A systems biology approach for pathway level analysis

@article{citeulike:1621696, abstract = {A common challenge in the analysis of genomics data is trying to understand the underlying phenomenon in the context of all complex interactions taking place on various signaling pathways. A statistical approach using various models is universally used to identify the most relevant pathways in a given experiment. Here, we show that the existing pathway analysis methods fail to take into consideration important biological aspects and may provide incorrect results in certain situations. By using a systems biology approach, we developed an impact analysis that includes the classical statistics but also considers other crucial factors such as the magnitude of each gene's expression change, their type and position in the given pathways, their interactions, etc. The impact analysis is an attempt to a deeper level of statistical analysis, informed by more pathway-specific biology than the existing techniques. On several illustrative data sets, the classical analysis produces both false positives and false negatives, while the impact analysis provides biologically meaningful results. This analysis method has been implemented as a Web-based tool, Pathway-Express, freely available as part of the Onto-Tools (http://vortex.cs.wayne.edu). 10.1101/gr.6202607}, author = {Draghici, Sorin and Khatri, Purvesh and Tarca, Adi L. and Amin, Kashyap and Done, Arina and Voichita, Calin and Georgescu, Constantin and Romero, Roberto }, citeulike-article-id = {1621696}, doi = {http://dx.doi.org/10.1101/gr.6202607}, journal = {Genome Res.}, keywords = {methods, pathway\_modeling, systems\_biology}, month = {September}, pages = {gr.6202607+}, posted-at = {2007-09-08 03:04:47}, priority = {2}, title = {A systems biology approach for pathway level analysis}, url = {http://dx.doi.org/10.1101/gr.6202607}, year = {2007} }

TY - JOUR ID - citeulike:1621696 L3 - citeulike-article-id:1621696 N2 - A common challenge in the analysis of genomics data is trying to understand the underlying phenomenon in the context of all complex interactions taking place on various signaling pathways. A statistical approach using various models is universally used to identify the most relevant pathways in a given experiment. Here, we show that the existing pathway analysis methods fail to take into consideration important biological aspects and may provide incorrect results in certain situations. By using a systems biology approach, we developed an impact analysis that includes the classical statistics but also considers other crucial factors such as the magnitude of each gene's expression change, their type and position in the given pathways, their interactions, etc. The impact analysis is an attempt to a deeper level of statistical analysis, informed by more pathway-specific biology than the existing techniques. On several illustrative data sets, the classical analysis produces both false positives and false negatives, while the impact analysis provides biologically meaningful results. This analysis method has been implemented as a Web-based tool, Pathway-Express, freely available as part of the Onto-Tools (http://vortex.cs.wayne.edu). 10.1101/gr.6202607 JF - Genome Res. TI - A systems biology approach for pathway level analysis SP - gr.6202607 KW - methods KW - pathway_modeling KW - systems_biology AU - Draghici, Sorin AU - Khatri, Purvesh AU - Tarca, Adi AU - Amin, Kashyap AU - Done, Arina AU - Voichita, Calin AU - Georgescu, Constantin AU - Romero, Roberto PY - 2007/09/04/ UR - http://dx.doi.org/10.1101/gr.6202607 ER -