Description: A simple and effective tool for computing and visualizing statistical power for meta-analysis,
including power analysis of main effects, test of homogeneity, subgroup analysis, and categorical moderator analysis.

including power analysis of main effects (Jackson & Turner, 2017)<doi:10.1002/jrsm.1240>,
test of homogeneity (Pigott, 2012)<doi:10.1007/978-1-4614-2278-5>, subgroup analysis,
and categorical moderator analysis (Hedges & Pigott, 2004)<doi:10.1037/1082-989X.9.4.426>.

Title: Robust P-Value Combination Methods
Description: The meta-analysis is performed to increase the statistical power by integrating the results from several experiments. The p-values are often combined in meta-analysis when the effect sizes are not available. The 'metapro' R package provides not only traditional methods (Becker BJ (1994, ISBN:0-87154-226-9), Mosteller, F. & Bush, R.R. (1954, ISBN:0201048523) and Lancaster HO (1949, ISSN:00063444)), but also new methods such as weighted Fisher’s method and ordmeta we developed. While the (weighted) Z-method is suitable for finding features effective in most experiments, (weighted) Fisher’s method and ordmeta are useful for detecting partially associated features. Thus, the users can choose the function based on their purpose.

person("Aaron", "Millan-Oropeza", email = "aaron.ibt@gmail.com", role = c("aut", "cre")),
person("Catherine", "Juste", email = "catherine.juste@inrae.fr", role = c("aut", "ctb")),
person("Ariane", "Bassignani", email = "ariane.bassignani@gmail.com", role = c("aut", "ctb")),
person("Céline", "Henry", email = "celine.henry@inrae.fr", role = c("aut", "ctb"))
Author: Aaron Millan-Oropeza [aut, cre],
Set of tools for descriptive analysis of metaproteomics data generated from high-throughput
mass spectrometry instruments. These tools allow to cluster peptides and proteins abundance,
expressed as spectral counts, and to manipulate them in groups of metaproteins. This information
can be represented using multiple visualization functions to portray the global metaproteome
landscape and to differentiate samples or conditions, in terms of abundance of metaproteins,
taxonomic levels and/or functional annotation. The provided tools allow to implement flexible
analytical pipelines that can be easily applied to studies interested in metaproteomics analysis.
Imports: ade4, dendextend, dplyr, ggforce, ggrepel, reshape2, stringr,