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Abstract:
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With the advent of high-throughput technologies making large-scale gene expression data readily available, developing appropriate computational tools to process these data and distill insights into systems biology and biomedicine has been an important part of the “big data” challenge. In this talk, we introduce a rank-based count statistic to assess “reverse” correlations between drug-induced gene differential expressions and disease-related gene differential expressions. The study is motivated by the belief that drugs which reverse the expression of disease associated genes have potential to be efficacious for treating the disease in question. We provide asymptotic analysis of the newly introduced statistics’ distributions and power, and evaluate their performance against existing measures of reverse correlation on simulated and real data. Our new statistics are fast to compute, robust against outliers, and show comparable and often better general performance.
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