The characteristic direction: a geometrical approach to identify differentially expressed genes

Clark, Neil R.; Hu, Kevin; Feldmann, Axel S.; Kou, Yan; Chen, Edward Y.; Duan, Qiaonan; Ma’ayan, Avi

doi:10.1186/1471-2105-15-79

Cited by 164 publications

(169 citation statements)

References 40 publications

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“…A key challenge in identifying "marker" genes is the choice of method that is used to assess selectivity of genes. Various parametric and nonparametric methods have been proposed in literature to identify differentially expressed genes between two groups [46,47] or between a group and other groups [48]. Furthermore, different methods have been developed in parallel to identify tissue-specific and tissue-selective genes that are unique markers with high specificity to their host tissue/cell type [49][50][51][52].…”

Section: Selection Of Marker Genes -The Good Bad and Uglymentioning

confidence: 99%

A Critical Survey of Deconvolution Methods for Separating Cell Types in Complex Tissues

Mohammadi

Zuckerman²,

Goldsmith

et al. 2017

Proc. IEEE

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Abstract-Identifying properties and concentrations of components from an observed mixture, known as deconvolution, is a fundamental problem in signal processing. It has diverse applications in fields ranging from hyperspectral imaging to denoising readings from biomedical sensors. This paper focuses on in-silico deconvolution of signals associated with complex tissues into their constitutive cell-type specific components, along with a quantitative characterization of the celltypes. Deconvolving mixed tissues/cell-types is useful in the removal of contaminants (e.g., surrounding cells) from tumor biopsies, as well as in monitoring changes in the cell population in response to treatment or infection. In these contexts, the observed signal from the mixture of cell-types is assumed to be a convolution, using a linear instantaneous (LI) mixing process, of the expression levels of genes in constitutive celltypes. The goal is to use known signals corresponding to individual celltypes along with a model of the mixing process to cast the deconvolution problem as a suitable optimization problem.In this paper, we present a survey and in-depth analysis of models, methods, and assumptions underlying deconvolution techniques. We investigate the choice of the different loss functions for evaluating estimation error, constraints on solutions, preprocessing and data filtering, feature selection, and regularization to enhance the quality of solutions, along with the impact of these choices on the performance of commonly used regression-based methods for deconvolution. We assess different combinations of these factors and use detailed statistical measures to evaluate their effectiveness. Some of these combinations have been proposed in the literature, whereas others represent novel algorithmic choices for deconvolution. We identify shortcomings of current methods and avenues for further investigation. For many of the identified shortcomings, such as normalization issues and data filtering, we provide new solutions. We summarize our findings in a prescriptive step-bystep process, which can be applied to a wide range of deconvolution problems.

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Section: Selection Of Marker Genes -The Good Bad and Uglymentioning

confidence: 99%

A Critical Survey of Deconvolution Methods for Separating Cell Types in Complex Tissues

Mohammadi

Zuckerman²,

Goldsmith

et al. 2017

Proc. IEEE

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“…Node (gene/protein) differential expression intensity is calculated based on a geometrical multivariate effective approach, called the Characteristic Direction (chdir) [20]. It uses a linear classification scheme, which defines a separating hyper-plane, the orientation of which can be interpreted to identify differentially expressed genes (DEG).…”

Section: Methodsmentioning

confidence: 99%

Detecting Perturbed Subpathways towards Mouse Lung Regeneration Following H1N1 Influenza Infection

et al. 2017

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It has already been established by the systems-level approaches that the future of predictive disease biomarkers will not be sketched by plain lists of genes or proteins or other biological entities but rather integrated entities that consider all underlying component relationships. Towards this orientation, early pathway-based approaches coupled expression data with whole pathway interaction topologies but it was the recent approaches that zoomed into subpathways (local areas of the entire biological pathway) that provided more targeted and context-specific candidate disease biomarkers. Here, we explore the application potential of PerSubs, a graph-based algorithm which identifies differentially activated disease-specific subpathways. PerSubs is applicable both for microarray and RNA-Seq data and utilizes the Kyoto Encyclopedia of Genes and Genomes (KEGG) database as reference for biological pathways. PerSubs operates in two stages: first, identifies differentially expressed genes (or uses any list of disease-related genes) and in second stage, treating each gene of the list as start point, it scans the pathway topology around to build meaningful subpathway topologies. Here, we apply PerSubs to investigate which pathways are perturbed towards mouse lung regeneration following H1N1 influenza infection.

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“…In [6] we introduced a new approach to differential expression that incorporates dimensionality reduction and a multivariate approach that respects the regulatory interactions between genes. This approach characterized differential expression as a direction in gene expression space, which we refer to as the Characteristic Direction.…”

Section: Introductionmentioning

confidence: 99%

Principle Angle Enrichment Analysis (PAEA): Dimensionally reduced multivariate gene set enrichment analysis tool

Clark

Szymkiewicz

Wang

et al. 2015

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Gene set analysis of differential expression, which identifies collectively differentially expressed gene sets, has become an important tool for biology. The power of this approach lies in its reduction of the dimensionality of the statistical problem and its incorporation of biological interpretation by construction. Many approaches to gene set analysis have been proposed, but benchmarking their performance in the setting of real biological data is difficult due to the lack of a gold standard. In a previously published work we proposed a geometrical approach to differential expression which performed highly in benchmarking tests and compared well to the most popular methods of differential gene expression. As reported, this approach has a natural extension to gene set analysis which we call Principal Angle Enrichment Analysis (PAEA). PAEA employs dimensionality reduction and a multivariate approach for gene set enrichment analysis. However, the performance of this method has not been assessed nor its implementation as a web-based tool. Here we describe new benchmarking protocols for gene set analysis methods and find that PAEA performs highly. The PAEA method is implemented as a user-friendly web-based tool, which contains 70 gene set libraries and is freely available to the community.

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The characteristic direction: a geometrical approach to identify differentially expressed genes

Cited by 164 publications

References 40 publications

A Critical Survey of Deconvolution Methods for Separating Cell Types in Complex Tissues

A Critical Survey of Deconvolution Methods for Separating Cell Types in Complex Tissues

Detecting Perturbed Subpathways towards Mouse Lung Regeneration Following H1N1 Influenza Infection

Principle Angle Enrichment Analysis (PAEA): Dimensionally reduced multivariate gene set enrichment analysis tool

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