A predictive and forecasting model for the Airpassengers dataset on R

Este proyecto realiza un análisis multivariado de las 10,000 empresas más grandes de Colombia, utilizando técnicas como el análisis de componentes principales (PCA) y análisis de correspondencias. El objetivo es identificar patrones y factores clave que afectan el desempeño empresarial, así como la distribución geográfica de estas empresas.

Single-sample Gene Set Enrichment Analysis (ssGSEA) is a powerful method used to determine the enrichment of gene sets in individual samples, offering insights into pathway activity at a single-sample resolution. This step-by-step analysis involves ranking gene expression values, identifying genes within predefined gene sets, and calculating enrichment scores. The process begins with ranking the expression values and sorting them in decreasing order. The presence of genes within a specific gene set is then determined, and their indices are identified. A vector of zeros is initialized and updated with weighted ranks for the genes in the set, followed by normalization and computation of the cumulative sum. Similarly, a vector for genes not in the set is created and normalized. The final enrichment score is derived by calculating the difference between the cumulative sums of the gene set and non-gene set vectors. This detailed dissection of ssGSEA enables researchers to comprehensively understand pathway dynamics and biological processes within individual samples.