The noisyR package is an end-to-end pipeline for quantifying and removing technical noise from HTS datasets. The three main pipeline steps are:

similarity calculation
noise quantification
noise removal

Each step can be finely tuned using hyperparameters; optimal, data-driven values for these parameters are also determined.

The package and some applications are described in more detail in this preprint and is actively maintained on https://github.com/Core-Bioinformatics/noisyR.

The count matrix approach uses the original, un-normalised count matrix, as provided after alignment and feature quantification; each sample is processed individually, only the relative expressions across samples are compared. Relying on the hypothesis that the majority of genes are not DE, most of the evaluations are expected to point towards a high similarity across samples.

Installation

To install the package, first install all bioconductor dependencies:

packages.bioc <- c("preprocessCore",
                   "IRanges",
                   "GenomicRanges",
                   "Rsamtools")
new.packages.bioc <- packages.bioc[!(packages.bioc %in% installed.packages()[,"Package"])]
if(length(new.packages.bioc)){
  if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
  BiocManager::install(new.packages.bioc)
}

Then, you can install noisyR (and all its other dependencies) from CRAN:

install.packages("noisyr")

To install the latest stable version from GitHub, first install CRAN dependencies:

packages.cran <- c("utils",
                   "grDevices",
                   "tibble",
                   "dplyr",
                   "magrittr",
                   "ggplot2",
                   "philentropy",
                   "doParallel",
                   "foreach"

noisyR count matrix approach workflow

Installation