Normalization and missing value imputation for label-free LC-MS analysis
By Yuliya V. Karpievitch, Alan R. Dabney, Richard D. Smith. BMC Bioinformatics, 2012
309 citations recorded by the source corpus.
Shotgun proteomic data are affected by a variety of known and unknown systematic biases as well as high proportions of missing values. Typically, normalization is performed in an attempt to remove systematic biases from the data before statistical inference, sometimes followed by missing value imputation to obtain a complete matrix of intensities. Here we discuss several approaches to normalization and dealing with missing values, some initially developed for microarray data and some developed specifically for mass spectrometry-based data.
Subfields: Molecular Biology, Spectroscopy
View Paper: https://www.semanticscholar.org/paper/d616814a50af4e079fe43a6fac545ea37545a0a0
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Citations in the corpus
- Data‐independent acquisition‐based SWATH‐MS for quantitative proteomics: a tutorial (2018, unknown): “Missing values can be either recovered using the re-quantification values provided from the across-run re-alignment analysis (Rost et al , 2016a) or using statistical imputation as provided by other tools (Karpievitch et al , 2012).”
- A systematic evaluation of normalization methods in quantitative label-free proteomics (2016, methodology): “This bias has been defined as variation caused by nonbiological sources, which is introduced by small variations in the experimental conditions in the course of carrying out the MS analysis [4].”
- Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies. (2016, methodology): “In a proteomics dataset, it is classically assumed that all the MAR values are also MCAR, so that one is little interested in MAR [5].”