# FindBestTransform: Find the power transformation that makes a data set... In PoiClaClu: Classification and Clustering of Sequencing Data Based on a Poisson Model

## Description

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.

## Usage

 `1` ```FindBestTransform(x) ```

## Arguments

 `x` A n-by-p matrix of sequencing data, with n observations and p features.

## Value

Returns alpha, the power to which x should be raised.

Daniela Witten

## References

D Witten (2011) Classification and clustering of sequencing data using a Poisson model. To appear in Annals of Applied Statistics.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10``` ```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) ```

PoiClaClu documentation built on May 2, 2019, 8:29 a.m.