With the fast development of biological techniques, high-throughput omics data is available, which also raises many big problems in other subjects like mathematics: how to deal with such big data so that meaningful biological insights can be found. Among the various omics data, I mainly work on Matrix-Assisted Laser Desorption Ionization Mass
Spectrometry and Single Cell RNA Sequencing data. Although there already exist many mature analysis pipelines and machine learning algorithms for analysing these kinds of omics data, there remains improvement space. In this thesis, I firstly describe the relevant challenges of omics data analysis in detail. Then I explain popular analysis pipelines and algorithms which are frequently utilized during the analysis. Finally, I illustrate the modified analysis pipeline, math models and corresponding results.