Gramm: Get the association between metabolites and microbes

In gramm4R: Generalized correlation analysis and model construction strategy for metabolome and microbiome

Description

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

Usage

Gramm(A,B,C,metaNor,rarefaction,r,alpha)

Arguments

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.

Value

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) .

Author(s)

Mengci Li, Dandan Liang, Tianlu Chen and Wei Jia

References

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.

See Also

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

Examples

data("metabolites");data("microbes");data("covariates")
Gramm(metabolites,microbes,covariates)

gramm4R documentation built on Nov. 8, 2020, 5:41 p.m.