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...
11:19
Correlating Gene-specific DNA Methylation Changes with Expression and Transcriptional Activity of Astrocytic KCNJ10 (Kir4.1)
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Cited by 9 •
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.
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.
12:09
Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
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Cited by 35 •
2014
The Default Mode Network (DMN) in Temporal Lobe Epilepsy (TLE) is analyzed in the resting state of the brain using seed-based functional connectivity MRI (fcMRI).
01:09
Correlation
0 Views •
2023
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation. Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if: Variable a...
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.