A novel statistical approach for identification of the master regulator transcription factor
Sinjini Sikdar1, Susmita Datta2
1Department of Biostatistics, University of Florida, Gainesville, FL, 32611, USA.
Background:
Transcription factors are known to play key roles in carcinogenesis and therefore, are gaining popularity as potential therapeutic targets in drug development. A 'master regulator' transcription factor often appears to control most of the regulatory activities of the other transcription factors and the associated genes. This 'master regulator' transcription factor is at the top of the hierarchy of the transcriptomic regulation. Therefore, it is important to identify and target the master regulator transcription factor for proper understanding of the associated disease process and identifying the best therapeutic option.
Methods:
We present a novel two-step computational approach for identification of master regulator transcription factor in a genome. At the first step of our method we test whether there exists any master regulator transcription factor in the system. We evaluate the concordance of two ranked lists of transcription factors using a statistical measure. In case the concordance measure is statistically significant, we conclude that there is a master regulator. At the second step, our method identifies the master regulator transcription factor, if there exists one.
Results:
In the simulation scenario, our method performs reasonably well in validating the existence of a master regulator when the number of subjects in each treatment group is reasonably large. In application to two real datasets, our method ensures the existence of master regulators and identifies biologically meaningful master regulators. An R code for implementing our method in a sample test data can be found in http://www.somnathdatta.org/software .
Conclusion:
We have developed a screening method of identifying the 'master regulator' transcription factor just using only the gene expression data. Understanding the regulatory structure and finding the master regulator help narrowing the search space for identifying biomarkers for complex diseases such as cancer. In addition to identifying the master regulator our method provides an overview of the regulatory structure of the transcription factors which control the global gene expression profiles and consequently the cell functioning.
Insights
We developed a computational method to identify master regulator transcription factors, key drivers in gene regulation and cancer. This approach aids in understanding complex diseases and finding therapeutic targets.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Transcription factors are crucial in gene regulation and implicated in cancer development.
- Identifying 'master regulator' transcription factors is vital for understanding disease mechanisms and therapeutic strategies.
Purpose of the Study:
- To present a novel computational approach for identifying master regulator transcription factors.
- To validate the method's efficacy in both simulated and real biological datasets.
Main Methods:
- A two-step computational strategy is employed.
- The first step statistically assesses the existence of a master regulator by evaluating the concordance of ranked transcription factor lists.
- The second step identifies the specific master regulator if one is detected.
Main Results:
- The method demonstrates robust performance in simulations with sufficient sample sizes.
- Application to real-world gene expression data successfully identified biologically relevant master regulators.
- An R code implementation is available for public use.
Conclusions:
- A screening method for identifying master regulator transcription factors using gene expression data has been established.
- This approach facilitates biomarker discovery for complex diseases like cancer by refining the search space.
- The method offers insights into the regulatory network controlling global gene expression and cellular function.
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