The package implements methods to compare lists of genes based on comparing the corresponding functional profiles'.

TCGA processed RNA-Seq data for 9264 tumor and 741 normal samples across 24 cancer types and made them available as GEO accession [GSE62944](http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62944). GSE62944 data have been parsed into a SummarizedExperiment object available in ExperimentHub.

GSALightning provides a fast implementation of permutation-based gene set analysis for two-sample problem. This package is particularly useful when testing simultaneously a large number of gene sets, or when a large number of permutations is necessary for more accurate p-values estimation.

We presented the Genotype-imputed Gene Set Enrichment Analysis (GIGSEA), a novel method that uses GWAS-and-eQTL-imputed trait-associated differential gene expression to interrogate gene set enrichment for the trait-associated SNPs. By incorporating eQTL from large gene expression studies, e.g. GTEx, GIGSEA appropriately addresses such challenges for SNP enrichment as gene size, gene boundary, SNP distal regulation, and multiple-marker regulation. The weighted linear regression model, taking as weights both imputation accuracy and model completeness, was used to perform the enrichment test, properly adjusting the bias due to redundancy in different gene sets. The permutation test, furthermore, is used to evaluate the significance of enrichment, whose efficiency can be largely elevated by expressing the computational intensive part in terms of large matrix operation. We have shown the appropriate type I error rates for GIGSEA (<5%), and the preliminary results also demonstrate its good performance to uncover the real signal.

The GSRI package estimates the number of differentially expressed genes in gene sets, utilizing the concept of the Gene Set Regulation Index (GSRI).

This package provides a visual exploration tool for multiple sequence alignment and associated data. Supports MSA of DNA, RNA, and protein sequences using ggplot2'. Multiple sequence alignment can easily be combined with other ggplot2 plots, such as phylogenetic tree Visualized by ggtree', boxplot, genome map and so on. More features: visualization of sequence logos, sequence bundles, RNA secondary structures and detection of sequence recombinations.

This package provides functions for calculating and visualizing growth-rate inhibition (GR) metrics.

Detect Differential Clustering of Genomic Sites such as gene therapy integrations. The package provides some functions for exploring genomic insertion sites originating from two different sources. Possibly, the two sources are two different gene therapy vectors. Vectors are preferred that target sensitive regions less frequently, motivating the search for localized clusters of insertions and comparison of the clusters formed by integration of different vectors. Scan statistics allow the discovery of spatial differences in clustering and calculation of False Discovery Rates (FDRs) providing statistical methods for comparing retroviral vectors. A scan statistic for comparing two vectors using multiple window widths to detect clustering differentials and compute FDRs is implemented here.

Biological molecules in a living organism seldom work individually. They usually interact each other in a cooperative way. Biological process is too complicated to understand without considering such interactions. Thus, network-based procedures can be seen as powerful methods for studying complex process. However, many methods are devised for analyzing individual genes. It is said that techniques based on biological networks such as gene co-expression are more precise ways to represent information than those using lists of genes only. This package is aimed to integrate the gene expression and biological network. A biological network is constructed from gene expression data and it is used for Gene Set Enrichment Analysis.

Download genome and assembly reports from NCBI.

This package contains a targeted clustering algorithm for the analysis of microarray data. The algorithm can aid in the discovery of new genes with similar functions to a given list of genes already known to have closely related functions.

Package fills a helper package role for whole gDR suite. It helps to support good development practices by keeping style requirements and style tests for other packages. It also contains build helpers to make all package requirements met.

This GEWIST package provides statistical tools to efficiently optimize SNP prioritization for gene-gene and gene-environment interactions.

This package aims to import, parse, and analyze KEGG data such as KEGG PATHWAY and KEGG MODULE. The package supports visualizing KEGG information using ggplot2 and ggraph through using the grammar of graphics. The package enables the direct visualization of the results from various omics analysis packages.

This package is a comprehensive visualization tool specifically designed for exploring phylomorphospace. It not only simplifies the process of generating phylomorphospace, but also enhances it with the capability to add graphic layers to the plot with grammar of graphics to create fully annotated phylomorphospaces. It also provide some utilities to help interpret evolutionary patterns.

This package contains core functions to process and analyze drug response data. The package provides tools for normalizing, averaging, and calculation of gDR metrics data. All core functions are wrapped into the pipeline function allowing analyzing the data in a straightforward way.

Illumina Golden Gate Human Methylation Cancer Panel Version 1 annotation data (chip GGHumanMethCancerPanelv1) assembled using data from public repositories.

Geneset Ordinal Association Test Enrichment Analysis (GOATEA) provides a Shiny interface with interactive visualizations and utility functions for performing and exploring automated gene set enrichment analysis using the GOAT package. GOATEA is designed to support large-scale and user-friendly enrichment workflows across multiple gene lists and comparisons, with flexible plotting and output options. Visualizations pre-enrichment include interactive Volcano and UpSet (overlap) plots. Visualizations post-enrichment include interactive geneset dotplot, geneset treeplot, gene-effectsize heatmap, gene-geneset heatmap and STRING database of protein-protein-interactions network graph. GOAT reference: Frank Koopmans (2024) <doi:10.1038/s42003-024-06454-5>.

Given a vector of cluster memberships for a cell population, identifies a sequence of gates (polygon filters on 2D scatter plots) for isolation of that cell type.

Microarray expression matrix platform GPL6106 and clinical data for 67 septicemic patients and made them available as GEO accession [GSE13015](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13015). GSE13015 data have been parsed into a SummarizedExperiment object available in ExperimentHub. This data data could be used as an example supporting BloodGen3Module R package.

This package provides tools to compute and visualize overlaps between gene sets or genomic regions. Venn diagrams with proportional areas are provided, while UpSet plots are recommended for larger numbers of sets. The package supports GRanges and GRangesList inputs, and integrates with analysis workflows for ChIP-seq, ATAC-seq, and other genomic interval data. It generates clean, interpretable, and publication-ready figures.

This package contains utility functions used throughout the gDR platform to fit data, manipulate data, and convert and validate data structures. This package also has the necessary default constants for gDR platform. Many of the functions are utilized by the gDRcore package.

This package provides a package for gene set analysis based on the variability of expressions as well as a method to detect Alternative Splicing Events . It implements DIfferential RAnk Conservation (DIRAC) and gene set Expression Variation Analysis (EVA) methods. For detecting Differentially Spliced genes, it provides an implementation of the Spliced-EVA (SEVA).

The package is a part of the gDR suite. It helps to prepare raw drug response data for downstream processing. It mainly contains helper functions for importing/loading/validating dose-response data provided in different file formats.