Package: santaR 1.2.4

santaR: 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.

Authors:Arnaud Wolfer [aut, cre], Timothy Ebbels [ctb], Joe Cheng [ctb]

santaR_1.2.4.tar.gz
santaR_1.2.4.zip(r-4.7-any)santaR_1.2.4.zip(r-4.6-any)santaR_1.2.4.zip(r-4.5-any)
santaR_1.2.4.tgz(r-4.6-any)santaR_1.2.4.tgz(r-4.5-any)
santaR_1.2.4.tar.gz(r-4.7-any)santaR_1.2.4.tar.gz(r-4.6-any)
santaR_1.2.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
santaR/json (API)

# Install 'santaR' in R:
install.packages('santaR', repos = c('https://adwolfer.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/adwolfer/santar/issues

Pkgdown/docs site:https://adwolfer.github.io

Datasets:

On CRAN:

Conda:

pcamethods (>= 1.92.0)

6.49 score 11 stars 71 scripts 282 downloads 18 exports 66 dependencies

Last updated from:6f7c7cdc8d. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK166
source / vignettesOK258
linux-release-x86_64OK162
macos-release-arm64OK144
macos-oldrel-arm64OK168
windows-develOK109
windows-releaseOK109
windows-oldrelOK115
wasm-releaseOK124

Exports:get_eigen_DFget_eigen_DFoverlay_listget_eigen_splineget_groupingget_ind_time_matrixget_param_evolutionplot_nbTP_histogramplot_param_evolutionsantaR_auto_fitsantaR_auto_summarysantaR_CBandsantaR_fitsantaR_plotsantaR_pvalue_distsantaR_pvalue_dist_withinsantaR_pvalue_fitsantaR_pvalue_fit_withinsantaR_start_GUI

Dependencies:base64encBiobaseBiocGenericsbslibcachemclicodetoolscommonmarkcpp11crosstalkdigestdoParallelDTevaluatefarverfastmapfontawesomeforeachfsgenericsggplot2gluegridExtragtablehighrhtmltoolshtmlwidgetshttpuvisobanditeratorsjquerylibjsonliteknitrlabelinglaterlazyevallifecyclemagrittrMASSmemoisemimeotelpcaMethodsplyrpromisesR6rappdirsRColorBrewerRcppreshape2rlangrmarkdownS7sassscalesshinysourcetoolsstringistringrtinytexvctrsviridisLitewithrxfunxtableyaml

Advanced command line functions
Preparing input data | DF search functions | Fitting, Confidence bands and plotting | P-values calculation | See Also
Rendered fromadvanced-command-line-functions.Rmdusingknitr::rmarkdown

Last update: 2019-10-24
Started: 2018-02-04

Automated command line analysis
Parallel processing | Automated Reporting | Save results for GUI | See Also
Rendered fromautomated-command-line.Rmdusingknitr::rmarkdown

Last update: 2019-10-24
Started: 2018-02-04

Getting Started with the santaR package
Installation | Overview | Getting Started | Further Reading
Rendered fromgetting-started.Rmdusingknitr::rmarkdown

Last update: 2019-10-24
Started: 2018-02-04

How to prepare input data for santaR
Data format | Introducing the dataset 'acuteInflammation' | Preparing the csv input for the graphical user interface | See Also
Rendered fromprepare-input-data.Rmdusingknitr::rmarkdown

Last update: 2019-10-24
Started: 2018-02-04

Plotting options
Basic plot | Adding options | Updating plot with ggplot2 grammar | Multiplots | See Also
Rendered fromplotting-options.Rmdusingknitr::rmarkdown

Last update: 2019-10-24
Started: 2018-02-04

santaR Theoretical Background
Concept of Functional Data Analysis | Main hypothesis | Signal extraction using splines | Group mean curve fitting | Intra-class variability (confidence bands on the group mean curves) | Detection of significantly altered time-trajectories | See Also
Rendered fromtheoretical-background.Rmdusingknitr::rmarkdown

Last update: 2019-10-24
Started: 2018-02-04

Selecting an optimal number of degrees of freedom
Intuitive parametrisation of smoothing | Automated model assessment and selection approaches | Latent time-trajectories for df selection | Smoothness and experimental design | See Also
Rendered fromselecting-optimal-df.Rmdusingknitr::rmarkdown

Last update: 2019-10-24
Started: 2018-02-04

santaR: Graphical user interface
Rendered fromsantaR-GUI.pdf.asisusingR.rsp::asis

Last update: 2018-02-04
Started: 2018-02-04

Readme and manuals

Help Manual

Help pageTopics
Measurement of 22 inflammatory mediators across timeacuteInflammation
Calculate the Akaike Information Criterion for a smooth.splineAIC_smooth_spline
Calculate the Akaike Information Criterion Corrected for small observation numbers for a smooth.splineAICc_smooth_spline
Calculate the Bayesian Information Criterion for a smooth.splineBIC_smooth_spline
Compute the optimal df and weighted-df using 5 spline fitting metricget_eigen_DF
Plot for each eigenSpline the automatically fitted spline, splines for all df and a spline at a chosen dfget_eigen_DFoverlay_list
Compute eigenSplines across a datasetget_eigen_spline
Generate a Ind x Time + Var data.frame concatenating all variables from input variableget_eigen_spline_matrix
Generate a matrix of group membership for all individualsget_grouping
Generate a Ind x Time DataFrame from input dataget_ind_time_matrix
Compute the value of different fitting metrics over all possible df for each eigenSplineget_param_evolution
Calculate the penalised loglikelihood of a smooth.splineloglik_smooth_spline
Plot an histogram of the number of time-trajectories with a given number of time-pointsplot_nbTP_histogram
Plot the evolution of different fitting parameters across all possible df for each eigenSplineplot_param_evolution
santaR: A package for Short AsyNchronous Time-series Analysis in RsantaR-package SANTAR santaR
Automate all steps of santaR fitting, Confidence bands estimation and p-values calculation for one or multiple variablessantaR_auto_fit
Summarise, report and save the results of a santaR analysissantaR_auto_summary
Compute Group Mean Curve Confidence BandssantaR_CBand
Generate a SANTAObj for a variablesantaR_fit
Plot a SANTAObjsantaR_plot
Evaluate difference in group trajectories based on the comparison of distance between group mean curvessantaR_pvalue_dist
Evaluate difference between a group mean curve and a constant modelsantaR_pvalue_dist_within
Evaluate difference in group trajectories based on the comparison of model fit (F-test) between one and two groupssantaR_pvalue_fit
Evaluate difference between a group mean curve and a constant model using the comparison of model fit (F-test)santaR_pvalue_fit_within
santaR Graphical User InterfacesantaR_start_GUI
Mean scaling of each columnscaling_mean
Unit-Variance scaling of each columnscaling_UV