An information theoretic thresholding method for detecting protein colocalizations in stacks of fluorescence images.

In this note, we present a new method that allows us to determine threshold values for separating presence and absence of proteins in a stack of fluorescence images describing a spatial distribution of proteins across a biological object (like a slice of nervous tissue, a sample of blood cells, etc.). We apply this method to stacks of fluorescence images and find that the resulting threshold values are almost identical with threshold values found using completely independent methods based on technological and biological aspects of the images in question.

[1]  Walter Schubert,et al.  Polymyositis, Topological Proteomics Technology and Paradigm for Cell Invasion Dynamics , 2002 .

[2]  Linda Sage.  The molecular face of prostate cancer. , 2009, Journal of proteome research.

[3]  Walter Schubert,et al.  Toponome mapping in prostate cancer: detection of 2000 cell surface protein clusters in a single tissue section and cell type specific annotation by using a three symbol code. , 2009, Journal of proteome research.

[4]  Jana ZvBrovh,et al.  Information theoretical approach to constitution and reduction of medical data , 1997 .

[5]  Solomon Kullback,et al.  Information Theory and Statistics , 1970, The Mathematical Gazette.

[6]  Nobuyuki Otsu,et al.  ATlreshold Selection Method fromGray-Level Histograms , 1979 .

[7]  R. Nelsen.  An Introduction to Copulas , 1998 .

[8]  Solomon Kullback,et al.  Information Theory and Statistics , 1960 .

[9]  Walter Schubert,et al.  A three‐symbol code for organized proteomes based on cyclical imaging of protein locations , 2007, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[10]  Walter Schubert,et al.  Breaking the biological code , 2007, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[11]  Walter Schubert,et al.  Toponomics and neurotoponomics: a new way to medical systems biology , 2008, Expert review of proteomics.

[12]  Evgueni A. Haroutunian,et al.  Information Theory and Statistics , 2011, International Encyclopedia of Statistical Science.

[13]  Katie Cottingham.  Human Toponome Project | Human Proteinpedia is open for (free) business , 2008 .

[14]  Andreas W. M. Dress,et al.  Poisson Numbers and Poisson Distributions in Subset Surprisology , 2005 .

[15]  Walter Schubert,et al.  Fluorescence detection of protein clusters in individual cells and tissue sections by using toponome imaging system: sample preparation and measuring procedures , 2007, Nature Protocols.

[16]  Walter Schubert,et al.  Topological proteomics, toponomics, MELK-technology. , 2003, Advances in biochemical engineering/biotechnology.

[17]  M. Studený,et al.  The Multiinformation Function as a Tool for Measuring Stochastic Dependence , 1998, Learning in Graphical Models.

[18]  Christian Stephan,et al.  Interlocking transcriptomics, proteomics and toponomics technologies for brain tissue analysis in murine hippocampus , 2008, Proteomics.

[19]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[20]  Bill Ravens,et al.  An Introduction to Copulas , 2000, Technometrics.

[21]  W. Schubert,et al.  Analyzing proteome topology and function by automated multidimensional fluorescence microscopy , 2006, Nature Biotechnology.

[22]  Walter Schubert,et al.  Functional architecture of the cell nucleus: towards comprehensive toponome reference maps of apoptosis. , 2008, Biochimica et biophysica acta.

[23]  Michael I. Jordan.  Learning in Graphical Models , 1999, NATO ASI Series.

[24]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .