Tag: Hypothesis testing

HyPhy 2.5.31 – Hypothesis testing using Phylogenies

HyPhy 2.5.31

:: DESCRIPTION

HyPhy is an open-source software package for the analysis of genetic sequences using techniques in phylogenetics, molecular evolution, and machine learning. It features a complete graphical user interface (GUI) and a rich scripting language for limitless customization of analyses. Additionally, HyPhy features support for parallel computing environmen. HyPhy intended to perform maximum likelihood analyses of genetic sequence data and equipped with tools to test various statistical hypotheses. HYPHY was designed with maximum flexibility in mind and to that end it incorporates a simple high level programming language which enables the user to tailor the analyses precisely to his or her needs. These include relative rate and ratio tests, several methods of ML based phylogeny reconstruction, bootstrapping, model selection, positive selection, molecular clock tests and many more

::DEVELOPER

Acme Computational Molecular Evolution Group

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows /  Mac OsX / Linux

:: DOWNLOAD

  HyPhy

:: MORE INFORMATION

Citation

Sergei L. Kosakovsky Pond, Simon D. W. Frost and Spencer V. Muse (2005)
HyPhy: hypothesis testing using phylogenies.
Bioinformatics 21(5): 676-679

HCE 3.5 – Interactive Power Analysis for Microarray Hypothesis Testing and Generation

HCE 3.5

:: DESCRIPTION

The HCE (Hierarchical Clustering Explorer) power analysis tool was designed to import any pre-existing microarray project, and interactively test the effects of user-defined definitions of α (significance), β (1-power), sample size, and effect size. The tool generates a filter for all probe sets or more focused ontology-based subsets, with or without noise filters that can be used to limit analyses of a future project to appropriately powered probe sets. We studied projects from three organisms (Arabidopsis, rat, human), and three probe set algorithms (MAS5.0, RMA, dChip PM/MM). We found large differences in power results based on probe set algorithm selection and noise filters. RMA provided exquisite sensitivity for low numbers of arrays, but this came at a cost of high false positive results (24% false positive in the human project studied). Our data suggests that a priori power calculations are important for both experimental design in hypothesis testing, and hypothesis generation, as well as for selection of optimized data analysis parameters.

::DEVELOPER

Ben Shneiderman, Jinwook Seo

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

  HCE

:: MORE INFORMATION

Citation

Jinwook Seo, Heather Gordish-Dressman, Eric P. Hoffman,
” An Interactive Power Analysis Tool for Microarray Hypothesis Testing and Generation,”
Bioinformatics, Vol. 22, No. 7, pp. 808-814, 2006.