knitr::opts_chunk$set(cache=TRUE) knitr::opts_chunk$set(autodep=FALSE) knitr::opts_chunk$set(echo=TRUE) knitr::opts_chunk$set(error=FALSE) knitr::opts_chunk$set(warning=FALSE) knitr::opts_chunk$set(message=FALSE)
data = TESS3enchoSen::sampleTESS2.3(100, 20000, 1, 3 , TESS3enchoSen::SampleDistDFromCenterQ(0.1)) tmpFile = tempfile() tmpFile.geno = paste0(tmpFile,".geno") tmpFile.coord = paste0(tmpFile,".coord") LEA::write.geno(data$X,tmpFile.geno) write.table(data$coord,tmpFile.coord,row.names = FALSE, col.names = FALSE)
# With the good K tess3r.obj = tess3r::TESS3(tmpFile.geno, tmpFile.coord, K = 3, ploidy = 1) tess3enchosen.obj = TESS3enchoSen::TESS3(data$X,data$coord, K=3, ploidy = 1, lambda = 1.0) BioCompToolsR::rmse_withBestPermutation(tess3r::Q(tess3r.obj,K = 3, run = 1), tess3enchosen.obj$Q) BioCompToolsR::rmse_withBestPermutation(tess3r::G(tess3r.obj,K = 3, run = 1), tess3enchosen.obj$G) # With an other K K = 5 tess3r.obj = tess3r::TESS3(tmpFile.geno, tmpFile.coord, K = K, ploidy = 1) tess3enchosen.obj = TESS3enchoSen::TESS3(data$X,data$coord, K=K, ploidy = 1, lambda = 1.0, max.iteration = 35) BioCompToolsR::rmse_withBestPermutation(tess3r::Q(tess3r.obj,K = K, run = 1), tess3enchosen.obj$Q) BioCompToolsR::rmse_withBestPermutation(tess3r::G(tess3r.obj,K = K, run = 1), tess3enchosen.obj$G)
at.geno = "~/PatatorHomeDir/Data/At/At.geno" at.coord = "~/PatatorHomeDir/Data/At/coord_european.txt" data.at = list() data.at$X = LEA::read.geno(at.geno) data.at$coord = read.table(at.coord)
K = 3 tess3r.obj <- tess3r::TESS3(at.geno, at.coord, K = K,ploidy = 2, entropy = FALSE, project = "new") tess3enchosen.obj = TESS3enchoSen::TESS3(data.at$X, data.at$coord, K = K, ploidy = 2, lambda = 1.0) BioCompToolsR::rmse_withBestPermutation(tess3r::Q(tess3r.obj,K = K, run = 1), tess3enchosen.obj$Q) BioCompToolsR::rmse_withBestPermutation(tess3r::G(tess3r.obj,K = K, run = 1), tess3enchosen.obj$G)
TESS3enchosen use more memory (because we store data un a double matrix).
Plot results :
TESS3enchoSen::PlotAncestryCoef(tess3r::Q(tess3r.obj,K = K, run = 1),data.at$coord) TESS3enchoSen::PlotAncestryCoef(tess3enchosen.obj$Q,data.at$coord)
K = 3 snmf.obj <- LEA::snmf(at.geno, K = K,ploidy = 2, entropy = FALSE, project = "new")
Plot results :
TESS3enchoSen::PlotAncestryCoef(LEA::Q(snmf.obj,K = K, run = 1),data.at$coord)
# K = 3 # tess3enchosen.oqa.obj = TESS3enchoSen::TESS3(data.at$X, # data.at$coord, K = K, ploidy = 2, lambda = 1.0, method = "OQA")
# TESS3enchoSen::PlotAncestryCoef(tess3enchosen.oqa.obj$Q,data.at$coord)
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