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. 2024 Dec 2;4(1):vbae193.
doi: 10.1093/bioadv/vbae193. eCollection 2024.

Optimizing design of genomics studies for clonal evolution analysis

Arjun Srivatsa  1 , Russell Schwartz  1   2
Affiliations

Optimizing design of genomics studies for clonal evolution analysis

Arjun Srivatsa et al. Bioinform Adv. .

Abstract

Motivation: Genomic biotechnology has rapidly advanced, allowing for the inference and modification of genetic and epigenetic information at the single-cell level. While these tools hold enormous potential for basic and clinical research, they also raise difficult issues of how to design studies to deploy them most effectively. In designing a genomic study, a modern researcher might combine many sequencing modalities and sampling protocols, each with different utility, costs, and other tradeoffs. This is especially relevant for studies of somatic variation, which may involve highly heterogeneous cell populations whose differences can be probed via an extensive set of biotechnological tools. Efficiently deploying genomic technologies in this space will require principled ways to create study designs that recover desired genomic information while minimizing various measures of cost.

Results: The central problem this paper attempts to address is how one might create an optimal study design for a genomic analysis, with particular focus on studies involving somatic variation that occur most often with application to cancer genomics. We pose the study design problem as a stochastic constrained nonlinear optimization problem. We introduce a Bayesian optimization framework that iteratively optimizes for an objective function using surrogate modeling combined with pattern and gradient search. We demonstrate our procedure on several test cases to derive resource and study design allocations optimized for various goals and criteria, demonstrating its ability to optimize study designs efficiently across diverse scenarios.

Availability and implementation: https://github.com/CMUSchwartzLab/StudyDesignOptimization.

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Conflict of interest statement

None declared.

Figures

Figure 1.
Figure 1.
A flowchart of the main algorithm is depicted above. The algorithm repeatedly estimates surrogate functions from simulation calls chosen to explore regions of the search space of uncertain quality while exploiting regions of known high solution quality via focused local search.
Figure 2.
Figure 2.
Illustration of the optimization algorithm on a simplified example. The algorithm starts with a Latin hypercube sample of the input space. New points are explored in three main ways to balance exploitation of known high-quality solutions with exploration of poorly characterized regions of the search space. Points depicted in green are generated via a gradient descent search step with a numerical estimation of the gradient. Points depicted in gold and by the gold mesh in the left image are drawn from regions of high variance in the Gaussian process estimation and explored in the next round via the lower acquisition bound criteria. The minimal point in blue on the left is explored via a mesh search of points in the local neighborhood in the next round.
Figure 3.
Figure 3.
Visualizations from the five experimental queries. (a, b, d) 2D plots of coverage and read length versus score highlighting how different tradeoffs in the study design space by experiment yield different high-scoring regions. (c) Plots read length versus score colored by round of optimization. (e) Plots number of samples and coverage, showing how a higher cost-prioritization favors low coverage high sample points.
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