biosvd: Package for high-throughput data processing, outlier...

In biosvd: Package for high-throughput data processing, outlier detection, noise removal and dynamic modeling

Description

Biosvd package for high-throughput data processing, outlier detection, noise removal and dynamic modeling

Details

This package allows for high-throughput data processing, outlier detection, noise removal and dynamic modeling, based on the framework of Singular Value Decomposition. It provides the user with summary graphs and an interactive html report.

Author(s)

Anneleen Daemen [e-mail], Matthew Brauer [e-mail]

Examples

example( report )

Example output

Attaching package: 'biosvd'

The following object is masked from 'package:grDevices':

    palette

The following objects are masked from 'package:base':

    apply, matrix


report> ## Metabolomics starvation data obtained from http://genomics-pubs.princeton.edu/StarvationMetabolomics/Download.shtml
report> data(StarvationData)

report> ## Computes the eigensystem for the actual data
report> eigensystem <- compute(StarvationData)

report> ## Exclude the eigenfeatures representing steady-state intensity
report> eigensystem <- exclude(eigensystem)

report> ## Computes the eigensystem on the variance in the data after filtering out stead-state intensity
report> eigensystem <- compute(eigensystem, apply="variance")

report> ## No exclusion of eigenfeatures representing steady-scale variance
report> eigensystem <- exclude(eigensystem, excludeEigenfeatures=0)

report> ## Generate report for eigenfeatures 1 and 2
report> params <- new("EigensystemPlotParam")

report> if (.Platform$OS.type %in% "windows") path(params) <- getwd()

report> report(eigensystem, params)

report> ## Generate report for eigenfeatures 2 and 3
report> whichPolarAxes(params) <- c(3,2)

report> report(eigensystem, params)

biosvd documentation built on April 28, 2020, 6:32 p.m.