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

PAST takes GWAS output and assigns SNPs to genes, uses those genes to find pathways associated with the genes, and plots pathways based on significance. Implements methods for reading GWAS input data, finding genes associated with SNPs, calculating enrichment score and significance of pathways, and plotting pathways.

Interactions between proteins occur in many, if not most, biological processes. Most proteins perform their functions in networks associated with other proteins and other biomolecules. This fact has motivated the development of a variety of experimental methods for the identification of protein interactions. This variety has in turn ushered in the development of numerous different computational approaches for modeling and predicting protein interactions. Sometimes an experiment is aimed at identifying proteins closely related to some interesting proteins. A network based statistical learning method is used to infer the putative functions of proteins from the known functions of its neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions.

Platform Design Info for Affymetrix HTA-2_0.

Platform Design Info for The Manufacturer's Name ATH1-121501.

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

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.

This is a simple data package including various data sets derived from the estrogen data for use with the puma (Propagating Uncertainty in Microarray Analysis) package.

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

An automated pipeline for the detection, integration and reporting of predefined features across a large number of mass spectrometry data files. It enables the real time annotation of multiple compounds in a single file, or the parallel annotation of multiple compounds in multiple files. A graphical user interface as well as command line functions will assist in assessing the quality of annotation and update fitting parameters until a satisfactory result is obtained.

Platform Design Info for Affymetrix Clariom_D_Human.

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

This package provides tools to test correlation between gene expression and phenotype in a way that is efficient, structured, fast and scalable. GSEA is also provided.

Platform Design Info for The Manufacturer's Name RN_U34.

This is a supplemental data package for the plotgardener package. Includes example datasets used in plotgardener vignettes and example raw data files. For details on how to use these datasets, see the plotgardener package vignettes.

Platform Design Info for The Manufacturer's Name Canine_2.

Platform Design Info for Affymetrix miRNA-3_1.

Platform Design Info for The Manufacturer's Name Plasmodium_Anopheles.

Sample data for PREDA package. (annotations objects synchronized with GeneAnnot custom CDFs version 2.2.0).

Platform Design Info for The Manufacturer's Name AG.

Protein Group Code Algorithm (PGCA) is a computationally inexpensive algorithm to merge protein summaries from multiple experimental quantitative proteomics data. The algorithm connects two or more groups with overlapping accession numbers. In some cases, pairwise groups are mutually exclusive but they may still be connected by another group (or set of groups) with overlapping accession numbers. Thus, groups created by PGCA from multiple experimental runs (i.e., global groups) are called "connected" groups. These identified global protein groups enable the analysis of quantitative data available for protein groups instead of unique protein identifiers.

Platform Design Info for Affymetrix MoGene-2_1-st.

Regularization and score distributions for position count matrices.

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