GSEA-P is a Java-based desktop application for Gene Set Enrichment Analysis (GSEA), a computational method that determines whether a predefined set of genes shows statistically significant, consistent differences between two biological states. GSEA-P 2.0 is a major improvement over the previous version, featuring a leading-edge analysis component, seamless integration with the Molecular Signature Database (MSigDB), and an embedded browser for searching and mapping gene sets to various microarray platforms. Users can directly import gene sets from MSigDB for analysis. The software also includes improved visualizations and links to Gene Set Cards, which provide concise annotations of gene sets. Over 3500 users have contributed feedback that has been incorporated into this new release. GSEA assesses the significance of over-representation of a gene set in a ranked list of genes correlated with a phenotype. It calculates an Enrichment Score (ES) by walking through the list, increasing a cumulative sum when genes are in the set and decreasing it otherwise. The ES is interpreted as a weighted Kolmogorov-Smirnov statistic. The leading-edge subset, which consists of genes in the set appearing before the ES reaches its maximum, is crucial for evaluating GSEA results. The significance of the ES is estimated using a permutation test. GSEA normalizes the ES for each gene set to account for set size variations, yielding a normalized enrichment score (NES), and calculates a false discovery rate (FDR) for each NES. The effectiveness of GSEA depends on how well the gene sets represent meaningful coordinated gene expression behaviors. The MSigDB contains over 3000 gene sets of various types, including those representing genes in the same chromosome, metabolic pathways, co-expressed genes, conserved regulatory motifs, and expression neighborhoods of cancer-related genes. Users can use this database or define their own gene sets. GSEA-P 2.0 includes features such as a gene set browser, comprehensive documentation, and a Gene Set Cards resource. It also supports batch analysis mode and the Chip2Chip utility for mapping identifiers between platforms. The software provides richly annotated HTML reports and interactive tools for leading-edge analysis and visualization. The documentation includes a user guide, tutorial, FAQs, and examples of GSEA analysis.GSEA-P is a Java-based desktop application for Gene Set Enrichment Analysis (GSEA), a computational method that determines whether a predefined set of genes shows statistically significant, consistent differences between two biological states. GSEA-P 2.0 is a major improvement over the previous version, featuring a leading-edge analysis component, seamless integration with the Molecular Signature Database (MSigDB), and an embedded browser for searching and mapping gene sets to various microarray platforms. Users can directly import gene sets from MSigDB for analysis. The software also includes improved visualizations and links to Gene Set Cards, which provide concise annotations of gene sets. Over 3500 users have contributed feedback that has been incorporated into this new release. GSEA assesses the significance of over-representation of a gene set in a ranked list of genes correlated with a phenotype. It calculates an Enrichment Score (ES) by walking through the list, increasing a cumulative sum when genes are in the set and decreasing it otherwise. The ES is interpreted as a weighted Kolmogorov-Smirnov statistic. The leading-edge subset, which consists of genes in the set appearing before the ES reaches its maximum, is crucial for evaluating GSEA results. The significance of the ES is estimated using a permutation test. GSEA normalizes the ES for each gene set to account for set size variations, yielding a normalized enrichment score (NES), and calculates a false discovery rate (FDR) for each NES. The effectiveness of GSEA depends on how well the gene sets represent meaningful coordinated gene expression behaviors. The MSigDB contains over 3000 gene sets of various types, including those representing genes in the same chromosome, metabolic pathways, co-expressed genes, conserved regulatory motifs, and expression neighborhoods of cancer-related genes. Users can use this database or define their own gene sets. GSEA-P 2.0 includes features such as a gene set browser, comprehensive documentation, and a Gene Set Cards resource. It also supports batch analysis mode and the Chip2Chip utility for mapping identifiers between platforms. The software provides richly annotated HTML reports and interactive tools for leading-edge analysis and visualization. The documentation includes a user guide, tutorial, FAQs, and examples of GSEA analysis.