Scotty – Power Analysis for RNA Seq Experiments

Scotty

:: DESCRIPTION

Scotty is a tool to assist in the designing of RNA Seq experiments that have adequate power to detect differential expression at the level required to achieve experimental aims.

::DEVELOPER

The MarthLab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/MacOsX/Windows
  • MatLab

:: DOWNLOAD

 Scotty

:: MORE INFORMATION

Citation

Scotty: a web tool for designing RNA-Seq experiments to measure differential gene expression.
Busby MA, Stewart C, Miller CA, Grzeda KR, Marth GT.
Bioinformatics. 2013 Mar 1;29(5):656-7. doi: 10.1093/bioinformatics/btt015. Epub 2013 Jan 12.

ESPRESSO 1.0 – Account Assessment Errors on Outcome and Exposures in Power Analysis for Association Studies

ESPRESSO 1.0

:: DESCRIPTION

ESPRESSO (Estimating Sample-size and Power in R by Exploring Simulated Study Outcomes) is a simulation based tool, written in the R language that supports power and sample size calculations for stand-alone studies and analyses nested in cohort studies. The large number of participants required for adequately powered studies are quite expensive so it is important that an accurate sample size is identified.

::DEVELOPER

ESPRESSO team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux /  Windows
  • R

:: DOWNLOAD

 ESPRESSO

:: MORE INFORMATION

Citation:

ESPRESSO: taking into account assessment errors on outcome and exposures in power analysis for association studies.
Gaye A, Burton TW, Burton PR.
Bioinformatics. 2015 Apr 22. pii: btv219.

SEQPower 1.0RC1 – Power Analysis for Sequence-based Association Studies

SEQPower 1.0RC1

:: DESCRIPTION

SEQPower provides statistical power analysis and sample size estimation for sequence-based association studies

::DEVELOPER

SEQPower team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/MacOsX

:: DOWNLOAD

  SEQPower

:: MORE INFORMATION

Citation

Bioinformatics. 2014 Apr 28. pii: btu296. [Epub ahead of print]
Power analysis and sample size estimation for sequence-based association studies.
Wang GT, Li B, Lyn Santos-Cortez RP, Peng B, Leal SM.

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.

POLY 0.4.0 – Polygenic analysis and Power analysis of human Quantitative Traits

POLY 0.4.0

:: DESCRIPTION

POLY is a computer program for polygenic analysis and power analysis of human quantitative traits. It is convenient to use POLY to analyze multiple models / traits in large pedigrees

:DEVELOPER

Wei-Min Chen

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/MacOsX
  • C++ Compiler
:: DOWNLOAD

 POLY

:: MORE INFORMATION

Citation

Genet Epidemiol. 2006 Sep;30(6):471-84.
Estimating the power of variance component linkage analysis in large pedigrees.
Chen WM, Abecasis GR.