proteiNorm - A User-Friendly Tool for Normalization and Analysis of TMT and Label-Free Protein Quantification.
proteiNorm - A User-Friendly Tool for Normalization and Analysis of TMT and Label-Free Protein Quantification.
复制标题
ProteiNorm-一个用户友好的工具，用于TMT和无标记蛋白质定量的标准化和分析。
DOI：
10.1021/acsomega.0c02564
复制
发表时间：
2020-10-13
期刊：
影响因子：
4.1
通讯作者：
Byrum SD
中科院分区：
文献类型：
--
作者：
Graw S;Tang J;Zafar MK;Byrd AK;Bolden C;Peterson EC;Byrum SD
质谱技术的进步使我们能够以更高的质量和速度收集更全面的数据。随着生成的数据量迅速增加，简化分析的必要性变得更加明显。已知蛋白质组学数据经常受到来自未知来源的系统性偏倚的影响，并且未能充分规范化数据可能导致错误的结论。为了让研究人员能够通过用户友好的界面轻松评估和比较不同的归一化方法，我们开发了“proteiNorm”。目前的ProteiNorm的实现提供了肽和样品水平的初步过滤器，然后对几种流行的标准化方法进行评估，并对缺失值进行可视化。然后，用户选择适当的归一化方法和几种插补方法之一，用于随后比较不同的差异表达方法和估计统计功效。ProteiNorm的应用及其结果的解释在两个串联质量标签多重（TMT6plex和TMT10plex）和一个无标记加标质谱示例数据集上进行了演示。这三个数据集揭示了归一化方法如何在不同的实验设计中表现不同，以及需要对每个质谱实验的归一化方法进行评估。通过ProteiNorm，我们提供了一个用户友好的工具来识别适当的归一化方法，并选择合适的方法进行差异表达分析。
The technological advances in mass spectrometry allow us to collect more comprehensive data with higher quality and increasing speed. With the rapidly increasing amount of data generated, the need for streamlining analyses becomes more apparent. Proteomics data is known to be often affected by systemic bias from unknown sources, and failing to adequately normalize the data can lead to erroneous conclusions. To allow researchers to easily evaluate and compare different normalization methods via a user-friendly interface, we have developed “proteiNorm”. The current implementation of proteiNorm accommodates preliminary filters on peptide and sample levels followed by an evaluation of several popular normalization methods and visualization of the missing value. The user then selects an adequate normalization method and one of the several imputation methods used for the subsequent comparison of different differential expression methods and estimation of statistical power. The application of proteiNorm and interpretation of its results are demonstrated on two tandem mass tag multiplex (TMT6plex and TMT10plex) and one label-free spike-in mass spectrometry example data set. The three data sets reveal how the normalization methods perform differently on different experimental designs and the need for evaluation of normalization methods for each mass spectrometry experiment. With proteiNorm, we provide a user-friendly tool to identify an adequate normalization method and to select an appropriate method for differential expression analysis.
登录
查看更多内容
影响因子：
14.9
作者：
Ritchie ME;Phipson B;Wu D;Hu Y;Law CW;Shi W;Smyth GK
通讯作者：
Smyth GK
影响因子：
7
作者：
Silva, JC;Gorenstein, MV;Geromanos, SJ
通讯作者：
Geromanos, SJ
影响因子：
4.4
作者：
Chawade, Aakash;Alexandersson, Erik;Levander, Fredrik
通讯作者：
Levander, Fredrik
影响因子：
14.8
作者：
Tyanova, Stefka;Temu, Tikira;Cox, Juergen
通讯作者：
Cox, Juergen
影响因子：
12.3
作者：
Lin H;Zhu W;Silva JC;Gu X;Buell CR
通讯作者：
Buell CR