FastLORS: Joint modelling for expression quantitative trait loci mapping in R

Rhyne, Jacob; Jeng, X. Jessie; Chabrière, Éric; Tzeng, Jung Ying

doi:10.1002/sta4.265

Cited by 1 publication

(3 citation statements)

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“…YMCEN then selects the other two tuning parameters with a grid search. Finally, a method we call Three-Stage MCEN (MCEN-3S) takes an approach similar to that proposed by Rhyne et al (2020) and Yang et al (2013). In the first stage of this method, a joint lasso model is fit to obtain initial estimates for the regression coefficients and the tuning parameter λ 1 is selected based on this initial fit.…”

Section: Computational Issues and Considerationsmentioning

confidence: 99%

“…Hence this reduces the three dimensional grid search of the MCEN method proposed by Price and Sherwood (2018) to three one-dimensional grid searches. We note that a major difference between this approach and the approaches of Rhyne et al (2020) and others, is their method is evaluated on a regression problem, while the cluster selection problem requires evaluation of the gap-statistic which is at times computationally complex.…”

Section: Computational Issues and Considerationsmentioning

confidence: 99%

“…We note that Bergstra and Bengio (2012) provides guidance on how to choose the values for testing tuning parameters and discusses a random approach vs a targeted grid search. Others such as Rhyne et al (2020) and Yang et al (2013) who propose a two stage selection of tuning parameters that results in a one dimensional grid search for each. To scale this multiple one dimensional grid search approach, complexity must be taken into account and will differ from setting to setting.…”

Section: Computational Issues and Considerationsmentioning

confidence: 99%

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Detecting clusters in multivariate response regression

Price

Allenbrand

Sherwood

2021

WIREs Computational Stats

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Multivariate regression, which can also be posed as a multitask machine learning problem, is used to better understand multiple outputs based on a given set of inputs. Many methods have been proposed on how to utilize shared information about responses with applications in fields such as economics, genomics, advanced manufacturing, and precision medicine. Interest in these areas coupled with the rise of large data sets (“big data”) has generated interest in how to make the computations more efficient, but also to develop methods that account for the heterogeneity that may exist between responses. One way to exploit this heterogeneity between responses is to use methods that detect groups, also called clusters, of related responses. These methods provide a framework that can increase computational speed and account for complexity of relationships of a large number of responses. With this flexibility, comes additional challenges such as how to identify these clusters of responses, model selection, and the development of more complex algorithms that combine concepts from both the supervised and unsupervised learning literature. We explore current state of the art methods, present a framework to better understand methods that utilize or detect clusters of responses, and provide insights on the computational challenges associated with this framework. Specifically we present a simulation study that discusses the challenges with model selection when detecting clusters of responses of interest. We also comment on extensions and open problems that are of interest to both the research and practitioner communities. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods

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Section: Computational Issues and Considerationsmentioning

confidence: 99%

Section: Computational Issues and Considerationsmentioning

confidence: 99%