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)
binutils-gold-s390x-linux-gnu
gold ELF linker for the s390x-linux-gnu target (deprecated)
Gold is intended to have complete support for ELF and to run as fast as
possible on modern systems. For normal use it is a drop-in replacement
for the older GNU linker.
binutils-gold-sparc64-linux-gnu
gold ELF linker for the sparc64-linux-gnu target (deprecated)
Gold is intended to have complete support for ELF and to run as fast as
possible on modern systems. For normal use it is a drop-in replacement
for the older GNU linker.
r-bioc-s4vectors
BioConductor S4 implementation of vectors and lists
The S4Vectors package defines the Vector and List virtual classes and a
set of generic functions that extend the semantic of ordinary vectors
and lists in R. Package developers can easily implement vector-like or
list-like objects as concrete subclasses of Vector or List. In addition,
a few low-level concrete subclasses of general interest (e.g. DataFrame,
Rle, and Hits) are implemented in the S4Vectors package itself (many
more are implemented in the IRanges package and in other Bioconductor
infrastructure packages).
r-bioc-tcgabiolinksgui.data
Data for the TCGAbiolinksGUI package
Supporting data for the GNU R TCGAbiolinksGUI package.
This package contains the following objects:
- For gene annotation:
- gene.location.hg38
- gene.location.hg19
- For Glioma Classifier function:
- glioma.gcimp.model
- glioma.idh.model
- glioma.idhmut.model
- glioma.idhwt.model
- For linkedOmics database:
- linkedOmicsData
r-bioc-tximport
transcript-level estimates for biological sequencing
Imports transcript-level abundance, estimated counts and
transcript lengths, and summarizes into matrices for use with
downstream gene-level analysis packages. Average transcript
length, weighted by sample-specific transcript abundance
estimates, is provided as a matrix which can be used as an
offset for different expression of gene-level counts.