Making microarray and RNA-seq gene expression data comparable

Measuring gene expression levels in the cell is an important tool in biomedical sciences. It can be used in new drug development, disease diagnostics and many other areas. Currently, two most popular platforms for measuring gene expression are microarrays and RNA-sequencing (RNA-seq). Making the gene expression results more comparable between these two platforms is an important topic which has not yet been investigated enough. In this thesis, we present a novel method, called PREBS, that addresses this issue. Our method adjusts RNA-seq data computational processing in a way that makes the resulting gene expression measures more similar to microarray based gene expression measures. We compare our method against two other RNA-seq processing methods, RPKM and MMSEQ, and evaluate each method's agreement with microarrays by calculating correlations between the platforms. We show that our method reaches the highest level of agreement among all of the methods in absolute expression scale and has a similar level of agreement as the other methods in differential expression scale. Additionally, this thesis provides some background on gene expression, its measurement and computational analysis of gene expression data. Moreover, it gives a brief literature review on the past microarray{RNA-seq comparisons