Journal ArticleDOI
Inferring gene regulatory networks by integrating ChIP-seq/chip and transcriptome data via LASSO-type regularization methods
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TLDR
Two extended models of LASSO, L0 and L1/2 regularization models are applied to infer gene regulatory network from both high-throughput gene expression data and transcription factor binding data in mouse embryonic stem cells to demonstrate the efficiency and applicability of these two models.About:
This article is published in Methods.The article was published on 2014-06-01. It has received 66 citations till now. The article focuses on the topics: Gene regulatory network & Lasso (statistics).read more
Citations
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Journal ArticleDOI
Gene regulatory network inference using fused LASSO on multiple data sets.
TL;DR: The study indicates that the combination of sparse regression techniques with other biologically meaningful constraints is a promising framework for gene regulatory network reconstructions.
Journal ArticleDOI
The single-cell eQTLGen consortium.
Monique G. P. van der Wijst,DH de Vries,Hilde E. Groot,Gosia Trynka,CC Hon,Marc Jan Bonder,Oliver Stegle,Martijn C. Nawijn,Youssef Idaghdour,P. van der Harst,Chun Jimmie Ye,Joseph E. Powell,Fabian J. Theis,Ahmed Mahfouz,Ahmed Mahfouz,Matthias Heinig,Lude Franke +16 more
TL;DR: The sc-eQTLGen consortium is founded, aimed at pinpointing the cellular contexts in which disease-causing genetic variants affect gene expression, and provides a set of study design considerations for future single-cell eQTL studies.
Journal ArticleDOI
Microbiome Multi-Omics Network Analysis: Statistical Considerations, Limitations, and Opportunities.
TL;DR: This review overviews some of the most important network methods for integrative analysis, with an emphasis on methods that have been applied or have great potential to be applied to the analysis of multi-omics integration of microbiome data.
Journal ArticleDOI
Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells.
Emily R. Miraldi,Emily R. Miraldi,Maria Pokrovskii,Aaron Watters,Dayanne M. Castro,Nicholas De Veaux,Jason A. Hall,June-Yong Lee,Maria Ciofani,Aviv Madar,Nick Carriero,Dan R. Littman,Dan R. Littman,Richard Bonneau +13 more
TL;DR: This work proposes methods for TRN inference in a mammalian setting, using ATAC-seq data to improve gene expression modeling, and highlights newly discovered roles for individual TFs and groups of TFs ("TF-TF modules") in Th17 gene regulation.
Journal ArticleDOI
Understanding gene regulatory mechanisms by integrating ChIP-seq and RNA-seq data: statistical solutions to biological problems.
Claudia Angelini,Valerio Costa +1 more
TL;DR: It is shown how integrating ChIP-seq and RNA-seq data can help to elucidate gene regulatory mechanisms and propose potential directions for statistical data integration.
References
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Least angle regression
Bradley Efron,Trevor Hastie,Iain M. Johnstone,Robert Tibshirani,Hemant Ishwaran,Keith Knight,Jean-Michel Loubes,Jean-Michel Loubes,Pascal Massart,Pascal Massart,David Madigan,David Madigan,Greg Ridgeway,Greg Ridgeway,Saharon Rosset,Saharon Rosset,Ji Zhu,Robert A. Stine,Berwin A. Turlach,Sanford Weisberg +19 more
TL;DR: A publicly available algorithm that requires only the same order of magnitude of computational effort as ordinary least squares applied to the full set of covariates is described.
Journal ArticleDOI
An Iterative Thresholding Algorithm for Linear Inverse Problems with a Sparsity Constraint
TL;DR: It is proved that replacing the usual quadratic regularizing penalties by weighted 𝓁p‐penalized penalties on the coefficients of such expansions, with 1 ≤ p ≤ 2, still regularizes the problem.
Posted Content
An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
Abstract: We consider linear inverse problems where the solution is assumed to have a sparse expansion on an arbitrary pre-assigned orthonormal basis. We prove that replacing the usual quadratic regularizing penalties by weighted l^p-penalties on the coefficients of such expansions, with 1 < or = p < or =2, still regularizes the problem. If p < 2, regularized solutions of such l^p-penalized problems will have sparser expansions, with respect to the basis under consideration. To compute the corresponding regularized solutions we propose an iterative algorithm that amounts to a Landweber iteration with thresholding (or nonlinear shrinkage) applied at each iteration step. We prove that this algorithm converges in norm. We also review some potential applications of this method.
Journal ArticleDOI
Regression shrinkage and selection via the lasso: a retrospective
TL;DR: In this article, the authors give a brief review of the basic idea and some history and then discuss some developments since the original paper on regression shrinkage and selection via the lasso.
Journal ArticleDOI
Sparse Approximate Solutions to Linear Systems
TL;DR: It is shown that the problem is NP-hard, but that the well-known greedy heuristic is good in that it computes a solution with at most at most $\left\lceil 18 \mbox{ Opt} ({\bf \epsilon}/2) \|{\bf A}^+\|^2_2 \ln(\|b\|_2/{\bf