[BioC] maSigPro and "vars" argument

[andrea.grilli at ior.it]

Hi María,
thank you for your detailed explanation.

Only some doubt remain regarding point 3: looking at my data as  
example, I get 6
different variables (and than 6 different results), but it's difficult  
to me to
understand to what correspond each variable, in particular how to  
manage the 6 different
groups of clustering I get as results.
Maybe my problem is to understand how variable itself is created: I  
know it comes from
regression model, but nothing more.

Thanks in advance,
Andrea



Citando Mª José Nueda <mj.nueda at ua.es>:

> Dear Andrea,
>
> 1) Your experimental design is correct.
> 2) Your explanation about the 2 groups you have when vars="groups" is
> also correct. Normally the first group is a reference (the control
> group) and maSigPro looks for genes that have differences between other
> treatments and the control. If you want to find genes with changes in
> time for the second group you can make 2 things: -Selecting group2 as
> the reference (first group) or, as you say, spliting the data in 2
> groups. But this last option doesn't give you genes with differences
> between groups.
> 3) Using vars="each" you get a many lists as variables you have in the
> model. The meaning "biologically speaking" depends on the study. This
> is an option that allows look for specific questions (differents to
> "all" or "groups") that a user can be interested in.  For instance,  if
> you are looking for all the genes with linear changes but not quadratic
> changes or whatever. You can manage these lists of genes to get the
> question you desire.
>
> If you don't understand my answer, please contact me again. Thank you
> for using maSigPro.
>
> María J. Nueda.
>
> --------------------------------------------------
> From: <andrea.grilli at ior.it>
> Sent: Thursday, September 08, 2011 5:41 PM
> To: <bioconductor at r-project.org>
> Subject: [BioC] maSigPro and "vars" argument
>
>> Hi to all,
>> I'm analyzing time series experiment with maSigPro package as first  
>>   time, and I get problems to understand if experimental design is   
>>  correct or not, in particular I'm doubtful with "vars" argument.
>>
>> Data comes from Affymetrix gene chip from 2 different cell lines, 4  
>>   time points, 2 replicates at each time. I normalized with RMA,  
>> and   filtered out low expressed/low changing genes, getting from   
>> initial  54k probes about 12k probes.
>>
>> I'm interested in genes varying (i)in either cell lines between the  
>>  different time points (ii) between the two cell lines across time.
>>
>> I did the analysis with vars argument as "groups", getting these   
>> comparisons:
>>> (ts.analysis$sig.genes$)
>> ts.analysis$sig.genes$Group1        ts.analysis$sig.genes$Group2vsGroup1
>>
>> So, If I well understood, I have 2 gene sets of significant genes,   
>> the first with those changing across time in Group1 cells, the   
>> second with those changing in Group2 vs Group1 cells across time.
>>
>> My questions: how can I also get significant genes for Group2??   
>> Should I split the experiment in two parts and performing separately?
>> Last question: using vars = "each", what I exactly get? I mean   
>> biologically speaking...
>>
>>
>>
>> This is my design matrix:
>>          Time Replicates Group1 Group2
>> wt22_g21    21          1            1    0
>> wt22_g7      7          2            1    0
>> wt36_g21    21          1            1    0
>> wt36_g7      7          2            1    0
>> Saos1_g21   21          5            0    1
>> Saos2_g21   21          5            0    1
>> Saos1_g7     7          6            0    1
>> Saos2_g7     7          6            0    1
>> wt22_g0      0          3            1    0
>> wt22_g14    14          4            1    0
>> wt36_g0      0          3            1    0
>> wt36_g14    14          4            1    0
>> Saos1_g0     0          7            0    1
>> Saos2_g0     0          7            0    1
>> Saos1_g14   14          8            0    1
>> Saos2_g14   14          8            0    1
>>
>> This is the command line:
>>> ts.analysis <- maSigPro (Data, parameters2, min.obs=4, rsq=0.7,   
>>> step.method="backward", pdf = TRUE, main = "./results.pdf", alfa =  
>>>   0.05, degree = 2, k = 9, vars = "groups")
>>
>> I checked in Bioconductor documentation, but things remain confused to me.
>> Any clarification is really appreciated,
>> Thanks,
>> Andrea
>>