README.md

In Yuliaxis/kernInt: Kernel Integration of Microbiome Analysis Methods & Data

R package 'kernInt'

• Current version: 0.1.0
• Author: Elies Ramon
• e-mail: [e-mail]

Purpose

kernInt uses the kernel framework to unify supervised and unsupervised microbiome analyses, while paying special attention to spatial and temporal integration. If you find our package useful, please cite:

Ramon E, Belanche-Muñoz L, Molist F, Quintanilla R, Perez-Enciso M, and Ramayo-Caldas Y (2021) kernInt: A Kernel Framework for Integrating Supervised and Unsupervised Analyses in Spatio-Temporal Metagenomic Datasets. Front. Microbiol. 12:609048.doi: 10.3389/fmicb.2021.609048

Installation and usage

In R console:

if (!requireNamespace("devtools")) install.packages("devtools")
devtools::install_github("elies-ramon/kernInt")

If metagenomeSeq was not installed previously:

if (!requireNamespace("BiocManager")) install.packages("BiocManager")
BiocManager::install("metagenomeSeq")

Once the package is installed, it can be used anytime typing:

library(kernInt)

Package Overview

Main features

• Integration of supervised (classification, regression) and unsupervised (kernel PCA, hierarchical clustering, outlier detection) analyses.
• Microbial signatures of the classification and regression models.
• Automatic training/test splitting of the input data, k-Cross Validation and SVM classification and regression.
• Integration of data from different sources via Multiple Kernel Learning (MKL)
• Previously unpublished longitudinal pig gut microbiome dataset
• Implementation of kernels for compositional data (Aitchison-RBF kernel, compositional linear)
• Implementation of kernels suitable for functional data (functional RBF, functional linear)
• Kernels derived from classical ecology distances, as Jaccard and Jensen-Shannon, are also available.

Example datasets

We offer three metagenomic datasets with the package: a single point soil dataset, a human health dataset with an spatial component, and a novel longitudinal dataset concerning pig production. Also, to better illustrate the longitudinal treatment of data, we include the classical Berkeley Growth Dataset.

Soil data: Bacterial abundances in 88 soils from across North and South America. Metadata as soil pH, annual season precipitation and temperature, country, elevation, etc. is available.

Smokers: Microorganism abundances of right and left oro- and nasopharynx in 29 smokers and 33 nonsmokers.

Pig data: Previously unpublished longitudinal gut microbiome dataset of 153 piglets during their first week of life.

Growth: Berkeley longitudinal height data of 54 girls and 39 boys (93 individuals in total) from ages 0 to 18.

Vignette

An online in-depth vignette covering the kernel framework, with step-to-step usage and detailed examples can be found here.

The same vignette can be also accessed offline when the package is loaded, typing:

browseVignettes("kernInt")

If no vignette is found, try again after doing this:

devtools::install_github("elies-ramon/kernInt", build_vignettes = TRUE)

Yuliaxis/kernInt documentation built on Feb. 20, 2022, 12:38 a.m.