Platform Design Info for The Manufacturer's Name HT_HG-U133A.

Platform Design Info for The Manufacturer's Name HG_U95C.

Platform Design Info for The Manufacturer's Name MG_U74Av2.

This package provides a GUI interface for the DAPAR package. The package Prostar (Proteomics statistical analysis with R) is a Bioconductor distributed R package which provides all the necessary functions to analyze quantitative data from label-free proteomics experiments. Contrarily to most other similar R packages, it is endowed with rich and user-friendly graphical interfaces, so that no programming skill is required.

Platform Design Info for Affymetrix DroGene-1_0-st.

This package was automatically created by package AnnotationForge version 1.11.21. The probe sequence data was obtained from http://www.affymetrix.com. The file name was P\_aeg1a\_probe\_tab.

The package is an R wrapper for Progenetix REST API built upon the Beacon v2 protocol. Its purpose is to provide a seamless way for retrieving genomic data from Progenetix database—an open resource dedicated to curated oncogenomic profiles. Empowered by this package, users can effortlessly access and visualize data from Progenetix.

Platform Design Info for The Manufacturer's Name Chicken.

This package provides a package containing an environment representing the Plasmodium_Anopheles.cdf file.

Platform Design Info for The Manufacturer's Name MG_U74Cv2.

Platform Design Info for The Manufacturer's Name Cotton.

This package contains all the datasets used in the PepsNMR package.

Platform Design Info for Affymetrix FelGene-1_1-st.

This package contains data required to run examples in prebs package. The data files include: 1) Small sample bam files for demonstration purposes 2) Probe sequence mappings for Custom CDF (taken from http://brainarray.mbni.med.umich.edu/brainarray/Database/CustomCDF/genomic_curated_CDF.asp) 3) Probe sequence mappings for manufacturer's CDF (manually created using bowtie).

This package provides a Bioconductor data package for the Taylor et al (2010) dataset.

Platform Design Info for The Manufacturer's Name HT_HG-U133_Plus_PM.

This package provides support for parallelized estimation of GLMs/GEEs, catering for dispersed data.

Platform Design Info for Affymetrix EquGene-1_1-st.

This R package helps the user identify k-mers (e.g. di- or tri-nucleotides) present periodically in a set of genomic loci (typically regulatory elements). The functions of this package provide a straightforward approach to find periodic occurrences of k-mers in DNA sequences, such as regulatory elements. It is not aimed at identifying motifs separated by a conserved distance; for this type of analysis, please visit MEME website.

This package uses a statistical framework for rapid and accurate detection of aneuploid cells with local copy number deletion or amplification. Our method uses an EM algorithm with mixtures of Poisson distributions while incorporating cytogenetics information (e.g., regional deletion or amplification) to guide the classification (partCNV). When applicable, we further improve the accuracy by integrating a Hidden Markov Model for feature selection (partCNVH).

Pancreatic ductal adenocarcinoma (PDA) has a relatively poor prognosis and is one of the most lethal cancers. Molecular classification of gene expression profiles holds the potential to identify meaningful subtypes which can inform therapeutic strategy in the clinical setting. The Pancreatic Cancer Adenocarcinoma Tool-Kit (PDATK) provides an S4 class-based interface for performing unsupervised subtype discovery, cross-cohort meta-clustering, gene-expression-based classification, and subsequent survival analysis to identify prognostically useful subtypes in pancreatic cancer and beyond. Two novel methods, Consensus Subtypes in Pancreatic Cancer (CSPC) and Pancreatic Cancer Overall Survival Predictor (PCOSP) are included for consensus-based meta-clustering and overall-survival prediction, respectively. Additionally, four published subtype classifiers and three published prognostic gene signatures are included to allow users to easily recreate published results, apply existing classifiers to new data, and benchmark the relative performance of new methods. The use of existing Bioconductor classes as input to all PDATK classes and methods enables integration with existing Bioconductor datasets, including the 21 pancreatic cancer patient cohorts available in the MetaGxPancreas data package. PDATK has been used to replicate results from Sandhu et al (2019) [https://doi.org/10.1200/cci.18.00102] and an additional paper is in the works using CSPC to validate subtypes from the included published classifiers, both of which use the data available in MetaGxPancreas. The inclusion of subtype centroids and prognostic gene signatures from these and other publications will enable researchers and clinicians to classify novel patient gene expression data, allowing the direct clinical application of the classifiers included in PDATK. Overall, PDATK provides a rich set of tools to identify and validate useful prognostic and molecular subtypes based on gene-expression data, benchmark new classifiers against existing ones, and apply discovered classifiers on novel patient data to inform clinical decision making.

Platform Design Info for Affymetrix Clariom_S_Mouse_HT.

Platform Design Info for The Manufacturer's Name Vitis_Vinifera.

This package provides a Bioconductor data package for the Grasso (2012) Prostate Cancer dataset.