coXpress: A Tool for Analyzing Differential Co-Expression in Gene Expression Data
Author Information
Author(s): Michael Watson
Primary Institution: Institute for Animal Health
Hypothesis
Can coXpress effectively identify groups of genes that are differentially co-expressed under varying experimental conditions?
Conclusion
coXpress can be used to find groups of genes that display differential co-expression patterns in microarray datasets.
Supporting Evidence
- coXpress identifies groups of genes that are highly correlated in one set of experiments but show little correlation in another.
- The software provides a p-value for each group to assess the significance of co-expression.
- Robustness testing showed that the identified groups are consistent across multiple resampling iterations.
Takeaway
coXpress is a software that helps scientists find groups of genes that work together differently in different situations.
Methodology
coXpress uses hierarchical clustering and a resampling method to identify differentially co-expressed gene groups.
Potential Biases
Genes may be incorrectly grouped due to variations in expression across sub-populations.
Limitations
The choice of where to cut the clustering tree is arbitrary and may not reflect biological realities.
Participant Demographics
The study involved gene expression data from 38 tumor mRNA samples, including 27 acute lymphoblastic leukaemia (ALL) cases and 11 acute myeloid leukaemia (AML) cases.
Statistical Information
P-Value
0.007638
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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