Amazon cloud-based computing for flow/mass cytometry data analysis

• Main Author: Than, Kyaw Min
• Other Authors: Lin Feng
• Format: Final Year Project
• Language: English
• Published: 2016
• Subjects: DRNTU::Engineering::Computer science and engineering
• Online Access: http://hdl.handle.net/10356/66770
• Institution: Nanyang Technological University

Flow/Mass cytometry data analysis is essential in the study of diverse phenotypes and functions at the single-cell level. Even though we have effectively processed the expressions values of different samples of genes with cytometry, it is still a key challenge to quantify and visualize high-dimensional datasets. Therefore, my main objective is to build an efficient data analysis system that will assist scientists to discover more findings. The datasets, obtained from National Center for Biotechnology Information (NCBI), which innovate science and health by providing access to genomic and biomedical datasets, are analysed. I have performed data cleaning, integration, normalization, extraction and loading of millions of data points on transcriptome profiles of Homo Sapiens (human) monocyte and dendritic cell subsets (human data). The data is then loaded into database and incorporated with web application development. Web application on cytometry data analysis has been deployed by utilising Spring Framework (MVC model). Moreover, developing mobile application (iOS & Android) has become efficient using cross platform deployable Ionic Framework. The data analysis system will serve scientists as a useful app assisting in studies of cell analysis at singular cell level.