Hi,

`The improvement mentioned by Zhian is available in the version of pegas`

`that is currently on GitHub (which will be the next version on CRAN).`

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Best, Emmanuel Le 15/02/2018 à 16:34, Zhian Kamvar a écrit :

Hi,This is more of a question for the R-SIG-GENETICS group as it no longerdeals with poppr any more, so I'm continuing it here. More knowledgablefolk, feel free to correct me.(1) Could I estimate the relative contribution of variances using SSD?e.g. 40434.10/77188.44=52.38%, It's the contribution of between groupsvariances?sigma2 represents the variance, so you should use that to estimaterelative contribution. The next version of pegas will present the phivalues for you.(2) What is the meaning of "sigma2" and "a"?See above.(3) Is P<0.05 conventionally enough to conclude significant differencefor molecular data ? My result is 0.048 but I doubt it's poweraccording to field observation. Should I set more strict critiria?0.01 for exampleI personally put very little trust in p-values for deciding on whetheror not there is an effect. The choice of 0.05 as the cutoff iswell-known to be arbitrary, so I think you should use your judgement ofwhat you know about the natural history of these populations and yoursample size to aid your conclusions.Hope that helps, Zhian ----- Zhian N. Kamvar, Ph. D. Postdoctoral Researcher (Everhart Lab) Department of Plant Pathology University of Nebraska-Lincoln ORCID: 0000-0003-1458-7108On Feb 13, 2018, at 23:00 , Yu NING <ningyu_s...@qq.com<mailto:ningyu_s...@qq.com>> wrote:Hi, I have some difficulties in interpreting the result of AMOVA. Theresults show:| Bly_amova Analysis of Molecular Variance Call: pegas::amova(formula = Blygl_dis ~ popslot, data = strata(Bly_gl), is.squared = TRUE) SSD MSD df popslot 40434.10 3110.316 13 Error 36754.33 1470.173 25 Total 77188.44 2031.275 38 Variance components: sigma2 P.value popslot 589.75 0.048 Error 1470.17 Variance coefficients: a 2.781065 | I wonder:(1) Could I estimate the relative contribution of variances using SSD?e.g. 40434.10/77188.44=52.38%, It's the contribution of between groupsvariances?(2) What is the meaning of "sigma2" and "a"?(3) Is P<0.05 conventionally enough to conclude significant differencefor molecular data ? My result is 0.048 but I doubt it's poweraccording to field observation. Should I set more strict critiria?0.01 for exampleI've tried to searched for pegas tutorial or forum but failed. Anyadvice here will be highly appreciated. : )在 2018年2月12日星期一 UTC+8下午11:31:06，Zhian Kamvar写道： Hello, I think I know why you were having this problem: 1. You didn't have anything defined in your strata [1]. 2. You didn't have a variable called "pop" defined in your workspace 3. Pegas saw a variable called "pop", which was a function, and gave a warning ("elements in the rhs of the formula are not all factors") However, given your code below, I'm not exactly sure why it works because "popslot" is not the same as "popslot_Kob". This tells me that you may run into problems in the future. You can define your strata with a data frame where each row represents a sample and each column is a different population grouping. A simple way to define it for the population: strata(Kob_snpclo) <- data.frame(population = pop(Kob_snpclo)) From there, you would use "population" on the right hand side of the formula in AMOVA. Cheers, Zhian [1]: If you are unfamiliar with strata, check out the strata tutorial: https://github.com/thibautjombart/adegenet/wiki/Tutorials <https://github.com/thibautjombart/adegenet/wiki/Tutorials> ----- Zhian N. Kamvar, Ph. D. Postdoctoral Researcher (Everhart Lab) Department of Plant Pathology University of Nebraska-Lincoln ORCID: 0000-0003-1458-7108On Feb 12, 2018, at 04:47 , Yu NING <ningy...@qq.com <http://qq.com/>> wrote: Now I have found one way to work it out: | popslot_Kob<-pop(Kob_snpclo) Blygl_dis<-bitwise.dist(Bly_gl,percent=FALSE) Bly_amova<-pegas::amova(Blygl_dis~popslot,data=strata(Bly_gl),is.squared=TRUE) | Just add one line to extract the pop information. But I still wonder why error showed when I use: | Bly_amova<-pegas::amova(Blygl_dis~pop,data=strata(Bly_gl),is.squared=TRUE) | It's a little wired.... 在 2018年2月11日星期日 UTC+8上午11:26:11，Yu NING写道： Hello, Thanks for your help. But other problems showed up: | > Blygl_dis<- bitwise.dist(Bly_gl, percent = FALSE) > Bly_amova<- pegas::amova(Blygl_dis ~ pop, data = strata(Bly_gl), is.squared = TRUE) Error in FUN(X[[i]], ...) : 'bin' must be numeric or a factor In addition: Warning message: In pegas::amova(Blygl_dis ~ pop, data = strata(Bly_gl), is.squared = TRUE) : elements in the rhs of the formula are not all factors | Is there something wrong in my data organization? This is the data info: | > Bly_gl /// GENLIGHT OBJECT ///////// // 39 genotypes, 7,227 binary SNPs, size: 969.5 Kb 11255 (3.99 %) missing data // Basic content @gen: list of 39 SNPbin @ploidy: ploidy of each individual (range: 2-2) // Optional content @ind.names: 39 individual labels @loc.names: 7227 locus labels @chromosome: factor storing chromosomes of the SNPs @position: integer storing positions of the SNPs @pop: population of each individual (group size range: 2-3) @other: a list containing: elements without names | 在 2018年1月23日星期二 UTC+8下午11:38:39，Zhian Kamvar写道： Hello,I have browsed the manual but I couldn't find how to calculate metrics of differentiation in/ poppr/ , and /poppr.amova()/ is also not applicable for snpclone object. Is there a way to solve it ?Unfortunately, calculating AMOVA on genlight objects is not currently possible in poppr [1]. I would recommend to use the pegas implementation like so: library("pegas") myDist <- bitwise.dist(myGenlight, percent = FALSE) res <- pegas::amova(myDist ~ pop/subpop, data = strata(myGenlight), is.squared = TRUE) Replace "myGenlight" with the name of your data and "pop/subpop" with the hierarchy present in your strata slot.Moreover, If I want to export my snpclone object whichis MLG filtered, how should I code?If you want to export the object for use in another Rscript, I would recommend using saveRDS() to save the object to a file and then readRDS() to read that file in a new session*. If you only need the MLG assignments, then he underlying genetic data does not change when you filter MLGs, so you can export the assignments as a CSV file along with your individual names and population data. I hope that helps. Zhian [1]: https://github.com/grunwaldlab/poppr/issues/72 <https://github.com/grunwaldlab/poppr/issues/72>. This is not a simple problem and may still take a bit longer to implement as I am now only working on poppr in my spare time. * If you defined your MLGs using a matrix, then you must also save that matrix and reload it with the same name. ----- Zhian N. Kamvar, Ph. D. Postdoctoral Researcher (Everhart Lab) Department of Plant Pathology University of Nebraska-Lincoln ORCID: 0000-0003-1458-7108On Jan 23, 2018, at 08:51 , Yu NING <ningy...@qq.com <http://qq.com/>> wrote: Hi~ I have browsed the manual but I couldn't find how to calculate metrics of differentiation in/poppr/ , and/poppr.amova()/is also not applicable for snpclone object. Is there a way to solve it ? Moreover, If I want to export my snpclone object which is MLG filtered, how should I code? Thanks very much -- You received this message because you are subscribed to the Google Groups "poppr" group. 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