Description: Simulation methods for the evolution of antibody repertoires. The heavy and light chain variable region of both human and C57BL/6 mice can be simulated in a time-dependent fashion. Both single lineages using one set of V-, D-, and J-genes or full repertoires can be simulated. The algorithm begins with an initial V-D-J recombination event, starting the first phylogenetic tree. Upon completion, the main loop of the algorithm begins, with each iteration representing one simulated time step. Various mutation events are possible at each time step, contributing to a diverse final repertoire.

Title: Improved False Positive Control of Gene-Permuting GSEA with
Description: Gene-permuting GSEA (or preranked GSEA) generates a lot of false positive gene-sets due to the inter-gene correlation in each gene set. Such false positives can be successfully reduced by filtering with the one-tailed absolute GSEA results. This package provides a function that performs gene-permuting GSEA calculation with or without the absolute filtering.

Description: Gene-set enrichment analysis (GSEA) is popularly used to assess the enrichment of differential signal in a pre-defined gene-set without using a cutoff threshold for differential expression. The significance of enrichment is evaluated through sample- or gene-permutation method. Although the sample-permutation approach is highly recommended due to its good false positive control, we must use gene-permuting method if the number of samples is small. However, such gene-permuting GSEA (or preranked GSEA) generates a lot of false positive gene-sets as the inter-gene correlation in each gene set increases. These false positives can be successfully reduced by filtering with the one-tailed absolute GSEA results. This package provides a function that performs gene-permuting GSEA calculation with or without the absolute filtering. Without filtering, users can perform (original) two-tailed or one-tailed absolute GSEA.

Imports: Rcpp, Biobase, stats, DESeq, limma