Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies

Journal of Proteome Research

Volumn 15, Issue 4, 2016, Pages 1116-1125

  Lazar, Cosmin ^a,c,d   Gatto, Laurent ^e,f   Ferro, Myriam ^a,c,d   Bruley, Christophe ^a,c,d   Burger, Thomas ^a,b,c,d  

^a UNIV GRENOBLE ALPES   (France)

^b CNRS   (France)

^c CEA GRENOBLE   (France)

^d INSERM   (France)

^e NONE   (United Kingdom)

^f Cambridge Center for Proteomics   (United Kingdom)

Author keywords

label free relative quantitative proteomics; missing value imputation

Indexed keywords

ACCURACY; ARTICLE; DATA PROCESSING; DETERMINISTIC MINIMUM IMPUTATION; HUMAN; IMPUTATION WITH SINGULAR VALUE DECOMPOSITION; K NEAREST NEIGHBOR; MAXIMUM LIKELIHOOD METHOD; MISSING COMPLETELY AT RANDOM; MISSING NOT AT RANDOM; MOLECULAR DYNAMICS; PRIORITY JOURNAL; PROBABILISTIC MINIMUM IMPUTATION; PROTEIN AGGREGATION; PROTEIN ANALYSIS; PROTEOMICS; QUANTITATIVE ANALYSIS; ADENOCARCINOMA; ALGORITHM; CARCINOMA, NON-SMALL-CELL LUNG; CHEMISTRY; COMPARATIVE STUDY; COMPUTER SIMULATION; INFORMATION PROCESSING; LUNG NEOPLASMS; MASS SPECTROMETRY; METABOLISM; STATISTICAL ANALYSIS; STATISTICS AND NUMERICAL DATA;

PEPTIDE; TUMOR PROTEIN;

ADENOCARCINOMA; ALGORITHMS; CARCINOMA, NON-SMALL-CELL LUNG; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; DATASETS AS TOPIC; HUMANS; LUNG NEOPLASMS; MASS SPECTROMETRY; NEOPLASM PROTEINS; PEPTIDES; PROTEOMICS;

EID: 84963706086     PISSN: 15353893     EISSN: 15353907     Source Type: Journal    
DOI: 10.1021/acs.jproteome.5b00981     Document Type: Article

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