Question: Using limma with contrast matrix ,replicate spots, donor effects
0
14.8 years ago by
Pita120
Pita120 wrote:
Thank you Anne I will look at that example, and try to work things out today. The one thing I dont understand is how to combine creating the correlations for the duplicate spots in the chips with a contrast matrix, or even if I need to? Peter At 11:11 AM 1/20/2005, Arne.Muller@sanofi-aventis.com wrote: >Hello, > >Maybe the Donor effect is a random effect. You could give it a go with a >mixed effects model in R > > lme(..., random = ~ 1|Donor) > >Gordon Smyth once pointed out to me and others on this list that this >would be similar to randomized block model that's implemented in limma >(section 10.3 of the limma guide). > >See https://stat.ethz.ch/pipermail/bioconductor/2004-December/006998.html >for the complete posting. > > regards, > > Arne > > > -----Original Message----- > > From: bioconductor-bounces@stat.math.ethz.ch > > [mailto:bioconductor-bounces@stat.math.ethz.ch]On Behalf Of Pita > > Sent: 20 January 2005 16:48 > > To: bioconductor > > Subject: [BioC] Using limma with contrast matrix ,replicate > > spots, donor > > effects > > > > > > 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. > > > > _______________________________________________ > > Bioconductor mailing list > > Bioconductor@stat.math.ethz.ch > > https://stat.ethz.ch/mailman/listinfo/bioconductor > >
hiv limma • 681 views
modified 14.8 years ago by Gordon Smyth39k • written 14.8 years ago by Pita120
Answer: Using limma with contrast matrix ,replicate spots, donor effects
0
14.8 years ago by
Gordon Smyth39k
Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
Gordon Smyth39k 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.
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. > >
Answer: Using limma with contrast matrix ,replicate spots, donor effects
0
14.8 years ago by
Gordon Smyth39k
Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
Gordon Smyth39k wrote:
At 06:50 AM 25/01/2005, Pita wrote: >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? That sounds correct. Gordon >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.