Time course experiment with limma
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@cecilia-mcgregor-1508
Last seen 9.6 years ago
I'm planning a time course experiment and was told that with the design I plan I would not be able to use limma for analysis and need to use MANOVA. I want to know whether this is true and whether there is a different experimental design I can use that would make it possible for me to use limma. (Simply 'cause I've used limma before , but never used MANOVA). Any other comments about the experiment and experimental design is also welcome. Here follows a somewhat lenthy description of the experiment. The treatments are: (1) uninfected plants (2) plants infected with SPFMV-RC (a strain that leads to SPVD in dual infections) alone, (3) SPFMV-C (a strain that does not cause SPVD in dual infections) alone, (4) plants infected with SPCSV alone, (5) plants infected with SPFMV-RC and SPCSV together (SPVD), (6) plants infected with SPFMV-C and SPCSV together (No SPVD). We are trying to figure out what it is that happens to the plants defense system that allows for the severe disease in the dual infection (SPFMV-RC and SPCSV). But we are also interested in the development of the disease over time. We therefore interested in both comparisons of treatment groups within each time point and comparison of time points in each group. The 5 time points will be: 2 days after infections (DAI), 5 DAI, 10 DAI, 15 DAI, 20 DAI). We are collecting from the same individuals (plants) for all timepoints. So if I say we have 3 biological replications per treatment, it means that on Day 0, I inoculate 3 plants for each treatment, and these 3 plants are used for all samples throughout the time of the experiment. But when I sample I take whole leaves, so that means that I have to take different leaves every time I samples. So, same plants for all timepoints, but different leaves. In the greenhouse the plants are in a completely randomized design. Recap of information I gave in previous e-mail > - I have 6 treatments (including the untreated), > - 3 biological replicates per treatment per timepoint > - 5 timepoints > - The problem is that we have only 60 arrays! The original experiment was planned with 120 arrays, but the price from the supplier doubled from a year ago when we did our other experiments. > - These are two color cDNA arrays. The experimental design that I plan to use: (1) Loops comparing DAI within each treatment: For each treatment, choose a single biological replication and connect its mRNA samples from different DAI using a loop. Note that each mRNA sample goes in two slides (with alternating labeling). Each loop will use 5 slides, and you?ll have 6 of those loops with a total of 30 slides comparing DAIs. (2) Loops comparing treatments within each time point: For each time point (DAI), choose two biological replications from each treatment and connect the mRNA samples following the structure given below. This is also a loop, but not a ?connected loop? as above. This kind of loop favors biological replication over technical replication. Here, note that each mRNA sample goes in one slide only, but there is still balance in labeling across treatments. There will be 5 loops of 6 slides each, with a total of 30 slides for comparing treatments. Can this type of design be analyzed with limma? I was told that I would have to use MANOVA. Is there a more appropriate design for this experiment, or a different appropriate design that could be analyzed with limma? Any help would be very much appreciated. Cecilia McGregor Post-Doc Sweetpotato Breeding and Genetics Lab JC Miller Hall room 236 Louisiana State University Baton Rouge LA, 70803 USA Phone: (225) 578 2173
Genetics limma Genetics limma • 1.2k views
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Naomi Altman ★ 6.0k
@naomi-altman-380
Last seen 2.9 years ago
United States
Dear Cecilia You cannot analyze this design properly in limma because you have 3 sources of correlation - 2 samples from the same RNA, 2 channels on the same array, and multiple time points on the same plant. Limma allows only 1 source of correlation. I have not used MAANOVA, but I am familiar with the statistical method used, and to me it seems like a very good alternative to limma. (NOT MANOVA, which is different.) 2 notes based on my experience: 1) You are wasting half your arrays by having 2 samples from each RNA. 2) If you are taking multiple samples from the same plants, you need to be sure that the wound response has died out between time points. --Naomi At 03:40 PM 11/28/2006, Cecilia McGregor wrote: >I'm planning a time course experiment and was told that with the >design I plan I would not be able to use limma for analysis and need >to use MANOVA. I want to know whether this is true and whether there >is a different experimental design I can use that would make it >possible for me to use limma. (Simply 'cause I've used limma before >, but never used MANOVA). Any other comments about the experiment >and experimental design is also welcome. > >Here follows a somewhat lenthy description of the experiment. > >The treatments are: >(1) uninfected plants >(2) plants infected with SPFMV-RC (a strain that leads to SPVD in >dual infections) alone, >(3) SPFMV-C (a strain that does not cause SPVD in dual infections) alone, >(4) plants infected with SPCSV alone, >(5) plants infected with SPFMV-RC and SPCSV together (SPVD), >(6) plants infected with SPFMV-C and SPCSV together (No SPVD). > >We are trying to figure out what it is that happens to the plants >defense system that allows for the severe disease in the dual >infection (SPFMV-RC and SPCSV). > >But we are also interested in the development of the disease over time. > >We therefore interested in both comparisons of treatment groups >within each time point and comparison of time points in each group. > >The 5 time points will be: 2 days after infections (DAI), 5 DAI, 10 >DAI, 15 DAI, 20 DAI). > >We are collecting from the same individuals (plants) for all >timepoints. So if I say we have 3 biological replications per >treatment, it means that on Day 0, I inoculate 3 plants for each >treatment, and these 3 plants are used for all samples throughout >the time of the experiment. But when I sample I take whole leaves, >so that means that I have to take different leaves every time I >samples. So, same plants for all timepoints, but different leaves. > >In the greenhouse the plants are in a completely randomized design. > >Recap of information I gave in previous e-mail > > - I have 6 treatments (including the untreated), > > - 3 biological replicates per treatment per timepoint > > - 5 timepoints > > - The problem is that we have only 60 arrays! The original > experiment was planned with 120 arrays, but the price from the > supplier doubled from a year ago when we did our other experiments. > > - These are two color cDNA arrays. > > >The experimental design that I plan to use: >(1) Loops comparing DAI within each treatment: For each treatment, >choose a single biological replication and connect its mRNA samples >from different DAI using a loop. Note that each mRNA sample goes in >two slides (with alternating labeling). Each loop will use 5 slides, >and you'll have 6 of those loops with a total of 30 slides comparing DAIs. >(2) Loops comparing treatments within each time point: For each time >point (DAI), choose two biological replications from each treatment >and connect the mRNA samples following the structure given below. >This is also a loop, but not a "connected loop" as above. This kind >of loop favors biological replication over technical replication. >Here, note that each mRNA sample goes in one slide only, but there >is still balance in labeling across treatments. There will be 5 >loops of 6 slides each, with a total of 30 slides for comparing treatments. > >Can this type of design be analyzed with limma? I was told that I >would have to use MANOVA. Is there a more appropriate design for >this experiment, or a different appropriate design that could be >analyzed with limma? > >Any help would be very much appreciated. > >Cecilia McGregor > >Post-Doc >Sweetpotato Breeding and Genetics Lab >JC Miller Hall room 236 >Louisiana State University >Baton Rouge >LA, 70803 >USA > >Phone: (225) 578 2173 > >_______________________________________________ >Bioconductor mailing list >Bioconductor at stat.math.ethz.ch >https://stat.ethz.ch/mailman/listinfo/bioconductor >Search the archives: >http://news.gmane.org/gmane.science.biology.informatics.conductor Naomi S. Altman 814-865-3791 (voice) Associate Professor Dept. of Statistics 814-863-7114 (fax) Penn State University 814-865-1348 (Statistics) University Park, PA 16802-2111
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