Evidence map›Paper›PMID 31601178›Full record

ArticleBMC bioinformatics2019

Random forest-based imputation outperforms other methods for imputing LC-MS metabolomics data: a comparative study.

Marietta Kokla, Jyrki Virtanen, Marjukka Kolehmainen, Jussi Paananen, Kati Hanhineva

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 107 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
107citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

107 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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47 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Marietta KoklaInstitute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio Campus, P.O. Box 1627, FI-70211, Kuopio, Finland. marietta.kokla@uef.fi.ORCID http://orcid.org/0000-0002-8152-2024
Jyrki VirtanenInstitute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio Campus, P.O. Box 1627, FI-70211, Kuopio, Finland.
Marjukka KolehmainenInstitute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio Campus, P.O. Box 1627, FI-70211, Kuopio, Finland.
Jussi PaananenInstitute of Biomedicine, University of Eastern Finland, Kuopio Campus, P.O. Box 1627, FI-70211, Kuopio, Finland.
Kati HanhinevaInstitute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio Campus, P.O. Box 1627, FI-70211, Kuopio, Finland.

Funding

Biotieteiden ja Ympäristön Tutkimuksen Toimikunta 2779886
6 · The paper itself

Abstract

backgroundLC-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.

resultsHere 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.

conclusionType 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.

Indexed as

BiasChromatography, LiquidHumansMass SpectrometryMetabolomicsHigh dimensional dataImputationMARMCARMetabolomicsMissing valuesMNARRF

Identifiers

PMID31601178
PMCPMC6788053

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.