r-bioc-qusage 2.20.0-1 source package in Ubuntu

Changelog

r-bioc-qusage (2.20.0-1) unstable; urgency=medium

  * Team upload.
  * New upstream version

 -- Dylan Aïssi <email address hidden>  Mon, 11 Nov 2019 08:20:54 +0100

Upload details

Uploaded by:
Debian R Packages Maintainers on 2019-11-11
Uploaded to:
Sid
Original maintainer:
Debian R Packages Maintainers
Architectures:
all
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Focal release on 2020-01-30 universe misc

Builds

Focal: [FULLYBUILT] amd64

Downloads

File Size SHA-256 Checksum
r-bioc-qusage_2.20.0-1.dsc 2.1 KiB 718b953e9052cef7bf85ab36bf293aff284066e3d871b4c1904120bc96c3d1db
r-bioc-qusage_2.20.0.orig.tar.gz 9.4 MiB c43463b37231f2825272b93fcafe240f6b6319da127e98949f3431d3f832d339
r-bioc-qusage_2.20.0-1.debian.tar.xz 2.9 KiB b57adf71dc483e023daf30a656f772d9147bf61d440849e1beece95e220ba046

Available diffs

No changes file available.

Binary packages built by this source

r-bioc-qusage: qusage: Quantitative Set Analysis for Gene Expression

 This package is an implementation the Quantitative Set
 Analysis for Gene Expression (QuSAGE) method described in
 (Yaari G. et al, Nucl Acids Res, 2013). This is a novel Gene
 Set Enrichment-type test, which is designed to provide a
 faster, more accurate, and easier to understand test for gene
 expression studies. qusage accounts for inter-gene correlations
 using the Variance Inflation Factor technique proposed by Wu et
 al. (Nucleic Acids Res, 2012). In addition, rather than simply
 evaluating the deviation from a null hypothesis with a single
 number (a P value), qusage quantifies gene set activity with a
 complete probability density function (PDF). From this PDF, P
 values and confidence intervals can be easily extracted.
 Preserving the PDF also allows for post-hoc analysis (e.g.,
 pair-wise comparisons of gene set activity) while maintaining
 statistical traceability. Finally, while qusage is compatible
 with individual gene statistics from existing methods (e.g.,
 LIMMA), a Welch-based method is implemented that is shown to
 improve specificity. For questions, contact Chris Bolen
 (cbolen1@gmail.com) or Steven Kleinstein
 (steven.kleinstein@yale.edu)