dataesr/scientific-paragraphs-categorization huggingface dataset:
The chemical specificity of the assay influences the pathways discoverable using ORA The analytical platform and specific assay used for a metabolomics study can be expected to introduce bias into the pathways which might be detected by ORA. Assays typically differ in their ability to detect compounds with different physico-chemical properties (e. g. , polarity). While it is increasingly common for metabolomics experiments to incorporate multiple assays, most studies will still be biased in the compounds they can detect. We would expect to be able to access different pathways depending on the compounds assayed, resulting in disparate ORA results. Using the Stevens et al. dataset as an example, which contains compounds identified using four different assay types, we mapped these compounds onto the KEGG pathway network using iPath 3.
Discussion As metabolomics continues to grow as a field of study with a multitude of applications within various disciplines, deriving meaningful conclusions from such data becomes increasingly important. ORA is one of the most popular approaches used to draw functional interpretations from metabolomics data. However, to date, there have been no published investigations of the consequences of varying input parameters on ORA results derived using metabolomics data. Understanding the sensitivity of ORA to tuning parameters, especially how it is influenced by metabolomics-specific factors, will play a crucial role in its successful application. In the present study, we sought to investigate the effects of varying inputs on ORA results, which we demonstrated using in-silico simulations based on five untargeted metabolomics datasets.
Such an assumption is highly unlikely to be true given that most technologies can only detect a small fraction of the metabolome and may lead to false-positive pathways. Additionally, the size of the background set is an important consideration, with larger sets generally yielding higher numbers of SEPs. MSbased approaches can usually detect a larger number of compounds than NMR-based methods, for example, at least for typical 1D NMR methods that are commonly used for profiling  [25]. Users need to consider whether their metabolomics dataset is large enough to provide sufficient statistical power such that ORA results can be considered useful. Defining the ideal assay-specific background set for a particular dataset remains an area for further study. The approach used in this work was to use all identified compounds, which although conservative, is the safest approach minimising the number of false-positive pathways.
The level of misidentification will vary depending on the analytical platform used and remains a key bottleneck, more so in MS-based studies, where the number of metabolites detected often exceeds that of NMR-based studies  [27]. In this study, we simulated metabolite misidentification by randomly swapping a small percentage of compounds in each of the datasets with compounds of either a similar molecular weight (± 20ppm) or an identical chemical formula. Even at a low level of misidentification of 4%, we found appreciable pathway loss and gain rates for all datasets. Hence, we suggest that ORA is sensitive to even low levels of metabolite misidentification, resulting in the emergence of false-positive and false-negative SEPs in the results. Another essential input of ORA is the pathway database or list of metabolite sets used.
The inherent differences between pathway databases will undoubtedly impact the PA results, regardless of the method used  [28]. In the case of ORA, which is based on the hypergeometric formula, pathway size will influence results by rendering smaller pathways more significant and larger pathways less significant  [29]. The number of pathways tested using ORA will also directly impact the adjusted significance level if multiple testing correction methods are applied, and the more pathways tested the more statistical power is lost. A related caveat is that the most widely used multiple testing approaches (e. g. Bonferroni, BH FDR) do not account for correlations between pathways and therefore such methods may be too conservative and undermine pathway significance  [2]. A further important consideration for pathway database evaluation is the type of compound identifiers used in the pathway.
KEGG and BioCyc use database-specific identifiers, whereas Reactome uses ChEBI identifiers. It is necessary to convert the identifiers present in a metabolomics dataset to their database-specific equivalent, which often results in loss of information as not all identifiers will necessarily map directly to a database compound or be mapped to a pathway  [30]. For example, in the Stevens et al. dataset, over 900 compounds were assigned to Metabolon identifiers, but less than half of these compounds could be mapped to KEGG identifiers. Another characteristic of metabolomics (and in particular lipidomics) is the discrepancy between the chemical precision of identification between the pathway databases and the dataset. For instance, in databases classes of lipids are often gathered into a single element (e.
g. , "a triglyceride") while lipidomics allows more in-depth annotation (e. g. , "TG 16/18/18"). Computational solutions based on chemical ontologies exist to establish a link between dataset elements and pathway database ones  [31] , but this will also have an impact on PA results since several data elements will map to a single node in the pathway database. The incompleteness of pathway databases, together with the evolution of pathway definitions between releases, are key factors highlighting the necessity of using an up-to-date resource; not doing so can have a detrimental effect on PA results  [32]. Furthermore, the magnitude of changes across database releases demonstrated in this work suggests that ORA results are somewhat short-lived and perhaps valid only at a given time, hence they should be periodically revised using an updated database.
Frainay et al. examined the coverage of analytes in the human metabolic network and found poor coverage of pathways involving eicosanoids, vitamins, heme, and bile acid metabolism  [33]. Finally, although an extensive comparison of pathway databases is beyond the scope of this paper, several excellent studies have examined this in detail to which we refer the interested reader  [28, 34, 35]. A general recommendation is to use multiple pathway databases and derive a consensus signature across these, if possible, reinforced by current knowledge of the underlying biochemistry of the system investigated. The use of integrative databases encompassing several pathway databases, such as the ConsensusPathDB  [36] , or interactive tools to simultaneously visualise pathways from different databases such as PathMe  [37]  may be beneficial and reflect ongoing efforts to harmonise pathway resources.
In this work we have focused on ORA, but many other PA methods exist  [1, 38, 39]. While functional class scoring and topology-based methods can overcome certain limitations associated with ORA, such as the need to select compounds of interest, or not taking metabolitelevel statistics into account, many of our findings are also relevant to these methods. Pathway database selection, metabolite misidentification rate, and assay chemical bias will impact the majority of metabolomics PA methods. Alongside the present work, further studies examining the input parameters of other PA methods for metabolomics data will be invaluable in establishing a set of best-practice guidelines for their application. This study is limited by the lack of availability of a ground-truth dataset where the identities of enriched pathways are known.
Possible sources of ground-truth data include simulations based on genome-scale metabolic models, in which enzymes in specific pathways are knocked out or the flux through reactions altered. Alternatively, one could insert artificial pathway signals into simulated or real data by altering the relative abundance levels of metabolites involved in the target pathways. Experimental datasets such as gene knockouts or knock-downs offer more realistic forms of ground truth datasets, which more accurately reflect the complexity of a biological system. Both simulated and experimental ground-truth datasets have limitations, however, such as the former being too simplistic, or the inability to pinpoint the exact pathway(s) affected by a perturbation in the latter. Nevertheless, such datasets might enable quantification of a wider variety of performance metrics than available here.
Another limitation is that in the majority of examples, a p-value threshold of P � 0. 1 was used without multiple testing correction to select SEPs. As metabolomics experiments usually identify far fewer compounds than transcriptomic experiments identify genes, ORA based on metabolites appears to have much lower power to identify significant pathways and as such in the example datasets few, if any, pathways remained significant after multiple testing correction was applied. The purpose of the present research was to evaluate the suitability of ORA for metabolomics PA and assess the effects of varying input data and parameters. We have investigated the three main input parameters: the background set, the list of compounds of interest, and the pathway database, as well as metabolomics-specific considerations such as metabolite misidentification and assay chemical bias.
By means of in-silico simulations based on experimental datasets, all of the aforementioned variables have been shown to introduce varying levels of bias and uncertainty into ORA results, which has significant implications for those using ORA to analyse metabolomics data. In particular, use of an assay-specific background set is often ignored, yet has a critical effect on the output. Overall, this study has been the first detailed investigation into the application of ORA to metabolomics data, with wide-ranging findings that have implications not only to ORA but also a variety of other PA methods in metabolomics. We therefore offer the community a set of recommendations for application, as well as suggested minimal reporting criteria, which may contribute to the future development of bestpractice guidelines for the application of ORA to metabolomics data.
Suggested recommendations for the application of ORA to metabolomics data 1. Specify a realistic background set based on the analytical platform used in the experiment. A conservative yet practical approach is to use all the metabolites that have been identified in the assay. 2. Use an organism-specific pathway set if the organism is supported by the pathway database. 3. Perform ORA using multiple pathway databases and derive a consensus pathway signature using the results if possible. 4. Use multiple-testing correction to select both DA metabolites and, where feasible, significant pathways. sample of typical metabolomics studies to illustrate the effects of changing ORA parameters. The inclusion criteria for a dataset were: i) it should be publicly available, ii) it should contain over 100 annotated metabolites, and iii) there should be at least two study groups.
For consistency, all datasets used in this work are based on mass-spectrometry (MS). The first dataset is available at MTBLS135 from the MetaboLights repository and consists of 12 Hi-Myc genotype and 12 wild-type Mus musculus tissue samples  [40]. The second dataset from Yachida et al. 2019  [41]  consists of 149 healthy control and 148 colorectal cancer human stool samples (stages I-IV). The third dataset is available at MTBLS136 and consists of 667 control samples and 332 estrogen users  [42]. The fourth dataset is from Quiros et al. 2017  [43]  from which we compared 8 HeLa cell replicates treated with actinonin to 8 HeLa cell replicates treated with doxycycline. The final dataset is available from EBI BioStudies (S-BSST5) and consists of >3,800 single-gene E. coli knockouts each with 3 biological replicates  [44].
Data from the positive and negative ionisation modes was combined to provide the final matrix of putative compound identifications and relative abundances for each. We selected two knockout strains to investigate from this dataset which were amongst those with the highest effect size (based on the number of significant pathways detected using ORA): ΔyfgM and ΔdcuS. It is important to note that two datasets, Quiros et al. 2017 and Fuhrer et al. 2017, did not use any separation step in their analytical platform, and therefore there may be a higher degree of uncertainty in the metabolite identifications. Post-processing of metabolomics datasets. All metabolomics datasets and corresponding metadata used in this study are publicly available from the MetaboLights repository  [45] , the BioStudies database  [46] , or in the supplementary information of the original publication (Table  1 ).
Details of metabolomics data pre-processing, as well as sample preparation, data acquisition, and compound identification can be found in the original publication for each dataset. For the purposes of this study, the pre-processed raw metabolite abundance matrices consisting of n samples by m metabolites were downloaded as. csv or. xlsx files and post-processed identically. Missing abundance values were imputed using the minimum value of each metabolite divided by 2. All abundance values in the matrix were then log 2 transformed and features (metabolites) were auto-scaled by subtracting the mean and dividing by the standard deviation. Metabolite identifier harmonisation. In order to map compounds to the three pathway databases investigated in this study (KEGG, Reactome, and BioCyc), metabolite identifiers in each dataset were converted to the corresponding identifier type.
For the conversion of compound names to KEGG identifiers, the MetaboAnalyst 4. 0  [47]  ID conversion tool was used (https://www. metaboanalyst. ca/MetaboAnalyst/upload/ConvertView. xhtml). For Reactome, KEGG compounds were mapped to ChEBI identifiers using the Python bioservices package (v 1. 7. 1) [48]. For BioCyc, the web-based metabolite translation service (https://metacyc. org/ metabolite-translation-service. shtml) was used to convert from KEGG to BioCyc identifiers. Selection of differentially abundant metabolites. The list of metabolites of interest was determined using a series of two-tailed student's t-tests to determine whether each metabolite in the dataset was significantly associated with the outcome of interest. P-values were adjusted using the Benjamini-Hochberg False discovery rate (BH FDR) procedure  [49]  to account for multiple testing.
Performing pathway enrichment Pathway database details. For the purposes of this paper, the pathway sets used contained only compounds (including small molecules, metabolites, and drugs). KEGG pathways and their corresponding compounds were downloaded using the KEGG REST API (https://www. kegg. jp/kegg/rest/keggapi. html) in October 2020, corresponding to KEGG release 96. Reactome pathways release 75 were downloaded from https://reactome. org/download-data. BioCyc pathways v24. 5 were exported from https://biocyc. org/ using the SmartTables function. ORA implementation. ORA was implemented using a custom Python script that utilised the scipy stats fisher_exact function (right-tailed) to calculate pathway p-values. Only pathways containing at least 3 compounds were used as input for ORA. p-values were calculated if the parameter k (number of differentially abundant metabolites in the i th pathway) was � 1.
Metabolite misidentification Implementation details. All simulations were performed using Python (v 3. 8). Simulations with an element of randomisation were repeated 100 times, and results are reported as the mean of 100 random samplings of the simulation, alongside the standard error of the mean. Simulating metabolite misidentification. Chemical formula and molecular weight information for each metabolite was obtained using the KEGG REST API. For each level of metabolite misidentification, we randomly selected f % (f = 0, 1,. X%) of compounds that had at least one other compound with a molecular weight within ±20ppm (approximately isobaric compound) present in the KEGG pathway set. For each randomly selected compound, one of its isobaric compounds was randomly selected and the identifier of this compound then replaced the original identifier in the dataset, thereby simulating misidentification by mass.
This research was funded in whole, or in part, by the  Wellcome Trust  [ 222837/Z/21/Z ]. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. CW is supported by a  Wellcome Trust   PhD Studentship  [ 222837/Z/21/Z ]. RPJL receives support from the  UK Medical Research Council  ( MR/R008922/1 ). JC is supported by a statefunded PhD contract ( MESRI (Minister of Higher Education, Research and Innovation) ). FJ is supported by the  French Ministry of Research and National Research Agency  as part of the  French MetaboHUB, the national metabolomics and fluxomics infrastructure  (Grant  ANR-INBS-0010 ), and  MetClassNet  project ( ANR-19-CE45-0021  and  DFG: 431572533 ). TE gratefully acknowledges partial support from  BBSRC  grant  BB/T007974/1 ,  NIH  grant  R01 HL133932-01  and the  NIHR Imperial Biomedical Research Centre (BRC).
Particle size The oil droplet size was measured by a Mastersizer 2000 (Malvern Instruments Co. , Ltd. , Worcestershire, UK). The emulsion was dispersed in the corresponding buffer to fit the obscuration range (0-12%). The refractive indexes of oil and dispersant (water) were set at 1. 48 and 1. 33, respectively. The mean of five scans was calculated for each measurement. Moreover, measurements were repeated on three replicates. Data were then analyzed by Mastersizer 3000 software. The particle size distribution was determined by the application of the Mie theory. Then, the volume-surface average diameter (d 3. 2 ) and volume average diameter (d 4. 3 ) were expressed in µm as the mean values of three replicates ± standard deviation.
Particle charge The oil droplet charge was evaluated by measuring the zeta-potential (mV). A zetameter CAD Zetacompact® (CAD Instrument, Les Essarts Le Roi, France) was used to measure these two parameters. To start, emulsions were greatly diluted in the corresponding buffer solution until a sparser particle distribution. The measurements were carried out at room temperature. Data were presented as an average of three repetitions (separate injections).
Iron-buffer interaction by UV spectroscopy Solutions of iron at 0. 1 mM and buffers (citrate and phosphate) at 1 mM were prepared. These concentrations were selected to mimic those of prepared emulsions. The UV spectra of these solutions as well as solutions of iron and buffers separately were recorded. Also, solutions of citrate buffer and iron were prepared at different pH values (2. 5, 3. 9, 5. 6, and 7. 2). Each sample was measured using a UV-Vis spectrophotometer (UVmc ®, SAFAS, Monaco) with a data interval of 1 nm and a band path of 2 nm. Spectra were recorded in the range of 200 to 800 nm.
The data interval was set at 1 nm and the band path at 2 nm. The concentration of aldehydes was calculated using the standard curve. The standard curve was prepared with tetramethoxypropane (TMP): eight standards, ranging from 0 to 30 µM and r 2 = 0. 999. TBArs value was expressed in millimolars of equivalents malondialdehyde per liter of emulsion (mM eq MDA. L -1 of emulsion).
In addition, the decomposition of hydroperoxides starts earlier in the presence of a high amount of PUFA, which confirms the rapid drop of the CD values (Fig. 1a ). Besides, the initial Fig. 1. Lipid oxidation as measured by conjugated dienes (CD mmol eq peroxide. Kg -1 of oil) for 20% (v/v) tuna oilinwater emulsions with citrate buffer (1 mM) and iron (II) (0. 1 mM). Two conditions were studied: (▴) under control atmosphere, air (dioxygen solubilized in the emulsion) and (■) after purging with argon (hypoxic condition). Samples were kept in dark at 30 °C. Data are means of three repetitions. Error bars represent standard deviations iron content in tuna oil was estimated as 0. 71 ± 0. 35 mg. Kg -1 of oil (Table  1 ). Iron content in fish oil should be within the range of 0. 5 to 7 mg. Kg -1 for crude oil and ≤1.
Buffer role in lipid oxidation A new set of samples was prepared with iron solution at 0. 1 mM. Two buffers were tested: citrate and phosphate. The pH was set at 7 (buffer at 1 mM). The oxidation level was monitored using the CD value. CD values increased rapidly from approx. 50 to 250 mmol eq peroxide. Kg -1 oil (Fig. 2a ). It was noticed that the buffer type impacted on lipid oxidation. Only after three days, the CD values of these two emulsions were significantly different (one-way ANOVA based on buffer's type; p < 0. 05). Moreover, the induction time was determined by the time when the CD value was significantly different than the day before (one-way ANOVA with the time factor, Supplementary data (C)). Induction times were between seven to ten days for citrate and four days for phosphate (designated by arrows in Fig.
Impact of buffer on iron location and activity in the emulsion Emulsions were prepared with citrate buffer (1 mM), CTAB as an emulsifier, and iron (II) solution at 0. 1 mM. Four pH levels were tested: 2. 5, 3. 9, 5. 6, and 7. 2. All samples were kept in dark at 30 °C. First, the particle size was measured. The values of d 3. 2 ranged from 0. 53 to 0. 57 µm and d 4:3 from 0. 56 to 0. 62 µm. At pH 7. 2, droplets were slightly bigger. But no significant changes were seen regarding the pH (one-way ANOVA analysis p < 0. 05, applied on d 4:3 and d 3:2 values) (details in Supplementary data (D)). All emulsions were stable: no coalescence or flocculation was observed over the storage period. The particle charge was evaluated by the variations of mobility (µm/s/V/ cm) and zeta-potential (mV) (Supplementary data (D)).
In addition, the TMS shift was calculated for the developed method. Spectra treatment was done using Top Spin 3. 6. 2 software. Phase correction was applied to all spectra and areas in the spectrum were integrated according to the chemical shift range 0. 5-1. 0 ppm related to methyl protons on a c or more carbon (Hg); 1. 0-1. 9 ppm related to protons attached to a saturated b or more carbon (Hb); 1. 9-4. 5 ppm related to protons attached to saturated a carbon (Ha) and 6. 3-9. 3 ppm related to aromatic protons (Har). According to the different performed analyses, Table  2  shows how the samples of this study exhibit large diversity and variability in properties, in terms of range, mean and standard deviation of each parameter.
Acknowledgment Thanks are due to  L. Bellon ,  L. Bocquet ,  S. Ciliberto ,  S. Dago ,  D. Guéry-Odelin ,  I. Palaia ,  I. Pagonabarraga ,  C. Plata ,  A. Prados , and  B. Rotenberg  for useful discussions. The work was partially funded by  Horizon 2020 program  through  766972-FET-OPEN-NANOPHLOW.
ACKNOWLEDGMENTS A. B. , T. S. and S. S. S. thank  FRIAS  ( Freiburg Institute of Advanced Studies ) for support of the present cooperation through the  Research Focus "Designed Quantum Transport in Complex Materials ". All authors thank  A. Bermudez ,  H. -P. Breuer , and  H. Haeffner  for useful discussions. F. S. acknowledges funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013 ) Grant Agreement No. 319286  Q-MAC. M. G. acknowledges support by the  LabEx ENS-ICFP :  ANR-10-LABX-0010/ANR-10-IDEX-0001-02 PSL*. T. S. acknowledges support from  Deutsche Forschungsgemeinschaft  [SCHA 973]. S. S. S. thanks the  People Programme (Marie Curie Actions ) of the European Union's  Seventh Framework Programme  ( FP7/2007-2013 ), under the  Research Executive Agency  Grant  609305 , and  FRIAS  for hosting him as an  External Senior Fellow.