Bayesian modelling of high-throughput sequencing assays with malacoda
Fig 2
MPRA data and malacoda priors.
A) The table shows a subset of our primary MPRA data. The highlighted cell containing 759 barcode counts is influenced both by the sequencing depth of its sample (blue column) and the unknown input DNA concentration of its barcode (red row). B) A simplified Kruschke diagram of the generative model underlying malacoda. After evaluating the joint posterior on all model parameters, a 95% posterior interval on a variant’s transcription shift (shaded area) may be used for a binary decision between “functional” or “non-functional”. This example TS posterior shows a negative shift that excludes zero, meaning the variant in question would be called as “functional”. C) A conceptual diagram demonstrating three prior types available in the malacoda framework. The marginal prior (left) weights all variants in the assay equally, while the grouped and conditional priors utilize informative annotations as weights in the prior estimation process.