In Malaysia, there are 81 (as on February 15, 2013) higher education institutions including satellite branches of the foreign universities. In northern part of the Peninsular Malaysia, AIMST University is the first private not-for-profit university and aims to become a premier private university in the country and the region. The workshop described in this article was designed to develop and enhance the capacity of academic staff-in-leadership-role for the University. This type of workshops may be a good method to enhance the leadership qualities of the head of each unit, department, school and faculty in each university.
Single-cell analysis (SCA) improves the detection of cancer, the immune system, and chronic diseases from complicated biological processes. SCA techniques generate high-dimensional, innovative, and complex data, making traditional analysis difficult and impractical. In the different cell types, conventional cell sequencing methods have signal transformation and disease detection limitations. To overcome these challenges, various deep learning techniques (DL) have outperformed standard state-of-the-art computer algorithms in SCA techniques. This review discusses DL application in SCA and presents a detailed study on improving SCA data processing and analysis. Firstly, we introduced fundamental concepts and critical points of cell analysis techniques, which illustrate the application of SCA. Secondly, various effective DL strategies apply to SCA to analyze data and provide significant results from complex data sources. Finally, we explored DL as a future direction in SCA and highlighted new challenges and opportunities for the rapidly evolving field of single-cell omics.