A nonparametric likelihood ratio test to identify differentially expressed genes from microarray data.

Microarray experiments contribute significantly to the progress in disease treatment by enabling a precise and early diagnosis. One of the major objectives of microarray experiments is to identify differentially expressed genes under various conditions. The statistical methods currently used to analyse microarray data are inadequate, mainly due to the lack of understanding of the distribution of microarray data. We present a nonparametric likelihood ratio (NPLR) test to identify differentially expressed genes using microarray data. The NPLR test is highly robust against extreme values and does not assume the distribution of the parent population. Simulation studies show that the NPLR test is more powerful than some of the commonly used methods, such as the two-sample t-test, the Mann-Whitney U-test and significance analysis of microarrays (SAM). When applied to microarray data, we found that the NPLR test identifies more differentially expressed genes than its competitors. The asymptotic distribution of the NPLR test statistic and the p-value function is presented. The application of the NPLR method is shown, using both synthetic and real-life data. The biological significance of some of the genes detected only by the NPLR method is discussed.

A run-based procedure to identify time-lagged gene clusters in microarray experiments.

MOTIVATION: The analysis of gene-expression data obtained from microarray experiments can be useful to identify regulatory relationship between genes. Genes with a common functional role have similar expression patterns across different microarray experiments. These similar expression patterns are perhaps due to co-regulation of genes in the same functional group. Most of the existing methods available for the identification of the regulatory relationships are either made for comparing two genes at a time or methods are not computationally efficient in the identification of the regulatory relationships. The procedures adopted by these methods do not use complete information contained in the data set. In this paper, we propose a statistical procedure, which will use the information contained in the data set to cluster genes that show similar patterns. The proposed procedure compares several genes at a time instead of pair-wise comparisons as done in some of the other procedures. The proposed procedure provides gene clusters based on time-lagged data sets with more details. The proposed method provides a numerical value that would facilitate in comparing different sets of data obtained from different expressions. It also provides the identification of the gene involved and the time point at which the observation is made so that proper medicine can be developed for the gene-specific and time-specific disease. RESULTS: We applied the proposed procedure on the Spellman data set (Mol. Biol. Cell 1998; 9(12): 3273-3297) and compared our procedure with some of the other existing procedures. We found that our procedure is more computationally efficient than Ji and Tan (Bioinformatics 2005; 21:509-516), event method and edge detection procedures. The proposed procedure also provides more details about the clusters than Ji and Tan (Bioinformatics 2005; 21:509-516), event method and edge detection procedures. The proposed procedure is really simple to apply as compared with other available procedures in the literature including Ji and Tan (Bioinformatics 2005; 21:509-516), event method and edge detection procedures.

Identification of hair cycle-associated genes from time-course gene expression profile using fractal analysis.

Microarray technology permits one to monitor thousands of processes going on inside the cell. This tool has been used to study gene expression profiles associated with the hair-growth cycle. We provide a novel method called the fractal analysis method to identify hair-growth cycle associated genes from time course gene expression profiles. Fractal analysis is a much better method than the computational method used by Lin et al. (2004). The fractal dimension obtained by fractal analysis process also indicates the irregularity in hair-growth pattern. The computational method used by Lin et al. (2004) was unable to make any inference about the hair-growth pattern.

Fractal binding and dissociation kinetics of heart-related compounds on biosensor surfaces.

A fractal analysis is presented for the binding and dissociation of different heart-related compounds in solution to receptors immobilized on biosensor surfaces. The data analyzed include LCAT (lecithin cholesterol acyl transferase) concentrations in solution to egg white apoA-I rHDL immobilized on a biosensor chip surface (1), native, mildly oxidized, and strongly oxidized LDL in solution to a heparin-modified Au-surface of a surface plasmon resonance (SPR) biosensor (2), and TRITC-labeled HDL in solution to a bare optical fiber surface (3). Single-and dual-fractal models were used to fit the data. Values of the binding and the dissociation rate coefficient(s), affinity values, and the fractal dimensions were obtained from the regression analysis provided by Corel Quattro Pro 8.0 (4). The binding rate coefficients are quite sensitive to the degree of heterogeneity on the sensor chip surface. Predictive equations are developed for the binding rate coefficient as a function of the degree of heterogeneity present on the sensor chip surface and on the LCAT concentration in solution and for the affinity as a function of the ratio of fractal dimensions present in the binding and the dissociation phases. The analysis presented provided physical insights into these analyte-receptor reactions occurring on different biosensor surfaces.