Short Asynchronous Time-Series Analysis

A graphical and automated pipeline for the analysis of short time-series in R ('santaR'). This approach is designed to accommodate asynchronous time sampling (i.e. different time points for different individuals), inter-individual variability, noisy measurements and large numbers of variables. Based on a smoothing splines functional model, 'santaR' is able to detect variables highlighting significantly different temporal trajectories between study groups. Designed initially for metabolic phenotyping, 'santaR' is also suited for other Systems Biology disciplines. Command line and graphical analysis (via a 'shiny' application) enable fast and parallel automated analysis and reporting, intuitive visualisation and comprehensive plotting options for non-specialist users.


santaR

Interactive package for Short AsyNchronous Time-series Analysis (SANTA), implemented in R and Shiny

Overview

Longitudinal studies in Systems Biology face multiple challenges that are not suitably addressed by current time-series statistical methods: it is difficult to simultaneously account for biological variability, measurement error, missing observations, asynchronous sampling, nonlinearity and low number of time points (e.g. 4-10) with a high number of variables.

To address these challenges, santaR (Short AsyNchronous Time-series Analysis) provides a Functional Data Analysis (FDA) approach -where the fundamental units of analysis are curves representing each individual across time-, in a graphical and automated pipeline for robust analysis of short time-series studies.

Analytes levels are descriptive of the underlying biological state and evolve smoothly through time. For a single analyte, the time trajectory of each individual is described with a smooth curve estimated by smoothing splines. For a group of individuals, a curve representing the group mean trajectory is also calculated. These individual and group mean curves become the new observational unit for subsequent data analysis, that is, the estimation of the intra-class variability and the identification of trajectories significantly altered between groups.

Designed initially for metabolomic, santaR is also suited for other Systems Biology disciplines. Implemented in R and Shiny, santaR is developed as a complete and easy-to-use statistical software package, which enables command line and GUI analysis, with fast and parallel automated analysis and reporting. Comprehensive plotting options as well as automated summaries allow clear identification of significantly altered analytes for non-specialist users.

Installation

Install the development version of the package directly from GitHub with:

if(!require("devtools")) install.packages("devtools")
devtools::install_github("adwolfer/santaR")

If the dependency pcaMethods is not successfully installed, Bioconductor must be added to the default repositories with:

setRepositories(ind=1:2)

Usage

To get started santaR's graphical user interface implements all the functions for short asynchronous time-series analysis:

library(santaR)
 
santaR_start_GUI(browser = TRUE)
#  To exit press ESC in the command line

The graphical user interface is divided in 4 sections, corresponding to the main steps of analysis:

Import, DF search, Analysis and Export:

  • The Import tab manages input data in comma separated value (csv) format or as an RData file containing a previous analysis. Once data is imported the DF search and Analysis tabs become available.
  • DF search implements the tools for the selection of an optimal number of degrees of freedom (df).
  • With the data imported and a pertinent df selected, Analysis regroups the interface to visualise and identify variables significantly altered over time. A plotting interface enables the interactive visualisation of the raw data points, individual trajectories, group mean curves and confidence bands for all variables, which subsequently can be saved. Finally, if inter-group differential trajectories have been characterised, all significance testing results (with correction for multiple testing) are presented in interactive tables.
  • The Export tab manages the saving of results and automated reporting. Fitted data can be saved as an RData file for future analysis or reproduction of results. csv tables containing significance testing results can also be generated and summary plot for each significantly altered variable saved for rapid evaluation.

Vignettes and Demo data

More information is available in the graphical user interface as well as in the following vignettes:

A dataset containing the concentrations of 22 mediators of inflammation over an episode of acute inflammation is also available. The mediators have been measured at 7 time-points on 8 subjects, concentration values have been unit-variance scaled for each variable. A subset of the data is presented below:

## Metadata
acuteInflammation$meta
time ind group
4 ind_6 Group2
4 ind_7 Group1
4 ind_8 Group2
8 ind_1 Group1
8 ind_2 Group2
8 ind_3 Group1
## Data
acuteInflammation$data
var_1 var_2 var_3 var_4
2.668 2.464 1.365 1.743
-0.3002 0.05366 0.4509 0.01572
3.777 2.543 1.858 2.213
-0.3275 0.1564 0.585 0.03299
0.708 0.4893 -0.08219 0.9345
-0.4101 -0.03727 -0.2914 -0.7239

Other tips

The GUI is to be prefered to understand the methodology, select the best parameters on a subset of the data before running the command line, or to visually explore results.

If a very high number of variables is to be processed, santaR's command line functions are more efficient, as they can be integrated in scripts and the reporting automated.

Copyright

santaR is licensed under the GPLv3

As a summary, the GPLv3 license requires attribution, inclusion of copyright and license information, disclosure of source code and changes. Derivative work must be available under the same terms.

© Arnaud Wolfer (2017)

News

Reference manual

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install.packages("santaR")

1.0 by Arnaud Wolfer, a year ago


https://github.com/adwolfer


Browse source code at https://github.com/cran/santaR


Authors: Arnaud Wolfer [aut, cre] , Timothy Ebbels [ctb] , Joe Cheng [ctb] (Shiny javascript custom-input control)


Documentation:   PDF Manual  


GPL-3 license


Imports plyr, foreach, doParallel, pcaMethods, ggplot2, gridExtra, reshape2, iterators, shiny, shinythemes

Suggests knitr, rmarkdown, pander, R.rsp


See at CRAN