View source for Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics.


== General information ==
Webb-Robertson, B.-J. M.; Wiberg, H. K.; Matzke, M. M.;
Brown, J. N.; Wang, J.; McDermott, J. E.; Smith, R. D.; Rodland, K. D.;
Metz, T. O.; Pounds, J. G.; Waters, K. M.; et al. [https://doi.org/10.1371/journal.pone.0074335: Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics.] J. Proteome Res. 2015,
14 (5), 1993−2001.

[https://doi.org/10.1371/journal.pone.0074335: DOI: 10.1371/journal.pone.0074335]

=== Summary ===
Evaluation of performance and caveats of 9 imputation algorithms applied on a LC-MS data set.

=== Study outcomes ===
==== Outcome O1 ====
Most imputation methods perform well, no single algorithm or imputation strategy (single, local, global) outperforms, sometimes even no imputation is superior in subsequent classification analysis.

==== Outcome O2 ====
Local similarity-based approaches are in general the most accuarate and robust methods. Such as least-squares adaptive (LSA) or regularized expectation maximization (REM) (Figure 4)

==== Outcome O3 ====
The 'best' imputation method highly depends on the data and the goal of the downstream analysis and therewith advantageous methods are hard to define (Figure 3)

==== Further outcomes ====
With left-censored data the number of missing values highly depends on peptide intensity (Figure 1)

=== Study design and evidence level ===
==== General aspects ====
3 single-value approaches (LOD1,LOD2,RTI), 5 local similarity approaches (KNN, LLS, LSA, REM, MBI) and 2 global-structure approaches (PPCA, BPCA) were evaluated which allows comparison and discussion of different imputation strategies.
They were applied to 3 real datasets of different type and species, which represent a broad biological application.

=== Further comments and aspects ===

=== References ===