tailieunhanh - Báo cáo y học: " Predicting transcription factor activities from combined analysis of microarray and ChIP data: a partial least squares approach"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học quốc tế cung cấp cho các bạn kiến thức về ngành y đề tài: Predicting transcription factor activities from combined analysis of microarray and ChIP data: a partial least squares approach | Theoretical Biology and Medical Modelling BioMed Central Research Open Access Predicting transcription factor activities from combined analysis of microarray and ChIP data a partial least squares approach Anne-Laure Boulesteix and Korbinian Strimmer Address Department of Statistics University of Munich Ludwigstr. 33 D-80539 Munich Germany Email Anne-Laure Boulesteix - Korbinian Strimmer - Corresponding author Published 24 June 2005 Received 15 April 2005 Theoretical Biology and Medical Modelling 2005 2 23 doi 1742-4682-2-AcCepted 24 June 2005 23 This article is available from http content 2 1 23 2005 Boulesteix and Strimmer licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Background The study of the network between transcription factors and their targets is important for understanding the complex regulatory mechanisms in a cell. Unfortunately with standard microarray experiments it is not possible to measure the transcription factor activities TFAs directly as their own transcription levels are subject to post-translational modifications. Results Here we propose a statistical approach based on partial least squares PLS regression to infer the true TFAs from a combination of mRNA expression and DNA-protein binding measurements. This method is also statistically sound for small samples and allows the detection of functional interactions among the transcription factors via the notion of meta -transcription factors. In addition it enables false positives to be identified in ChIP data and activation and suppression activities to be distinguished. Conclusion The proposed method performs very well both for simulated data and for .