r-bioc-qusage 2.30.0-1 source package in Ubuntu
Changelog
r-bioc-qusage (2.30.0-1) unstable; urgency=medium * Team upload. * New upstream version * Standards-Version: 4.6.1 (routine-update) -- Andreas Tille <email address hidden> Fri, 13 May 2022 15:04:19 +0200
Upload details
- Uploaded by:
- Debian R Packages Maintainers
- Uploaded to:
- Sid
- Original maintainer:
- Debian R Packages Maintainers
- Architectures:
- all
- Section:
- misc
- Urgency:
- Medium Urgency
See full publishing history Publishing
| Series | Published | Component | Section |
|---|
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| r-bioc-qusage_2.30.0-1.dsc | 2.1 KiB | 1851f9247d4e4e8450a0afa347eed3ce8d73a90977e691fe803ce018d0b7c54c |
| r-bioc-qusage_2.30.0.orig.tar.gz | 9.5 MiB | 4160708f7132d3c41eaa988cf83575d873a65ad7804e571306aca0743c824c52 |
| r-bioc-qusage_2.30.0-1.debian.tar.xz | 3.1 KiB | a0e17e2128b2b58c385043e5fa8082ff1905a58a3804759cb45488db97ccaf53 |
Available diffs
- diff from 2.28.0-1 to 2.30.0-1 (948 bytes)
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)
