June 2023

Medical Mass Spectrometry Vol. 7 No. 1

Mini Review

Bioinformatics and data science for mass spectrometry data analysis
1

2

3, 4

Kei Zaitsu *, Seiichiro Eguchi , Akira Iguchi

Multimodal Informatics and Wide-data Analytics Laboratory (MiWA-Lab.), Department of Computational Systems Biology,
Faculty of Biology-Oriented Science and Technology, Kindai University, 930 Nishi Mitani, Kinokawa, Wakayama 649‒6493, Japan2
Department of Neurosurgery, Tokyo Womenʼs Medical University, 8‒1 Kawada-cho Shinjuku-ku, Tokyo 162‒8666, Japan
3
Geological Survey of Japan, National Institute of Advanced Industrial Science and Technology (AIST),AIST Tsukuba Central 7, 1‒1‒1 Higashi, Tsukuba, Ibaraki 305‒8567, Japan
4
Research laboratory on environmentally-conscious developments and technologies [E-code],
National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki 305‒8567, Japan
1

Abstract

Mass spectrometry data contributing to proteome and metabolome analyses play an important role in omics sci-

ences. Moreover, the integration of multilayer data called trans-omics has steadily become popular in life sciences, increasing the signiﬁcance of bioinformatics and data science. This minireview, therefore, outlines commonly used bioinformatics,
and data science tools, mainly based on our previous studies using mass spectrometry data. Here, we not only introduce
some bioinformatics platforms and data pipelines but also provide a concise explanation of multivariate correlation network analyses and machine learning as well as time-series data analysis. Finally, future perspectives on applying bioinformatics and data sciences to mass spectrometry data are outlined.
Key words: bioinformatics, data science, omics, data-driven analysis

Introduction

sc-RNA-seq have dramatically increased due to its innova3‒5)
tiveness in cell sorting and high-throughput sequencing .

Recent developments in omics technologies have madethe acquisition of comprehensive bioinformation from vari-

However, mass spectrometry still plays a pivotal role inous biological samples and/or organisms much easier than1)ever . Transcriptome analysis via RNA sequencing (RNA-omics techniques given its ability for proteome and/or
metabolome proﬁling. In addition, the integration of multi-seq) has been widely used in various scientiﬁc ﬁelds, allow-

layer omics data called trans-omics analyses has gradually

ing researchers to proﬁle expression patterns of tens ofbecome popular in life sciences, providing researchers

2)

1)

thousands of genes . Moreover, this technology has been
extended to single-cell RNA-seq (sc-RNA-seq) for elucidat-

access to composite multivariate data .

ing the heterogeneity of cells, and studies involving

data or grasp data structures, implementing bioinformatics,

To unveil the latent relationship among such multivariate
and data sciences to your own data is mandatory, for which*Corresponding author

numerous visualizing, and/or eﬃcient bioinformatics tools

Kei Zaitsu

6‒8)

have been available for free . Owing to the widespreaduse of open-source coding languages, such as R, andMultimodal Informatics and Wide-data Analytics Laboratory(MiWA-Lab.), Department of Computational Systems Biol-

Python, implementing, and/or customizing such valuablebioinformatics tools has never been more easier. In particu-

ogy, Faculty of Biology-Oriented Science and Technology,

Kindai University, 930 Nishi Mitani, Kinokawa, Wakayama
649‒6493, Japanlar, data-driven data analyses, such as multivariate and network analyses, are considerably helpful for discovering sig-

Tel: ＋81‒736‒77‒0345 (ext. 4232)
E-mail: kzaitsu@waka.kindai.ac.jp

Received: January 19, 2023. Accepted: March 7, 2023.

niﬁcant explanatory variables from complicated omics data
without special presumptions. For instance, correlation net-

Epub April 7, 2023.

work analysis can successfully estimate the relationship

DOI: 10.24508/mms.2023.06.006

among explanatory variables in omics data

9‒12)

17

and detect

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Medical Mass Spectrometry Vol. 7 No. 1

communities in the targeted biological network. Moreover,centrality analyses, such as a betweenness centrality analy-

method using stable isotopes of targeted compounds has
still been commonly used for MS-based metabolomics. Oursis, can elucidate hub molecules in a biological network,group has also reported that a total intensity-based normalization method was the optimal inter-batch normalization

9)

which are bottlenecks in the targeted network . Machinelearning techniques, such as random forest (RF) and sup-technique for gas chromatography/tandem mass spectrome10)
try-based metabolomics . For scaling of mass spectrome-port vector machine (SVM), have also become popular forconstructing discriminant or regression models of multivar-

try data, unit variance scaling is commonly used, whilepareto-scaling is often used for orthogonal projection toiate data, and these techniques are often used for selecting
signiﬁcant molecules (i.e., potential biomarkers) for dis-

16,37).
latent structures discriminant analysis (OPLS-DA)

13)

criminating groups or mechanisms .

Among bioinformatics platforms and data pipelines forMathematical and computational modeling techniques
have been used for estimating credible intervals (CI)

metabolome data, MetaboAnalyst (the latest version 5.0), a
web-based comprehensive platform developed by Wishartinstead of conﬁdence intervals through classical statistical
14,15). For instance, time-series data analyses,
analyses28)
et al. , might be the most popular. Similar to other bioinformatics platforms, MetaboAnalyst is user-friendly, andincluding Bayesian state space modeling, are applied totime-series data obtained through mass spectrometry given

can perform multivariate analyses, such as principal component analysis (PCA), projection to latent structures dis-that it is fundamentally inappropriate to apply classical sta14)
tistical analysis to time-series data .

criminant analysis (PLS-DA), and OPLS-DA. This platform

As described earlier, the signiﬁcance of bioinformatics,

set enrichment analysis, metabolic pathway analysis, andand data sciences on mass spectrometry multivariate data ismultivariate receiver operating characteristic (ROC) curve
analysis based on PLS-DA, SVM, or RF.

also allows for analysis of variance (ANOVA), metabolite

expected to increase more than ever, making the outliningof commonly used bioinformatics tools for mass spectrome-

In omics studies, multiple comparisons commonly cause
29)

problems with the analysis of statistical signiﬁcance .
Therefore, appropriate correction methods, such as Bonfer-

try data highly meaningful. This minireview, therefore,
introduces representative bioinformatics tools that havebeen applied to mass spectrometry data, especially metabo-roni and false discovery rate (FDR) correction meth-10‒12,14,16,17).
We also address bioinformatics platforms and data pipe-

30,31), are generally required for multiple comparisons.

lome data, mainly based on our previous studies

ods

lines, the application of correlation network analysis andBonferroni correction method adjust familywise error rate,
while FDR correction method literally control false discovery rate, where q-values are generally used instead of p-val-

machine learning to metabolome data, and mathematical,
and computational modeling for time-series data. Finally,
future perspectives on applying bioinformatics and data sci-

ues with FDR correction. Our previous study demonstrated
that the newly developed R-based platform called PiT-

ences to mass spectrometry data are outlined.

MaP

1.

cal analyses with FDR correction for multivariate data. In
the PiTMaP algorithm, PLS-DA is used for selecting vari-

17)

Overview of Bioinformatics Platforms andData Pipelines

can automatically perform two- or multi-pair statisti-

ables that are subjected to signiﬁcant analyses, allowing for

To date, numerous bioinformatics tools for mass spec-

trometry multivariate data have been reported, and previous

the objective selection of variables for signiﬁcant analysis.
PiTMaP also automatically generates box-and-whisker plots

review articles are helpful in outlining such tools. Priorto bioinformatics analyses, normalization is generally per-for all explanatory variables and score and loading plots for
PCA and PLS-DA, in addition to automatically drawing

formed for mass spectrometry data because matrix eﬀects

box-and-whisker plots for signiﬁcantly altered variables.
Fig. 1 shows some parts of the results obtained automati-

17‒28)(i.e., ionization enhancement or ionization suppression) can
disrupt the quantitativity in mass spectrometry. There arecally within 30 s via PiTMaP when applied to hepatic
metabolome data from mouse models of acetaminophen-in-

various normalization techniques such as internal standard(IS) normalization and locally weighted scatterplot smooth-

duced liver-injury and control mice. Such bioinformatics

ing (LOESS)-based normalization methods for mass spec-

data pipelines can dramatically improve the eﬃciency ofmultivariate data analysis considering their ability to auto-

10)

trometry data . As far as we know, IS normalization18June 2023

Fig. 1.

Medical Mass Spectrometry Vol. 7 No. 1

Results automatically created by PiTMaP.

(a) PLS-DA score and loading plots for the control and acetaminophen (APAP)-induced liver injury model mice with a VIP criterion of 1.0. Red: APAP model mice and blue green: control mice in PLS-DA score plots. Dotted and solid circle in the score plotsshow 95 and 99% conﬁdence intervals of all plots, respectively. Colored circles show 95% conﬁdence intervals of each cohort, and(b) box-and-whisker plots for signiﬁcantly altered metabolites. Reprinted with permission from Anal Chem 92(12): 8514‒8522,
2020. Copyright @ 2020 American Chemical Society.

. Moreover, the trans-omics
approach strongly depends on data-driven network analysis.
9,11,32,33)

matically and objectively provide results. Our team has

also to metagenome data

been improving PiTMaP to develop a web application that
implements other bioinformatics tools to achieve high-eﬃ-

Zhou et al. reported a comprehensive web-based platform
for multi-omics called OmicsAnalyst, where a correlation

ciency bioinformatics data analyses.

network analysis is applicable for multi-omics data . Cor7)

2. Data-driven Bioinformatics

relation network analysis can extract hub molecules, which

2.1.

are the most inﬂuential variables in the targeted biologicalnetworks. To identify such hub molecules, centrality analy-

Correlation network analysis

As mentioned earlier, data-driven bioinformatics is essen-

9‒12). There are several types of centrality indices, namely betweenness cen-tial for identifying potential biomarkers and/or inﬂuential

sis of network has been commonly used

hub molecules in targeted biological networks. Correlation
network analysis is based on pairwise Peasonʼs or Spear-

trality (BC), degree centrality (DC), closeness centralitymanʼs correlation coeﬃcients, and it is widely applied not(CC), and eigenvector centrality (EC). DC is the simplest

only to transcriptome, proteome, and metabolome data but

centrality index, and the high DC value simply means that19

June 2023

Fig. 2.

Medical Mass Spectrometry Vol. 7 No. 1

Network analysis results for the blood metabolome of maternal mice exposed to a phthalate during pregnancy.
The criterion was set at R＞0.75, and the size of each node (circle) was proposed to the corresponding betweenness centrality(BC) values. Each color (i.e., red, green, yellow and etc.) indicates the clusters determined by the hierarchical cluster analysis(Reprinted from ACS Omega 7(27): 23717‒23726, 2022).

with other molecules. EC is an expansion of DC, and it isdemonstrates the importance of hub molecules in the targeted network. Each color of node also indicates the clus-

deﬁned by total sum of neighbor moleculesʼ centralities. BC

ters determined by the hierarchical cluster analysis. Fig. 2

and CC are based on the shortest paths. CC considers theshows the hub metabolites in the blood metabolome ofmolecule with the shortest paths as the hub molecule, while
BC considers the molecule that is the most frequently

maternal mice exposed to a phthalate during pregnancy,
and these metabolites could be associated with fetal lethal-passed through in the shortest paths as the hub molecule.
Centrality analysis can rank explanatory variables in a tar-ity via phthalate exposure . Fig. 3 also shows hub molecules in HepG2 cells exposed to some mitochondrial toxi-

geted network according to importance via the centrality
index, with variables having high centrality values literally

cants, and these hub molecules could be molecular
indicators for discriminating mitochondrial toxicity mecha-

being at the center of the targeted network.

nisms . These results demonstrated that a correlation net-

the molecule with high DC value have many interactions

11)

12)

Our previous studies showed that correlation network

work analysis could be a powerful tool for unveiling hidden

analyses could successfully extract hub molecules in themetabolome network (Figs. 2 and 3). In our studies,

key factors in a targeted biological network. However,given that pseudo-correlations could potentially exist

betweenness centrality was commonly used for extracting

during correlation network analysis, we need to pay close

the hub molecules. In Figs. 2 and 3, the size of each nodeattention to the biological interpretation of the results.
To draw such correlation-based networks, setting athreshold for the selection of correlated variables is neces-(circle) was proposed to the corresponding betweenness
centrality values, and thus, bigger size of circle visually20June 2023

Fig. 3.

Medical Mass Spectrometry Vol. 7 No. 1

Network analysis results for metabolome data obtained from HepG2 cells exposed to some mitochondrial toxicants.
The criterion was set at R＞0.75, and the size of each node (circle) was proposed to the corresponding betweenness centrality (BC)values Hub-metabolites were determined by BC values. Each color (i.e., purple, yellow green and etc.) indicates the clusters determined by the hierarchical cluster analysis (Reprinted with permission from Toxicol Appl Pharmacol 457(15): 116316, 2022).

sary. To date, r＞0.75‒0.80 has been the commonly

2.2.

Machine learning

In medical and medicinal sciences, one of the most interesting things for omics data is searching for potential bio-

accepted threshold for correlation network analysis.
Though this threshold has been experimentally accepted bymany researchers, the use of such values still remains arbi-

markers that can discriminate disease groups from healthy

trary. In fact, correlation coeﬃcient-based thresholds could

control groups or predict diseases and/or their levels. For

be theoretically determined. In line with this, Langfelder

this purpose, diﬀerentially expressed gene (DEG) analysis

and Horvath demonstrated weighted correlation network

had generally been performed for transcriptome data.
For instance, Robinson et al. reported that the R-based bio-

2,35)

34)

analysis (WGCNA) , which can keep overall information

informatics tool called edgeR can create MA-plot, an application of a Bland‒Altman plot for gene data, and visualizeof correlation coeﬃcients while reducing false positives;
however, given its somewhat tedious implementation, its

35)

DEGs . For metabolome data, multivariate analyses, suchas PLS-DA, and OPLS-DA, as well as PCA-regression andapplications to omics data have been limited at this time.

21June 2023

Fig. 4.

Medical Mass Spectrometry Vol. 7 No. 1

Time-series changes in (a-1, b-1, and c-1) L-glutamic acid (L-Glu) and (a-2, b-2, and c-2) gamma-aminobutyric acid
(GABA) levels in the microdialysate every 1 min, and their credible intervals (blue band) estimated by Bayesianstate-space model using the steady state levels.

The model could detect signiﬁcant deviation (*) of the neurotransmitters via depolarization induced by replacing high-potassium
containing artiﬁcial cerebrospinal ﬂuid (Reprinted with permission from Talanta 234(1): 122620, 2021).

PLS-regression (PLSR), have been commonly used . In
our previous studies, PLSR was used for predicting biologi36)

16, 37)cal states using metabolome data

we programmed that hyperparameters of RF (e.g., mtry)
were automatically tuned and area under curve by each
ROC curve were automatically calculated, determining themost important variables for discriminating groups auto-.

In addition to these commonly-used approaches, machine

11,12)

maticallylearning, such as RF, and SVM, have recently been applied
to omics-data to construct discriminant and prediction mod-. This will facilitate the application ofmachine learning to omics data.

38,39)

given that these methods can be easily implemented
by R or Python more than initially expected. In our previels

2.3.

Time-series data analysis

Mathematical and computational modeling techniques

ous studies, RF, and ROC curves were used for selecting

have been used for understanding biological data

important metabolites that can discriminate groups, where
22

6,14,15,40).

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Medical Mass Spectrometry Vol. 7 No. 1

which can more easily provide time-series data. Now, weapply a time-series analysis to real-time monitoring data

Sriyudthsak et al. reviewed mathematical modeling anddynamic simulation of metabolic reaction systems for time
15)

obtained by this technique. Our successful demonstration ofa platform for such time-series data would certainly

series data of metabolome . Hirai and Shiraishi also
reported on the mathematical modeling of plant metabolic
40)
systems for time-series data .

increase the value of ambient ionization mass spectrometry.

Unlike the aforementioned cross-sectional data (i.e., one
time-point data), time-series data contain trends, cycle,

3. Future Perspectives
As mentioned earlier, researchers have already been able
to access big omics data, and the signiﬁcance of bioinformat-

irregularity, and seasonality; thus, appropriate modelingmethods, such as autoregressive, autoregressive moving
average, and state space models, are generally required forunderstanding time-series data. In our previous report, we

ics and data science has been steadily increasing. Moreover,applied a Bayesian state-space model to time-series data

be combined in near future. For instance, Zhu et al. reported
44)
on the application of single-cell multimodal omics , where

diverse data obtained via multimodal analytical methods willobtained from combinational use of in vivo microdialysis
14)

and ambient ionization mass spectrometry . In this study,
quantitative time-series data of neurotransmitters (GABA

they emphasized “the power of many.” Bredikhin et al. also

and glutamate) were obtained from each living mouse brain

MUON . Thus, mass spectrometric information, which is
positioned at an intermediate level of genomic and pheno-

reported on the multimodal omics analysis framework called8)

via an in vivo microdialysis technique. Conventionally,time-series data obtained from microdialysis were statisti-

typic information in multi-omics, is expected to becomecally analyzed by applying the t-test and/or ANOVA to eachmore a signiﬁcant part of multimodality in terms of bridging
each level. In addition to analytical multimodality, spatiotem-

time point, although such statistical tests are fundamentally
inappropriate for analyzing time-series data given theirporal information is expected to become more valuable in the45, 46)

future. Thus, more focus will be placed on single cell mass

autocovariance (autocorrelation) and periodicity. In ourstudy, therefore, we implemented the Bayesian state-space

spectrometry and spatiotemporal mass spectrometry

model using the R and Stan software and applied the modelMultimodality and spatiotemporal information will be a key

to estimate the CI based on the steady state (initial state)
14)levels of the neurotransmitters . Here, we used the follow-

factor in elucidating hidden molecular mechanisms, withsuch “wide data” requiring further improvements in bioinfor-

ing equation 1 to estimate the CI range.

matics and data science tools.

47, 48)pred (t＋i)＝Nomal [2*μpred (t＋i − 1) - μpred (t＋i − 2), σ ].

4. Conclusions(Eq. 1)In this mini review, we outlined the commonly-used bioin-

formatics and data science tools for mass spectrometry data,
mainly based on our previous studies. To date, several user-

In this equation, t is time, i is diﬀerence, pred(t＋i) is aprediction value, μpred (t) is level component at t, and σ isstandard deviation of process error. We ran 4 chains of
8,000 from the posterior distribution and discarded the ﬁrst

friendly platforms/data pipelines have been reported, with
their usability being continuously improved. We demon-

2,000 ones before inference. Model diagnostics were also

strated that multivariate and correlation network analyses

conﬁrmed using Rhat values. As shown in Fig. 4, theBayesian state-space model successfully estimated the CI

have been useful for understanding omics data and that

range of the neurotransmitters from their steady state, andmachine learning, such as RF, and SVM, is expected to
become more popular for discriminant and/or prediction anal-

could detect signiﬁcant deviations in the neurotransmitters
via depolarization induced by replacing the high-potassi-

yses of omics data. Also, mathematical, and computationalmodeling techniques are required for analyzing time-series

um-containing artiﬁcial cerebrospinal ﬂuid. This approachdata. Given the feasibility of implementation by freely avail-enables us to evaluate variations in the target biomolecules
within the time-series data without testing for statistical sig-

able coding languages such as R and Python, bioinformatics
and data sciences have already been mandatory for any scien-

niﬁcance. In addition, our group has developed in vivo real-

tists, and these techniques can provide valuable information

time monitoring techniques for metabolites in mouseto elucidate hidden molecular mechanisms in organisms.

,

Finally, future perspectives suggest that mass spectrometric41‒43)

tissues using ambient ionization mass spectrometry23June 2023

Medical Mass Spectrometry Vol. 7 No. 1

multivariate data will play a more important role in multi-

vealed key maternal metabolites related to fetal lethality

modal and spatiotemporal information in the future.

via Di(2-ethylhexyl)phthalate exposure in pregnant mice.
ACS Omega 7: 23717‒23726, 2022.

Acknowledgements

12) Hibino Y, Iguchi A, Zaitsu K: Preliminary study to classi-

We are very grateful to Dr. Tomomi Asano, Dr. Masaru

fy mechanisms of mitochondrial toxicity by in vitro me-

Taniguchi, Dr. Daisuke Kawakami, Dr. Yui Hibino, and Mr.

tabolomics and bioinformatics. Toxicol Appl Pharmacol

Kazuaki Hisatsune for their contribution to our research team.

457: 116316, 2022.

We would like to thank anonymous reviewers who strongly13) Reel PS, Reel S, Pearson E, Trucco E, Jeﬀerson E: Usingmachine learning approaches for multi-omics data analy-

improved this manuscript with the feedback and comments.

sis: A review. Biotechnol Adv 49: 107739, 2021.

Conﬂicts of Interest14) Kawakami D, Tsuchiya M, Murata T, Iguchi A, Zaitsu K:

There are no conﬂicts of interest to declare.

Rapid quantiﬁcation of extracellular neurotransmitters inmouse brain by PESI/MS/MS and longitudinal data analy-

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