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In this article, an analysis method is introduced based on public transcriptomic datasets and single-cell datasets, which could be used to comprehensively describe the role of single genes in tumors, including shaping the tumor immune microenvironment, shaping tumor molecular subtypes, and predicting the prognosis of tumor patients. At the same time, the introduction of single-gene data can not only avoid the randomness and heterogeneity brought about by the analysis of a single transcriptome but also allow for a deeper exploration of which specific clusters of cells the gene is expressed in, as well as further research into the role the gene plays within the pathway. Considering that many researchers may not be proficient in single-cell analysis, an online website is introduced in this method article, which is called TISCH2 (http://tisch.compbio.cn/), thus helping everyone to finish the single-cell analysis. In addition, the application of 101 machine learning methods has played an indispensable role in constructing the most accurate prognostic model. In conclusion, it is believed that this integrated single-gene analysis method that combines bioinformatics analysis, machine learning, and single-cell analysis can play an indispensable and crucial role in the study of the functions of single genes in tumor progression, as well as in the study of the functions of individual genes in pathways.