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Many practical applications require optimization of multiple, computationally expensive, and possibly competing objectives that are well-suited for multi-objective Bayesian optimization (MOBO) procedures. However, for many types of biomedical data, measures of data analysis workflow success are often heuristic and therefore it is not known a priori which objectives are useful. Thus, MOBO methods that return the full Pareto front may be suboptimal in these cases. Here we propose a novel MOBO method that adaptively updates the scalarization function using properties of the posterior of a multi-output Gaussian process surrogate function. This approach selects useful objectives based on a flexible set of desirable criteria, allowing the functional form of each objective to guide optimization. We demonstrate the qualitative behaviour of our method on toy data and perform proof-of-concept analyses of single-cell RNA sequencing and highly multiplexed imaging datasets.

BUM-HMM is a statistically robust modeling pipeline for interpreting high-throughput RNA structure probing data, including that from transcriptome-wide experiments. Structure probing coupled with high-throughput sequencing could revolutionize our understanding of the role of RNA structure in regulation of gene expression. Despite recent technological advances, intrinsic noise and high sequence coverage requirements greatly limit the applicability of these techniques. Here we describe a probabilistic modeling pipeline that accounts for biological variability and biases in the data, yielding statistically interpretable scores for the probability of nucleotide modification transcriptome wide. Using two yeast data sets, we demonstrate that our method has increased sensitivity, and thus our pipeline identifies modified regions on many more transcripts than do existing pipelines. Our method also provides confident predictions at much lower sequence coverage levels than those recommended for reliable structural probing. Our results show that statistical modeling extends the scope and potential of transcriptome-wide structure probing experiments.

Mutational signatures are patterns of mutation types, many of which are linked to known mutagenic processes. Signature activity represents the proportion of mutations a signature generates. In cancer, cells may gain advantageous phenotypes through mutation accumulation, causing rapid growth of that subpopulation within the tumour. The presence of many subclones can make cancers harder to treat and have other clinical implications. Reconstructing changes in signature activities can give insight into the evolution of cells within a tumour. Recently, we introduced a new method, TrackSig, to detect changes in signature activities across time from single bulk tumour sample. By design, TrackSig is unable to identify mutation populations with different frequencies but little to no difference in signature activity. Here we present an extension of this method, TrackSigFreq, which enables trajectory reconstruction based on both observed density of mutation frequencies and changes in mutational signature activities. TrackSigFreq preserves the advantages of TrackSig, namely optimal and rapid mutation clustering through segmentation, while extending it so that it can identify distinct mutation populations that share similar signature activities.

A report on the Wellcome Trust Conference on Computational RNABiology held in Hinxton, UK, 17 to 19 October 2016.