Merging Cross-Platform Gene Expression Data

Gene expression studies enable scientists to investigate which genes are actively transcribed, providing insights into the differences between normal and diseased cells and allowing for more accurate disease detection and monitoring. There are currently two major technologies used for gene expression analysis: DNA microarray and RNA sequencing. While DNA microarray has generated invaluable data over the decades, it can be relatively expensive and has limitations in detecting certain transcripts. Therefore, RNA sequencing is likely to become the predominant tool for transcriptome analysis in the near future. Although both technologies capture similar information, differences in the platforms and technologies used to collect them can make direct comparisons challenging in practice. In our research project, we applied different approaches, such as Linear Regression, Constrained B-spline, and novel techniques such as MatchMixeR, to increase the comparability of both platforms. Our goal is to advance our understanding of the mechanisms underlying gene expression and disease