Phenotypes comparison based on a pathway consensus approach. Assess the relationship between candidate genes and a set of phenotypes based on additional genes related to the candidate (e.g. Pathways or network neighbors).

Platform Design Info for The Manufacturer's Name Barley1.

Platform Design Info for The Manufacturer's Name MG_U74B.

Platform Design Info for Affymetrix GenomeWideSNP_5.

Platform Design Info for Affymetrix Clariom_S_Mouse.

Platform Design Info for The Manufacturer's Name Mu11KsubB.

Platform Design Info for NimbleGen feinberg_hg18_me_hx1.

This package contains the data employed in the vignette of the PathNet package. These data belong to the following publication: PathNet: A tool for pathway analysis using topological information. Dutta B, Wallqvist A, and Reifman J., Source Code for Biology and Medicine 2012 Sep 24;7(1):10.

Platform Design Info for The Manufacturer's Name NuGO_Hs1a520180.

peakCombiner, a fully R based, user-friendly, transparent, and customizable tool that allows even novice R users to create a high-quality consensus peak list. The modularity of its functions allows an easy way to optimize input and output data. A broad range of accepted input data formats can be used to create a consensus peak set that can be exported to a file or used as the starting point for most downstream peak analyses.

This package calculates probabilistic pathway scores using gene expression data. Gene expression values are aggregated into pathway-based scores using Bayesian network representations of biological pathways.

Platform Design Info for The Manufacturer's Name Medicago.

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

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.

PathNet uses topological information present in pathways and differential expression levels of genes (obtained from microarray experiment) to identify pathways that are 1) significantly enriched and 2) associated with each other in the context of differential expression. The algorithm is described in: PathNet: A tool for pathway analysis using topological information. Dutta B, Wallqvist A, and Reifman J. Source Code for Biology and Medicine 2012 Sep 24;7(1):10.

PaIRKAT is model framework for assessing statistical relationships between networks of metabolites (pathways) and an outcome of interest (phenotype). PaIRKAT queries the KEGG database to determine interactions between metabolites from which network connectivity is constructed. This model framework improves testing power on high dimensional data by including graph topography in the kernel machine regression setting. Studies on high dimensional data can struggle to include the complex relationships between variables. The semi-parametric kernel machine regression model is a powerful tool for capturing these types of relationships. They provide a framework for testing for relationships between outcomes of interest and high dimensional data such as metabolomic, genomic, or proteomic pathways. PaIRKAT uses known biological connections between high dimensional variables by representing them as edges of ‘graphs’ or ‘networks.’ It is common for nodes (e.g. metabolites) to be disconnected from all others within the graph, which leads to meaningful decreases in testing power whether or not the graph information is included. We include a graph regularization or ‘smoothing’ approach for managing this issue.

High-throughput omics data are often affected by systematic biases introduced throughout all the steps of a clinical study, from sample collection to quantification. Normalization methods aim to adjust for these biases to make the actual biological signal more prominent. However, selecting an appropriate normalization method is challenging due to the wide range of available approaches. Therefore, a comparative evaluation of unnormalized and normalized data is essential in identifying an appropriate normalization strategy for a specific data set. This R package provides different functions for preprocessing, normalizing, and evaluating different normalization approaches. Furthermore, normalization methods can be evaluated on downstream steps, such as differential expression analysis and statistical enrichment analysis. Spike-in data sets with known ground truth and real-world data sets of biological experiments acquired by either tandem mass tag (TMT) or label-free quantification (LFQ) can be analyzed.

Base annotation databases for pig, intended ONLY to be used by AnnotationDbi to produce regular annotation packages.

Account for missing values in label-free mass spectrometry data without imputation. The package implements a probabilistic dropout model that ensures that the information from observed and missing values are properly combined. It adds empirical Bayesian priors to increase power to detect differentially abundant proteins.

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 package containing an environment representing the PrimeView.cdf file.

Platform Design Info for The Manufacturer's Name HG-U219.

Runs PANDA, an algorithm for discovering novel network structure by combining information from multiple complementary data sources.

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