Bayesian Analysis of High Throughput Data

Duplicate or triplicate experimental replicates are commonplace in the high throughput literature. However, it has not been tested whether this is statistically defensible or not. To address this issue, we use probabilistic programming to develop a simple hierarchical model for analyzing high throughput measurement data. With the model and simulated data, we show that a small increase in replicate experiments can quantitatively improve accuracy in measurement. We also provide posterior densities for statistical parameters used in the evaluation of HT data. Finally, we provide an extensible open source implementation that ingests data structured in a simple format and produces posterior densities of estimated measurement and assay evaluation parameters.