Single Cell RNA-Sequencing Using Machine Learning

Single-cell RNA-sequencing becoming an effective tool for the diagnosis and prognosis of cancer and other disease. To significant advances in knowledge, improved methods for medical diagnosis and superior medical treatments with more effective and targeted results. A fundamental step in single-cell RNA-sequencing involves the identification of similarities between cells using mathematical techniques. As the field is still in its infancy, no best-practices have been determined for either the maths to use or their optimal software implementation.
This project sought to develop and analyse some new methods for the classification of cells into clusters of resemblance. It sought to create computational templates to supplement existing pipelines for single-cell RNAsequencing analysis. It implemented new techniques for gene-selection, dimensionality reduction and clustering algorithms.