Description Usage Arguments Value Author(s) References See Also Examples

The entire strategy to get the association between metabolites and microbes, using linear and nonlinear methods, and plot the regression figures.

1 |

`A` |
A SummarizedExperiment object contains data of metabolome, where rows represent features of metabolites and columns represent samples. |

`B` |
A SummarizedExperiment object contains data of microbiome, where rows represent features of microbes and columns represent samples. |

`C` |
A SummarizedExperiment object contains data of covariates, where rows represent features of covariates and columns represent samples. |

`metaNor` |
Should metabolome data normalized? Using normalization when your metabolites are qualitative; and no normalization when the metabolites are quantitative. Default:TRUE. |

`rarefaction` |
Resample an OTU table such that all samples have the same library size. Here refers to a repeated sampling procedure to assess species richness, first proposed in 1968 by Howard Sanders.(see wikipedia for more detail.) Default:FALSE. |

`r` |
The linear regression coefficients threshold for using nonlinear method. Default: 0.5. |

`alpha` |
The linear regression p-value threshold for using nonlinear method.Default: 0.05. |

`pretreatment` |
The result of pretreatment |

`correlation` |
The result of correlation, see naiveGramm for detail |

A file named "R value top 10 pairs.pdf" will be created automatically (corrlation coefficient top 10 pairs) .

Mengci Li, Dandan Liang, Tianlu Chen and Wei Jia

Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V., Egozcue, J. J., Microbiome Datasets Are Compositional: And This Is Not Optional. Front. Microbiol. 2017, 8 (2224). Chambers, J. M. (1992) Linear models. Chapter 4 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole. D. Reshef, Y. Reshef, H. Finucane, S. Grossman, G. McVean, P. Turnbaugh, E. Lander, M. Mitzenmacher, P. Sabeti. (2011) Detecting novel associations in large datasets. Science 334, 6062. D. Albanese, M. Filosi, R. Visintainer, S. Riccadonna, G. Jurman, C. Furlanello. minerva and minepy: a C engine for the MINE suite and its R, Python and MATLAB wrappers. Bioinformatics (2013) 29(3): 407-408.

preGramm for pretreatment;nlfitGramm for nonlinear fitting;naiveGramm for naive correlation method.

1 2 | ```
data("metabolites");data("microbes");data("covariates")
Gramm(metabolites,microbes,covariates)
``` |

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