Gene Correlation Network
Gene Correlation Network - Related Videos
02:20
Correlations
0 Views •
2020
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
09:49
Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
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2021
Network analysis was applied to evaluate the association of various ecological microbial communities, such as soil, water and rhizosphere. Presented here is a protocol on how to use the WGCNA algorithm to analyze different co-occurrence networks that may occur in the microbial communities due to different ecological environments.
11:19
Correlating Gene-specific DNA Methylation Changes with Expression and Transcriptional Activity of Astrocytic KCNJ10 (Kir4.1)
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Cited by 10 •
2015
DNA methylation is capable of maintaining stable levels of gene expression as well as allowing for dynamic changes in gene expression in response to a variety of stimuli. We detail techniques that allow the study of gene-specific changes in DNA methylation and the effect of these changes on gene expression.
10:40
CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis
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Cited by 1 •
2022
This protocol describes a high-throughput clustered regularly interspaced short palindromic repeats (CRISPR) gene editing workflow for microRNA cluster network analysis that allows the rapid generation of a panel of genetically modified cell lines carrying unique miRNA cluster member deletion combinations as large as 35 kb within a single experiment.
10:44
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Cited by 3 •
2021
The Inherent Dynamics Visualizer is an interactive visualization package that connects to a gene regulatory network inference tool for enhanced, streamlined generation of functional network models. The visualizer can be used to make more informed decisions for parameterizing the inference tool, thus increasing confidence in the resulting models.
FAQs
The analysis compares expression patterns for pairs of genes across the available samples. Genes showing similar statistical behavior receive stronger relationships, whereas weaker relationships may be reduced or excluded during network construction. This organization allows researchers to examine coordinated activity across many genes and identify groups whose expression changes together under the sampled biological conditions.
A correlation threshold can retain only relationships that meet a selected strength criterion, producing a more selective network for organizing genes into modules. Weighted approaches preserve differences in relationship strength rather than treating retained connections equally. The choice therefore influences network structure, module composition, and which coordinated expression patterns become prominent during downstream analysis.
Co-expression modules group genes with related expression patterns, making it possible to examine coordinated programs instead of isolated gene pairs. Network topology can then highlight hub genes, which occupy prominent positions within the modeled structure. These features help prioritize candidate pathways or genes for further investigation, but prominence in the network does not establish biological causation.
A statistical relationship between expression patterns indicates coordinated behavior across the analyzed samples, not a confirmed regulatory interaction. Two genes may appear connected without one directly controlling the other. This distinction is essential when interpreting a Gene Correlation Network, because network associations can suggest candidates or biological programs while still requiring experimental work to examine direct regulation or causality.
A typical workflow begins with gene-expression measurements collected across samples, followed by calculation of pairwise correlations. Researchers then apply correlation thresholds or use weighted relationships to organize the genes into co-expression modules. Finally, they examine network topology and module-level patterns to identify prominent genes and coordinated programs that merit further biological interpretation or experimental investigation.
In neuroscience, module-level patterns can be examined in relation to neuronal development, synaptic function, brain regions, or neurological disease. The network provides a way to connect coordinated gene-expression programs with these subject-specific contexts. Researchers can use the resulting modules and topology to identify candidate pathways or hub genes for follow-up studies, while maintaining the distinction between association and mechanism.
Network and module analysis can identify coordinated gene programs, prominent hub genes, and candidate pathways associated with the sampled biological context. In neuroscience, these candidates may relate to development, synaptic activity, regional brain biology, or disease-associated expression patterns. Such findings guide further experimental investigation rather than serving as definitive evidence of direct regulation or causal function.