Use of multiple singular value decompositions to analyze complex intracellular calcium ion signals
| Autor: | Martinez, Josue G., Huang, Jianhua Z., Burghardt, Robert C., Barhoumi, Rola, Carroll, Raymond J. |
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| Rok vydání: | 2010 |
| Předmět: | |
| Zdroj: | Annals of Applied Statistics 2009, Vol. 3, No. 4, 1467-1492 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1214/09-AOAS253 |
| Popis: | We compare calcium ion signaling ($\mathrm {Ca}^{2+}$) between two exposures; the data are present as movies, or, more prosaically, time series of images. This paper describes novel uses of singular value decompositions (SVD) and weighted versions of them (WSVD) to extract the signals from such movies, in a way that is semi-automatic and tuned closely to the actual data and their many complexities. These complexities include the following. First, the images themselves are of no interest: all interest focuses on the behavior of individual cells across time, and thus, the cells need to be segmented in an automated manner. Second, the cells themselves have 100$+$ pixels, so that they form 100$+$ curves measured over time, so that data compression is required to extract the features of these curves. Third, some of the pixels in some of the cells are subject to image saturation due to bit depth limits, and this saturation needs to be accounted for if one is to normalize the images in a reasonably unbiased manner. Finally, the $\mathrm {Ca}^{2+}$ signals have oscillations or waves that vary with time and these signals need to be extracted. Thus, our aim is to show how to use multiple weighted and standard singular value decompositions to detect, extract and clarify the $\mathrm {Ca}^{2+}$ signals. Our signal extraction methods then lead to simple although finely focused statistical methods to compare $\mathrm {Ca}^{2+}$ signals across experimental conditions. Comment: Published in at http://dx.doi.org/10.1214/09-AOAS253 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org) |
| Databáze: | arXiv |
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