RobNorm: model-based robust normalization method for labeled quantitative mass spectrometry proteomics data | Zendy

Meng Wang | Zendy; Lihua Jiang | Zendy; Ruiqi Jian | Zendy; Joanne Chan | Zendy; Qing Liu | Zendy; M Snyder | Zendy; Hua Tang | Zendy

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RobNorm: model-based robust normalization method for labeled quantitative mass spectrometry proteomics data

Author(s) -

Meng Wang,

Lihua Jiang,

Ruiqi Jian,

Joanne Chan,

Qing Liu,

M Snyder,

Hua Tang

Publication year - 2020

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa904

Subject(s) - normalization (sociology) , outlier , computer science , robustness (evolution) , database normalization , data mining , pattern recognition (psychology) , artificial intelligence , biology , biochemistry , sociology , anthropology , gene

Data normalization is an important step in processing proteomics data generated in mass spectrometry experiments, which aims to reduce sample-level variation and facilitate comparisons of samples. Previously published methods for normalization primarily depend on the assumption that the distribution of protein expression is similar across all samples. However, this assumption fails when the protein expression data is generated from heterogenous samples, such as from various tissue types. This led us to develop a novel data-driven method for improved normalization to correct the systematic bias meanwhile maintaining underlying biological heterogeneity.

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