Srivastava Sudhir, Merchant Michael, Rai Anil, Rai Shesh N
Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India.
Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, Kentucky, United States of America.
J Proteomics Bioinform. 2019;12(3):48-55. doi: 10.35248/0974-276x.19.12.496. Epub 2019 Apr 4.
The quantitative measurements based on liquid chromatography (LC) coupled with mass spectrometry (MS) often suffer from the problem of missing values and data heterogeneity from technical variability. We considered a proteomics data set generated from human kidney biopsy material to investigate the technical effects of sample preparation and the quantitative MS.
We studied the effect of tissue storage methods (TSMs) and tissue extraction methods (TEMs) on data analysis. There are two TSMs: frozen (FR) and FFPE (formalin-fixed paraffin embedded); and three TEMs: MAX, TX followed by MAX and SDS followed by MAX. We assessed the impact of different strategies to analyze the data while considering heterogeneity and MVs. We have used analysis of variance (ANOVA) model to study the effects due to various sources of variability.
We found that the FFPE TSM is better than the FR TSM. We also found that the one-step TEM (MAX) is better than those of two-steps TEMs. Furthermore, we found the imputation method is a better approach than excluding the proteins with MVs or using unbalanced design.
基于液相色谱(LC)与质谱(MS)联用的定量测量常常面临缺失值问题以及技术变异性导致的数据异质性问题。我们考虑了一个源自人类肾脏活检材料的蛋白质组学数据集,以研究样品制备和定量质谱的技术效果。
我们研究了组织存储方法(TSMs)和组织提取方法(TEMs)对数据分析的影响。有两种TSMs:冷冻(FR)和甲醛固定石蜡包埋(FFPE);以及三种TEMs:MAX、TX后接MAX和SDS后接MAX。在考虑异质性和缺失值的同时,我们评估了不同数据分析策略的影响。我们使用方差分析(ANOVA)模型来研究各种变异性来源所产生的影响。
我们发现FFPE TSM优于FR TSM。我们还发现一步法TEM(MAX)优于两步法TEM。此外,我们发现插补方法比排除有缺失值的蛋白质或使用不平衡设计是更好的方法。