Multiscale processing of mass spectrometry data
Multiscale processing of mass spectrometry data
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DOI：
10.1111/j.1541-0420.2005.00504.x
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发表时间：
2006-06-01
期刊：
影响因子：
1.9
通讯作者：
Yasui, Y.
中科院分区：
文献类型：
--
作者：
Randolph, T. W.;Yasui, Y.
这项工作解决了从蛋白质质谱数据中提取信号含量的问题。这些光谱的多尺度分解是用来专注于本地规模为基础的结构，通过定义特定的规模功能。特征的量化伴随着用于计算特征的位置的有效方法，该方法避免了对信噪比或带宽的估计。基于尺度的直方图用作类谱密度函数，指示数据中特征的高密度区域。这些区域提供了在其中对特征进行量化并跨样品进行比较的箱。作为初步步骤，粗尺度箱内的显著特征的位置可用于光谱的粗配准。的多尺度分解，基于尺度的特征定义，计算的特征位置，和随后的量化的特征进行了通过一个不变性的小波分析。
This work addresses the problem of extracting signal content from protein mass spectrometry data. A multiscale decomposition of these spectra is used to focus on local scale-based structure by defining scale-specific features. Quantification of features is accompanied by an efficient method for calculating the location of features which avoids estimation of signal-to-noise ratios or bandwidths. Scale-based histograms serve as spectral-density-like functions indicating the regions of high density of features in the data. These regions provide bins within which features are quantified and compared across samples. As a preliminary step, the locations of prominent features within coarse-scale bins may be used for a crude registration of spectra. The multiscale decomposition, the scale-based feature definition, the calculation of feature locations, and subsequent quantification of features are carried out by way of a translation-invariant wavelet analysis.