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Extracting gene expression patterns and identifying co-expressed genes from microarray data reveals biologically responsive processes.

Chou JW, Zhou T, Kaufmann WK, Paules RS, Bushel PR - BMC Bioinformatics (2007)

Bottom Line: However, the variation associated with microarray data and the complexity of the experimental designs make the acquisition of co-expressed genes a challenge.Among them were mitotic cell cycle, DNA replication, DNA repair, cell cycle checkpoint, and G0-like status transition.EPIG can be applied to data sets from a variety of experimental designs.

View Article: PubMed Central - HTML - PubMed

Affiliation: Microarray Group, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA. chou@niehs.nih.gov

ABSTRACT

Background: A common observation in the analysis of gene expression data is that many genes display similarity in their expression patterns and therefore appear to be co-regulated. However, the variation associated with microarray data and the complexity of the experimental designs make the acquisition of co-expressed genes a challenge. We developed a novel method for Extracting microarray gene expression Patterns and Identifying co-expressed Genes, designated as EPIG. The approach utilizes the underlying structure of gene expression data to extract patterns and identify co-expressed genes that are responsive to experimental conditions.

Results: Through evaluation of the correlations among profiles, the magnitude of variation in gene expression profiles, and profile signal-to-noise ratio's, EPIG extracts a set of patterns representing co-expressed genes. The method is shown to work well with a simulated data set and microarray data obtained from time-series studies of dauer recovery and L1 starvation in C. elegans and after ultraviolet (UV) or ionizing radiation (IR)-induced DNA damage in diploid human fibroblasts. With the simulated data set, EPIG extracted the appropriate number of patterns which were more stable and homogeneous than the set of patterns that were determined using the CLICK or CAST clustering algorithms. However, CLICK performed better than EPIG and CAST with respect to the average correlation between clusters/patterns of the simulated data. With real biological data, EPIG extracted more dauer-specific patterns than CLICK. Furthermore, analysis of the IR/UV data revealed 18 unique patterns and 2661 genes out of approximately 17,000 that were identified as significantly expressed and categorized to the patterns by EPIG. The time-dependent patterns displayed similar and dissimilar responses between IR and UV treatments. Gene Ontology analysis applied to each pattern-related subset of co-expressed genes revealed underlying biological processes affected by IR- and/or UV- induced DNA damage.

Conclusion: EPIG competed with CLICK and performed better than CAST in extracting patterns from simulated data. EPIG extracted more biological informative patterns and co-expressed genes from both C. elegans and IR/UV-treated human fibroblasts. Using Gene Ontology analysis of the genes in the patterns extracted by EPIG, several key biological categories related to p53-dependent cell cycle control were revealed from the IR/UV data. Among them were mitotic cell cycle, DNA replication, DNA repair, cell cycle checkpoint, and G0-like status transition. EPIG can be applied to data sets from a variety of experimental designs.

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The patterns extracted by EPIG from the combined UV- and IR- treated data. In each of these patterns, 1 to 18, the first half with open circles were UV-treated and the second half with solid circles were IR-treated. For each treatment, there were three individual cell lines, F1-HTERT, F3-HTERT and F10-HTERT, positioned from left to right. Each cell line consisted of eight data points with four different treatment conditions, i.e., sham-treatment and 2, 6, and 24 h post-treatment colored red, green, blue and magenta, respectively. The vertical axes with zero at the middle are the changes in gene expression (log2 intensity) relative to the sham-treated controls.
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Figure 4: The patterns extracted by EPIG from the combined UV- and IR- treated data. In each of these patterns, 1 to 18, the first half with open circles were UV-treated and the second half with solid circles were IR-treated. For each treatment, there were three individual cell lines, F1-HTERT, F3-HTERT and F10-HTERT, positioned from left to right. Each cell line consisted of eight data points with four different treatment conditions, i.e., sham-treatment and 2, 6, and 24 h post-treatment colored red, green, blue and magenta, respectively. The vertical axes with zero at the middle are the changes in gene expression (log2 intensity) relative to the sham-treated controls.

Mentions: We next applied EPIG to a microarray data set that combined gene expression profiles of both ionizing radiation (IR)- and ultraviolet (UV)-treated human fibroblast cells with two goals in mind: 1) to find similar and dissimilar responses between treatments and 2) to reveal differences in gene regulation upon DNA damage caused by IR or UV. In each of the two treatments, the data consisted of four biological states, i.e. sham-treated, 2 h-, 6 h-, and 24 h post UV- or IR-treatment. A gene expression profile consists of eight inter-groups, corresponding to four states from the two treatments. Each of the intra-groups contains six data points from three biological replicates and two technical replicates (dye-swap pairs) for a given treatment at a given time point. As such, each gene expression profile consisted of 48 data points. EPIG analysis using the whole data as its input resulted in total of 18 patterns as shown in Figure 4 with a total of 2661 co-expressed genes being identified. Each of the co-expressed genes was categorized to a particular pattern. Figure 5 is a heat map of the 2661 genes that are arranged in the order of pattern number from top to bottom. Table 3 lists the number of genes in each of the patterns and denotes their over-represented Gene Ontology biological processes[19].


Extracting gene expression patterns and identifying co-expressed genes from microarray data reveals biologically responsive processes.

Chou JW, Zhou T, Kaufmann WK, Paules RS, Bushel PR - BMC Bioinformatics (2007)

The patterns extracted by EPIG from the combined UV- and IR- treated data. In each of these patterns, 1 to 18, the first half with open circles were UV-treated and the second half with solid circles were IR-treated. For each treatment, there were three individual cell lines, F1-HTERT, F3-HTERT and F10-HTERT, positioned from left to right. Each cell line consisted of eight data points with four different treatment conditions, i.e., sham-treatment and 2, 6, and 24 h post-treatment colored red, green, blue and magenta, respectively. The vertical axes with zero at the middle are the changes in gene expression (log2 intensity) relative to the sham-treated controls.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2194742&req=5

Figure 4: The patterns extracted by EPIG from the combined UV- and IR- treated data. In each of these patterns, 1 to 18, the first half with open circles were UV-treated and the second half with solid circles were IR-treated. For each treatment, there were three individual cell lines, F1-HTERT, F3-HTERT and F10-HTERT, positioned from left to right. Each cell line consisted of eight data points with four different treatment conditions, i.e., sham-treatment and 2, 6, and 24 h post-treatment colored red, green, blue and magenta, respectively. The vertical axes with zero at the middle are the changes in gene expression (log2 intensity) relative to the sham-treated controls.
Mentions: We next applied EPIG to a microarray data set that combined gene expression profiles of both ionizing radiation (IR)- and ultraviolet (UV)-treated human fibroblast cells with two goals in mind: 1) to find similar and dissimilar responses between treatments and 2) to reveal differences in gene regulation upon DNA damage caused by IR or UV. In each of the two treatments, the data consisted of four biological states, i.e. sham-treated, 2 h-, 6 h-, and 24 h post UV- or IR-treatment. A gene expression profile consists of eight inter-groups, corresponding to four states from the two treatments. Each of the intra-groups contains six data points from three biological replicates and two technical replicates (dye-swap pairs) for a given treatment at a given time point. As such, each gene expression profile consisted of 48 data points. EPIG analysis using the whole data as its input resulted in total of 18 patterns as shown in Figure 4 with a total of 2661 co-expressed genes being identified. Each of the co-expressed genes was categorized to a particular pattern. Figure 5 is a heat map of the 2661 genes that are arranged in the order of pattern number from top to bottom. Table 3 lists the number of genes in each of the patterns and denotes their over-represented Gene Ontology biological processes[19].

Bottom Line: However, the variation associated with microarray data and the complexity of the experimental designs make the acquisition of co-expressed genes a challenge.Among them were mitotic cell cycle, DNA replication, DNA repair, cell cycle checkpoint, and G0-like status transition.EPIG can be applied to data sets from a variety of experimental designs.

View Article: PubMed Central - HTML - PubMed

Affiliation: Microarray Group, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA. chou@niehs.nih.gov

ABSTRACT

Background: A common observation in the analysis of gene expression data is that many genes display similarity in their expression patterns and therefore appear to be co-regulated. However, the variation associated with microarray data and the complexity of the experimental designs make the acquisition of co-expressed genes a challenge. We developed a novel method for Extracting microarray gene expression Patterns and Identifying co-expressed Genes, designated as EPIG. The approach utilizes the underlying structure of gene expression data to extract patterns and identify co-expressed genes that are responsive to experimental conditions.

Results: Through evaluation of the correlations among profiles, the magnitude of variation in gene expression profiles, and profile signal-to-noise ratio's, EPIG extracts a set of patterns representing co-expressed genes. The method is shown to work well with a simulated data set and microarray data obtained from time-series studies of dauer recovery and L1 starvation in C. elegans and after ultraviolet (UV) or ionizing radiation (IR)-induced DNA damage in diploid human fibroblasts. With the simulated data set, EPIG extracted the appropriate number of patterns which were more stable and homogeneous than the set of patterns that were determined using the CLICK or CAST clustering algorithms. However, CLICK performed better than EPIG and CAST with respect to the average correlation between clusters/patterns of the simulated data. With real biological data, EPIG extracted more dauer-specific patterns than CLICK. Furthermore, analysis of the IR/UV data revealed 18 unique patterns and 2661 genes out of approximately 17,000 that were identified as significantly expressed and categorized to the patterns by EPIG. The time-dependent patterns displayed similar and dissimilar responses between IR and UV treatments. Gene Ontology analysis applied to each pattern-related subset of co-expressed genes revealed underlying biological processes affected by IR- and/or UV- induced DNA damage.

Conclusion: EPIG competed with CLICK and performed better than CAST in extracting patterns from simulated data. EPIG extracted more biological informative patterns and co-expressed genes from both C. elegans and IR/UV-treated human fibroblasts. Using Gene Ontology analysis of the genes in the patterns extracted by EPIG, several key biological categories related to p53-dependent cell cycle control were revealed from the IR/UV data. Among them were mitotic cell cycle, DNA replication, DNA repair, cell cycle checkpoint, and G0-like status transition. EPIG can be applied to data sets from a variety of experimental designs.

Show MeSH
Related in: MedlinePlus