SGoF and SGoF+ 7.2 – Sequential Goodness of Fit for multiple testing

SGoF and SGoF+ 7.2

:: DESCRIPTION

SGoF+ is a new multiple test adjustment, based on a sequential goodness of fit test (SGoF) applied on a list of p-values, which increases its statistical power when the number of tests increases. SGoF+ can be a valuable alternative for multiple testing with high-dimensional biological data. Combined with the q-value estimation, which is also performed by the software, SGoF+ provides the power to detect true effects jointly with the reasonable proportion of false discoveries one should assume.

::DEVELOPER

Antonio Carvajal-Rodriguez 

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / MacOsX

:: DOWNLOAD

 SGoF+

:: MORE INFORMATION

Citation

Carvajal-Rodriguez A, Uña-Alvarez J (2011)
Assessing Significance in High-Throughput Experiments by Sequential Goodness of Fit and q-Value Estimation.
PLoS ONE 6(9): e24700

 

fuzzyFDR 1.0 – Find Fuzzy Decision Rules for Multiple Testing of Hypotheses with Discrete data

fuzzyFDR 1.0

:: DESCRIPTION

fuzzyFDR is an R package to find fuzzy decision rules for multiple testing of hypotheses with discrete data.

::DEVELOPER

Dr Alexandra M Lewin

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 fuzzyFDR

:: MORE INFORMATION

Citation

Kulinskaya, E. and Lewin, A. (2009)
On fuzzy familywise error rate and false discovery rate procedures for discrete distributions.
Biometrika 96(1):201-211.

weighted_FDR 1.2 – Apply Weighted False Discovery Rate for Multiple Testing

weighted_FDR.R 1.2

:: DESCRIPTION

weighted_FDR.R is a Software to Apply Weighted False Discovery Rate for Multiple Testing. When testing a large number of hypotheses, such as an association genome scan, the power to detect modest effects can be low, due to the penalty for multiple testing.  This is especially true when traditional approaches such as the Bonferroni correction are employed.  The false discovery (FDR) approach increases power in a multiple testing scenario, but it is still challenging to obtain significant results when very large numbers of tests are performed. To enhance power further, a weighted FDR approach can be employed that involves weighting the hypotheses based on prior data, such as a linkage scan, knowledge about biological pathways, candidate genes and so on. The wFDR procedure up-weights likely candidates and down-weights others, while maintaining control of the overall rate of false discoveries. The weights must average to 1 and be chosen independently of the association test statistics.

::DEVELOPER

COMPUTATIONAL GENETICS LAB

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 weighted_FDR.R

:: MORE INFORMATION

Citation:

Roeder K, Bacanu S-A, Wasserman L, Devlin B (2006)
Using linkage genome scans to improve power of association genome scans.
Am J Hum Genet. February 2006.