A flexible and comprehensive platform for analyzing gene expression data

Studying the original gene expression dataset is one of the essential methods for analyzing biological processes. Many platforms were developed to conduct this kind of study, such as GSEA, and the online gene list analysis portal Metascape. However, these well-known platforms sometimes are not friendly enough for inexperienced users due to the following reasons. Firstly, many biological experiments only have three duplicates, which make classical statistical methods lack of efficient and accuracy. Secondly, different experiments could result in different gene expression profiles, where standard differential expressed gene identification methods still have room to be further improved. Thirdly, many platforms work only for specific experimental conditions based on their default parameters, where users are not easily setup parameters for their own studies. In this study, we designed a comprehensive and flexible gene expression data analysis tool, where six novel differential expressed gene identification methods and three functional enrichment analysis methods were proposed. Majority parameters can be friendly setting by users and a variety of algorithms can be 9 according to the user’s own study designing. Experiments show that our platform provides an effective way for gene set series analysis, and has great performance in both practicality and convenience.