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UBC Theses and Dissertations

Mega-analysis of gene expression patterns across tissues in human and mouse Feng, Min


Expression patterns across tissues are a primary indicator of gene function. High-throughput technology created many cross-tissue data sets on a transcriptomic level (tissue panel data sets). However, the existence of multiple tissue panel data sets creates a challenge for the scientific community to decide if these data sets are equally valid or decide which data set to choose. To date, the multiple tissue panel data sets have not been well compared, nor fully evaluated. In my Master’s thesis, I collected a large number of public-available tissue panel data sets, harmonized them, integrated the data sets into a tissue expression atlas including human data and mouse data, compared and contrasted the data sets across the atlas, evaluated each data set preliminarily with a gene-specific disagreement index that I developed. I found in general, these data sets had a good agreement. However, in certain data sets the amount of disagreement was high, which indicated the qualities of these data sets were suspect. Applying the disagreement index, I was able to offer a summarized expression pattern in the tissue expression atlas with either consensus or disagreements outlined. I also developed a web-based prototype to access to this atlas. Furthermore, I explored the range of changes in gene expression patterns that may be caused by experimental conditions, such as diseases or drug treatments. I found most of the changes could not be as dramatic as a change from unexpressed to highly expressed, even though these changes were reported as statistically significant in literatures. Only a couple of conditions such as cancer or inflammation could cause an unexpressed-to-highly-expressed change, because tissue composition in those conditions were changed substantially.

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