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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
Published on: July 29, 2022
Clustering biological annotations and gene expression data to identify putatively co-regulated biological processes
Corneliu Henegar1, Raffaella Cancello, Sophie Rome
1Inserm, U755 Nutriomique, 75004 Paris, France. corneliu@henegar.info
Journal of Bioinformatics and Computational Biology
|September 29, 2006
Summary
This study introduces FunCluster, an R tool for identifying co-regulated biological processes from gene expression data. Analysis revealed immune molecule synthesis in adipose tissue and protein metabolism in skeletal muscle.
Area of Science:
- Bioinformatics
- Systems Biology
- Genomics
Background:
- Functional profiling is crucial for analyzing microarray gene expression data.
- Identifying co-regulated biological processes aids in understanding biological interactions.
Purpose of the Study:
- To develop and present an original approach for identifying co-regulated biological processes based on shared co-expressed genes.
- To implement this approach as an R language tool named FunCluster.
Main Methods:
- Developed an R language implementation called FunCluster.
- Tested FunCluster on two distinct gene expression datasets.
- Performed discriminatory functional analysis on the datasets.
Main Results:
- Analysis of human white adipose tissue revealed non-adipose cells' role in inflammatory and immunity molecule synthesis.
- Analysis of human skeletal muscle highlighted novel functional classes related to protein metabolism and muscle contraction regulation.
Conclusions:
- FunCluster effectively identifies co-regulated biological processes from gene expression data.
- The tool provides insights into cellular functions in different biological contexts, such as adiposity and insulin regulation.
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