Description Usage Arguments Value Author(s) References Examples

We propose a class of microbiome based sum of powered score (MiSPU) tests based on a newly defined generalized taxon proportion that combines observed microbial composition information with phylogenetic tree information. Different from the existing methods, a MiSPU test is based on a weighted score of the generalized taxon proportion in a general framework of regression, upweighting more likely to be associated microbial lineages. Our simulations demonstrated that one or more MiSPU tests were more powerful than MiRKAT while correctly controlling type I error rates. An adaptive MiSPU (aMiSPU) test is proposed to combine multiple MiSPU tests with various weights, approximating the most powerful MiSPU for a given scenario, consequently being highly adaptive and high powered across various scenarios.

1 2 |

`y` |
Outcome of interest. It can be a disease indicator; =0 for controls, =1 for cases. Or it can be a quantitative trait. A vector with length n (number of observations). |

`X` |
OTU count table, row - n sample, column - q OTU |

`tree` |
Rooted phylogenetic tree of R class “phylo” |

`cov` |
Covariates. A matrix with dimension n by p (n :number of observation, p : number of covariates). |

`model` |
Use "gaussian" for a quantitative trait, and use "binomial" for a binary trait. |

`pow` |
The gamma which controls the weight. Larger pow puts more weight on the variables that have larger absolute score. |

`n.perm` |
number of permutations or bootstraps. |

A list object, including the results for MiSPU_u, MiSPU_w and aMiSPU.

Chong Wu

Pan, W., et al.(2014) A powerful and adaptive association test for rare variants, Genetics, 197(4), 1081-95

Chong, W., Pan, W. (2015) An Adaptive Association Test for Microbiome Data, submitted.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
data(throat.otu.tab)
data(throat.tree)
data(throat.meta)
Y.tmp =throat.meta[,3]
Y = rep(0,dim(throat.meta)[1])
Y[Y.tmp=="Smoker"] = 1
cov.tmp = throat.meta[,c(10,12)]
cov = matrix(1,dim(throat.meta)[1],2)
cov[cov.tmp[,1]== "None",1] = 0
cov[cov.tmp[,2]== "Male",2] = 0
start.time = proc.time()
X = as.matrix(throat.otu.tab)
out = MiSPU(Y,X, throat.tree,cov,model = "binomial", pow = c(2:8, Inf), n.perm = 1000)
out
``` |

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