Hi @gregcaporaso,

Yes, the easiest way to use unifrac is

`qiime diversity core-metrics-phylogenetic`

.

I faced a roadblock in this as I needed to generate rep-seqs for each body site but I used the grouped table as input to do so. This subsequently led to problems down the road while running core-metrics-phylogenetic as it was not recognizing the subject column so I had to replace all the sample IDs with the respective subjects to run core-metrics-phylogenetic (Hopefully this was the right thing to do).

Here are the results, however the p values are still greater than 0.05. Does a high p value mean there is no correlation between the two sites after factoring in individual subjects? Based on my (limited) understanding, the null hypothesis in Mantel Test is that distance matrix 1 is independent of distance matrix 2, therefore in this case, null hypothesis is not rejected and the distance matrices are independent. (Side Question: What if p value was less than 0.05? Does that mean there is some dependence (correlation) between distance matrices?)

Unweighted Unifrac:

Mantel test results | |
---|---|

Method | Spearman |

Sample size | 6 |

Permutations | 999 |

Alternative hypothesis | two-sided |

Spearman rho | 0.425 |

p-value | 0.097 |

Weighted Unifrac:

Mantel test results | |
---|---|

Method | Spearman |

Sample size | 6 |

Permutations | 999 |

Alternative hypothesis | two-sided |

Spearman rho | 0.007143 |

p-value | 0.983 |

Bray Curtis:

Mantel Test Results | |
---|---|

Method | Spearman |

Sample size | 7 |

Permutations | 999 |

Alternative hypothesis | two-sided |

Spearman rho | 0.01236 |

p-value | 0.961 |

Thank you again!