Hi Andrea, In the regression model of each gene you will have included the "statistically significant" variables that are the variables that make change the response (gene-expression). For each gene you can have several significant variables. Examples: - A gene with flat profile for group1 and linear trend for group2, in the model: time and time^2 will not be significant and time_group2 will be significant. - A gene with linear profile for group1 and quadratic profile for group2, will have time, time2_group2 significant and time2 no significant. Anyway this is difficult to interpret. I think that if you are looking for genes that change in whatever way you have to choose vars="all" and you will have only one group with significant genes. If you need an specific question, then you have to decide how to use the 6 groups of genes you have when vars="each". Good luck, Mar?a. -------------------------------------------------- From: <andrea.grilli@ior.it> Sent: Monday, September 12, 2011 2:59 PM To: "M? Jos?Nueda" <mj.nueda at="" ua.es=""> Cc: <bioconductor at="" r-project.org=""> Subject: Re: [BioC] maSigPro and "vars" argument > Hi Mar?a, > 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 M? Jos? 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. >> >> Mar?a 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 >>> >