r-bioc-qusage 2.24.0-1 source package in Ubuntu

Changelog

r-bioc-qusage (2.24.0-1) unstable; urgency=medium

  * Team upload.
  * New upstream version
  * debhelper-compat 13 (routine-update)

 -- Dylan Aïssi <email address hidden>  Mon, 02 Nov 2020 15:11:46 +0100

Upload details

Uploaded by:
Debian R Packages Maintainers on 2020-11-02
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
Hirsute proposed on 2020-11-02 universe misc

Builds

Hirsute: [FULLYBUILT] amd64

Downloads

File Size SHA-256 Checksum
r-bioc-qusage_2.24.0-1.dsc 2.1 KiB 0f695a40f4633d833673500cbee17a28d8dd6106833410bad68c8f3a532b2baa
r-bioc-qusage_2.24.0.orig.tar.gz 9.4 MiB 07b239567ddf55221196d6c75d1033290e00ae45c66e21d56cdc99f192f0c3c4
r-bioc-qusage_2.24.0-1.debian.tar.xz 3.0 KiB 5de1e1988b2397416866b4f7dcf104c210591b28c9f62cf924b77a9f9de7e9d6

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)