c-lasso - a Python package for constrained sparse and robust regression and classification

Simpson, Léo; Combettes, Patrick L.; Müller, Christian L.

doi:10.21105/joss.02844

Cited by 9 publications

(7 citation statements)

References 15 publications

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“…On the other hand, if the Python (Python Software Foundation) language is used, the c‐lasso package (Simpson et al., 2021) enables the performance of sparse and robust linear regressions and classifications with linear equality constraints on the model's parameters. This programme manages several estimators for inferring the unknown coefficients, including regularised SVMs.…”

Section: Advanced Data Analysis and Visualisationmentioning

confidence: 99%

Critical review of 16S rRNA gene sequencing workflow in microbiome studies: From primer selection to advanced data analysis

Regueira‐Iglesias,

Balsa‐Castro,

Blanco‐Pintos

et al. 2023

Molecular Oral Microbiology

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The multi‐batch reanalysis approach of jointly reevaluating gene/genome sequences from different works has gained particular relevance in the literature in recent years. The large amount of 16S ribosomal ribonucleic acid (rRNA) gene sequence data stored in public repositories and information in taxonomic databases of the same gene far exceeds that related to complete genomes. This review is intended to guide researchers new to studying microbiota, particularly the oral microbiota, using 16S rRNA gene sequencing and those who want to expand and update their knowledge to optimise their decision‐making and improve their research results. First, we describe the advantages and disadvantages of using the 16S rRNA gene as a phylogenetic marker and the latest findings on the impact of primer pair selection on diversity and taxonomic assignment outcomes in oral microbiome studies. Strategies for primer selection based on these results are introduced. Second, we identified the key factors to consider in selecting the sequencing technology and platform. The process and particularities of the main steps for processing 16S rRNA gene‐derived data are described in detail to enable researchers to choose the most appropriate bioinformatics pipeline and analysis methods based on the available evidence. We then produce an overview of the different types of advanced analyses, both the most widely used in the literature and the most recent approaches. Several indices, metrics and software for studying microbial communities are included, highlighting their advantages and disadvantages. Considering the principles of clinical metagenomics, we conclude that future research should focus on rigorous analytical approaches, such as developing predictive models to identify microbiome‐based biomarkers to classify health and disease states. Finally, we address the batch effect concept and the microbiome‐specific methods for accounting for or correcting them.

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Section: Advanced Data Analysis and Visualisationmentioning

confidence: 99%

Critical review of 16S rRNA gene sequencing workflow in microbiome studies: From primer selection to advanced data analysis

Regueira‐Iglesias,

Balsa‐Castro,

Blanco‐Pintos

et al. 2023

Molecular Oral Microbiology

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“…spgl1 is a NumPy based implementation of spectral projected gradient for L1 minimization. c-lasso (Simpson et al, 2021) is a Python package for constrained sparse regression and classification. This is also CPU only.…”

Section: Statement Of Needmentioning

confidence: 99%

CR-Sparse: Hardware accelerated functional algorithms for sparse signal processing in Python using JAX

Kumar¹

2021

JOSS

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“…where β o and β k are the respective vectors of coefficients for the original and knockoff features such that β = (β o , β k ). The problem can be solved with c-lasso solver in Python (Simpson et al, 2021).…”

Section: Modified Compositional Lasso Model For Microbiome Knockoff S...mentioning

confidence: 99%

“…where γ = (γ o , γ k ) is the concatenation of the node effects of the original features and their knockoff counterparts. The problem can be solved with c-lasso solver in Python (Simpson et al, 2021).…”

Section: Incorporating Phylogenetic Informationmentioning

confidence: 99%

Bayesian Knockoff Generators for Robust Inference Under Complex Data Structure

Märtens¹,

Banerjee²,

Qi³

et al. 2021

Preprint

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The recent proliferation of medical data, such as genetics and electronic health records (EHR), offers new opportunities to find novel predictors of health outcomes. Presented with a large set of candidate features, interest often lies in selecting the ones most likely to be predictive of an outcome for further study such that the goal is to control the false discovery rate (FDR) at a specified level. Knockoff filtering is an innovative strategy for FDR-controlled feature selection. But, existing knockoff methods make strong distributional assumptions that hinder their applicability to real world data. We propose Bayesian models for generating high quality knockoff copies that utilize available knowledge about the data structure, thus improving the resolution of prognostic features. Applications to two feature sets are considered: those with categorical and/or continuous variables possibly having a population substructure, such as in EHR; and those with microbiome features having a compositional constraint and phylogenetic relatedness. Through simulations and real data applications, these methods are shown to identify important features with good FDR control and power.

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c-lasso - a Python package for constrained sparse and robust regression and classification

Cited by 9 publications

References 15 publications

Critical review of 16S rRNA gene sequencing workflow in microbiome studies: From primer selection to advanced data analysis

Critical review of 16S rRNA gene sequencing workflow in microbiome studies: From primer selection to advanced data analysis

CR-Sparse: Hardware accelerated functional algorithms for sparse signal processing in Python using JAX

Bayesian Knockoff Generators for Robust Inference Under Complex Data Structure

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