comparison of two sets of developmental study
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yan zhou ▴ 80
@yan-zhou-2479
Last seen 10.2 years ago
Hi, All, I have two microarray datasets. Each set have data from 8 different developmental stages( A-H, each stage have at least 3-4 biological replicates); The developmental stage are the same between the two; The only difference between the two are the tissue source. It's known that the two development are similar but not the same. I was wondering if anyone would have good suggestions as to 1. how to capture genes that are changing along development in each tissure seperately, 2. and also how to capture genes behave totally different between the two developmental courses. Thank you. Yan
Microarray Microarray • 1.2k views
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@steve-lianoglou-2771
Last seen 21 months ago
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Hi, Some comments in line, but as a general note I feel like your questions are a bit vague: On Thu, Apr 15, 2010 at 2:23 PM, Yan Zhou <yan.zhou at="" fccc.edu=""> wrote: > Hi, All, > > I have two microarray datasets. Each set have data from ?8 different > developmental stages( A-H, each stage have at least 3-4 biological > replicates); The developmental stage are the same between the two; The only > difference between the two are the tissue source. It's known that the two > development are similar but not the same. I was wondering if anyone would > have good suggestions as to > 1. how to capture genes that are changing along development in each tissure > seperately, What does it mean to be "changing in each tissue separately". Can you put this in more precise terms? Are you looking for genes that show, say, anti-correlated expression patterns between the two tissue types across your time course? Or do you want to look at tissue A, and see which genes are differentially expressed from time 0 to time 1, 2, etc. > 2. ?and also how to capture genes behave totally different between the two > developmental courses. Maybe you mean anti-correlated here? -steve -- Steve Lianoglou Graduate Student: Computational Systems Biology | Memorial Sloan-Kettering Cancer Center | Weill Medical College of Cornell University Contact Info: http://cbio.mskcc.org/~lianos/contact
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Hi, Steve, Thank you for your reply. Sorry that I didn't make it clear. My interests are two layers: 1. Genes changing along development in tissue A and B seperately; More precisely, genes change in A along development; Same as in tissure B; 2. Anti-correlated expression patterns as you mentioned in the two tissues in order to find key players which drive the two development differently. Any suggestions will be appreciated! Yan Steve Lianoglou wrote: >Hi, > >Some comments in line, but as a general note I feel like your >questions are a bit vague: > >On Thu, Apr 15, 2010 at 2:23 PM, Yan Zhou <yan.zhou at="" fccc.edu=""> wrote: > > >>Hi, All, >> >>I have two microarray datasets. Each set have data from 8 different >>developmental stages( A-H, each stage have at least 3-4 biological >>replicates); The developmental stage are the same between the two; The only >>difference between the two are the tissue source. It's known that the two >>development are similar but not the same. I was wondering if anyone would >>have good suggestions as to >>1. how to capture genes that are changing along development in each tissure >>seperately, >> >> > >What does it mean to be "changing in each tissue separately". > >Can you put this in more precise terms? Are you looking for genes that >show, say, anti-correlated expression patterns between the two tissue >types across your time course? > >Or do you want to look at tissue A, and see which genes are >differentially expressed from time 0 to time 1, 2, etc. > > > >>2. and also how to capture genes behave totally different between the two >>developmental courses. >> >> > >Maybe you mean anti-correlated here? > >-steve > > >
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Hi, On Mon, Apr 19, 2010 at 10:54 AM, Yan Zhou <yan.zhou at="" fccc.edu=""> wrote: > Hi, Steve, > > Thank you for your reply. Sorry that I didn't make it clear. My interests > are two layers: > > 1. Genes changing along development in tissue A ?and B seperately; More > precisely, genes change in A along development; Same as in tissure B; It sounds like you want to call genes that are differentially expressed along your time series. You can use limma for that, but there are also several BioC packages that specifically deal with timecourse data, such as: * maSigPro: http://bioconductor.org/packages/2.5/bioc/html/maSigPro.html and http://bioinformatics.oxfordjournals.org/cgi/content/full/22/9/1096 * betr: http://bioconductor.org/packages/2.5/bioc/html/betr.html The vignettes that accompany them have references to publications you can read to figure out what others are doing. > 2. Anti-correlated expression patterns as you mentioned in the two tissues > in order to find key players which drive the two development differently. Assuming you have expr.a, and expr.b, which are the normalized expression datasets (rows=genes, cols=times) for tissue a and b, respectively, I reckon you can simply run cor.test along each row-pair from the two matrices and look for highly negative correlation coefficients w/ significant p.values as a start, no? For example: R> cors <- lapply(1:nrow(expr.a), function(i) { cor.test(expr.a[i,], expr.b[i,]) }) R> interesting <- which(sapply(cors, function(x) x$p.value < 0.05 && x$estimate < 0)) `interesting` will have the indices of rows in the expression matrix that have uncorrected p-values < 0.05 and are negative. -steve -- Steve Lianoglou Graduate Student: Computational Systems Biology | Memorial Sloan-Kettering Cancer Center | Weill Medical College of Cornell University Contact Info: http://cbio.mskcc.org/~lianos/contact
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Steve, Thank you for your suggestions. For my developmental data, the time between each stage are not measureable. I do know the sequence of development, but don't know the time it takes to go from stage 1 to 2, or 2 to 3 and so on. Can I still treat it like time course data? You suggestions about the cor.test is pretty smart. I'll try that. Thank you. Yan Steve Lianoglou wrote: >Hi, > >On Mon, Apr 19, 2010 at 10:54 AM, Yan Zhou <yan.zhou at="" fccc.edu=""> wrote: > > >>Hi, Steve, >> >>Thank you for your reply. Sorry that I didn't make it clear. My interests >>are two layers: >> >>1. Genes changing along development in tissue A and B seperately; More >>precisely, genes change in A along development; Same as in tissure B; >> >> > >It sounds like you want to call genes that are differentially >expressed along your time series. > >You can use limma for that, but there are also several BioC packages >that specifically deal with timecourse data, such as: > >* maSigPro: http://bioconductor.org/packages/2.5/bioc/html/maSigPro.html >and http://bioinformatics.oxfordjournals.org/cgi/content/full/22/9/1096 > >* betr: http://bioconductor.org/packages/2.5/bioc/html/betr.html > >The vignettes that accompany them have references to publications you >can read to figure out what others are doing. > > > >>2. Anti-correlated expression patterns as you mentioned in the two tissues >>in order to find key players which drive the two development differently. >> >> > >Assuming you have expr.a, and expr.b, which are the normalized >expression datasets (rows=genes, cols=times) for tissue a and b, >respectively, I reckon you can simply run cor.test along each row- pair >from the two matrices and look for highly negative correlation >coefficients w/ significant p.values as a start, no? For example: > >R> cors <- lapply(1:nrow(expr.a), function(i) { > cor.test(expr.a[i,], expr.b[i,]) >}) >R> interesting <- which(sapply(cors, function(x) x$p.value < 0.05 && >x$estimate < 0)) > >`interesting` will have the indices of rows in the expression matrix >that have uncorrected p-values < 0.05 and are negative. > >-steve > > >
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Hi Yan, On Mon, Apr 19, 2010 at 3:40 PM, Yan Zhou <yan.zhou at="" fccc.edu=""> wrote: > Steve, > > Thank you for your suggestions. For my developmental data, the time between > each stage are not measureable. I do know the sequence of development, but > don't know the time it takes to go from stage 1 to 2, or 2 to 3 and so on. > Can I still treat it like time course data? Honestly I'm not real familiar with time-course analysis/literature, or these packages. In working with some timecourse data in the past, I was looking at differentially expressed genes between each time point compared with time-0. I'm not saying that's the correct thing to do, but if you want to do that, you can do it with limma and an appropriate contrasts matrix. Perhaps others can comment who have more experience with these analyses. -steve -- Steve Lianoglou Graduate Student: Computational Systems Biology | Memorial Sloan-Kettering Cancer Center | Weill Medical College of Cornell University Contact Info: http://cbio.mskcc.org/~lianos/contact
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