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CMRF: analyzing differential gene regulation in two group perturbation experiments
Nirmalya Bandyopadhyay1, Manas Somaiya, Sanjay Ranka
1Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32603, USA. nirmalya@cise.ufl.edu.
Background:
Microarray experiments often measure expressions of genes taken from sample tissues in the presence of external perturbations such as medication, radiation, or disease. The external perturbation can change the expressions of some genes directly or indirectly through gene interaction network. In this paper, we focus on an important class of such microarray experiments that inherently have two groups of tissue samples. When such different groups exist, the changes in expressions for some of the genes after the perturbation can be different between the two groups. It is not only important to identify the genes that respond differently across the two groups, but also to mine the reason behind this differential response. In this paper, we aim to identify the cause of this differential behavior of genes, whether because of the perturbation or due to interactions with other genes.
Results:
We propose a new probabilistic Bayesian method CMRF based on Markov Random Field to identify such genes. CMRF leverages the information about gene interactions as the prior of the model. We compare the accuracy of CMRF with SSEM and Student's t test and our old method SMRF on semi-synthetic dataset generated from microarray data. CMRF obtains high accuracy and outperforms all the other three methods. We also conduct a statistical significance test using a parametric noise based experiment to evaluate the accuracy of our method. In this experiment, CMRF generates significant regions of confidence for various parameter settings.
Conclusions:
In this paper, we solved the problem of finding primarily differentially regulated genes in the presence of external perturbations when the data is sampled from two groups. The probabilistic Bayesian method CMRF based on Markov Random Field incorporates dependency structure of the gene networks as the prior to the model. Experimental results on synthetic and real datasets demonstrated the superiority of CMRF compared to other simple techniques.
Insights
We developed a new Bayesian method, CMRF, to identify differentially expressed genes in microarray data from two sample groups. CMRF accurately identifies genes affected by perturbations and gene interactions, outperforming existing methods.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Microarray experiments analyze gene expression changes due to external factors like disease or medication.
- Gene expression can be altered directly or indirectly through gene interaction networks.
- Differential gene expression analysis is crucial for understanding biological responses to perturbations.
Purpose of the Study:
- To identify genes exhibiting differential expression between two sample groups under perturbation.
- To determine the underlying causes of differential gene behavior, including perturbation effects and gene interactions.
Main Methods:
- Proposed a novel probabilistic Bayesian method named CMRF (Causal Markov Random Field).
- Leveraged gene interaction network information as prior knowledge within the CMRF model.
- Utilized semi-synthetic datasets derived from microarray data for comparative analysis.
Main Results:
- CMRF demonstrated high accuracy in identifying differentially regulated genes.
- CMRF outperformed existing methods, including SSEM, Student's t-test, and SMRF.
- Statistical significance tests confirmed the accuracy and reliability of CMRF across various parameter settings.
Conclusions:
- Successfully addressed the challenge of identifying differentially regulated genes in two-group microarray data under perturbation.
- The CMRF method effectively incorporates gene network structures into a Bayesian framework.
- Experimental validation on both synthetic and real datasets confirmed CMRF's superior performance over simpler techniques.
