Generalized correlation measure using count statistics for gene expression data with ordered samples

Wang, Y. X. Rachel; Liu, Ke; Theusch, Elizabeth; Rotter, Jerome I.; Medina, Marisa W.; Waterman, Michael S.; Huang, Haiyan

doi:10.1093/bioinformatics/btx641

Cited by 11 publications

(7 citation statements)

References 29 publications

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“…Other coexpression measures can be more appropriate in other contexts as discussed in refs. 15–17. While in most cases, the bait genes cluster separately from the candidate genes, in some instances candidate gene(s) will cluster with the bait genes (e.g., when a gray dot clusters within the red dots as shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Given an M × T matrix, which contains the expression values of T genes across M samples, we first compute a T × T similarity matrix A with entry A[i,j] representing the absolute value of Spearman rank correlation between gene i and gene j across the M samples (note that alternative gene coexpression or association measures, such as those introduced and discussed in refs. 15–17, can be used per users’ choice and study goal). Next, we performed an eigen decomposition of the normalized graph Laplacian L = I − D −1/2 AD −1/2 and formed a T × K matrix G with column G[, j] representing the eigenvector corresponding to the jth smallest non-0 eigenvalue of L. Here, D is a diagonal matrix in which entry D[i,i] is the sum of the ith row of A.…”

Section: Methodsmentioning

confidence: 99%

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GeneFishing to reconstruct context specific portraits of biological processes

Liu

Theusch²,

Zhou

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

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Rapid advances in genomic technologies have led to a wealth of diverse data, from which novel discoveries can be gleaned through the application of robust statistical and computational methods. Here, we describe GeneFishing, a semisupervised computational approach to reconstruct context-specific portraits of biological processes by leveraging gene–gene coexpression information. GeneFishing incorporates multiple high-dimensional statistical ideas, including dimensionality reduction, clustering, subsampling, and results aggregation, to produce robust results. To illustrate the power of our method, we applied it using 21 genes involved in cholesterol metabolism as “bait” to “fish out” (or identify) genes not previously identified as being connected to cholesterol metabolism. Using simulation and real datasets, we found that the results obtained through GeneFishing were more interesting for our study than those provided by related gene prioritization methods. In particular, application of GeneFishing to the GTEx liver RNA sequencing (RNAseq) data not only reidentified many known cholesterol-related genes, but also pointed to glyoxalase I (GLO1) as a gene implicated in cholesterol metabolism. In a follow-up experiment, we found that GLO1 knockdown in human hepatoma cell lines increased levels of cellular cholesterol ester, validating a role for GLO1 in cholesterol metabolism. In addition, we performed pantissue analysis by applying GeneFishing on various tissues and identified many potential tissue-specific cholesterol metabolism-related genes. GeneFishing appears to be a powerful tool for identifying related components of complex biological systems and may be used across a wide range of applications.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

GeneFishing to reconstruct context specific portraits of biological processes

Liu

Theusch²,

Zhou

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

View full text Add to dashboard Cite

show abstract

“…For time series data, another prevalent feature is the presence of time shifts between association patterns, reflecting the fact that regulation may take effect after a time delay. Methods for handling the time lag issue include time-shifted Pearson's correlation [100], time-shifted expression rank pattern analysis [229] and time sequence alignment algorithms [1,69,111,252].…”

Section: Inferring Gene-gene Relationships Using Expression Datamentioning

confidence: 99%

Network Modeling in Biology: Statistical Methods for Gene and Brain Networks

Wang

et al. 2021

Statist. Sci.

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The rise of network data in many different domains has offered researchers new insights into the problem of modeling complex systems and propelled the development of numerous innovative statistical methodologies and computational tools. In this paper, we primarily focus on two types of biological networks, gene networks and brain networks, where statistical network modeling has found both fruitful and challenging applications. Unlike other network examples such as social networks where network edges can be directly observed, both gene and brain networks require careful estimation of edges using measured data as a first step. We provide a discussion on existing statistical and computational methods for edge estimation and subsequent statistical inference problems in these two types of biological networks.

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“…It should be noted that a kernel of the form (1) implicitly assumes uncorrelatedness between components in the sample vector, which implies that the kernel can be isotropic. However, observed samples usually present some sort of mutual correlation [34,35].…”

Section: Innovative Contributionmentioning

confidence: 99%

Anisotropic Gaussian kernel adaptive filtering by Lie-group dictionary learning

et al. 2020

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The present paper proposes a novel kernel adaptive filtering algorithm, where each Gaussian kernel is parameterized by a center vector and a symmetric positive definite (SPD) precision matrix, which is regarded as a generalization of scalar width parameter. In fact, different from conventional kernel adaptive systems, the proposed filter is structured as a superposition of non-isotropic Gaussian kernels, whose non-isotropy makes the filter more flexible. The adaptation algorithm will search for optimal parameters in a wider parameter space. This generalization brings the need of special treatment of parameters that have a geometric structure. In fact, the main contribution of this paper is to establish update rules for precision matrices on the Lie group of SPD matrices in order to ensure their symmetry and positive-definiteness. The parameters of this filter are adapted on the basis of a leastsquares criterion to minimize the filtering error, together with an ℓ 1-type regularization criterion to avoid overfitting and to prevent the increase of dimensionality of the dictionary. Experimental results confirm the validity of the proposed method.

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Generalized correlation measure using count statistics for gene expression data with ordered samples

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 11 publications

References 29 publications

GeneFishing to reconstruct context specific portraits of biological processes

GeneFishing to reconstruct context specific portraits of biological processes

Network Modeling in Biology: Statistical Methods for Gene and Brain Networks

Anisotropic Gaussian kernel adaptive filtering by Lie-group dictionary learning

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