| LoBrA {LoBrA} | R Documentation |
LoBrA: A package for modeling longitudinal breath data
Description
The LoBrA package provides important data objects and functions to analyze longitudinal metabolomic (breath) data.
Introduction
Novel metabolomic technologies paved the way for longitudinal analysis of exhaled air and online monitoring of fast progressing diseases. This package implements an automated analysis approach of longitudinal data from different omics technologies, such as ion mobility spectrometry of human exhaled air and demonstrates how including temporal signals increases the statistical power in biomarker identification. It can handel multiple irregular 4D time series data. More precisely, it can simultaniously handel the data of multiple experiements each observing multiple components. Therefore, it allows repeated measurements of a component, irregular sampling, and unequal temporal spacing of the time points.
LoBrA Analysis
A typical LoBrA analysis is will comprise the following steps
1. Background Screening: Using the function screening and selectComponents to select the Components that most likely do not originate from background noise.
2. Model Selection: First, a set of spline models based on different number of splits and split positions are generated by the function lobraModelSelection. Subsequently, these models are evaluated using different quality criteria, i.e. 'AIC', 'BIC' and 'LogLik'. Finally, the most appropriate model is selected.
3. Evaluation of the non-background components on the selected model, using the longitudinal 'Gouderman' linear mixed effect model in function modelGoudermanLongitudinal.
Author(s)
Maintainer: Anne-Christin Hauschild [Copyright holder]
Authors:
Sandrah P. Eckel
Jan Baumbach