.png){kind=icon}
返回
Optimization of Statistical Methods Impact on Quantitative Proteomics Data
.png){kind=icon}
OA
AI
DOI：10.1021/acs.jproteome.5b00183.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
As tools for quantitative label-free mass spectrometry (MS) rapidly develop, a consensus about the best practices is not apparent. In the work described here we compared popular statistical methods for detecting differential protein expression from quantitative MS data using both controlled experiments with known quantitative differences for specific proteins used as standards as well as real experiments where differences in protein abundance are not known a priori. Our results suggest that data-driven reproducibility-optimization can consistently produce reliable differential expression rankings for label-free proteome tools and are straightforward in their application.
Keyword:
proteomics
label-free mass spectrometry
quantitative analysis
statistical methods
reproducibility
ROTS
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.4K
被引数:
2.3W
机构
引用论文
Significance analysis of microarrays applied to the ionizing radiation response微阵列应用于电离辐射响应的意义分析OAAI
OAAI分享收藏分享收藏Identification and relative quantitation of protein mixtures by enzymatic digestion followed by capillary reversed-phase liquid chromatography-tandem mass spectrometry.png)
ANALYTICAL CHEMISTRY
ANALYTICAL CHEMISTRYIF6.7
PREAI
分享收藏ProteomeXchange provides globally coordinated proteomics data submission and disseminationNATURE BIOTECHNOLOGYOAAI
NATURE BIOTECHNOLOGYIF41.7
OAAI分享收藏分享收藏Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present蛋白质组学中的定量质谱: 2007年目前的关键综述更新
PREAI
分享收藏
查看更多内容