At 12:17 AM 1/22/2005, Gordon Smyth wrote: >Having within-array replicate spots on your arrays makes no difference at >all to the design and contrast matrices. (With one exception, which is >that you can't fit a random block effect in limma and estimate a duplicate >spot correlation at the same time.) Is there something which has caused >you to become concerned about this? I was originally going to subtract out t_0 from my t_6, t_24, and t_72 as my experiments are against a universal, and express all my ratios relative to t_0. Ann Muller pointed out to me that the issue of a randomized block. Now since I am not from a strong statistical blood-line (I am more of a programmer and biochemist, than a stats person), I now need to go read up on randomized block designs because I don't know much about them. So this is how the randomized block thing came about, not that I knew anything about randomized block designs. So I guess in my case I do have replicate spots and yes it seems that I could apply a randomized block in my case, but as you pointed out limma does not support this. I had not realized how to get the duplicate spot correlations done _at the same time_ as calculating the contrasts, I was looking at example 14.1 and got confused. I read through all the function descriptions and found that I could include from the start the 'ndups' with: RG$printer <- getLayout(RG$genes, guessdups=TRUE) which took takes care of the dupes for me. I am ok with this issue now. I have spent more time with the documentation in general and I think I am getting a better handle on how limma works. I will have to practice with some basic statistical examples to get used to interpreting the statistics and knowing what models to apply. >I suggest you try accommodating the donor effect simply by including a set >of coefs for the donor effects in your design matrix. You form the design >matrix as you would for an additive two-way anova with donor as one of the >two factors. Comparisons between infections, infect types, and infect >times will then be in effect made _within_ donor. I will try this. Thanks Peter >Gordon > >>Date: Thu, 20 Jan 2005 10:48:21 -0500 >>From: Pita <pwilkinson_m@xbioinformatics.org> >>Subject: [BioC] Using limma with contrast matrix ,replicate spots, >> donor effects >>To: bioconductor <bioconductor@stat.math.ethz.ch> >> >>This question is because I am misunderstanding how certain things fit >>together in Limma. There is no example like this in the documentation, and >>I am trying to figure out how to do this based on examples section 10.5 >>and 14.1. >> >>sorry for the lengthy post, this is a complicated one, but it might be an >>interesting case example for some of you. >> >>A simplified version of my experiment follows >> >>Background: >> >>Blood from 8 separate donors have been collected and undergone a cell sort. >>The sorted cells that we are interested in were divided and infected with >>HIV according to the following table (the letters do not mean the literal >>HIV subtype in this case, I have just simplified it to A,B,C and >>N=non-infected.). >> >>Filename Cy3 Cy5 Donor >>1 Ref N_0 1 >>2 Ref N_6 1 >>3 Ref N_24 1 >>4 Ref N_74 1 >>5 Ref A_0 1 >>6 Ref A_6 1 >>7 Ref A_24 1 >>8 Ref A_74 1 >>9 Ref B_0 1 >>10 Ref B_6 1 >>11 Ref B_24 1 >>12 Ref B_74 1 >>13 Ref C_0 1 >>14 Ref C_6 1 >>15 Ref C_24 1 >>16 Ref C_72 1 >>...for 7 more donors >> >>- I have a series of 2 channel array hybridizations against a common >>reference >>- the array used uses DUPLICATE spots (spacially spotted in pairs). >>- N is non-infected(this exp its HIV), >>- A,B,C are three different infection types >>- 0,6,24 are the times that the cells were harvested and RNA isolated. >>- A_0 is infected at time 0 which is different from non-infected 0 (N_0) >>in that A_0 is after 2 hours of incubation with the virus. >>- Total of 8 donors >> >>The question I have is how to deal with the ' donor effect' using Limma. >>First case (1): I could assume that my donor variability is much less than >>the variability in the treatments and just plow ahead(probably worth >>trying). In the second case (2), the problem being that there can be quite >>the donor variability so I am thinking that what might be better is if I >>subtract the 0 time point for each infection type WITHIN each donor from >>all the others so that all expression values are relative to 0: >> >>For >>example Donor1 N_72-N_0, N_24-N_0, N_6-N_0, A_72-A_0, >>A_24-A_0, A_6-A_0, etc >> Donor1 >>N_72-N_0, N_24-N_0, N_6-N_0, A_72-A_0, A_24-A_0, A_6-A_0, etc >> >> >>I would like to compare the difference between each donor for the >>non-infected N to characterize the donor variability so that I understand >>it and I would like to compare the infection types for each time point in >>the 2 different ways (cases). My ultimate goal it to compare the infection >>types at each time point against each other while reducing the noise due to >>donor variability. >> >>There are 2 things i need to know how to do >> >>How do I combine creating the contrast matrix and use it with calculating >>duplicate spot correlation in 14.1, for case 1? >>How do I create a contrast matrix to account for normalising against time 0 >>as in case (2) and then combine that with the duplicate spot correlation? >> >> >>lastly, are there in fact other proven methods for dealing with donor >>variability ? >> >>Thanks for any insight. >> >>Peter W. > > >

I am assuming that for the design matrix, I just need to add a column for each donor and plug in the 1's for the appropriate rows to my existing design that specifies the infection type timepoints (A_0,A_6, ... C_42,C_72, Donor1, Donor2, etc ...) ??? Is this correct? Peter At 12:17 AM 1/22/2005, Gordon Smyth wrote: >Having within-array replicate spots on your arrays makes no difference at >all to the design and contrast matrices. (With one exception, which is >that you can't fit a random block effect in limma and estimate a duplicate >spot correlation at the same time.) Is there something which has caused >you to become concerned about this? > >I suggest you try accommodating the donor effect simply by including a set >of coefs for the donor effects in your design matrix. You form the design >matrix as you would for an additive two-way anova with donor as one of the >two factors. Comparisons between infections, infect types, and infect >times will then be in effect made _within_ donor. > >Gordon > >>Date: Thu, 20 Jan 2005 10:48:21 -0500 >>From: Pita <pwilkinson_m@xbioinformatics.org> >>Subject: [BioC] Using limma with contrast matrix ,replicate spots, >> donor effects >>To: bioconductor <bioconductor@stat.math.ethz.ch> >> >>This question is because I am misunderstanding how certain things fit >>together in Limma. There is no example like this in the documentation, and >>I am trying to figure out how to do this based on examples section 10.5 >>and 14.1. >> >>sorry for the lengthy post, this is a complicated one, but it might be an >>interesting case example for some of you. >> >>A simplified version of my experiment follows >> >>Background: >> >>Blood from 8 separate donors have been collected and undergone a cell sort. >>The sorted cells that we are interested in were divided and infected with >>HIV according to the following table (the letters do not mean the literal >>HIV subtype in this case, I have just simplified it to A,B,C and >>N=non-infected.). >> >>Filename Cy3 Cy5 Donor >>1 Ref N_0 1 >>2 Ref N_6 1 >>3 Ref N_24 1 >>4 Ref N_74 1 >>5 Ref A_0 1 >>6 Ref A_6 1 >>7 Ref A_24 1 >>8 Ref A_74 1 >>9 Ref B_0 1 >>10 Ref B_6 1 >>11 Ref B_24 1 >>12 Ref B_74 1 >>13 Ref C_0 1 >>14 Ref C_6 1 >>15 Ref C_24 1 >>16 Ref C_72 1 >>...for 7 more donors >> >>- I have a series of 2 channel array hybridizations against a common >>reference >>- the array used uses DUPLICATE spots (spacially spotted in pairs). >>- N is non-infected(this exp its HIV), >>- A,B,C are three different infection types >>- 0,6,24 are the times that the cells were harvested and RNA isolated. >>- A_0 is infected at time 0 which is different from non-infected 0 (N_0) >>in that A_0 is after 2 hours of incubation with the virus. >>- Total of 8 donors >> >>The question I have is how to deal with the ' donor effect' using Limma. >>First case (1): I could assume that my donor variability is much less than >>the variability in the treatments and just plow ahead(probably worth >>trying). In the second case (2), the problem being that there can be quite >>the donor variability so I am thinking that what might be better is if I >>subtract the 0 time point for each infection type WITHIN each donor from >>all the others so that all expression values are relative to 0: >> >>For >>example Donor1 N_72-N_0, N_24-N_0, N_6-N_0, A_72-A_0, >>A_24-A_0, A_6-A_0, etc >> Donor1 >>N_72-N_0, N_24-N_0, N_6-N_0, A_72-A_0, A_24-A_0, A_6-A_0, etc >> >> >>I would like to compare the difference between each donor for the >>non-infected N to characterize the donor variability so that I understand >>it and I would like to compare the infection types for each time point in >>the 2 different ways (cases). My ultimate goal it to compare the infection >>types at each time point against each other while reducing the noise due to >>donor variability. >> >>There are 2 things i need to know how to do >> >>How do I combine creating the contrast matrix and use it with calculating >>duplicate spot correlation in 14.1, for case 1? >>How do I create a contrast matrix to account for normalising against time 0 >>as in case (2) and then combine that with the duplicate spot correlation? >> >> >>lastly, are there in fact other proven methods for dealing with donor >>variability ? >> >>Thanks for any insight. >> >>Peter W. > >