PubMed · 11479565
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A Dove. 2001. Structures without crystals.. https://doi.org/10.1038/90785
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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.
Incremental truncation is a method for constructing libraries of every one base pair truncation of a segment of DNA. Incremental truncation libraries can be created using a time-dependent nuclease method or through the incorporation of alpha-phosphothioate dNTPs by PCR or by primer extension (THIO(pcr) truncation and THIO(extension) truncation, respectively). Libraries created by the fusion of two truncation libraries, known as ITCHY libraries, can be created using the above methods or by the incremental truncation-like method SHIPREC. Knowing and being able to tailor the distribution of truncations in incremental truncation, ITCHY and SHIPREC libraries would be beneficial for their use in protein engineering and other applications. However, the experimental determination of the distributions would require extensive, cost-prohibitive, DNA sequencing to obtain statistically relevant data. Instead, a theoretical prediction of the distributions was developed. Time-dependent incremental truncation libraries had the most uniform distribution of truncation lengths, but were biased against longer truncations. Essentially uniform distribution over the desired truncation range (from zero to N(max) base pairs) required that truncations be prepared up to at least 1.2-1.5 N(max). THIO(pcr) and THIO(extension) truncation libraries had a very nonuniform distribution of truncation lengths with a bias against longer truncations. Such nonuniformity could be significantly diminished by decreasing the incorporation rate of alphaS-dNTPs but at the expense of having a large fraction of the DNA truncated beyond the desired range or completely degraded. ITCHY libraries created using time-dependent truncation had the most uniform distribution of possible fusions and had the highest fraction of the library being parental-length fusions. However, the distribution of parental-length fusions was biased against fusions near the beginning/ends of genes unless the truncation libraries are prepared with a uniform distribution up to N(max). In contrast, SHIPREC libraries and THIO(pcr) ITCHY libraries, by the very nature of the nonuniform distributions of the truncated DNA, are ensured of having a uniform distribution of fusion points in parental-length fusions. This comes at the expense of having a smaller fraction of the library being parental-length fusions; however, this limitation can be overcome by performing size selection on the library.