Find a constant alpha, 0<alpha<=1, such that x raised to the power alpha approximately follows the simple Poisson log linear model that says that the (i,j) element of x is Poisson with mean si times gj, where si is a sample-specific term and gj is a feature-specific term. Alpha is selected via a grid search.
A n-by-p matrix of sequencing data, with n observations and p features.
Returns alpha, the power to which x should be raised.
D Witten (2011) Classification and clustering of sequencing data using a Poisson model. To appear in Annals of Applied Statistics.
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set.seed(1) dat <- CountDataSet(n=20,p=100,sdsignal=2,K=4,param=10) alpha <- FindBestTransform(dat$x) # This is the best transformation! dd <- PoissonDistance(dat$x^alpha,type="mle", transform=FALSE) # OR we could get the samething automatically: dd2 <- PoissonDistance(dat$x,type="mle",transform=TRUE) # or like this: dd3 <- PoissonDistance(dat$x,type="mle",transform=TRUE,alpha=alpha) ColorDendrogram(hclust(dd$dd), y=dat$y, branchlength=10)
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