Package: digiNORM 0.1.0

Grant C. OConnell

digiNORM: Data-Driven Digital PCR Normalization

Adopts the general least squares-based data-driven normalization strategy developed by Heckmann et al. (2011) <doi:10.1186/1471-2105-12-250> to correct for technical variance in gene expression data generated via digital polymerase chain reaction (dPCR). Performs normalization of raw copy numbers and also calculates relative variability metrics that can be used to assess the impact of normalization on variance.

Authors:Grant C. O'Connell [aut, cre]

digiNORM_0.1.0.tar.gz
digiNORM_0.1.0.zip(r-4.7-any)digiNORM_0.1.0.zip(r-4.6-any)
digiNORM_0.1.0.tar.gz(r-4.7-any)digiNORM_0.1.0.tar.gz(r-4.6-any)
digiNORM_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
digiNORM/json (API)

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

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 303 downloads 4 exports 0 dependencies

Last updated from:1a6e3ef34d. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK114
source / vignettesOK174
linux-releaseOK161
windows-develOK60
windows-releaseOK91
wasm-releaseOK89

Exports:correction_factorsdigi_normnormalization_weightsrelative_variability

Dependencies: