DAnTE

DAnTE performs statistical analysis of quantitative bottom-up, shotgun proteomics and other high-throughput datasets to provide normalization, missing-value imputation, peptide-to-protein rollup, visualization, and hypothesis testing accommodating unbalanced designs and random effects.

Key Features:

• Normalization Methods: Implements a range of normalization techniques for quantitative proteomics and microarray data.
• Missing Value Imputation Algorithms: Provides algorithms to impute missing values commonly observed in high-throughput datasets.
• Peptide-to-Protein Rollup Methods: Aggregates peptide-level measurements into protein-level quantification.
• Extensive Plotting Functions: Offers plotting functions to visualize complex proteomics and microarray datasets.
• Comprehensive Hypothesis-Testing Scheme: Supports hypothesis testing for unbalanced data and models that incorporate random effects.

Scientific Applications:

• Shotgun proteomics analysis: Quantification and statistical analysis of proteins from bottom-up, shotgun proteomics data.
• Microarray data analysis: Normalization and statistical processing of microarray datasets.
• High-throughput dataset analysis: Application to various high-throughput biological studies requiring normalization, imputation, rollup, visualization, and hypothesis testing.

Methodology:

Uses normalization techniques, missing value imputation algorithms, peptide-to-protein rollup methods, plotting functions, and a hypothesis-testing framework that handles unbalanced data and random effects.