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Handling missing rows in multi-omics data integration: multiple imputation in multiple factor analysis framework

Overview of attention for article published in BMC Bioinformatics, October 2016
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80 Mendeley
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Title
Handling missing rows in multi-omics data integration: multiple imputation in multiple factor analysis framework
Published in
BMC Bioinformatics, October 2016
DOI 10.1186/s12859-016-1273-5
Pubmed ID
Authors

Valentin Voillet, Philippe Besse, Laurence Liaubet, Magali San Cristobal, Ignacio González

Abstract

In omics data integration studies, it is common, for a variety of reasons, for some individuals to not be present in all data tables. Missing row values are challenging to deal with because most statistical methods cannot be directly applied to incomplete datasets. To overcome this issue, we propose a multiple imputation (MI) approach in a multivariate framework. In this study, we focus on multiple factor analysis (MFA) as a tool to compare and integrate multiple layers of information. MI involves filling the missing rows with plausible values, resulting in M completed datasets. MFA is then applied to each completed dataset to produce M different configurations (the matrices of coordinates of individuals). Finally, the M configurations are combined to yield a single consensus solution. We assessed the performance of our method, named MI-MFA, on two real omics datasets. Incomplete artificial datasets with different patterns of missingness were created from these data. The MI-MFA results were compared with two other approaches i.e., regularized iterative MFA (RI-MFA) and mean variable imputation (MVI-MFA). For each configuration resulting from these three strategies, the suitability of the solution was determined against the true MFA configuration obtained from the original data and a comprehensive graphical comparison showing how the MI-, RI- or MVI-MFA configurations diverge from the true configuration was produced. Two approaches i.e., confidence ellipses and convex hulls, to visualize and assess the uncertainty due to missing values were also described. We showed how the areas of ellipses and convex hulls increased with the number of missing individuals. A free and easy-to-use code was proposed to implement the MI-MFA method in the R statistical environment. We believe that MI-MFA provides a useful and attractive method for estimating the coordinates of individuals on the first MFA components despite missing rows. MI-MFA configurations were close to the true configuration even when many individuals were missing in several data tables. This method takes into account the uncertainty of MI-MFA configurations induced by the missing rows, thereby allowing the reliability of the results to be evaluated.

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Mendeley readers

The data shown below were compiled from readership statistics for 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 1%
Unknown 79 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 28%
Researcher 19 24%
Student > Master 6 8%
Student > Bachelor 6 8%
Student > Doctoral Student 3 4%
Other 10 13%
Unknown 14 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 13 16%
Agricultural and Biological Sciences 11 14%
Mathematics 11 14%
Computer Science 10 13%
Engineering 6 8%
Other 11 14%
Unknown 18 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 March 2017.
All research outputs
#6,451,721
of 11,293,566 outputs
Outputs from BMC Bioinformatics
#2,568
of 4,195 outputs
Outputs of similar age
#123,089
of 259,656 outputs
Outputs of similar age from BMC Bioinformatics
#74
of 140 outputs
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So far Altmetric has tracked 4,195 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 34th percentile – i.e., 34% of its peers scored the same or lower than it.
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