Package: QuantNorm 1.0.5

QuantNorm: Mitigating the Adverse Impact of Batch Effects in Sample Pattern Detection

Modifies the distance matrix obtained from data with batch effects, so as to improve the performance of sample pattern detection, such as clustering, dimension reduction, and construction of networks between subjects. The method has been published in Bioinformatics (Fei et al, 2018, <doi:10.1093/bioinformatics/bty117>). Also available on 'GitHub' <https://github.com/tengfei-emory/QuantNorm>.

Authors:Teng Fei, Tianwei Yu

QuantNorm_1.0.5.tar.gz
QuantNorm_1.0.5.zip(r-4.5)QuantNorm_1.0.5.zip(r-4.4)QuantNorm_1.0.5.zip(r-4.3)
QuantNorm_1.0.5.tgz(r-4.5-any)QuantNorm_1.0.5.tgz(r-4.4-any)QuantNorm_1.0.5.tgz(r-4.3-any)
QuantNorm_1.0.5.tar.gz(r-4.5-noble)QuantNorm_1.0.5.tar.gz(r-4.4-noble)
QuantNorm_1.0.5.tgz(r-4.4-emscripten)QuantNorm_1.0.5.tgz(r-4.3-emscripten)
QuantNorm.pdf |QuantNorm.html
QuantNorm/json (API)

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

Bug tracker:https://github.com/tengfei-emory/quantnorm/issues

Datasets:
  • ENCODE - Normalized ENCODE raw counts data for both human and mouse.
  • brain - Brain RNA-Seq data for both human and mouse.

On CRAN:

Conda:

batch-effects

3.65 score 9 stars 9 scripts 139 downloads 1 mentions 2 exports 0 dependencies

Last updated 6 years agofrom:ddb0076bff. Checks:1 OK, 8 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 26 2025
R-4.5-winNOTEMar 26 2025
R-4.5-macNOTEMar 26 2025
R-4.5-linuxNOTEMar 26 2025
R-4.4-winNOTEMar 26 2025
R-4.4-macNOTEMar 26 2025
R-4.4-linuxNOTEMar 26 2025
R-4.3-winNOTEMar 26 2025
R-4.3-macNOTEMar 26 2025

Exports:connection.matrixQuantNorm

Dependencies: