Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies
CEA Grenoble · Inserm · +7 more institutions
Abstract
Missing values are a genuine issue in label-free quantitative proteomics. Recent works have surveyed the different statistical methods to conduct imputation and have compared them on real or simulated data sets and recommended a list of missing value imputation methods for proteomics application. Although insightful, these comparisons do not account for two important facts: (i) depending on the proteomics data set, the missingness mechanism may be of different natures and (ii) each imputation method is devoted to a specific type of missingness mechanism. As a result, we believe that the question at stake is not to find the most accurate imputation method in general but instead the most appropriate one. We…
Citation impact
- FWCI
- 22.44
- Percentile
- 100%
- References
- 32
Authors
5- CLCosmin LazarCorresponding
CEA Grenoble, Inserm, Université Grenoble Alpes, Commissariat à l'Énergie Atomique et aux Énergies Alternatives
- LGLaurent Gatto
- MFMyriam Ferro
Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Institut de Recherche Interdisciplinaire de Grenoble, Inserm, Université Grenoble Alpes, CEA Grenoble
- CBChristophe Bruley
Commissariat à l'Énergie Atomique et aux Énergies Alternatives, CEA Grenoble, Université Grenoble Alpes, Institut de Recherches en Technologies et Sciences pour le Vivant, Inserm
- TBThomas Bürger
Inserm, Laboratoire des Sciences et Techniques de l’Information de la Communication et de la Connaissance, CEA Grenoble, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Université de Bretagne Occidentale, Centre National de la Recherche Scientifique, Université Grenoble Alpes
Topics & keywords
- Imputation (statistics)
- Missing data
- Computer science
- Data mining
- Data set
- Artificial intelligence
- Machine learning