Informatics and statistics for analyzing 2-d gel electrophoresis images.
Informatics and statistics for analyzing 2-d gel electrophoresis images.
复制标题
DOI：
10.1007/978-1-60761-444-9_16
复制
发表时间：
2010
期刊：
影响因子：
--
通讯作者：
Yang, Guang-Zhong
中科院分区：
文献类型：
--
作者：
Dowsey, Andrew W;Morris, Jeffrey S;Gutstein, Howard B;Yang, Guang-Zhong
尽管最近在通过集成液相色谱和质谱（LC/MS）的“鸟枪”肽分离方面取得了进展，但蛋白质组覆盖率和再现性仍然受到这种方法的限制，并且获得足够的重复运行用于生物标志物发现是一个挑战。由于这些原因，最近的研究表明，有一个持续的需要，蛋白质分离的二维凝胶电泳（2-DE）。然而，与传统的2-DE信息学，数字化的图像减少到符号数据，通过斑点检测和定量蛋白质比较差异表达斑点匹配之前。最近，已经出现了一种更稳健和自动化的范例，其中在整个图像集上检测斑点之前，凝胶在图像域中直接对齐。在这一章中，我们描述了这两种方法的方法，并讨论了统计推理时发现的差异蛋白质表达的陷阱。
Despite recent progress in “shotgun” peptide separation by integrated liquid chromatography and mass spectrometry (LC/MS), proteome coverage and reproducibility are still limited with this approach and obtaining enough replicate runs for biomarker discovery is a challenge. For these reasons, recent research demonstrates that there is a continuing need for protein separation by two-dimensional gel electrophoresis (2-DE). However, with traditional 2-DE informatics, the digitized images are reduced to symbolic data through spot detection and quantification before proteins are compared for differential expression by spot matching. Recently, a more robust and automated paradigm has emerged where gels are directly aligned in the image domain before spots are detected across the whole image set as a whole. In this chapter, we describe the methodology for both approaches and discuss the pitfalls present when reasoning statistically about the differential protein expression discovered.