Abstract
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.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China under Grant Nos. 61972320, 61772426, 61702161, 61702420, 61702421, and 61602386, the education and teaching reform research project of Northwestern Polytechnical University (Grant No 2020JGY23), the Fundamental Research Funds for the Central Universities under Grant No. 3102019DX1003, the Key Research and Development and Promotion Program of Henan Province of China under Grant 182102210213, the Key Research Fund for Higher Education of Henan Province of China under Grant 18A520003, and the Top International University Visiting Program for Outstanding Young Scholars of Northwestern Polytechnical University.
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Chen, B., Wang, C., Gao, L., Shang, X. (2020). A Flexible and Comprehensive Platform for Analyzing Gene Expression Data. In: Han, H., Wei, T., Liu, W., Han, F. (eds) Recent Advances in Data Science. IDMB 2019. Communications in Computer and Information Science, vol 1099. Springer, Singapore. https://doi.org/10.1007/978-981-15-8760-3_12
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DOI: https://doi.org/10.1007/978-981-15-8760-3_12
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