Comprehensive review of association estimators for the inference of gene networks

Kurt, Zeyneb; Aydin, Nizamettin; Altay, Gökmen

doi:10.3906/elk-1312-90

Cited by 11 publications

(9 citation statements)

References 140 publications

(357 reference statements)

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“…The mutual information values are then calculated by exploring all possible grids up to a maximal grid resolution. The definition and properties of MIC were described by Reshef et al (2011) and discussed by Kinney et al (2013) and Kurt et al (2016). MIC takes values from 0 to 1; the closer it is to 1, the stronger relationship between the variables is expected.…”

Section: Methodsmentioning

confidence: 99%

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Comparison of some correlation measures for continuous and categorical data

Skotarczak

Dobek

Moliński

2019

Biometrical Letters

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SummaryIn the literature there can be found a wide collection of correlation and association coefficients used for different structures of data. Generally, some of the correlation coefficients are conventionally used for continuous data and others for categorical or ordinal observations. The aim of this paper is to verify the performance of various approaches to correlation coefficient estimation for several types of observations. Both simulated and real data were analysed. For continuous variables, Pearson’s r2 and MIC were determined, whereas for categorized data three approaches were compared: Cramér’s V, Joe’s estimator, and the regression-based estimator. Two method of discretization for continuous data were used. The following conclusions were drawn: the regression-based approach yielded the best results for data with the highest assumed r2 coefficient, whereas Joe’s estimator was the better approximation of true correlation when the assumed r2 was small; and the MIC estimator detected the maximal level of dependency for data having a quadratic relation. Moreover, the discretization method applied to data with a non-linear dependency can cause loss of dependency information. The calculations were supported by the R packages arules and minerva.

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Section: Methodsmentioning

confidence: 99%

“…In the literature there can be found a wide collection of correlation and association coefficients used for different structures of data. Kurt et al (2016) give a comprehensive review of correlation coefficients, including their comparison. They compared the correlation coefficients used in the inference of gene networks.…”

Section: Introductionmentioning

confidence: 99%

Comparison of some correlation measures for continuous and categorical data

Skotarczak

Dobek

Moliński

2019

Biometrical Letters

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“…(Filtered and Normalized) estimator, we implemented the popular estimators the Pearson Correlation Coefficient (PCC) and the Spearman Correlation Coefficient (SCC). A comprehensive review and descriptions of dependency score estimators can be seen in [31]. Shortly, PCC captures linear relationship between the variables, whereas SCC is a ranked based version of PCC and captures monotonic relationships.…”

Section: Cell Type a Gene Expression Dataset (Filtered And Normalized)mentioning

confidence: 99%

Gene Regulatory Cross Networks: Inferring Gene Level Cell-to-Cell Communications of Immune Cells

Altay

Peters

2018

Preprint

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Corresponding authors: altay@lji.org and bpeters@lji.org. Abstract:Background Gene level cell-to-cell communications are crucial part of biology as they may be potential targets of drugs and vaccines against a disease condition of interest. Yet, there are only few studies that propose algorithms on this particularly important research field. ResultsIn this study, we first overview the current literature and define two general terms for the types of approaches in general for gene level cell-to-cell communications: Gene Regulatory Cross Networks (GRCN) and Gene Co-Expression Cross Networks (GCCN). We then propose two algorithms for each type, named as GRCNone and GCCNone. We applied them to reveal communications among 8 different immune cell types and evaluate their performances mainly via membrane protein database. Also, we show the biological relevance of the predicted crossnetworks with pathway enrichment analysis. We then provide an approach that prioritize the targets by ranking them before experimental validations. ConclusionsWe establish two main approaches and propose algorithms for genome-wide scale gene level cellto-cell communications between any two different cell-types. This study aims accelerating this relatively new avenue of research in cross-networks and points out the gap of it with the wellestablished single cell type gene networks. The proposed algorithms have the potential to reveal gene level interactions between normal and disease cell types. For instance, they might reveal the interaction of genes between tumor and normal cells, which are the potential drug-targets and thus can help finding new cures that might prevent the prevailing of tumor cells. Background:Gene network inference (GNI) methods can identify interactions between different genes and the gene products they encode. Gene regulatory networks (GRN), which can be inferred by GNI methods, help in the basic biological understanding of genes and their functions and are well studied within single cell condition [1]. In any GRN inference, the goal is to infer a network that consist of causal interactions between the genes. It is the main difference of GRN from the coexpression networks [2] that infers a network containing both causal and associated interactions.Given the huge number of significantly associated genes, most of the links of a co-expression network are associative. This way it can be considered mostly similar to kind of clustering of interactions among the genes in some more detail than basic clustering. In clustering, the clustered genes are assumed to be fully connected with each other. GRNs are, in theory, more precise to understand the causal mechanism of any interaction but harder to infer accurately. However, different cell types are also interacting with each other, especially immune cell types, to manifest various complex biological functions. So far, GNI methods have not focused on the gene level interactions between different cell types [3]. In fact, it was stated in the very recent publication [3] that gene netw...

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“…Estimating a random variable’s entropy is of great importance and with many applications, having received much attention in recent years [ 7 , 8 , 9 , 10 , 11 ]. Most of the known estimators are generally applied for the estimation of mutual information [ 12 ], but in this scenario entropy estimators will perform similarly, and thus the selection between different estimators is less of an issue. However, entropy has great application in cryptography as a vital tool for designing and analyzing encryption methods [ 13 , 14 , 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Selecting an Effective Entropy Estimator for Short Sequences of Bits and Bytes with Maximum Entropy

Rodríguez

Madarro-Capó

Legón-Pérez

et al. 2021

Entropy

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Entropy makes it possible to measure the uncertainty about an information source from the distribution of its output symbols. It is known that the maximum Shannon’s entropy of a discrete source of information is reached when its symbols follow a Uniform distribution. In cryptography, these sources have great applications since they allow for the highest security standards to be reached. In this work, the most effective estimator is selected to estimate entropy in short samples of bytes and bits with maximum entropy. For this, 18 estimators were compared. Results concerning the comparisons published in the literature between these estimators are discussed. The most suitable estimator is determined experimentally, based on its bias, the mean square error short samples of bytes and bits.

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Comprehensive review of association estimators for the inference of gene networks

Cited by 11 publications

References 140 publications

Comparison of some correlation measures for continuous and categorical data

Comparison of some correlation measures for continuous and categorical data

Gene Regulatory Cross Networks: Inferring Gene Level Cell-to-Cell Communications of Immune Cells

Selecting an Effective Entropy Estimator for Short Sequences of Bits and Bytes with Maximum Entropy

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