Missing values in gel‐based proteomics

@article{Albrecht2010MissingVI,
  title={Missing values in gel‐based proteomics},
  author={Daniela Albrecht and Olaf Kniemeyer and Axel A. Brakhage and Reinhard Guthke},
  journal={PROTEOMICS},
  year={2010},
  volume={10}
}
Gel‐based proteomics is a widely applied technique to measure abundances of proteins in various biological systems. Comparison of two or more biological groups involves matching of 2‐D gels. Depending on the software, this can result in spots showing missing values on several gels. Most studies ignore this fact or substitute all missing data by zero. Since a couple of years, scientists have realized that this is not the optimal way of analyzing their data and several studies were published… 
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63 Citations
Background Citations
14
Methods Citations
8

63 Citations

Citation Type

Citation Type

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This paper reviews and illustrates several different aspects of data analysis within the context of gel-based proteomics, summarizing the current state of research within this field and discussing the usefulness of available multivariate analysis tools both for data visualization and feature selection purposes.
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Combined usage of these methods are recommended as a novel and optimal way to detect significantly changing features in these data sets, suitable for large quantitative data sets from stable isotope labeling and mass spectrometry experiments and should be applicable to large data sets of any type.
WGCNA Application to Proteomic and Metabolomic Data Analysis.
Proper imputation of missing values in proteomics datasets for differential expression analysis
TLDR
This study investigated public DDA datasets of various tissue/sample types to determine the composition of MVs and developed simulated datasets that imitate the MV profile of real-life datasets, and compared the impact of various popular imputation methods on the analysis of differentially expressed proteins.
A Simple Optimization Workflow to Enable Precise and Accurate Imputation of Missing Values in Proteomic Datasets
TLDR
Overall, this study indicates that the most suitable imputation method depends on the overall structure and correlations of proteins within the data set and can be identified with the workflow presented here.
A Simple Optimization Workflow to Enable Precise and Accurate Imputation of Missing Values in Proteomic Data Sets.
TLDR
Overall, this study indicates that the most suitable imputation method relies on the overall structure of the data set and provides an example of an analytic framework that may assist in identifying the most appropriate imputation strategies for the differential analysis of proteins.
Quantitative plant proteomics
TLDR
Plant‐specific quantitative methods such as metabolic labeling methods that can take full advantage of plant metabolism and culture practices are described, and other potential advantages and challenges that may arise from the unique properties of plants are discussed.
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Integrative analysis of transcriptomic and proteomic data of Shewanella oneidensis: missing value imputation using temporal datasets.
TLDR
A non-linear data-driven stochastic gradient boosted trees (GBT) model is applied to impute missing proteomic values using a temporal transcriptomic and proteomic dataset of Shewanella oneidensis to demonstrate that such missing value imputation improved characterization of the temporal response of S. oneidense to chromate.
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