r-bioc-qusage 2.22.0-1build1 source package in Ubuntu

Changelog

r-bioc-qusage (2.22.0-1build1) groovy; urgency=medium

  * No-change rebuild against r-api-4.0

 -- Graham Inggs <email address hidden>  Sat, 30 May 2020 21:34:28 +0000

Upload details

Uploaded by:
Graham Inggs on 2020-05-30
Uploaded to:
Groovy
Original maintainer:
Debian R Packages Maintainers
Architectures:
all
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Hirsute release on 2020-10-23 universe misc
Groovy release on 2020-06-26 universe misc

Builds

Groovy: [FULLYBUILT] amd64

Downloads

File Size SHA-256 Checksum
r-bioc-qusage_2.22.0.orig.tar.gz 9.4 MiB ae97719a8b3fad371b3a7148e03a4db49c6a8b32d93d1bbbc2e1f9a845fbdd06
r-bioc-qusage_2.22.0-1build1.debian.tar.xz 3.1 KiB 6c329902b0a27286f58d13e69015bdbe12049bc335fca0cf3e5acac1c5e04e56
r-bioc-qusage_2.22.0-1build1.dsc 2.1 KiB 1857a3a32d2aa96bf7ba71c6876d0d0d4ffd3c27735ae0ae249e438fff396d29

View changes file

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)