After exchanging our ideas with clinical doctors and ordinary people around us, we summarize the benefits of our project to human practice into two parts...
Our algorithm provides a powerful tool to analyze, predict and cluster how cells undergo complex differentiation trajectories using single-cell RNA data, which is especially critical for cancer research because it is now urgently needed to locate certain key cells such as cancer stem cells (CSCs) and cells with great metastasis potential. And among many differentiation patterns, epithelial-mesenchymal transition (EMT) in recent years is shown to have great correlations with cancer metastasis. However, the exact paths and stages that cells undergo EMT have not yet been well studies. Using our models, one is able to dig out the possible transition patterns during EMT as well as the critical genes that might give rise to EMT in different patterns. This can help to guide the study of EMT. It can also play a part in other differentiation pattern such as the gain of stemness and many other critical biology events in cancer progression. Besides cancer research, our algorithm can also be applied in other fields as long as the cells differentiation trajectories toward one target are wanted, using the scRNA data.
Cancer is now the major suffer for human beings all over the world. As a medical student, one of our main targets of doing scientific research is to improve the well-being of patients in the world. On the one hand, our results help to drive the research of cancer. For example, it might be helpful to find out the critical genes driving epithelial-mesenchymal transition (EMT), which can be a good target for new chemotherapy drugs since EMT is one of the major events that cancer rely to gain the ability of metastasis. And in the long run, as the rapid progress of sequencing technologies, we can imagine someday when the single-cell RNA data becomes a common index of biopsy, and our model can serve as accessory diagnosis method for oncologists to get a better knowledge and judgement of the patients’ condition.