A Combinatorial Approach to the Analysis of Differential Gene Expression Data

2006 ◽  
pp. 223-238 ◽  
Author(s):  
Michael A. Langston ◽  
Lan Lin ◽  
Xinxia Peng ◽  
Nicole E. Baldwin ◽  
Christopher T. Symons ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Gene Expression Data ◽  
Expression Data ◽  
Combinatorial Approach ◽  
Differential Gene

scholarly journals SFARI Genes and where to find them; classification modelling to identify genes associated with Autism Spectrum Disorder from RNA-seq data

2021 ◽  
Author(s):  
Magdalena Navarro ◽  
T Ian Simpson
Keyword(s):  
Gene Expression ◽  
Autism Spectrum Disorder ◽  
Differential Gene Expression ◽  
Gene Expression Data ◽  
Gene List ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Expression Data ◽  
Link Type ◽  
Differential Gene

AbstractMotivationAutism spectrum disorder (ASD) has a strong, yet heterogeneous, genetic component. Among the various methods that are being developed to help reveal the underlying molecular aetiology of the disease, one that is gaining popularity is the combination of gene expression and clinical genetic data. For ASD, the SFARI-gene database comprises lists of curated genes in which presumed causative mutations have been identified in patients. In order to predict novel candidate SFARI-genes we built classification models combining differential gene expression data for ASD patients and unaffected individuals with a gene’s status in the SFARI-gene list.ResultsSFARI-genes were not found to be significantly associated with differential gene expression patterns, nor were they enriched in gene co-expression network modules that had a strong correlation with ASD diagnosis. However, network analysis and machine learning models that incorporate information from the whole gene co-expression network were able to predict novel candidate genes that share features of existing SFARI genes and have support for roles in ASD in the literature. We found a statistically significant bias related to the absolute level of gene expression for existing SFARI genes and their scores. It is essential that this bias be taken into account when studies interpret ASD gene expression data at gene, module and whole-network levels.AvailabilitySource code is available from GitHub (https://doi.org/10.5281/zenodo.4463693) and the accompanying data from The University of Edinburgh DataStore (https://doi.org/10.7488/ds/2980)[email protected]

scholarly journals Reconciling differential gene expression data with molecular interaction networks

2013 ◽  
Vol 29 (5) ◽  
pp. 622-629 ◽  
Author(s):  
Christopher L. Poirel ◽  
Ahsanur Rahman ◽  
Richard R. Rodrigues ◽  
Arjun Krishnan ◽  
Jacqueline R. Addesa ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Gene Expression Data ◽  
Molecular Interaction ◽  
Interaction Networks ◽  
Expression Data ◽  
Molecular Interaction Networks ◽  
Differential Gene

scholarly journals Python for gene expression

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 870
Author(s):  
Leonid Bystrykh
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Programming Language ◽  
Gene Expression Data ◽  
Expression Data ◽  
R Programming Language ◽  
Substantial Progress ◽  
Differential Gene ◽  
R Programming ◽  
Computational Part

Genome biology shows substantial progress in its analytical and computational part in the last decades. Differential gene expression is one of many computationally intense areas; it is largely developed under R programming language. Here we explain possible reasons for such dominance of R in gene expression data. Next, we discuss the prospects for Python to become competitive in this area of research in coming years. We indicate that Python can be used already in a field of a single cell differential gene expression. We pinpoint still missing parts in Python and possibilities for improvement.

scholarly journals FM-test: a fuzzy-set-theory-based approach to differential gene expression data analysis

2006 ◽  
Vol 7 (Suppl 4) ◽  
pp. S7 ◽  
Author(s):  
Lily R Liang ◽  
Shiyong Lu ◽  
Xuena Wang ◽  
Yi Lu ◽  
Vinay Mandal ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Differential Gene Expression ◽  
Set Theory ◽  
Gene Expression Data ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Expression Data ◽  
Gene Expression Data Analysis ◽  
Differential Gene

scholarly journals VisExpress: Visual exploration of differential gene expression data

2016 ◽  
Vol 16 (1) ◽  
pp. 48-73 ◽  
Author(s):  
Svenja Simon ◽  
Sebastian Mittelstädt ◽  
Bum Chul Kwon ◽  
Andreas Stoffel ◽  
Richard Landstorfer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Gene Expression Data ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Environmental Changes ◽  
Visual Exploration ◽  
Expression Data ◽  
Differential Gene Expression Analysis ◽  
Differential Gene

Biologists are keen to understand how processes in cells react to environmental changes. Differential gene expression analysis allows biologists to explore functions of genes with data generated from different environments. However, these data and analysis lead to unique challenges since tasks are ill-defined, require implicit domain knowledge, comprise large volumes of data, and are, therefore, of explanatory nature. To investigate a scalable visualization-based solution, we conducted a design study with three biologists specialized in differential gene expression analysis. We stress our contributions in three aspects: first, we characterize the problem domain for exploring differential gene expression data and derive task abstractions and design requirements. Second, we investigate the design space and present an interactive visualization system, called VisExpress. Third, we evaluate the usefulness of VisExpress via a Pair Analytics study with real users and real data and report on insights that were gained by our experts with VisExpress.

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