Microarray data enables biologists to extract differentially expressed genes (DEGs) across multiple phenotypes. While several pipelines and tools exist to perform microarray data analysis, they are targeted to users with moderate to advanced computational understanding and lack an easy-to-use, interactive and dynamic methodology to perform analysis assisted with comprehensive learning resources. In this study, we developed an interactive application “sMAP” (Standard Microarray Analysis Pipeline) to make transcriptome microarray data analysis more accessible in learning environments and to enable the identification of significant pathological biomarkers. In a case study of colorectal cancer, we showed that sMAP enabled us to reproduce previous findings and discover relevant pathways. sMAP provides a comprehensive set of tutorials and learning documentation to help early-stage researchers. The latest URLs of sMAP’s hosting, tutorial, and frequently updated documentation can be found at https://github.com/BI-STEM-Away/sMAP.