GEPSi: A Python Library to Simulate GWAS Phenotype Data

Reidenbach, Daniel A.; A, Lal; Slim, Lotfi; Mosafi, Ohad; Israeli, Johnny

doi:10.1101/2021.08.04.455085

Cited by 2 publications

(2 citation statements)

References 23 publications

(23 reference statements)

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“…For the Lasso and a few simple convex models, coordinate descent has been broadly disseminated to practitioners in off-the-shelf packages such as glmnet [Friedman et al, 2007] or scikit-learn [Pedregosa et al, 2011]. More recently, celer, a state-of-the-art convex working set algorithm [Massias et al, 2020] allowed for successful applications of the Lasso in very high dimensional problems in medicine [Reidenbach et al, 2021, Kim et al, 2021 or seismology [Muir and Zhan, 2021].…”

Section: Pred Rmse Left-outmentioning

confidence: 99%

Beyond L1: Faster and Better Sparse Models with skglm

Bertrand¹,

Klopfenstein²,

Bannier³

et al. 2022

Preprint

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We propose a new fast algorithm to estimate any sparse generalized linear model with convex or non-convex separable penalties. Our algorithm is able to solve problems with millions of samples and features in seconds, by relying on coordinate descent, working sets and Anderson acceleration. It handles previously unaddressed models, and is extensively shown to improve state-of-art algorithms. We provide a flexible, scikit-learn compatible package, which easily handles customized datafits and penalties.

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Section: Pred Rmse Left-outmentioning

confidence: 99%

Beyond L1: Faster and Better Sparse Models with skglm

Bertrand¹,

Klopfenstein²,

Bannier³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Accordingly, the corresponding simulators have also been developed. For instance, many recent simulators support the generation of multiple correlated phenotypes (Meyer and Birney, 2018; O’Reilly et al ., 2012; Porter and O’Reilly, 2017), and some simulators support the epistatic effects between causal genetic factors (Fernandes and Lipka, 2020; Reidenbach et al ., 2021).…”

Section: Introductionmentioning

confidence: 99%

OmeSim: a genetics-based nonlinear simulator for in-between-ome and phenotype

Long,

Zhang

2024

Preprint

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MotivationDeciphering genetic basis of complex traits via genotype-phenotype association studies is a long-standing theme in genetics. The availability of molecular omics data (such as transcriptome) has enabled researchers to utilize “in-between-omes” in association studies, for instance transcriptome-wide association study. Although many statistical tests and machine learning models integrating omics in genetic mapping are emerging, there is no standard way to simulate phenotype by genotype with the role of in-between-omes incorporated. Moreover, the involvement of in-between-omes usually bring substantial nonlinear architecture (e.g., co-expression network), that may be non-trivial to simulate. As such, rigorous power estimations, a critical step to test novel models, may not be conducted fairly.ResultsTo address the gap between emerging methods development and the unavailability of adequate simulators, we developed OmeSim, a phenotype simulator incorporating genetics, an in-between-ome (e.g., transcriptome), and their complex relationships including nonlinear architectures. OmeSim outputs detailed causality graphs together with original data, correlations, and associations structures between phenotypic traits and omes terms as comprehensive gold-standard datasets for the verifications of novel tools integrating an in-between-ome in genotype-phenotype association studies. We expect OmeSim to enable rigorous benchmarking for the future multi-omics integrations.Availabilityhttps://github.com/zhoulongcoding/OmeSimContactqingrun.zhang@ucalgary.ca

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GEPSi: A Python Library to Simulate GWAS Phenotype Data

Cited by 2 publications

References 23 publications

Beyond L1: Faster and Better Sparse Models with skglm

Beyond L1: Faster and Better Sparse Models with skglm

OmeSim: a genetics-based nonlinear simulator for in-between-ome and phenotype

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