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We are analyzing https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-10-200.

Title:
Seeking unique and common biological themes in multiple gene lists or datasets: pathway pattern extraction pipeline for pathway-level comparative analysis | BMC Bioinformatics | Full Text
Description:
Background One of the challenges in the analysis of microarray data is to integrate and compare the selected (e.g., differential) gene lists from multiple experiments for common or unique underlying biological themes. A common way to approach this problem is to extract common genes from these gene lists and then subject these genes to enrichment analysis to reveal the underlying biology. However, the capacity of this approach is largely restricted by the limited number of common genes shared by datasets from multiple experiments, which could be caused by the complexity of the biological system itself. Results We now introduce a new Pathway Pattern Extraction Pipeline (PPEP), which extends the existing WPS application by providing a new pathway-level comparative analysis scheme. To facilitate comparing and correlating results from different studies and sources, PPEP contains new interfaces that allow evaluation of the pathway-level enrichment patterns across multiple gene lists. As an exploratory tool, this analysis pipeline may help reveal the underlying biological themes at both the pathway and gene levels. The analysis scheme provided by PPEP begins with multiple gene lists, which may be derived from different studies in terms of the biological contexts, applied technologies, or methodologies. These lists are then subjected to pathway-level comparative analysis for extraction of pathway-level patterns. This analysis pipeline helps to explore the commonality or uniqueness of these lists at the level of pathways or biological processes from different but relevant biological systems using a combination of statistical enrichment measurements, pathway-level pattern extraction, and graphical display of the relationships of genes and their associated pathways as Gene-Term Association Networks (GTANs) within the WPS platform. As a proof of concept, we have used the new method to analyze many datasets from our collaborators as well as some public microarray datasets. Conclusion This tool provides a new pathway-level analysis scheme for integrative and comparative analysis of data derived from different but relevant systems. The tool is freely available as a Pathway Pattern Extraction Pipeline implemented in our existing software package WPS, which can be obtained at http://www.abcc.ncifcrf.gov/wps/wps_index.php
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Keywords {🔍}

gene, genes, analysis, terms, biological, enrichment, ppep, lists, datasets, pubmed, file, pathway, common, pattern, article, cancer, extraction, google, scholar, data, method, additional, enriched, pathwaylevel, themes, list, cas, wps, tissues, differential, studies, prostate, patterns, multiple, figure, pathways, study, derived, case, genelevel, testisrelated, results, unique, pipeline, methods, gobp, central, microarray, underlying, based,

Topics {✒️}

springer nature integrin-mediated cell-matrix interactions transforming growth factor-beta author information authors pathway-level pattern extraction human protein-encoding transcriptomes ptm-based pattern extraction genome-wide expression patterns pathway-level enrichment patterns pathway-based classification methods pure pathway/network approach gene-level based approach pathway-level comparative analysis gene-based classification methods discussion gene-term association network curated gene-gene relationships derived pathway-level signatures gene-level based analysis gene-level based methods common pathway-level signatures authors scientific editing gene-level paradigm failed considers sample-level variations pathway-level analysis scheme gene-level method appeared retrieved gene-term relations gene-term association networks pathway pattern extraction accompany drug-selected mutations privacy choices/manage cookies tissue-specific biological processes pgc-1-responsive genes involved summary statistics-based method list-specific biological processes z-score approach supplementary files dhanasekaran sm multiple high-throughput datasets biomed central k-means clustering algorithm pattern extraction results authors’ original file testis-related biological processes related pathways/gene sets gene list files gene expression patterns demo page rhodes dr pathway-level patterns

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      headline:Seeking unique and common biological themes in multiple gene lists or datasets: pathway pattern extraction pipeline for pathway-level comparative analysis
      description:One of the challenges in the analysis of microarray data is to integrate and compare the selected (e.g., differential) gene lists from multiple experiments for common or unique underlying biological themes. A common way to approach this problem is to extract common genes from these gene lists and then subject these genes to enrichment analysis to reveal the underlying biology. However, the capacity of this approach is largely restricted by the limited number of common genes shared by datasets from multiple experiments, which could be caused by the complexity of the biological system itself. We now introduce a new Pathway Pattern Extraction Pipeline (PPEP), which extends the existing WPS application by providing a new pathway-level comparative analysis scheme. To facilitate comparing and correlating results from different studies and sources, PPEP contains new interfaces that allow evaluation of the pathway-level enrichment patterns across multiple gene lists. As an exploratory tool, this analysis pipeline may help reveal the underlying biological themes at both the pathway and gene levels. The analysis scheme provided by PPEP begins with multiple gene lists, which may be derived from different studies in terms of the biological contexts, applied technologies, or methodologies. These lists are then subjected to pathway-level comparative analysis for extraction of pathway-level patterns. This analysis pipeline helps to explore the commonality or uniqueness of these lists at the level of pathways or biological processes from different but relevant biological systems using a combination of statistical enrichment measurements, pathway-level pattern extraction, and graphical display of the relationships of genes and their associated pathways as Gene-Term Association Networks (GTANs) within the WPS platform. As a proof of concept, we have used the new method to analyze many datasets from our collaborators as well as some public microarray datasets. This tool provides a new pathway-level analysis scheme for integrative and comparative analysis of data derived from different but relevant systems. The tool is freely available as a Pathway Pattern Extraction Pipeline implemented in our existing software package WPS, which can be obtained at http://www.abcc.ncifcrf.gov/wps/wps_index.php
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         Computer Appl. in Life Sciences
         Algorithms
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      name:Advanced Biomedical Computing Center, Advanced Technology Program, SAIC-Frederick Inc, Frederick, USA

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