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Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis

In untargeted metabolomics analysis, several factors (e.g., unwanted experimental & biological variations and technical errors) may hamper the identification of differential metabolic features, which requires the data-driven normalization approaches before feature selection. So far, ≥16 normalization methods have been widely applied for processing the LC/MS based metabolomics data. However, the performance and the sample size dependence of those methods have not yet been exhaustively compared and no online tool for comparatively and comprehensively evaluating the performance of all 16 normalization methods has been provided. In this study, a comprehensive comparison on these methods was conducted. As a result, 16 methods were categorized into three groups based on their normalization performances across various sample sizes. The VSN, the Log Transformation and the PQN were identified as methods of the best normalization performance, while the Contrast consistently underperformed across all sub-datasets of different benchmark data. Moreover, an interactive web tool comprehensively evaluating the performance of 16 methods specifically for normalizing LC/MS based metabolomics data was constructed and hosted at http://server.idrb.cqu.edu.cn/MetaPre/. In summary, this study could serve as a useful guidance to the selection of suitable normalization methods in analyzing the LC/MS based metabolomics data.

untargeted metabolomics data, while data-driven ones are better choices for untargeted LC/MS based metabolomics data 15 . The capacities of 11 data-driven normalization methods ("normalization method" in short for the rest of this paper) for processing nuclear magnetic resonance (NMR) based metabolomics data were systematically compared 21 . Two methods (the Quantile and the Cubic Splines) were identified as the "best" performed normalization methods, while other two methods (the Contrast and the Li-Wong) could "hardly" reduce bias at all and could not improve the comparability between samples 21 . For gas chromatography mass spectrometry (GC/ MS) based metabolomics, a comparative research on the performances of 8 normalization methods discovered two (the Auto Scaling and the Range Scaling) of "overall best performance" 12 . Similar to NMR and GC/MS, the LC/ MS is one of the most popular sources of current metabolomics data, and it is of great importance to analyze the differential influence of those methods on LC/MS based data. Ejigu et al. measured the performance of 6 methods according to their "average metabolite specific coefficient of variation (CV)" 15 . The CV showed that the Cyclic Loess and the Cubic Splines performed "slightly better" than other methods, but no statistical difference among CVs of those methods was observed 15 .
For the past decade, no less than 16 methods have been developed for normalizing the LC/MS based metabolomics data 13,22,23 , some of which (e.g., the VSN 24 , the Quantile 25 , the Cyclic Loess 26 ) are directly adopted from those previously used for processing transcriptomics data. Both metabolomics data and transcriptomics data are high-dimensional. However, the dimension of transcriptomics data can reach 10 thousands, while that of metabolomics data is about a few thousands. Moreover, unlike transcriptomics, correlation among metabolites identified from metabolomics data may not indicate a common biological function 27 . Apart from the above differences, there are significant similarities between two OMICs data: (1) right-skewed distribution 23 , (2) great data sparsity 28 , (3) substantial amount of noise 29,30 and (4) significantly varied sample sizes 31,32 . Due to these similarities, it is feasible to apply some of the normalization methods used in transcriptomics data analysis to the metabolomics one.
Those 16 methods specifically normalizing LC/MS based metabolomics data can be classified into two groups 21 . Methods in group one (including the Contrast Normalization 33 , the Cubic Splines 34 , the Cyclic Loess 35 , the Linear Baseline Scaling 25 , the MSTUS 22 , the Non-Linear Baseline Normalization 36 , the Probabilistic Quotient Normalization 37 and the Quantile Normalization 25 ) aim at removing the unwanted sample-to-sample variations, while methods of the second group (including the Auto Scaling 38 , the Level Scaling 12 , the Log Transformation 39 , the Pareto Scaling 40 , the Power Scaling 41 , the Range Scaling 42 , the VSN 43,44 and the Vast Scaling 45 ) adjust biases among various metabolites to reduce heteroscedasticity. However, the performance and the sample size dependence of those methods widely adopted in current metabolomics studies (e.g., the Pareto Scaling and the VSN) 28, 46 have not yet been exhaustively compared in the context of LC/MS metabolomics data analysis.
Moreover, several comprehensive metabolomics pipelines are currently available online, where various normalization algorithms are integrated in as one step in their corresponding analysis chain. These online pipelines include the MetaboAnalyst 28 , the Metabolomics Workbench 47 , the MetaDB 48 , the MetDAT 49 , the MSPrep 50 , the Workflow4Metabolomics 51 and the XCMS online 52 . Based on a comprehensive review, the number of normalization algorithms provided by the above pipelines varies significantly from 2 (the Workflow4Metabolomics) to 13 (the MetaboAnalyst). 6 out of those 7 pipelines only provide < 50% of those 16 methods analyzed in this study. The MetaboAnalyst is the only pipeline offering 13 methods, but some methods reported as "well-performed" in LC/ MS based metabolomics analysis (e.g., the VSN and the PQN) 28,37,46 are not provided. The inadequate coverage of these methods may weaken the applicability range of those pipelines. Moreover, the suitability of a normalization method was reported to be greatly dependent on the nature of the analyzed data 53 , a comparative performance evaluation among methods is therefore essential to the determination of the most appropriate method for professional/inexperienced researchers. However, no comparative evaluation among those normalization methods was conducted in the above pipelines. So far, the Normalyzer 53 is the only online tool offering comparative evaluation of 12 different normalization methods treating high-throughput OMICs data 53 . In particular, this tool accepted a variety of data types including metabolomics, proteomics, DNA microarray and the real-time polymerase chain reaction data 53 . However, since the Normalyzer was designed to process a wide range of OMICs, it did not cover 8 of those 16 methods specifically for LC/MS based metabolomics studies. Thus, it is in urgent need to construct a publicly available tool for comparatively and comprehensively evaluating the performances of methods used specifically for normalizing LC/MS based metabolomics data.
In this study, a comprehensive comparison on the normalization capacities of 16 methods was conducted. Firstly, the differential metabolic features selected based on each method were validated by a benchmark spike-in dataset and by experimentally validated markers. To further understand the influence of sample size on the method performance, 10 sub-datasets of various sample size were generated to evaluate the variation of normalization performance among 16 methods, and to categorize these methods into 3 groups (superior, good and poor performance group). Finally, a web-based tool used to comprehensively evaluate the performance of all 16 methods was constructed. In sum, this study could serve as valuable guidance to the selection of suitable normalization methods in analyzing the LC/MS based metabolomics data.

Materials and Methods
Benchmark datasets collection and sub-datasets generation. Five criteria were used to select datasets from the MetaboLights (http://www.ebi.ac.uk/metabolights/) 32 in this study, which include: (1) data type set as "study"; (2) technology set as "mass spectrometry"; (3) organism set as "homo sapiens"; (4) study validation set as "fully validated"; (5) untargeted LC/MS based metabolomics data with > 100 samples selected by manual literature and dataset reviews. Based on the above criteria, 4 benchmark datasets were collected for analysis, which include the positive (ESI+ ) and negative (ESI− ) ionization modes of both MTBLS28 54 and MTBLS17 55 . For MTBLS17, only the dataset of experiment 1 with >100 studied samples was included. For the remaining text of this paper, MTBLS17 was used to stand for the dataset of experiment 1 in Ressom's work 55 54 , and MTBLS17 ESI+ and ESI− gave profiles of 189 samples (60 HCC patients and 129 people with cirrhosis) and 185 samples (59 HCC patients and 126 people with cirrhosis), respectively 55 .
To construct training and validation datasets and sub-datasets of various sample size, random sampling and k-means clustering were applied. Taking MTBLS28 ESI+ as an example, 1,005 samples were divided into training dataset (400 lung cancer patients and 500 healthy individuals) and validation dataset (105 samples) by random sampling. Moreover, to generate the sub-datasets from training dataset, the k-means clustering 56 was used to sample 10 sub-datasets of various sample size. In particular, the number of lung cancer patients versus that of healthy individuals were 50 vs. 40 LC/MS based metabolomics data pre-processing. Biological variance and technical error are two key factors introducing biases to the metabolomics data. Biological variance arises from the spread of metabolic signals detected from various biological samples 57 , while technical error results from machine drift 58 . In particular, biological variances (e.g., varying concentration levels of bio-fluid, different cell sizes, varying sample measurements) are commonly encountered in metabolomics data 13 , while technical errors (e.g., a sudden drop in peak intensities or measurements on different instruments) are the major issues in large-scale metabolomics studies 58 . Apart from those above methods widely adopted to remove biological variances 22 , quality-control (QC) samples were used to significantly reduce technical errors 58 .
Moreover, sparsity is the nature of metabolomics data, which can be represented by a substantial amount of missing values (10~40%), which can affect up to 80% of all metabolic features 59 . The direct assignment of zero to the missing values could be useful for cluster analysis, but it may lead to poor performance or even malfunction if normalization method is applied 50 , especially for those methods based on the logarithm (e.g., the Log Transformation) 50,53 . Several missing value imputation methods are currently available, among which the KNN algorithm 60 was reported as the most robust one for analyzing mass spectrometry based metabolomics data 60 . Therefore, the KNN algorithm was adopted in this work to impute the missing signals of the metabolic features.
In this study, a widely adopted data pre-processing procedure 54,60,61 was applied, which included sample filtering, data matrix construction and signal filtering & imputing (Fig. 1). In particular, (1) samples with signal interruption or not detectable internal standard were removed based on Mathé's work 54 ; (2) peak detection, retention time correction and peak alignment 54 were applied to the UHPLC/Q-TOF-MS raw data (in CDF format) using the xcmsSet, the group and the rector functions in the XCMS package 62 with both the full width at half-maximum (fwhm) and the retention time window (bw) set as 10; (3) metabolic features detected in < 20% of QC samples 61 or with large variations 54 were removed based on Mathé's work, and missing signals of the remaining metabolic features were imputed by the KNN algorithm 60 . The detailed workflow of data pre-processing used in this study was illustrated in Fig. 1.
Normalization methods analyzed in this study. 16 methods were analyzed in this work, which include the Auto Scaling (unit variance scaling, UV) 38 , the Contrast Normalization 33 , the Cubic Splines 34 , the Cyclic Locally Weighted Regression (Cyclic Loess) 35 , the Level Scaling 12 , the Linear Baseline Scaling 25 , the Log Transformation 39 , the MS Total Useful Signal (MSTUS) 22 , the Non-Linear Baseline Normalization (Li-Wong) 36 , the Pareto Scaling 40 , the Power Scaling 41 , the Probabilistic Quotient Normalization (PQN) 37 , the Quantile Normalization 25 , the Range Scaling 42 , the Variance Stabilization Normalization (VSN) 43,44 and the Vast Scaling 45 .
Auto Scaling (unit variance scaling, UV) is one of the simplest methods adjusting metabolic variances 21 , which scales metabolic signals based on the standard deviation of metabolomics data. This method makes all metabolites of equal importance, but analytical errors may be amplified due to dilution effects 21 . Auto scaling has been used to improve the diagnosis of bladder cancer using gas sensor arrays 63 and to identify urinary nucleoside markers from urogenital cancer patients 64 .
Contrast Normalization is originated from the integration of MA-plots and logged Bland-Altman plots, which assumes the presence of non-linear biases 21 . The use of a log function in this method may impede the processing of zeros and negative numbers, which requires the conversion of non-positive numbers to an extremely small value 21 . The contrast method has been employed to reveal the role of polychlorinated biphenyls in non-alcoholic fatty liver disease by metabolomics analysis 65 .
Cubic Splines is one of the non-linear baseline methods assuming the existence of non-linear relationships between baseline and individual spectra 21 . Cubic splines has been adopted to reduce variability in DNA microarray experiments by normalizing all signal channels to a target array 34 . Moreover, this method has been performed to evaluate differential effects of clinical and biological variables in breast cancer patients 66 .
Similar to contrast normalization, Cyclic Locally Weighted Regression (Cyclic Loess) comes also from the combination of MA-plot and logged Bland-Altman plot by assuming the existence of non-linear bias 21 . However, cyclic loess is the most time-consuming one among those studied normalization methods, and the amount of time grows exponentially as the number of sample increases 67 . This method has been used to discover microRNA candidates regulating human osteosarcoma 68 .
Level Scaling transforms metabolic signal variation into variation relative to the average metabolic signal by scaling according to the mean signal 12 . This method is especially suitable for the circumstances when huge relative variations are of great interest (e.g., studying the stress responses, identifying relatively abundant biomarkers) 12 . Level Scaling has been used to identify urinary nucleoside markers from urogenital cancer patients 64 .
Linear Baseline Scaling maps each sample spectrum to the baseline based on the assumption of a constant linear relationship 21 . However, this assumption of a linear correlation among sample spectra may be oversimplified 21 . This method has been conducted to identify differential metabolomics profiles among the banana's 5 different senescence stages 69 . Moreover, linear baseline scaling has been performed to discover the toxicity profiling of capecitabine in patients with inoperable colorectal cancer 70 .
Log Transformation converts skewed metabolomics data to symmetric via the non-linear transformation, which is usually used to adjust heteroscedasticity and transform metabolites' relations from multiplication to addition 12 . In metabolomics, relations among metabolites may not always be additive, this method is thus needed to identify multiplicative relation with linear techniques 12 . This method has been used to delineate potential role of sarcosine in prostate cancer progression 71 .
MS Total Useful Signal (MSTUS) utilizes the total signals of metabolites that are shared by all samples by assuming that the number of increased and decreased metabolic signals is relatively equivalent 22,72 . However, the validity of this hypothesis is questionable since an increase in the concentration of one metabolite may not necessarily be accompanied by a decrease in that of another metabolite 72,73 . MSTUS has been reported as among the best choices for overcoming sample variability in urinary metabolomics 73 and used to identify diagnostic and prognostic markers for lung cancer patients 54 .
Non-Linear Baseline Normalization (Li-Wong) is one of the normalization methods aiming at removing unwanted sample-to-sample variations 21 . This method is first used to analyze oligonucleotide arrays based on a multiplicative parametrization 36,74 , and currently adopted to improve NMR-based metabolomics analysis 21 . This method has already been successfully integrated into the dChip 74 .
Different from the auto scaling, Pareto Scaling uses the square root of the standard deviation of the data as scaling factor 40 . Therefore, comparing to the auto scaling, this method is able to reduce more significantly the weights of large fold changes in metabolite signals, but the dominant weight of extremely large fold changes may still be unchanged 21 . Pareto scaling has been performed for improving the pattern recognition for targeted 75 and untargeted 76 metabolomics data.
Power Scaling aims at correcting for the pseudo scaling and the heteroscedasticity 12 . Different from the log transformation, the method is able to handle and zero values 12 . Power scaling has been used to study the serum amino acid profiles and their variations in colorectal cancer patients 77 .
Probabilistic Quotient Normalization (PQN) transforms the metabolomics spectra according to an overall estimation on the most probable dilution 37 . This algorithm has been reported to be significantly robust and accurate comparing to the integral and the vector length normalizations 37 . PQN has been used to discover potential diagnostic technique for ovarian and breast cancers from urine metabolites 78 .
Quantile Normalization aims at achieving the same distribution of metabolic feature intensities across all samples, and the quantile-quantile plot in this method is used to visualize the distribution similarity 21 . Quantile normalization has been used to probe differential molecular profiling between pancreatic adenocarcinoma and chronic pancreatitis 79 , and currently adopted to improve NMR-based metabolomics analysis 21 . Range Scaling scales the metabolic signals by the variation of biological responses 63 . A disadvantage of this method lies in a limited number (usually only 2) of values used to describe the variation unlike other scaling methods taking all measurements into account using the standard deviation, which makes this algorithm relatively sensitive to outliers 12 . Because all variation levels of the metabolites are treated equally by the range scaling, it has been used to fuse mass spectrometry-based metabolomics data 42 .
Variance Stabilization Normalization (VSN) is one of the non-linear methods aiming at remain variances unchanged across the whole data range 21 . The method is reported to be a preferred approach for exploratory analysis such as the principal component analysis 80 . VSN was originally developed for normalizing single and two-channel microarray data 81 , and currently used to determine metabolic profiles of liver tissue during early cancer development 82 .
As an extension of the auto scaling, Vast Scaling scales the metabolic signals based on the coefficient of variation 12 . Vast scaling has been used to identify prognostic factors for breast cancer patients from the magnetic resonance based metabolomics 83 .
Detailed descriptions on these methods could be found in Supplementary Note S1, and their source codes programed in this study could be found in Supplementary Note S2.
Assessment of the normalization performance by classification algorithm. Firstly, the differential metabolic features were identified by VIP value (> 1) of the partial least squares discriminant analysis (PLS-DA) 84 in R package ropls 85 together with p-value (< 0.05) of Student t-test 71 . All computational assessments were conducted in R (http://www.r-project.org) version 3.2.4 running on 64-bit Mac OS X EI Capitan (v10.11.5) platform. Source codes of related programs designed in this study could be found in Supplementary Note S2.
Secondly, classification algorithm was applied to assess the performance of each normalization method based on the identified differential metabolic features. Several classification algorithms were adopted to evaluate the performance of normalization methods, which include the Support Vector Machine (SVM) 21 , the k-Nearest Neighbors (k-NN) 86 , the Gaussian Mixture Model (GMM) 87 , and so on. As illustrated in Fig. 1, the SVM algorithm in the R package e1071 (http://cran.r-project.org/web/packages/e1071) was selected to assess normalization performance in this study. In the process of training the classification models, 10-fold cross validation was used to optimize parameters, and the validation dataset was then used to assess the classification performance of the selected differential features by the receiver operating characteristic (ROC) plots generated by R package ROCR 88 . Source codes of the classification algorithm programed in this study could be found in Supplementary Note S2.
Identification of the performance relationship among normalization methods. The hierarchical clustering 56,89,90 was adopted to identify the relationship of sample size dependent performance among 16 methods. Firstly, the area under the curve values (AUCs) of a specific method among 10 sub-datasets of various sample size were used to generate a 10 dimensional vector. Secondly, hierarchical clustering was adopted to investigate the relationship among vectors, and therefore among corresponding methods. As an assessment of consistency between different distance metrics, two metrics (the Manhattan and the Euclidean) were applied: In Eq. (1) and Eq. (2), i refers to each AUC of method a and b. Clustering approach adopted is the Ward's minimum variance method 91 , which is used to reduce the total within-cluster variance to the maximum extent. In this work, Ward's minimum variance module in R package was used 92 . Source codes of the hierarchical clustering algorithm programed in this study could be found in Supplementary Note S2.

Construction of web-based tool for evaluating performance of 16 normalization method. A
web-based tool named as MetaPre for comprehensively evaluating the normalization performance of all 16 methods was constructed and hosted at http://server.idrb.cqu.edu.cn/MetaPre/. MetaPre was developed in R environment, and further extended using HTML, CSS and JavaScript. The R package Shiny (http://shiny.rstudio.com/) was used to construct web application (comprised of a front end and a back end). R package DiffCorr 93 and vsn from Bioconductor Project 94 were utilized to support background processes. MetaPre server was deployed at Apache HTTP web server v2.2.15 (http://httpd.apache.org).

Validation of the differential metabolic features selected based on 16 normalization methods.
Supplementary Table S1 showed the number of differential metabolic features identified by PLS-DA based on 16 normalization methods. As demonstrated, the numbers of features selected based on some methods were the same as each other, while the numbers identified by some others varied significantly. SVM classifier based on those features was used in this work, the validity of these features were therefore crucial for assessing performances of 16 methods. In this study, two lines of evidence were provided for this assessment. First, a benchmark spike-in dataset from Franceschi's work 95 was analyzed. As shown in Supplementary Table S2, the performances on identifying spike-in compounds based on 16 methods were equivalent to that of Franceschi's work, which indirectly reflected the reliability of strategy applied in this study. Secondly, 2 markers (creatine riboside and 561.3432) from positive and other 2 markers (cortisol sulfate and N-acetylneuraminic acid) from negative ionization mode were experimentally validated in Mathé's work 54 Table S4 showed the ACC values of each method trained by 10 sub-datasets based on MTBLS17 (ESI+ and ESI− ). For the training set of 170 samples from MTBLS17, the Contrast method always underperformed comparing to other methods, which was similar to that of MTBLS28. However, the top-ranked normalization methods for each ionization mode of each dataset vary significantly, which is in accordance with Chawade's conclusion that the effectiveness of a method in normalizing data relied on the nature of the analyzed data 53 . Thus, this significant variation reminded us that it is essential to take various sample size into account, if one try to compare the performance among normalization methods.

Variation of normalization performances among 16 methods based on benchmark datasets.
The receiver operating characteristic (ROC) curves and the area under the curve values (ACCs) were used to illustrate the performances of 16 methods in Fig. 2 Fig. 2a and b consisted of 900 samples (400 lung cancer patients and 500 healthy individuals), and that of  Group. The VSN and the PQN had been discovered as robust and well-performed methods in metabolomics for various dilutions of biological samples 37,96 . The Log Transformation was reported to be a powerful tool for making skewed distributions symmetric 12 , it was therefore a very suitable method for treating metabolomics data (the distribution of which was right-skewed) 23 . Moreover, some methods (e.g., the VSN) in G-A was also found to be the most capable one in reducing variation between technical replicates in proteomics, and consistently well-performed in identifying differential expression profiles 97 . The Contrast was the only one method in group C (G-C, the Poor Performance Group), the performance of which was consistently the worst across 10 sub-datasets among all 16 methods. As reported by Kohl et al. 21 , the Contrast hardly reduced bias at all and could not improve comparability among samples 21 .
Moreover, the remaining 12 methods in group B (Good Performance Group) could be further divided into G-B1 (including 6 methods occasionally classified to the top area of Fig. 3) and G-B2 (including 6 methods consistently staying in the middle area of Fig. 3). As illustrated in Fig. 4, although slightly underperformed comparing to G-A, methods in G-B1 showed good normalization performances across 10 sub-datasets of various sample size. Furthermore, the majority of the methods in G-B2 followed a similar fluctuation trends across various sample sizes, with the Li-Wong distinguished as an outlier. The Li-Wong performed the worst among other assessed methods in reducing within-and between-group variations 96 , and could hardly reduce the biases among samples at all 21 .
Similar to the Manhattan metric (Fig. 3), 16 methods could also be re-categorized with the Euclidean metric. As illustrated in Supplementary Figure S1, the categorization generated based on the Euclidean metric identified 3 groups with exactly the same methods in each group as that of the Manhattan metric, which reflected the independent nature of method categorization on different distance metrics. Moreover, in Supplementary Figure S1d, the Li-Wong was clustered into the bottom area (magenta) together with the Contrast, which again reflected its unsuitability in analyzing LC/MS based metabolomics data 21,96 . Online interactive analysis tool for normalizing LC/MS based metabolomics data. With R package Shiny (http://shiny.rstudio.com/), an interactive web tool, named MetaPre, was developed in this study and hosted at http://server.idrb.cqu.edu.cn/MetaPre/. The MetaPre constructed to normalize LC/MS based metabolomics data could be easily accessed by modern web browsers such as Chrome, Foxfire, IE, Safari, and so on. Meanwhile, the local version of MetaPre was freely provide in this study and could also be readily downloaded from https://github.com/libcell/MetaPre in Github. The procedure for using online version of the MetaPre was provided in Fig. 5, which included 4 steps: (1) uploading the dataset; (2) data pre-processing; (3) data normalization; (4) performance evaluation. Uploading the dataset provided the option to upload data with or without QC samples. In large-scale metabolomics study (especially the LC/MS based one), not all samples can be analyzed in the same experimental batch 61 . To cope with these difficulties, QC samples were frequently applied 58,61 . In the MetaPre, batch correction based on QC samples was provided, which made this tool one of the few currently available online servers 51,98 offering such kind of function.
Data pre-processing offered the function to correct metabolic features and impute missing signals. For data with QC samples, the MetaPre firstly applied within-block signal correction 61 to correct metabolic features. Then, multiple popular imputing algorithms were provided to fill missing signals. For data without QC samples, only the process of missing signal imputing was implemented.
Data normalization integrated 16 normalization methods discussed in this study to remove the unwanted biological variations. After selecting any of these methods, the normalized data matrix was displayed on the web page and a corresponding csv file could be downloaded directly. Moreover, two box plots used to visualizing the distributions of data before and after normalization were illustrated on the web page.
Performance evaluation was quantified based on AUC values of the constructed SVM models. Firstly, the differential metabolic features were identified by VIP value (>1) of PLS-DA model. Then, SVM models were constructed based on these identified differential features. After k-folds cross validation, ROC curve together with its AUC value were calculated and displayed on the web page.
MetaPre is valuable online tool to select suitable methods for normalizing LC/MS based metabolomics data, and is a useful complement to the currently available tools in modern metabolomics analysis.

Conclusion
Based on the 4 datasets tested in this work, 16 methods for normalizing LC/MS based metabolomics data were categorized into three groups based on their normalization performances across various sample sizes, which included the superior (3 methods), good (12 methods) and poor (1 method) performance groups. The VSN, the Log Transformation and the PQN were identified as methods of the best normalization performance, while the Contrast consistently underperformed across all sub-datasets of different benchmark data among those 16 methods. Moreover, an interactive web tool comprehensively evaluating the performance of all 16 methods for normalizing LC/MS based metabolomics data was constructed and hosted at http://server.idrb.cqu.edu.cn/MetaPre/. In sum, this study could serve as guidance to the selection of suitable normalization methods in analyzing the LC/ MS based metabolomics data.