PubMed · 7648330
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1995. Theory and simulation.. https://doi.org/10.1016/0959-440x(95)80086-7
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Simulation of realistic omics data is a key input for benchmarking studies that help users obtain optimal computational pipelines. Omics data involves large numbers of measured features on each sample and these measures are generally correlated with each other. However, simulation too often ignores these correlations, perhaps due to computational and statistical hurdles of doing so. To alleviate this, we describe three approaches for generating omics-scale data with correlated measures which mimic real datasets. These approaches are all based on a Gaussian copula approach with a covariance matrix that decomposes into a diagonal part and a low-rank part. This decomposition allows for extremely efficient simulation, overcoming a hurdle for adoption of past methods. We use these approaches to demonstrate the importance of including correlation in two benchmarking applications. First, we show that variance of results from the popular DESeq2 method increases when dependence is included. Second, we demonstrate that CYCLOPS, a method for inferring circadian time of collection from transcriptomics, improves in performance when given gene-gene dependencies in some circumstances. We provide an R package, dependentsimr, that has efficient implementations of these methods and can generate dependent data with arbitrary marginal distributions, including discrete (binary, ordered categorical, Poisson, negative binomial), continuous (normal), or with an empirical distribution.
The goal of cancer immunotherapy is to boost a patient's immune response to a tumour. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumour microenvironment, immune-modulating effects of conventional treatments and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modelling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumour classification, optimal treatment scheduling and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modellers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumour-immune biology. We conclude the review with recommendations for modellers both with respect to methodology and biological direction that might help keep modellers at the forefront of cancer immunotherapy development.
Pools of overlapping peptides corresponding to specific antigens are frequently used to identify T cell immune responses to vaccines or pathogens. While the response to the entire pool of peptides provides important information, it is often desirable to also know to which individual peptides within the pool the immune responses are directed. In this report, we analyzed various ways of deconvoluting an immune response to a pool of peptides to determine the number of different peptides to which the T cells are responding. We used a Monte Carlo simulation to optimize the construction of peptide pools that could identify responses to individual peptides using the fewest numbers of assays and patient material. We find that the number of assays required to deconvolute a pool increases by the logarithm of the number of peptides within the pool; however, the optimum configuration of pools changes dramatically according to the number of responses to individual peptides that are expected to be in the sample. Our simulation will help in the design of clinical trials in which the breadth of the response is being measured, by allowing a calculation for the minimum amount of blood that needs to be collected. In addition, our results guide the design and implementation of the experiments to deconvolute the responses to individual peptide epitopes.