ParaSAM: a parallelized version of the significance analysis of microarrays algorithm

Abstract

Motivation: Significance analysis of microarrays (SAM) is a widely used permutation-based approach to identifying differentially expressed genes in microarray datasets. While SAM is freely available as an Excel plug-in and as an R-package, analyses are often limited for large datasets due to very high memory requirements.Summary: We have developed a parallelized version of the SAM algorithm called ParaSAM to overcome the memory limitations. This high performance multithreaded application provides the scientific community with an easy and manageable client-server Windows application with graphical user interface and does not require programming experience to run. The parallel nature of the application comes from the use of web services to perform the permutations. Our results indicate that ParaSAM is not only faster than the serial version, but also can analyze extremely large datasets that cannot be performed using existing implementations.Availability:A web version open to the public is available at http://bioanalysis.genomics.mcg.edu/parasam. For local installations, both the windows and web implementations of ParaSAM are available for free at http://www.amdcc.org/bioinformatics/software/parasam.aspxContact: rmcindoe@mail.mcg.eduSupplementary information: Supplementary Data is available at Bioinformatics online.

Combining Small-Volume Metabolomic and Transcriptomic Approaches for Assessing Brain Chemistry

“…25,26 The data were further corrected for type 2 error using Storey’s false discovery rates (FDR) 27 and appropriate statistical packages and subroutines implemented in R 28 and Bioconductor. 29 Genes differentially expressed between the groups were selected at <0.5% FDR (typically one to three expected false-positives per set) and a more than 2-fold change in expression.…”

“…The raw signal intensity data was filtered for background and technical outliers, normalized for dye and array effects using the loess normalization procedure implemented by the Bioconductor package affy , and compared for differential gene expression using hypothesis testing statistics similar to those previously described. , The data were further corrected for type 2 error using Storey’s false discovery rates (FDR) and appropriate statistical packages and subroutines implemented in R and Bioconductor . Genes differentially expressed between the groups were selected at <0.5% FDR (typically one to three expected false-positives per set) and a more than 2-fold change in expression.…”