A Statistical Approach for Identifying the Best Combination of Normalization and Imputation Methods for Label-Free Proteomics Expression Data

Label-free proteomics expression data sets often exhibit data heterogeneity and missing values, necessitating the development of effective normalization and imputation methods. The selection of appropriate normalization and imputation methods is inherently data-specific, and choosing the optimal app...

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Veröffentlicht in:	Journal of proteome research Jg. 24; H. 1; S. 158
Hauptverfasser:	Sakthivel, Kabilan, Lal, Shashi Bhushan, Srivastava, Sudhir, Chaturvedi, Krishna Kumar, Khan, Yasin Jeshima, Mishra, Dwijesh Chandra, Madival, Sharanbasappa D, Vaidhyanathan, Ramasubramanian, Jha, Girish Kumar
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States 03.01.2025
Schlagworte:	Algorithms Data Interpretation, Statistical Humans Least-Squares Analysis Linear Models Proteomics - methods Proteomics - standards Proteomics - statistics & numerical data quality control normalization bottom-up approach protein differential expression analysis label-free proteomics missing value imputation
ISSN:	1535-3907, 1535-3907
Online-Zugang:	Weitere Angaben
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