# Influence of Missing Values Substitutes on Multivariate Analysis of Metabolomics Data

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Experimental Section

#### 2.1. Materials and Methods

#### 2.1.1. Cell Culture and Experimental Protocol

_{2}. Cells were divided into three groups: one group was placed in a 95% air and 5% CO

_{2}incubator (normoxia); one group placed in a 1% O

_{2}, 5% CO

_{2}balanced with N

_{2}hypoxybox (hypoxia) and one group was placed in an anoxic chamber (Bactron anaerobic chamber, Sheldon Manufacturing, Cornelius, OR, USA) where 5% CO

_{2}, 5% H

_{2}and 90% N

_{2}(BOC, Manchester, UK) was flowed over a palladium catalyst to remove any remaining oxygen (anoxia) for 24 h. Each of the three groups were split into three sub groups, which were dosed with 0, 0.1 or 1 µM doxorubicin for 16 h whilst remaining in the predefined oxygen condition for a further 24 h.

#### 2.1.2. Methanol Metabolite Extraction

#### 2.1.3. Metabolic Profiling Using GC-MS and Raw Data Processing

_{4}acid to account for the technical variability associated with chemical derivatisation and low sample injection volumes. The chromatogram peaks areas were exported as an ASCII file into Microsoft Excel

^{®}and these 52 metabolites were used for all further analyses.

#### 2.2. Software Tools

#### 2.3. Data Preparation

#### 2.4. Imputation Methods

#### 2.4.1. Imputation of Missing Values Using k-Nearest Neighbours (kNN).

#### 2.4.2. Missing Value Imputation Using Random Forest (RF)

#### 2.5. Unsupervised Learning

#### 2.5.1. Principal Components Analysis (PCA)

^{2}based on the test sets from the k-folds). Thus for PCA we applied 10-fold cross-validation and we repeated this 100 times. This allowed the explained variance for each model to be assessed. These was performed to determine the optimal number of PCs and from 1 to n PCs were used in this process and the overall percent explain variance represented as a box-whisker plot; further explanation of the PCA procedure can be found in supplementary information (SI).

#### 2.5.2. Hierarchical Cluster Analysis (HCA)

#### 2.6. Supervised Learning

#### 2.6.1. Linear Discriminant Analysis (LDA)

_{s}− N

_{𝑔}− 1) > N

_{v}

_{s}correspond to number of samples, N

_{g}to the number of groups, and N

_{v}reflects the number of inputs (features).

#### 2.6.2. Partial Least Squares-Discriminant Analysis (PLS-DA)

#### 2.7. Model Validation

## 3. Results and Discussion

**Figure 1.**Scree plot showing the effect of the five different missing value substitutes—zero (black boxplots), mean (red boxplots) and median (green boxplots), k-nearest neighbours (kNN) (blue boxplots) and RF (violet boxplots)—on the total explained variance recovered in principal component analysis (PCA). 10-fold cross-validation was repeated 100 times and the boxplots display statistical distribution among all 100 iterations. The arrows indicate the minimum number of components that are required to explain >80% variance.

**Figure 2.**PCA scores plots showing comparisons of five different missing value substitutes on for: (

**A**) zero; (

**B**) mean; (

**C**) median; (

**D**) kNN; and (

**E**) RF. Symbols represent: normoxia (black circles), hypoxia (red triangles) and anoxia (green pluses).

**Figure 3.**HCA based on Wards linkage for five different value substitutes: (

**A**) zero; (

**B**) mean; (

**C**) median; (

**D**) kNN; and (

**E**) random forest (RF). Boxes indicate where the majority of the following sample types are located. Text are coloured according to normoxia (black), hypoxia (red) or anoxia (green).

**Figure 4.**Comparison of prediction accuracy for normoxia, hypoxia and anoxia when five different missing value substitutes (zero, mean, median, kNN and RF) are used in (

**A**) principal component-linear discriminant analysis or (

**B**) partial least squares-discriminant analysis. The results are displayed as an average percentage of correct classification in the test sets from 100 bootstraps versus the number of PCs used for. The arrows indicate first local maxima at which an optimum classification rate has been accomplished. Lines are coloured according to zero (black), mean (red), median (green), kNN (blue) or RF (violet) imputations.

**Figure 5.**Principal components-linear discriminant analysis (PC-LDA) scores plots showing the comparison of the five different missing value substitutes—(

**A**) zero; (

**B**) mean; (

**C**) median; (

**D**) kNN and (

**E**) RF—for the classification of normoxia (black circles), hypoxia (red triangles) and anoxia (green pluses).

## 4. Conclusions

PC-LDA | PLS-DA | |||||||||
---|---|---|---|---|---|---|---|---|---|---|

Zero | Mean | Median | kNN | RF | Zero | Mean | Median | kNN | RF | |

Classification rate (%) * | 92.02 | 77.91 | 92.57 | 95.63 | 98.02 | 90.97 | 77.98 | 91.76 | 96.06 | 97.73 |

Number of latent variables (components) used | 7 | 21 | 22 | 19 | 22 | 7 | 6 | 8 | 9 | 10 |

Metabolite name | Normoxia | Hypoxia | Anoxia |
---|---|---|---|

Glycine | –2.59 | 0.98 | 0.70 |

Lactate | –0.25 | 0.13 | –0.35 |

Pyruvate | 0.14 | 0.25 | 1.54 |

Valine | 0.95 | 0.63 | 0.97 |

Leucine | 0.27 | 0.67 | 0.92 |

Glycerol | 0.59 | 4.03 | 1.45 |

Isoleucine | 0.12 | 1.29 | 1.05 |

Leucine | 1.15 | 2.07 | 1.80 |

Malonate | –2.29 | –0.61 | –1.05 |

Glycine | –0.55 | 0.03 | –0.09 |

Phosphate | 1.29 | 0.78 | 1.01 |

Threonine | –0.66 | 0.76 | 1.19 |

Alanine | 0.84 | 0.25 | 0.96 |

Threonine | 0.35 | 1.24 | 1.05 |

Succinate | 0.38 | 0.84 | 0.05 |

Benzoic acid | –0.60 | –1.06 | 4.15 |

Threitol/erythritol | 1.07 | 1.08 | 1.58 |

Malate | –0.09 | 0.91 | 0.45 |

4-hydroxyproline | –0.54 | 2.65 | 0.98 |

Aspartate | 0.82 | 0.57 | 1.22 |

4-aminobutyric acid | 0.70 | 0.91 | 0.03 |

Aspartate | –0.33 | 1.99 | –0.32 |

4-hydroxyproline | 0.10 | 0.00 | 0.86 |

Xylitol | 0.99 | 1.00 | 0.68 |

2-hydroxyglutaric acid | 0.89 | 1.24 | 0.22 |

4-hydroxybenzoic acid | –0.78 | 1.26 | 1.71 |

Methionine | 0.18 | 1.23 | 1.10 |

Creatinine | 0.32 | 0.49 | –0.55 |

Putrescine | 0.10 | 0.22 | 0.27 |

Hypotaurine | –0.07 | 0.27 | –0.62 |

Glutamate | 0.34 | 0.42 | 1.25 |

2-oxoglutarate | 0.32 | 0.36 | 0.56 |

Fructose | –0.20 | 0.46 | 2.00 |

Sorbose/fructose | 1.41 | 1.08 | 1.31 |

Sorbitol/galactose /glucose | 1.20 | 1.08 | 0.93 |

Sorbose/fructose | 1.55 | 1.56 | 1.34 |

Glycerol 3-phosphate | –0.68 | 0.80 | –0.20 |

Galactose/glucose | 2.28 | 1.33 | 1.99 |

Galactose/glucose | 2.36 | 0.35 | 2.21 |

Galactose/glucose | 3.01 | 1.52 | 1.97 |

Citrate | 0.50 | 1.17 | 0.46 |

N-acetyl aspartate | –0.90 | 0.59 | 0.78 |

Glucose | 2.15 | 0.63 | 2.39 |

Scyllo-inositol | 0.93 | 0.55 | 1.53 |

Lysine | 0.46 | 0.75 | 0.94 |

Myo-inositol | –1.65 | 0.29 | –0.09 |

Pantothenic acid | 1.58 | 0.38 | 0.72 |

Tyramine/tyrosine | 0.82 | 0.85 | 1.04 |

Hexadecanoic acid | –0.86 | 1.57 | 1.57 |

Octadecanoic acid | –2.67 | 0.97 | 2.67 |

Myo-inositol phosphate | 0.81 | 0.84 | 2.78 |

Lactose/maltose | 0.33 | 1.21 | 0.83 |

## Supplementary Files

**Supplementary File 1:**

## Acknowledgments

## Author Contributions

## Conflicts of Interest

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## Share and Cite

**MDPI and ACS Style**

Gromski, P.S.; Xu, Y.; Kotze, H.L.; Correa, E.; Ellis, D.I.; Armitage, E.G.; Turner, M.L.; Goodacre, R.
Influence of Missing Values Substitutes on Multivariate Analysis of Metabolomics Data. *Metabolites* **2014**, *4*, 433-452.
https://doi.org/10.3390/metabo4020433

**AMA Style**

Gromski PS, Xu Y, Kotze HL, Correa E, Ellis DI, Armitage EG, Turner ML, Goodacre R.
Influence of Missing Values Substitutes on Multivariate Analysis of Metabolomics Data. *Metabolites*. 2014; 4(2):433-452.
https://doi.org/10.3390/metabo4020433

**Chicago/Turabian Style**

Gromski, Piotr S., Yun Xu, Helen L. Kotze, Elon Correa, David I. Ellis, Emily Grace Armitage, Michael L. Turner, and Royston Goodacre.
2014. "Influence of Missing Values Substitutes on Multivariate Analysis of Metabolomics Data" *Metabolites* 4, no. 2: 433-452.
https://doi.org/10.3390/metabo4020433