, Jun 15, 2012 at 8:04 AM, Ingezz <[hidden email]> wrote:
>> Dear Peter,
>>
>> I am trying to apply the WGCNA meta-analysis for two (or more) microarray
>> datasets-tutorial to my own data.
>>
>>> mp=modulePreservation(multiExpr,multiColor,referenceNetworks=1,verbose=3,networkType="signed",
>>> nPermutations=30,maxGoldModuleSize=100,maxModuleSize=400)
>>
>> However, the error I am getting is:
>>
>> Error in .checkExpr(multiData, verbose, indent) :
>>  The submitted 'multiExpr' data contain genes or samples
>>   with zero variance or excessive counts of missing entries.
>>   Please use the function goodSamplesGenes on each set to filter out the
>> problematic
>>   genes and samples before running modulePreservation.
>>
>> Seems pretty clear, but applying goodSamplesGenes function results in no
>> exclusions for both arrays.
>>
>>>  GM2 <- goodSamplesGenes(M2, minFraction = 1/2, minNSamples =8, minNGenes
>>> =20)
>>
>> What could be the issue here? Should I increase the stringency? What
>> stringency should be used with goodsamplegenes for modulepreservation to
>> work?
>
> Hi Inge,
>
> first, I suggest that in the future you contact me directly, since
> others on this list probably have no idea what we're talking about.
>
> It is difficult to diagnose the problem without the data. I suggest
> you specify the verbose argument to both functions with a high value
> (e.g., 5) which should produce more informative output.
>
> I assume you are aware of the fact that the function goodSamplesGenes
> returns a list whose component allOK determines whether any genes or
> samples should be removed. Other components in the list specify which
> genes and/or samples should be removed. The actual removal needs to be
> done by you (I haven't written a function to do that yet but may do so
> in the future to make it more convenient). In this sense the error
> message is a bit misleading and I will change that as well.
>
> HTH,
>
> Peter
>
> ______________________________________________
> [hidden email] mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.

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[Ingezz]

Dear Peter,

I have another question about WGCNA. I am using the package for meta-analysis to find modules preserved in several datasets. However, I am unsure how to handle the softpower, because each dataset has its own ideal scale indepence value. When combining several datasets what should I do?
- pick the lowest scale indepence value and use this for all datasets?
- calculate an average scale indepence value of the datasets, and use this one?
- use different scale indepence values for different datasets, all though combining them later on?
- or something else, which I havent thought of?

Hope you can help me!

Many thanks!

Kind regards,
Inge

[plangfelder]

On Wed, Aug 1, 2012 at 6:30 AM, Ingezz <[hidden email]> wrote:

> Dear Peter,
>
> I have another question about WGCNA. I am using the package for
> meta-analysis to find modules preserved in several datasets. However, I am
> unsure how to handle the softpower, because each dataset has its own ideal
> scale indepence value. When combining several datasets what should I do?
> - pick the lowest scale indepence value and use this for all datasets?
> - calculate an average scale indepence value of the datasets, and use this
> one?
> - use different scale indepence values for different datasets, all though
> combining them later on?
> - or something else, which I havent thought of?

Hi Inge,

I'm not sure what you mean by "modules preserved in several datasets".
Are you calculating consensus modules? If so, I would choose
soft-thesholding powers that (a) give approximate scale-free topology
in each data set, and (b) give roughly comparable mean or median
connectivities across the data sets. You may choose a different power
for each data set. However, it is also fine to choose the same power
(such that the network topology is approximately scale-free in each
set) since the consensus module calculation includes a step in which
the input networks are roughly calibrated to make them comparable.

If you are calculating module preservation (function
modulePreservation), the function chooses the standard power by
network type since for module preservation the consistency of
soft-thresholding powers is more important.

Best,

Peter

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