ArticlePlant physiology2012
Application of the Gini correlation coefficient to infer regulatory relationships in transcriptome analysis.
Article in Plant physiology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
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- Transcriptome and metabolome analyses reveal regulatory networks associated with nutrition synthesis in sorghum seeds.Communications biology · 2024Article
- Extensive transcriptome data providing great efficacy in genetic research and adaptive gene discovery: a case study ofFrontiers in plant science · 2024Article
- Wound-Induced Temporal Reprogramming of Gene Expression during Agarwood Formation inPlants (Basel, Switzerland) · 2023Article
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- Design, execution, and interpretation of plant RNA-seq analyses.Frontiers in plant science · 2023Review
- Gene Co-Expression Network Tools and Databases for Crop Improvement.Plants (Basel, Switzerland) · 2022Review
- Comparative transcriptome analysis reveals regulatory network and regulators associated with proanthocyanidin accumulation in persimmon.BMC plant biology · 2021Article
- Key Regulators of Sucrose Metabolism Identified through Comprehensive Comparative Transcriptome Analysis in Peanuts.International journal of molecular sciences · 2021Article
- Predicting Protein Functions Based on Differential Co-expression and Neighborhood Analysis.Journal of computational biology : a journal of computational molecular cell biology · 2021Article
- Phenylpropanoid Biosynthesis Gene Expression Precedes Lignin Accumulation During Shoot Development in Lowland and Upland Switchgrass Genotypes.Frontiers in plant science · 2021Article
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- Genome and transcriptome of Papaver somniferum Chinese landrace CHM indicates that massive genome expansion contributes to high benzylisoquinoline alkaloid biosynthesis.Horticulture research · 2021Article
- AnnoLnc: A One-Stop Portal to Systematically Annotate Novel Human Long Noncoding RNAs.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Transcriptomic analysis at organ and time scale reveals gene regulatory networks controlling the sulfate starvation response of Solanum lycopersicum.BMC plant biology · 2020Article
- CAFU: a Galaxy framework for exploring unmapped RNA-Seq data.Briefings in bioinformatics · 2020Article
- A novel application of Gini coefficient for the quantitative measurement of bacterial aggregation.Scientific reports · 2019Article
Corrections and comments
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
One of the computational challenges in plant systems biology is to accurately infer transcriptional regulation relationships based on correlation analyses of gene expression patterns. Despite several correlation methods that are applied in biology to analyze microarray data, concerns regarding the compatibility of these methods with the gene expression data profiled by high-throughput RNA transcriptome sequencing (RNA-Seq) technology have been raised. These concerns are mainly due to the fact that the distribution of read counts in RNA-Seq experiments is different from that of fluorescence intensities in microarray experiments. Therefore, a comprehensive evaluation of the existing correlation methods and, if necessary, introduction of novel methods into biology is appropriate. In this study, we compared four existing correlation methods used in microarray analysis and one novel method called the Gini correlation coefficient on previously published microarray-based and sequencing-based gene expression data in Arabidopsis (Arabidopsis thaliana) and maize (Zea mays). The comparisons were performed on more than 11,000 regulatory relationships in Arabidopsis, including 8,929 pairs of transcription factors and target genes. Our analyses pinpointed the strengths and weaknesses of each method and indicated that the Gini correlation can compensate for the shortcomings of the Pearson correlation, the Spearman correlation, the Kendall correlation, and the Tukey's biweight correlation. The Gini correlation method, with the other four evaluated methods in this study, was implemented as an R package named rsgcc that can be utilized as an alternative option for biologists to perform clustering analyses of gene expression patterns or transcriptional network analyses.
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