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Research Article
Nishith Kumar
Bangabandhu Sheikh Mujibur Rahman Science and Technology University
Corresponding Author
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Mass spectrometry is a modern and sophisticated high-throughput analytical technique that enables large-scale metabolomics analyses. It yields a high dimensional large scale matrix (samples × metabolites) of quantified data that often contain missing cell in the data matrix as well as outliers which originate from several reasons, including technical and biological sources. Although, in the literature, several missing data imputation techniques can be found, however all the conventional existing techniques can only solve the missing value problems but not relieve the problems of outliers. Therefore, outliers in the dataset, deteriorate the accuracy of imputation. To overcome both the missing data imputation and outlier’s problem, here, we developed a new kernel weight function based missing data imputation technique (proposed) that resolves both the missing values and outliers. We evaluated the performance of the proposed method and other nine conventional missing imputation techniques using both artificially generated data and experimentally measured data analysis in both absence and presence of different rates of outliers. Performance based on both artificial data and real metabolomics data indicates that our proposed kernel weight based missing data imputation technique is a better performer than some existing alternatives. For user convenience, an R package of the proposed kernel weight based missing value imputation technique has been developed which is available at https://github.com/NishithPaul/tWLSA .
Keywords
Metabolomics, Missing data imputation, Weighted least square, Receiver operating characteristic (ROC) curve, Area under the ROC curve (AUC), Support vector machine (SVM)
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CURRENT STATUS: Under Review
Version 1
Posted 13 Jan, 2021
No community comments so far
Editorial decision: Major revision
On 05 Feb, 2021
Reviews received
Received 22 Jan, 2021
Reviewers agreed
On 20 Jan, 2021
Reviewers invited
Invitations sent on 17 Jan, 2021
Editor assigned
On 17 Jan, 2021
Editor invited
On 13 Jan, 2021
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On 11 Jan, 2021
First submitted
On 04 Jan, 2021
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A new preprint design is coming!
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This preprint is under consideration at Scientific Reports. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Nishith Kumar
Bangabandhu Sheikh Mujibur Rahman Science and Technology University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 13 Jan, 2021
No community comments so far
Editorial decision: Major revision
On 05 Feb, 2021
Reviews received
Received 22 Jan, 2021
Reviewers agreed
On 20 Jan, 2021
Reviewers invited
Invitations sent on 17 Jan, 2021
Editor assigned
On 17 Jan, 2021
Editor invited
On 13 Jan, 2021
Submission checks complete
On 11 Jan, 2021
First submitted
On 04 Jan, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Mass spectrometry is a modern and sophisticated high-throughput analytical technique that enables large-scale metabolomics analyses. It yields a high dimensional large scale matrix (samples × metabolites) of quantified data that often contain missing cell in the data matrix as well as outliers which originate from several reasons, including technical and biological sources. Although, in the literature, several missing data imputation techniques can be found, however all the conventional existing techniques can only solve the missing value problems but not relieve the problems of outliers. Therefore, outliers in the dataset, deteriorate the accuracy of imputation. To overcome both the missing data imputation and outlier’s problem, here, we developed a new kernel weight function based missing data imputation technique (proposed) that resolves both the missing values and outliers. We evaluated the performance of the proposed method and other nine conventional missing imputation techniques using both artificially generated data and experimentally measured data analysis in both absence and presence of different rates of outliers. Performance based on both artificial data and real metabolomics data indicates that our proposed kernel weight based missing data imputation technique is a better performer than some existing alternatives. For user convenience, an R package of the proposed kernel weight based missing value imputation technique has been developed which is available at https://github.com/NishithPaul/tWLSA .
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
This is a list of supplementary files associated with this preprint. Click to download.
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