Calibration using constrained smoothing with applications to mass spectrometry data

doi:10.1111/biom.12135

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	Calibration using constrained smoothing with applications to mass spectrometry data
	Feng, Xingdong 1; Sedransk, Nell 2; Xia, Jessie Q.3
刊名	BIOMETRICS
	2014-06
卷号	70 期号:2 页码:398-408
关键词	Functional data LoD and LoQ Piecewise smoothing Proteomics Regression splines Shape restrictions
ISSN号	0006-341X
DOI	10.1111/biom.12135
英文摘要	Linear regressions are commonly used to calibrate the signal measurements in proteomic analysis by mass spectrometry. However, with or without a monotone (e.g., log) transformation, data from such functional proteomic experiments are not necessarily linear or even monotone functions of protein (or peptide) concentration except over a very restricted range. A computationally efficient spline procedure improves upon linear regression. However, mass spectrometry data are not necessarily homoscedastic; more often the variation of measured concentrations increases disproportionately near the boundaries of the instruments measurement capability (dynamic range), that is, the upper and lower limits of quantitation. These calibration difficulties exist with other applications of mass spectrometry as well as with other broad-scale calibrations. Therefore the method proposed here uses a functional data approach to define the calibration curve and also the limits of quantitation under the two assumptions: (i) that the variance is a bounded, convex function of concentration; and (ii) that the calibration curve itself is monotone at least between the limits of quantitation, but not necessarily outside these limits. Within this paradigm, the limit of detection, where the signal is definitely present but not measurable with any accuracy, is also defined. An iterative approach draws on existing smoothing methods to account simultaneously for both restrictions and is shown to achieve the global optimal convergence rate under weak conditions. This approach can also be implemented when convexity is replaced by other (bounded) restrictions. Examples from Addona et al. (2009, Nature Biotechnology 27, 663-641) both motivate and illustrate the effectiveness of this functional data methodology when compared with the simpler linear regressions and spline techniques.
WOS研究方向	Life Sciences & Biomedicine - Other Topics ; Mathematical & Computational Biology ; Mathematics
语种	英语
出版者	WILEY-BLACKWELL
WOS记录号	WOS:000337621000015
内容类型	期刊论文
源URL	[http://10.2.47.112/handle/2XS4QKH4/1748]
专题	上海财经大学
通讯作者	Feng, Xingdong
作者单位	1.Shanghai Univ Finance & Econ, Sch Stat & Management, Minist Educ, Key Lab Math Econ SUFE, Shanghai 200433, Peoples R China; 2.Natl Inst Stat Sci, Res Triangle Pk, NC 27709 USA; 3.Verisk Innovat Analyt, San Francisco, CA 94111 USA
推荐引用方式 GB/T 7714	Feng, Xingdong,Sedransk, Nell,Xia, Jessie Q.. Calibration using constrained smoothing with applications to mass spectrometry data[J]. BIOMETRICS,2014,70(2):398-408.
APA	Feng, Xingdong,Sedransk, Nell,&Xia, Jessie Q..(2014).Calibration using constrained smoothing with applications to mass spectrometry data.BIOMETRICS,70(2),398-408.
MLA	Feng, Xingdong,et al."Calibration using constrained smoothing with applications to mass spectrometry data".BIOMETRICS 70.2(2014):398-408.