scholarly journals Glimma: interactive graphics for gene expression analysis

2017 ◽  
Author(s):  
Shian Su ◽  
Charity W. Law ◽  
Casey Ah-Cann ◽  
Marie-Liesse Asselin-Labat ◽  
Marnie E. Blewitt ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
R Package ◽  
Data Exploration ◽  
Interactive Graphics ◽  
Microarray Gene Expression ◽  
Additional Information ◽  
On Demand ◽  
Annotation Information

AbstractMotivationSummary graphics for RNA-sequencing and microarray gene expression analyses may contain upwards of tens of thousands of points. Details about certain genes or samples of interest are easily obscured in such dense summary displays. Incorporating interactivity into summary plots would enable additional information to be displayed on demand and facilitate intuitive data exploration.ResultsThe open-source Glimma package creates interactive graphics for exploring gene expression analysis with a few simple R commands. It extends popular plots found in the limma package, such as multi-dimensional scaling plots and mean-difference plots, to allow individual data points to be queried and additional annotation information to be displayed upon hovering or selecting particular points. It also offers links between plots so that more information can be revealed on demand. Glimma is widely applicable, supporting data analyses from a number of well established Bioconductor workflows (limma, edgeR and DESeq2) and uses D3/JavaScript to produce HTML pages with interactive displays that enable more effective data exploration by end-users. Results from Glimma can be easily shared between bioinformaticians and biologists, enhancing reporting capabilities while maintaining reproducibility.Availability and ImplementationThe Glimma R package is available from http://bioconductor.org/packages/devel/bioc/html/Glimma.html.

scholarly journals cDNA Microarray Gene Expression Analysis and Toxicological Phenotype for Anticancer Drug

2004 ◽  
Vol 66 (11) ◽  
pp. 1339-1345 ◽  
Author(s):  
Gyoung-Jae LEE ◽  
Wan-Seon LEE ◽  
Ki-Seon JEON ◽  
Chan-Hwi UM ◽  
Yang-Suk KIM ◽  
...  
Keyword(s):  
Gene Expression ◽  
Anticancer Drug ◽  
Expression Analysis ◽  
Cdna Microarray ◽  
Gene Expression Analysis ◽  
Microarray Gene Expression ◽  
Microarray Gene ◽  
Microarray Gene Expression Analysis

scholarly journals Microarray gene expression analysis of neutrophils from elderly septic patients

Genomics Data ◽  
2015 ◽  
Vol 6 ◽  
pp. 51-53 ◽  
Author(s):  
Diogo Vieira da Silva Pellegrina ◽  
Patricia Severino ◽  
Marcel Cerqueira Machado ◽  
Fabiano Pinheiro da Silva ◽  
Eduardo Moraes Reis
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Microarray Gene Expression ◽  
Microarray Gene ◽  
Microarray Gene Expression Analysis

scholarly journals NormExpression: an R package to normalize gene expression data using evaluated methods

2018 ◽  
Author(s):  
Zhenfeng Wu ◽  
Weixiang Liu ◽  
Xiufeng Jin ◽  
Deshui Yu ◽  
Hua Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Expression Profiles ◽  
R Package ◽  
Data Normalization ◽  
Expression Data ◽  
Normalization Methods ◽  
Normalize Gene Expression

AbstractData normalization is a crucial step in the gene expression analysis as it ensures the validity of its downstream analyses. Although many metrics have been designed to evaluate the current normalization methods, the different metrics yield inconsistent results. In this study, we designed a new metric named Area Under normalized CV threshold Curve (AUCVC) and applied it with another metric mSCC to evaluate 14 commonly used normalization methods, achieving consistency in our evaluation results using both bulk RNA-seq and scRNA-seq data from the same library construction protocol. This consistency has validated the underlying theory that a sucessiful normalization method simultaneously maximizes the number of uniform genes and minimizes the correlation between the expression profiles of gene pairs. This consistency can also be used to analyze the quality of gene expression data. The gene expression data, normalization methods and evaluation metrics used in this study have been included in an R package named NormExpression. NormExpression provides a framework and a fast and simple way for researchers to evaluate methods (particularly some data-driven methods or their own methods) and then select a best one for data normalization in the gene expression analysis.

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