Understanding human disease through integrated molecular and clinical analyses
- Traditional biomedical experiments are designed to study a single cohort for a single disease using a single technology. I show that studying disease with a narrow lens has lead researchers to make discoveries that are not reproducible because they are not representative of the real heterogeneity of disease. I developed and implemented methods for integrating datasets to identify robust disease signatures. By integrating data from over 40 studies and 7,000 patients, we establish a robust signature of systemic lupus erythematosus which correlates with disease activity and persists across blood, tissue, and sorted cell populations. We compare relationships of 104 diseases based on molecular and clinical manifestations from 41,000 gene expression samples and 2 million patient records. Finally, we contextualize single-cell RNA-seq data with bulk gene expression profiles to understand the relationships of novel cell subsets to known cell populations and human disease. By integrating biomedical datasets, my work has enabled an unbiased and multi-scale understanding of disease.
|Type of resource
|electronic resource; remote; computer; online resource
|1 online resource.
|Haynes, Winston Andrew
|Degree committee member
|Stanford University, Department of Biomedical Informatics.
|Statement of responsibility
|Winston Andrew Haynes.
|Submitted to the Department of Biomedical Informatics.
|Thesis Ph.D. Stanford University 2018.
- © 2018 by Winston Andrew Haynes
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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