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.