| Title |
Random forest-based imputation outperforms other methods for imputing LC-MS metabolomics data: a comparative study
|
|---|---|
| Published in |
BMC Bioinformatics, October 2019
|
| DOI | 10.1186/s12859-019-3110-0 |
| Pubmed ID | |
| Authors |
Marietta Kokla, Jyrki Virtanen, Marjukka Kolehmainen, Jussi Paananen, Kati Hanhineva |
| Abstract |
LC-MS technology makes it possible to measure the relative abundance of numerous molecular features of a sample in single analysis. However, especially non-targeted metabolite profiling approaches generate vast arrays of data that are prone to aberrations such as missing values. No matter the reason for the missing values in the data, coherent and complete data matrix is always a pre-requisite for accurate and reliable statistical analysis. Therefore, there is a need for proper imputation strategies that account for the missingness and reduce the bias in the statistical analysis. Here we present our results after evaluating nine imputation methods in four different percentages of missing values of different origin. The performance of each imputation method was analyzed by Normalized Root Mean Squared Error (NRMSE). We demonstrated that random forest (RF) had the lowest NRMSE in the estimation of missing values for Missing at Random (MAR) and Missing Completely at Random (MCAR). In case of absent values due to Missing Not at Random (MNAR), the left truncated data was best imputed with minimum value imputation. We also tested the different imputation methods for datasets containing missing data of various origin, and RF was the most accurate method in all cases. The results were obtained by repeating the evaluation process 100 times with the use of metabolomics datasets where the missing values were introduced to represent absent data of different origin. Type and rate of missingness affects the performance and suitability of imputation methods. RF-based imputation method performs best in most of the tested scenarios, including combinations of different types and rates of missingness. Therefore, we recommend using random forest-based imputation for imputing missing metabolomics data, and especially in situations where the types of missingness are not known in advance. |
X Demographics
The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 14% |
| Comoros | 1 | 14% |
| India | 1 | 14% |
| Brazil | 1 | 14% |
| United Kingdom | 1 | 14% |
| Unknown | 2 | 29% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 57% |
| Scientists | 3 | 43% |
Mendeley readers
The data shown below were compiled from readership statistics for 162 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 162 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 33 | 20% |
| Researcher | 27 | 17% |
| Student > Master | 19 | 12% |
| Student > Doctoral Student | 8 | 5% |
| Student > Bachelor | 7 | 4% |
| Other | 27 | 17% |
| Unknown | 41 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 20 | 12% |
| Agricultural and Biological Sciences | 18 | 11% |
| Computer Science | 12 | 7% |
| Medicine and Dentistry | 12 | 7% |
| Chemistry | 9 | 6% |
| Other | 41 | 25% |
| Unknown | 50 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 13 November 2019.
All research outputs
#13,175,336
of 23,577,654 outputs
Outputs from BMC Bioinformatics
#3,690
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Outputs of similar age
#160,877
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Outputs of similar age from BMC Bioinformatics
#74
of 135 outputs
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