Demonstrating that the data produced in metabolic phenotyping investigations (metabolomics/metabonomics) is of good quality is increasingly seen as a key factor in gaining acceptance for the results of such studies. The use of established quality control (QC) protocols, including appropriate QC samples, is an important and evolving aspect of this process. However, inadequate or incorrect reporting of the QA/QC procedures followed in the study may lead to misinterpretation or overemphasis of the findings and prevent future metanalysis of the body of work.reporting QA/QC procedures is suggested. A template that can be completed as studies progress to ensure that relevant data is collected, and further documents, are provided as on-line resources.The aim of this guidance is to provide researchers with a framework that encourages them to describe quality assessment and quality control procedures and outcomes in mass spectrometry and nuclear magnetic resonance spectroscopy-based methods in untargeted metabolomics, with a focus on reporting on QC samples in sufficient detail for them to be understood, trusted and replicated. There is no intent to be proscriptive with regard to analytical best practices; rather, guidance for generally employed in the generation of metabolomic data. Details such as sample matrices and sample preparation, the use of test mixtures and system suitability tests, blanks and technique-specific factors are considered and methods for reporting are discussed, including the importance of reporting the acceptance criteria for the QCs. To this end, the reporting of the QC samples and results are considered at two levels of detail: “minimal” and “best reporting practice” levels.Multiple topics should be considered when reporting QA/QC protocols and outcomes for metabolic phenotyping data. Coverage should include the role(s), sources, types, preparation and uses of the QC materials and samples The online version contains supplementary material available at 10.1007/s11306-022-01926-3. Metabolic phenotyping studies typically begin with experimental design, and progress through sample collection, sample preparation, sample analysis and data collection. The process culminates in data processing and mining, which includes many different workflows depending on the question being addressed  and in data repositories is the ability to demonstrate that the QA/QC measures used before and during the analysis of study-derived  samples were both appropriate, suitable in terms of their design and study objectives and that the criteria used for acceptance were reasonable, achieved and reported. If the level of detail provided is insufficient for this to be assessed, it will seriously reduce confidence in results and therefore the value of the work. In fact, such factors represent a strong argument for journal editors and reviewers to suggest that the manuscript should not be accepted for publication in its present form. This may lead to requests for revision and resubmission or rejection. A secondary consequence of a focus on standardized reporting of QA/QC practices and results is the need to inform and educate those newly entering the field of metabolomics on the many factors that can affect data quality and influence results. A focus on QA/QC and dissemination of clear (minimum and best reporting practice) QA/QC practices should have the effect of naturally improving the quality of all metabolomics work.Metabolomics researchers have undertaken efforts on standardizing reporting structures from early on in mammalian and plant studies  spectroscopy or mass spectrometry (MS). MS-based analysis includes both direct infusion MS (DI/MS or DIMS) ) and separation-based MS techniques, including liquid-(LC) and gas-chromatography (GC) and capillary electrophoresis (CE). There are numerous other methodologies being developed for both MS and NMR, including imaging, direct analysis and in vivo modalities. However, currently the majority of published studies are based on GC–MS, LC–MS, direct infusion and high-resolution liquid state NMR spectroscopy and those modes serve as the basis for this report. As other approaches become more widely applied, the guidelines presented here should be adaptable enough to allow users of technologies not specifically covered here to report the quality metrics that were used with their analytical methods used.For every stage (or segment) in the development and execution of a metabolomics study, there are important QA and QC steps that should be carefully performed and documented. Detailed QA/QC protocols need to be defined that, for In general, it is reasonable to expect that, whatever the method used, the analysis has been undertaken by a trained individual, that the procedure has been documented in detail (preferably with a written standard operating procedure (SOP)), that facilities are appropriate, the materials are of sufficient quality, and the equipment has been well maintained and appropriately used. Where SOPs are used it would clearly be of value to the community if, as best reporting practice, a version-controlled copy of the full SOP could also be supplied e.g., with DOI, Git or other accepted publication method. Reporting these details (and other QA aspects) as part of the manuscript QA section is crucial to strengthening the confidence of the report.In targeted MS assays, where a specific analyte is measured, method validation gives an indication of the reliability of the data that will be acquired when the method is applied for the analysis of experimental samples. However, in untargeted profiling this is not possible as the analytical procedure aims to detect and measure an undefined number of metabolites, the identity of most of which are unknown. Despite this, it is possible to apply QC procedures that enable the continuous monitoring of measurement performance to demonstrate analytical precision or, on the other hand, highlights unexpected/unwanted deleterious changes. Having such data either allows verification of the analytical data, showing either that it met the quality requirements of the investigator, or did not, resulting in subsequent investigations to establish why the quality thresholds were not reached, and therefore what to do to improve the analyses. In Fig. According to ISO/IEC 17025 “The resulting data should be recorded in such a way that trends are detectable. This monitoring should be planned. If the results of the analysis of data from monitoring activities are found to be outside pre-defined criteria, appropriate action shall be taken to prevent incorrect results from being reported”. It is important to emphasize that “appropriate action” should be taken which can include removal of data for specific metabolites or samples, or full re-analysis of samples or data. These guidelines can be adopted in untargeted profiling in chromatography-MS studies by setting up a quality control program that includes monitoring and documentation of, for example, the following: the type and number of QC samples used, randomization strategies, sample run order, number and frequency of QC samples per analytical batch, instrument parameter(s) monitored such as e.g., LC pressure curves, statistical analysis used for QC samples, illustrations used e.g. batch drift visualization, and control limits , and “best reporting practice” standards for QC data (the reporting standards that experienced metabolomics laboratories should aspire to be routinely reporting).The type of information that should be included in the documentation of a typical untargeted metabolomics study must cover a series of factors that determine the reliability of the data. These factors and the recommended reporting items are described in detail in the following paragraphs. For ease, key reporting information, for what we consider to currently represent both minimal and best reporting practice, is listed in Table OR1 of the Online Resources while a schematic diagram of the suggested minimum reporting requirements is shown in Fig. It is recognized that it is sufficient to record many of the details below as part of the normal methods section of a manuscript. QC samples should be analyzed using the same analytical methods as the experimental samples, and where this is not done, the actual method used and the reason for the difference should both be reported.For both NMR spectroscopy and MS studies the type of Quality Control materials used should be identified and their purpose should be clearly described.A number of similar QC sample types exist to enable the signals present in samples to be tracked over the course of the analysis for trends in properties such as e.g., intensity, retention time and mass accuracy . Phenotypic QC samples might be used to highlight differences in precision between experimental groups  vs control samples for signals differentiating between the classes . This might be the result of e.g., analytes having been “diluted out” in the study pool or are perhaps even entirely absent in one sample class, or so strongly elevated in concentration that they saturate the detector. Intra-study QC samples can be used in a single study batch (intra-batch) or across several batches from the same study (inter-batch) to enable the results for different batches of samples to be compared. Inter-batch QC samples may be used in short term studies that require several analytical runs to be performed over a few days, perhaps with preventative instrument maintenance carried out between runs. They may also be used by different laboratories that are collaborating on inter-laboratory trials. “Long Term Reference (LTR)” samples are analyzed across multiple batches often across multiple studies and are normally composed of bulk matrix samples . LTRs are generally used where many batches of samples, perhaps in several collaborating laboratories, are being assayed using the same methodology and there is the desire or intention to compare them or even to correct them  matrix-matched QC samples, usually pooled samples, are meant to be representative of the samples being studied  which were used in the collection or storage of the samples should be reported.If from experimental samples, which experimental samples were pooled to create the sample. For example, all or a subset of samples, of the same study or of a previous study. If commercial, the supplier information, any additional metadata known, product number, and batch/lot number.Origin: Storage and Preparation: Storage conditions and methods used to prepare the QC samples are important and should be reported. This should ideally include aliquot volumes, freeze/thaw cycles, and details on QC preparation. For example, assuming a sample preparation step, how were the QC samples collected and processed before extraction? Extraction of multiple individual QC aliquots enables the assessment of both extraction and instrumental variability. Conversely, if the QC was pooled post extraction i.e., one extract was injected multiple times, then only instrument, but not extraction, variability can be assessed. For this reason, it is important to note the preparation stage at which the sample was pooled. For example, if plasma samples were aliquoted to form the QC before any sample preparation or following protein precipitation.Scope of use: The intended use of samples should also be reported. This includes whether the QC samples were used to assess the precision within a batch, or the precision between batches, or for signal correction, or to compare results from a different study within one laboratory/ between laboratories or a combination of these, etc. More details of potential uses are provided in the analysis section.Clearly, the fact that the use of QC samples is now considered standard . The source and purity of the internal standards should be reported.The type of any blanks  used and how they were prepared.Procedures and methods used to prepare the process blank QC sample, including vial-types used and volumes of aliquots, especially if these were different from those used on the experimental samples. Details on solvents including the supplier and their purity should be included, as should the use of any additives such as buffers etc., and sodium chloride solution (where biphasic extractions are used).Acceptance criteria should be reported including e.g., the acceptance criteria for peaks which appear in the blanks or maximum acceptable carryoverIt is important to differentiate between process blanks and “solvent blanks” or “true blanks”. True blanks are mobile phase (LC) or background electrolyte (CE) which are analyzed independently, i.e., some form of dummy injection is made into the instrument. Process blanks are water or solvent samples prepared and treated exactly like experimental samples, including undergoing any extraction processes. They can be used to highlight contaminant peaks arising from vessels or chemicals used in sample processing. Both Process and True blanks will detect “system” peaks, such as mobile phase contaminants. Analyzing both enables the source of the contamination to be isolated. All blanks can also be used for detecting and quantifying sample carryover. Minimum reporting standards for blanks should include:If a synthetic mixture comprised of chemical standards is used, its composition, concentrations of the compounds used and, importantly, method of use, should be reported.For laboratory-prepared solutions, individual components of the standards mix, their concentrations and, ideally, the source and purity of each chemical.For commercial test mixtures/reference materials, report the product name and source, and product number etc.If this mix is used as an SST, minimum reporting practice requires reporting:Whilst perhaps not strictly related to sample QC, information that is material to the analysis should be provided for LC, GC or CE systems coupled with mass spectrometers. Minimum details would include the type(s) of chromatographic column used, its source and its characteristics, unless it is being used in a propriety method. For GC, the derivatization reagents used should be reported, and whether this was performed in batches or using just-in-time derivatization automation. Mass calibration standards and other materials used for sample cleaning or treatment should be provided if considered critical for the acquired metabolite fingerprint .2O/90% H2O or 100% D2O; it is rare to use other solvents in biological metabolomics studies, but in lipidomics less-polar solvents such as CDCl3 are often used. The chemical shift reference compound is usually added in this bulk solvent preparation to ensure uniform concentration across all the samples in the study, but if this is instead added as an extra step in the sample preparation from a concentrated stock solution, it is a crucial difference which must be reported clearly because the inherent errors in adding small volumes will affect any subsequent quantification. There are several chemical shift reference compounds commonly in use in NMR spectroscopy-based metabolomics, so it is crucial to positively identify which compound is used and its labeled deuteration level. Rarely, a project may include pH-indicating compounds or additional quantification internal standards; these should be fully described (name and concentration) in the QC report. Any analysis of the prepared bulk solvent by NMR spectroscopy (or other means) for impurities and suitability should be reported, noting whether this analysis was performed before any experimental samples were rehydrated, or diluted, with the bulk solvent to detect any inadvertent contamination.The preparation of QC samples for NMR spectroscopy studies is not too different from MS-based studies. But because there is no requirement for multiple internal standards for identification or quantification of analytes, any internal standards used should be carefully described and quantified. Typically, a bulk supply of solvent is prepared for a study for rehydration or dilution of experimental and QC samples immediately before analysis. The careful preparation of this bulk solvent, which may include a buffer, is crucial for quantification and replication across studies, so details of preparation should be reported. Minimum reporting should document the solvent, for example 10% DThe type and source of the NMR tubes used for the experimental samples and QC samples should be identified by manufacturer part number in the QA/QC report. If disposable, single-use NMR tubes are used, the QC report should indicate any contamination detected in solvent blanks run in the batches as there may be residues from the manufacturing process. If NMR tubes are recycled, the details of the tube cleaning and drying process should be reported as well.The instrumentation used to perform sample analysis is clearly critical to the type(s) of data that can be obtained. The minimum information that should be supplied is detailed below.Type(s) of mass spectrometer  or NMR spectrometer field strength(s) as well as the model, software, and manufacturers details must be reported. Similarly, if hyphenated to a separation method, clear details of all instrumentation should be provided including any sample handling robots or similar preparative equipment.best reporting practice data for MS tuning and calibration, mass resolution and accuracy should be provided, and the frequency of calibration should be clearly stated in the manuscript. As discussed earlier, if possible, chemical identity, parameter(s) and acceptance criteria for each calibrant should be stated.The method of instrument calibration, tuning, and details of any test compounds/mixtures used, and their origin should always be provided to demonstrate that the devices are operating to the specifications of the manufacturer. As part of For NMR spectroscopy, temperature calibration and stability are crucial to obtaining consistent results such as high-quality and consistent water suppression and consistent spectral linewidths and positions. Using standard protocols  and chromatographic resolution should be determined to ensure that they are within the predetermined tolerances required for optimal performance.Whilst more of a QA procedure rather than an SST, there may also be value in noting any other interventions that are critical for the quality of the data acquired such as maintenance cycles, routine column cleaning and equilibration, injection system, temperature and duration of column baking out etc.1H NMR spectroscopy, system suitability testing is very closely related to instrument performance acceptance procedures which should be common practice for NMR spectroscopists. Details of the specific instrument tests performed must be reported as part of minimum reporting practice. A few examples of the tests that are necessary to demonstrate suitability of an NMR instrument before a project begins are: water suppression experiments, signal-to-noise assessments and temperature calibration experiments. While other approaches may be chosen, often these tests can all be addressed by meeting instrument specifications for a 2.0 mM sucrose sample in 10% D2O/90% H2O standard sample Amounts/volumes of test materials used for analysis, including any additional information such as injection volumes, split ratios, etc.Results of any SST performed, utilizing pre-defined acceptance criteria and whether the acceptance criteria were met.SST reporting standards for all instrumental techniques should minimally include:Sample collection and handling are important areas of QA and are too involved and complex to fully cover here. They will form the subject of a future paper. However, since sample handling may clearly affect the QC results (or their interpretation), it is covered very briefly here to note that sample handling protocols and any deviation from them should be well controlled and documented. This includes any procedures occurring after the samples have arrived at the lab, including freeze/thawing, subsampling of single aliquots, aliquoting, storage conditions, thawing conditions for sample preparation, etc.Metabolism quenching protocolStorage conditions including temperature, duration and aliquot size of samples and QC samples.Thawing conditionsAliquoting schemeSample preparation protocols applied to experimental and QC samples . Typically, the same procedure is applied for QC and experimental samples.Minimum reporting requirements for sample handling include:The number of freeze–thaw cycles for experimental samples should be provided as best reporting practice. If a large number of samples are prepared all together or as batches and stored till the analysis or are analyzed directly and remain under the autosampler conditions for long periods of time, this should be stated. In GC–MS analysis protocols where derivatization takes place, time intervals between sample prep and analysis are often critical. Data that provide proof of sample integrity during sample treatment or analysis are desirable in these cases.As different procedures are followed depending on the applied technique, reporting requirements are listed for LC–MS, GC–MS and NMR spectroscopy platforms separately.(a) Method detailsm/z scan range, resolution, scan rate, collision energy(s)), additional specialized parameters e.g., for MSE acquisitions, 2D mass spectrometers, or for IMS, lock mass calibrant used in line/offline for TOF MS systems, etc.For minimal QC reporting, all critical details  that affect the analytical results should be reported unless it comes to a proprietary methodology where only selected information can be disclosed. In such cases, there should be a justification of why all method details are not given, and the QC results should be fully reported to enable readers to assess the method’s fitness for purpose. As new instruments are continually developed, it is difficult to present a comprehensive reporting list without it quickly becoming out of date. Guidelines for reporting have been presented here, but individuals should report all methods in enough detail that another individual can repeat the analysis exactly on the same model instrument. Reporting items should include the chromatographic column, type, dimensions, particle type and size, manufacturer, elution program , flow rates, injection volumes, split ratios, temperatures, source parameters , MS and MS/MS detection parameters ((b) Analytical sequence detailsHow many QC samples were prepared and analyzed? What was the frequency of analysis? Was more than one type of pooled QC used? e.g., phenotypic or long-term QC samples etc.Where “column conditioning” was undertaken conducted at the beginning of the run how was it performed. Often column conditioning is performed using a number of injections of a QC sample and the number of sample injections (and the volume used) should be reported. The acceptance criteria used to assess that the system was equilibrated based on the column conditioning should be reported.It is generally accepted that, whilst the QC samples are analyzed at regular intervals, the study samples should be analyzed in a randomized order. If samples and/or QC samples were randomized, the method of randomization should be reported.If multiple injections of test samples were made for QC purposes, then this should also be described.What number and nature of blanks analyzed were used?, and in which order? Was each aliquot analyzed once, or was repeated use of the same blank aliquot used? How many blanks were prepared and analyzed?Number of synthetic mixtures of metabolites /test mix solutions analyzed, in which order and with what frequency?Acceptance criteria for QCs should be provided, and confirmation that these criteria were met.The full and accurate description of the overall plan of analysis to assess the data quality during, or after, the analytical run represents a minimum reporting standard. This description should include the sample run order, , the number of samples per batch, the duration of the analysis and the total number and frequency of QC injections. It is generally accepted that, as a minimum, 5% to 10% of the samples analyzed should be QC samples, but the frequency of QC analysis should be justified by the number of experimental samples, instrument technical variation and the intended use of the QC samples. In addition, the following represent minimum reporting standards: The number of replicates per QC aliquot should be provided; for each individual analytical run, the number of individual QC samples and the number of injections from each vial should be given.For best practice reporting, the time period of a non-stop analytical batch, as well as the time interval between different batches for samples from the same study could usefully be reported. In addition details of scheduled or other maintenance of the system which occurred between batches from the same study, e.g., cleaning of the source, mass calibration, rinsing or exchange of the chromatographic column in LC, “bake out” in a GC system or the exchange of separation capillary in CE etc., represent useful supporting information on the conduct of the analytical aspects of the study.Method detailsDetails on how the QC samples and experimental samples were analyzed for spectral quality during the data acquisition phase should be reported. State whether or not each spectrum was assessed for QC metrics such as: linewidth, pulse width, baseline roll and water suppression quality. Clear details of how the spectra from different samples were collected should be provided such as whether or not this was done with fixed instrumental parameters (like receiver gain or pulse widths) or if these features were changed from sample to sample. It is desirable to report the range of times between solubilization of the sample and data acquisition because of the potential of sample degradation. Best reporting practice would be to describe whether or not sample spectra were examined for spurious contamination issues. Because sample stability can be a concern when a sample has to be reanalyzed as a result of spectral quality issues or sample degradation then this should be indicated. If re-analysis occurred some statement as to the timeframe for re-analysis is necessary. For example, was re-analysis performed as soon as possible after the issue was noted? or at the end of the whole experiment? Alternatively, the decision might be taken to delete this sample from the data set in which case it would be useful to indicate the criteria on which the decision was based.Often additional spectra  may be collected to aid in compound identification, so these auxiliary experiments should be described and details about whether these spectra were collected on every experimental sample or on a representative set should be stated.(b)Analytical sequence detailsAdditional analytical details that affect the batch data quality should be given. For example, is there evidence that the water suppression was consistent throughout the batches or the whole project? Was shimming of the magnet acceptable for each sample? Was the NMR spectroscopic analysis continuous for the project, or were batches run in a discontinuous manner over an extended time period?The procedures that follow instrumental sample analysis should be described, including all details of the processing of the LC–MS, GC–MS, DIMS, CE-MS or NMR spectral data into data matrices used for quality control analysis. The suggested reporting here applies only to the data analysis performed up to the point that the quality of data was shown and documented. It should be stressed however, that reporting requirements associated with the data analysis stage of a metabolomics study have been extensively described previously by Goodacre et al. . In geneFor MS based platforms, minimum reporting would include the software used , its source , and some detail regarding the following parameters: deconvolution setting, peak picking and chromatogram spectral alignment, mass tolerances and retention time thresholds, abundance thresholds, signal to noise ratio, retention time correction as well as noise filtering settings, and parameters for isotopic masses and adduct-clustering. These settings differ in the various software packages however, this information can aid the reproduction of the data from a similar system and provides a general overview of data processing conditions as these are roughly similar irrespective of the algorithm applied. Since default values may change over time or with different software versions, individuals are encouraged to report the actual values used.For NMR spectroscopy minimum reporting should include the software used in preparation for multivariate (or other) analysis. The details of post-acquisition processing for spectral quality such as any applied signal processing, baseline correction, phase correction or signal enhancement should be provided. The parameters used in data post processing prior to chemometric analysis should also be reported including spectral alignment, binning mode (if any) or feature selection parameters, and finally, any normalization , scaling and transformation applied. All spectral regions removed  should also be reported.For both MS- and NMR-based data sets, if features are excluded based on threshold settings dictated by signal-to-noise ratio, or the frequency of their presence in samples, blanks or QC samples, or those which are removed based on the variance of their intensities in the QC samples, these should be minimally reported as they affect the dimensionality of the final exported data matrix. For the reporting of QC data, whether the data processing applied was pre- or post-normalization it should be defined as this can influence QC acceptance criteria. It is also the case that sometimes row/column scaling or normalization of the data is applied based either on total signal or mean intensities of IS's or specific endogenous metabolite peaks. Information on these data transformations should be included in all manuscripts as they represent minimum reporting practice.Best reporting practice would encompass not only the processed data being made available on publication but that the raw data is also made accessible for all samples analyzed (both study and QC samples). This is suggested with the caveat that there are circumstances where immediate release of such data may not be possible but where embargoing may be appropriate.The predetermined acceptance criteria for a series of samples within a run based on test mix, blanks and QC analyses.R)).The peaks monitored from test mixtures or QC samples, target ion selection parameters, mass range, parameters examined  signal-to-noise for the experimental samples based on an added internal standard compound, the typical (or averaged) proton pulse width and the typical glucose resolution (if the samples contain glucose).Irrespective of the technique used, minimum reporting of data quality should include:Monitoring of random or systematic effects, including intra and inter batch effects. If the QC samples have been used to monitor random or systemic effects it is important to report how this was done e.g., where Principal Components Analysis (PCA) was applied, which samples were excluded? and which combination of PCs were used? If plots of selected ions against time/injection order were used to obtain a preliminary assessment of the data quality  the outcome should be reported. If such run order effects were seen by PCA, was further action via e.g., Time Series analysis etc., undertaken to explore them? Any action taken should be described, and the results reported. Ideally, where a numerical method was used alongside a visual method to monitor run order/batch effects, e.g., calculating the sum of squares (SSQ) deviation from the horizontal etc., the results should be reported.Precision of the analysis is based on the results for the QC sample analysis. Authors should define how these were calculated .Precision of detection is based on QC sample analysis. This is indicated by reporting the number of QC samples in which an individual metabolite or signal was detected.Subjective proof of data quality based on illustrative examples of the parameters monitored such as plots showing batch drift evaluation (intra- and inter- batch including significant drift in retention time or change in response) e.g., see  or spectra (NMR) to statistical analysis of acquired QC data. For MS-based platforms minimum acceptable reporting should include:Intra- and inter-batch QC RSDs for all reported compounds/ features.Objective numeric data should be provided to assess the degree of batch response or RT drift Details including name, version number and computer environment of any software package or algorithm used to correct batch drift including the parameters used for the algorithm .If a newly developed algorithm is applied for data quality assessment or correction/improvement based on QC series data, it should be described, and references should be provided .A description of all key reporting features and the rationale behind them is summarized in Table OR1 of the online resources, and an excel spreadsheet that can be updated as studies progress is also provided in the OR.Irrespective of the technique used, Best Reporting Practice of data quality should include:The use of both QA and QC procedures is recognized as fundamental for achieving reliable and consistent data in metabolomics. The recommendations presented here are not meant to constrain research but to ensure that, where these systems have been employed, this is stated and what has been done is clearly explained. In this opinion paper there is no official authority to mandate the procedures that must be used when employing QA/QC in untargeted metabolomics. However, the recommendation here is that authors state what they did, and to state this as transparently and as completely as possible. In other words, they should deliberately and purposefully avoid obfuscation. The documentation of the QA and QC of the analytical process of an untargeted metabolomics experiment enables experimenters to demonstrate that the work was conducted appropriately, and that the editors and reviewers, and therefore ultimately readers can have confidence in the resulting conclusions. However, recognizing that untargeted metabolic phenotyping is a discovery methodology any hypotheses emerging from it are inevitably provisional. Nevertheless, armed with knowledge that the data are of good quality provides the motivation to devote the resources required to undertake further targeted analysis, using quantitative and validated methods, to confirm, or refute, these hypotheses.https://www.mqacc.org/These recommendations will, no doubt, be modified as practices evolve and there will be future discussions on other important topics such as sample collection and storage, as well as data processing practices and their robust reporting. For such measures to be widely adopted requires consent and buy-in from the metabolomics and wider community. Towards this end, a number of virtual meetings are planned within twelve months of this publication to enable all interested parties to debate the points raised here and a final community consensus to be reached. The meeting dates and times will be published on the mQACC website: Supplementary file1 (DOCX 1837 kb)Supplementary file2 (PDF 162 kb)Supplementary file3 (XLSX 12 kb)Below is the link to the electronic supplementary material.


Eucommia ulmoides leaf (EUL), as a medicine and food homology plant, is a high-quality industrial raw material with great development potential for a valuable economic crop. There are many factors affecting the quality of EULs, such as different drying methods and regions. Therefore, quality and safety have received worldwide attention, and there is a trend to identify medicinal plants with artificial intelligence technology. In this study, we attempted to evaluate the comparison and differentiation for different drying methods and geographical traceability of EULs. As a superior strategy, the two-dimensional correlation spectroscopy (2DCOS) was used to directly combined with residual neural network (ResNet) based on Fourier transform near-infrared spectroscopy.(1) Each category samples from different regions could be clustered together better than different drying methods through exploratory analysis and hierarchical clustering analysis; (2) A total of 3204 2DCOS images were obtained, synchronous 2DCOS was more suitable for the identification and analysis of EULs compared with asynchronous 2DCOS and integrated 2DCOS; (3) The superior ResNet model about synchronous 2DCOS used to identify different drying method and regions of EULs than the partial least squares discriminant model that the accuracy of train set, test set, and external verification was 100%; (4) The Xinjiang samples was significant differences than others with correlation analysis of 19 climate data and different regions.This study verifies the superiority of the ResNet model to identify through this example, which provides a practical reference for related research on other medicinal plants or fungus.The online version contains supplementary material available at 10.1186/s13007-022-00935-6. Eucommia ulmoides Oliver is a unique tree species in China and belonging to Eucommia (Eucommiaceae), which is named as and Tuchong in Japanese and Du-Zhong in Chinese [E. ulmoides in the world. The traditional uses of E. ulmoides peel are used as medicine with a history of more than 2000 years. It mainly contains chemical composition, including flavonoids, terpenoids, steroids, lignans, iridoid terpenoids, etc., which possesses pharmacological activity, such as blood pressure-lowering, blood sugar-lowering, blood lipids-regulating, anti-inflammation, liver protection, anti-cancer, prevention of osteoporosis and others [Eucommia-related industries, the demand for Eucommia ulmoides leaves (EULs) is also increasing as an important part of Eucommia resources. The EULs are rich in chemical components and can be harvested every year with great potential for development. Recent studies have shown that the chemical components and medicinal activity of its leaves are similar to those of bark [Eucommia functional food (http://www.leadingir.com/trend/view/1525.html). Chinese . At presd others . This med others . However of bark , 7. WithE. ulmoides medicinal materials is affected by many complex factors, such as genotype, cultivation technologies, growth conditions , harvest time, processing, and storage conditions , cultivation environments, etc. These variable factors may cause changes in the chemical composition of E. ulmoide, leading to significant differences in their quality. Geographical variation is the main factor leading to differences in the chemical composition of EULs. And most authentic traditional herbal medicines contain enough active chemical components. Herbs grown in different environments can produce various secondary metabolite components, leading to differences in their intrinsic qualities. In addition, since EULs are mostly picked when the branches and leaves are lush in summer  and autumn , the volume is fluffy. After being placed, fresh leaves easily change color and taste, so they need to be processed in time. Different drying methods have their advantages in terms of drying cost, time, convenience, efficiency, and environmental impact. Three methods were commonly applied to plant drying, including natural, artificial, and comprehensive methods. Traditional drying methods are conducted in the shade, air, or sun. The drying method of EULs is dried at low temperature or drying, while there is no specific temperature limit, which has been listed in “China Pharmacopoeia” [However, the quality of copoeia” . The traParis polyphylla var. yunnanensis than the traditional model, such as support vector machines, PLS-DA [Dendrobium species have successfully identified by two-dimensional correlation spectroscopy combined with ResNet based on feature bands extracted by spectrum standard deviation [Traditional methods of identification technology such as nuclear magnetic resonance (NMR), high-performance liquid chromatography (HPLC), and mass spectroscopy  are generally applied to distinguish in foods, medicine, etc. Although these techniques are sensitive and accurate, they require costly instrumentation and maintenance, trained personnel to operate the instruments, and are time-consuming. From an economic point of view, this makes them less attractive as a technique for determining identification. As an alternative technology, spectroscopic technologies have the characteristics of sample pretreatment is simple, routine analysis is fast, operation is simple and easy, no reagents are required, and economical technique, which allows simultaneous multi-component analysis. It has been widely used in the fields of textile, environment, petroleum, agriculture, medicine, and so on , 10. Alt, PLS-DA . A studyeviation . In prevIn this study, the FT-NIR technology combined with ResNet and chemometrics to identify the different drying methods and geographical traceability of EULs, and the specific components of this study were: (1) to collect the FT-NIR spectral data of 534 EULs samples; (2) to explore the distribution trends of various samples with exploratory analysis of principal component analysis (PCA) and hierarchical cluster analysis (HCA); (3) to obtain three two-dimensional correlation spectroscopy (2DCOS) of Synchronous, asynchronous, and integrated images. (4) to establish and compare two models of PLS-DA and ResNet for identifying with different drying methods and regions of EULs samples; (5) Correlation analyses of 16 different region s and 19 climate data were performed and analyzed. Therefore, image processing of ResNet is extended to different research directions of medicinal plants or fungus, which may provide more concise methods without less loss of classification ability.E. ulmoides gum interfering with component detection). The sieved powders were placed in ziplock bags and stored at room temperature in the dark for subsequent experiments.A total 534 samples of EULs were collected in the second quarter of summer from seven provinces in China. The details sample information was displayed in Fig. −1, the resolution was 4 cm−1, and 32 scans were accumulated for each spectrum. The ordinate of the spectrum is the log (1/reflectance) of the reciprocal reflectance. Each sample was measured three times in parallel and the final average spectrum was obtained using the OMICA software that the ordinate was set as the absorbance. To eliminate the interference of air information, before each scan, the laboratory air (H2O and CO2) spectrum was recorded as the background and automatically subtracted. In addition, it is necessary to control experiment’s room temperature and humidity (25 °C/30% RH) to maintain the consistency of the experimental operating environment. Finally, the data processing was used by SIMCA-P+14.1 software to analyze the acquired spectral data for subsequent modeling.The sample powder was scanned using a NIR spectrometer  equipped with a diffuse reflectance accessory. Each sample was weighed (1.0 ± 0.05 g) with an electronic balance  and placed in a clean glass for scanning to avoid errors in anthropic factors. The scan range was 10,000-4000 cmExploratory analysis is an initial approach to study raw clustering results with a subset of variables or the entire dataset. The data is transformed into a new coordinate system and the correlation between the sample and the variable, can be seen through the extracted principal components. Thus, the classification trend of the sample can be visually analyzed by selecting the first two principal components in PCA to explain most of the spectral variable information. Unlike PCA, HCA determines the difference between the data point of each category according to all the data points’ distances to determine the similarity between them. The two closest data or categories are combined to generate a clustering tree. They were clustered into one group for samples with similar chemical information, while samples with larger differences were divided into different groups based on the theory of phylogenetic clustering algorithm . In thisTraditional Chinese medicines have the characteristics of “multi-component, multi-target and multi-channel”. Since the signals of different chemical components overlap each other, it is difficult to extract the information of interest and its overall quality evaluation is difficult. Therefore, we still need to resort to chemometric tools to find useful spectral features. The one-dimensional spectrum has the disadvantages of peak overlap and inconspicuous characteristic peaks. In contrast, generalized 2DCOS is an effective technology to improve spectral resolution and resolve peak overlap issues, which is widely used in physics, chemistry, medicine, and other fields , 24.t at n steps, the dynamic spectrum intensities are represented by the column vector Y at variable v [According to the theory of Noda, when measuring the equally spaced external perturbation riable v . The expv1 and v2 can be expressed as [Then, the synchronous  contains two spectral data (m = 2): the first is the average FT-NIR spectrum and the second is the i-th FT-NIR spectrum about each drying methods or regions of EULs. The synchronous 2DCOS, asynchronous 2DCOS, and i2DCOS spectra of the i-th sample of each drying method or region can be obtained through formulas Y (m × n)PLS-DA is a linear supervised classification method established based on the standard PLS regression algorithm. Finding the variable with the largest covariance with the classification matrix (Y) from the variable matrix (X), where Y is divided into two categories, Y = 1 represents that sample belongs to a specific classification, Y = 0 represents that the sample does not belong to a specific classification. Finally, the probability is obtained that each sample is classified into each category. The algorithm could explain the small number of sample observations and reduce the effect of multicollinearity between the samples. Stability evaluation of the PLS-DA model based on two parameters are cross-validation root mean squared error (RMSECV) and root mean square error of estimation (RMSEE). The smaller the value indicates that the stability of this model is better, and the predictive ability is better. In addition, the performance of the classification model is evaluated by combining specificity, sensitivity, and accuracy. The closer the value is to 1, the better the performance of the classification model. The detailed expression of sensitivity, specificity, and accuracy is calculated using true positive (TP), false positive (FP), true negative (TN), and false negative (FN), which are as follows:2-intercept and Q2-intercept. In this study, we tried to establish the PLS-DA model based on FT-NIR spectral data of EULs with different drying methods and regions. Using the Kennard-stone algorithm to divide the data set to improve the model recognition ability and avoid the irreproducibility of random selection. Data sets of different drying methods include 70% training set (374) and 30% test set (160), and the datasets of different regions are divided into 70% training set (372) and 30% test set (162) for the PLS-DA model.Besides, receiver operating characteristic (ROC), is graphically represented by using group probabilities obtained from PLS-DA analysis. The area under the curve (AUC) of ROC, as an indicator of predictive performance, the value close to 1 demonstrates high predictive power of the classification model . 200 timResNet is understood as performing “feature learning” or “representation learning”. The Anaconda and Python were installed as the data processing hardware platform and we selected Amazon’s MXNet as the ResNet framework. In addition, the MxBoard was installed for training process visualization. ResNet, as an excellent CNN, could overcome the problem of network degradation with increasing depth by residual block as displayed in Additional file H(x) −x, which is simplified for the entire data processing. According to the dimensions of input and output whether were the same, it’s used the identity residual block (Identity block) and the convolution residual block (Conv block) to build the model. If the dimensions of output F(x) are consistent with that of input, the model was established depending on the identity block, which is displayed in Fig. F(x) are in conformity with the input x, the shortcut path of a convolutional layer with a convolution kernel size (1 × 1) is added, this structure is called conv block which is displayed in Fig. The ResNet network ensures the integrity of information. Therefore, the learning objective is F(x) = In this study, we established a 12-layer ResNet model with the two types of residual blocks above whose input data are synchronous 2DCOS, asynchronous 2DCOS, and i2DCOS spectral images. Furthermore, the input data is passed through a layer of convolution operation, followed by BatchNorm normalization and Relu nonlinear activation. Then, four identity blocks and two conv blocks were used to extract features. Finally, the global average pooling, the flatten layer, and the full connection layer were executed that each layer functions differently in processed data.−1, which connected with the second overtone of C-H stretching vibrations of the CH3 and CH2 groups. The wideband at 6881 cm−1 was the first overtone of O–H stretching. The weak absorbance at 5775 cm−1 was related to C-H stretching of R-OHCH3, whereas an obvious peak at 5172 cm−1 was stretching of O-H and OH deformation of H2O. Moreover, the complex absorption peaks in the 5000-4000 cm−1 range, were C-H stretching and deformation group frequencies of polysaccharides and the C = O group frequencies and C-H stretching of carbohydrates.In order to reflect the originality of the data, the FT-NIR spectral data was obtained by MATLAB 14.1 only performed “automatic baseline correction” on it through OMICA software. All FT-NIR averaged spectra were shown in Fig. −1 as the limit, the wave number was in the range of 10,000-7000 cm−1, the absorption peaks of the four drying methods have similar trends, but the absorption intensities were not consistent and the absorption peak intensity of the sample from high to low was 40℃ > sun drying > shade drying > 60℃. On the other hand, when the wave number was in the range of 6000-4000 cm−1, the absorption peak intensity was the largest at 60℃, followed by the spectrum of shade drying, sun drying, and 40℃. Among them, the changing trend and absorption intensity of the absorption peaks of sun drying and 40℃ were the same, which indicated that the chemical composition content in these two drying methods was similar. The averaged spectra of seven geographical regions were also exhibited in Fig. −1, the absorption peaks of different regions were Guizhou, Xinjiang, Jiangxi, Hunan, Hubei, and Shaanxi in descending order. However, in the range of 5350–4000 cm−1, the absorption intensity of Shaanxi is the highest, which indicated that the samples from Shaanxi have strong absorption signals in this wavenumber range. From Fig. −1, and the absorption peak intensity of the Shaanxi sample based on drying was significantly higher than that of other regions.The averaged spectra of four drying methods were exhibited in Fig. In all results, the spectra of EULs in different drying methods and different regions had the same trend and the peaks appear in similar positions. It meant that the chemical components contained in EULs from different regions were similar, but the absorbance difference was obvious, which represented the content of chemical components was different between different drying methods and different regions. Besides, the differences in absorption intensity and peak shape in different drying methods Fig. A were muThe result of the PCA score plot based on the FT-NIR spectra data for different drying methods was shown in Fig. −1 for PC1 and the variables negatively influencing samples distribution were the 5800-4000 and 10,000-4600 cm−1 for PC1 and PC2 in Fig. −1 and 10,000-4000 cm−1 drop to negative values for PC1 and PC2, respectively. However, spectral variables and PC1 had a positive effect in the range 5700-4000 cm−1.According to the loading plot of the two principal components for different drying methods and regions as shown in Fig. In order to evaluate the similarity between different classified samples, HCA was established. This technology is designed to create groups that maximize internal cohesion and maximize external separation. The results of HCA for different drying methods and regions were shown in Fig. −1. It contained more important information on molecular structure, improving the resolution of 1D spectroscopy, which could be used to identify and study the interactions between substances or groups [v1 changes strongly before v2, a positive peak appears; if v1 changes strongly after v2, a negative peak appears. In this study, Figs. −1. It was related to substances such as water and carbohydrate that this result was consistent to “analysis of  FT-NIR spectra”. The information of asynchronous and integrated 2DCOS images was fragmented with less spectral information, it was not suitable for identification. In addition, the 2DCOS images of different drying methods and regions were shown in Additional file Synchronous, asynchronous, and integrated 2DCOS images were shown in Fig. r groups . The pear groups . Automat2 of R2Y-intercept of −0.0034, Q2-intercept of -0.0834, showing that the method has a good identify ability and no over-overlap phenomenon.Identifying individuals of different groups based on their spectral characteristics is one of the main tasks of chemometrics research. It’s best to use a fixed method and see how that method handles it to get a head start when analyzing two spectral data sources. In this study, the PLSDA model was firstly used to evaluate the differences of different drying methods and regions of EULs, and the variable characteristics of spectral sets were comprehensively studied. Based on the spectral data of different dry methods, the PLS-DA model was established that the best number of latent variables was 7 as displayed in Additional file Results of synchronous, asynchronous, and integrated 2DCOS images based on “Analysis of 2DCOS images”, ResNet models were established as displayed in Fig. In this work, when the number of epochs is 37, the accuracy of both the training set and the test set reached 100%, and the loss value was 0.0214 and tended to be flat from Fig. 2 = 0.536 and Q2 = 0.387). The best number of latent variables (LVs: 22) and permutation test of 200 times also showed that the model was rigorously set up with no overlap as displayed in Additional file The identify significant differences between groups of surveyed samples was executed by the PLS-DA model when studying differences between internal partial samples in different regions, which represented the total variance in the spectral information  were established. The confusion matrix of synchronous 2DCOS asynchronous 2DCOS, and integrated 2DCOS models were shown in Fig. E. eucommia tree is relatively slow in the juvenile period, and the fast-growing period appears in 7–20 years. It grows place more in low mountains, valleys, or low-slope sparse forests.The origin and growth environment of TCM are important, which greatly affects the efficacy of TCM. As a major factor, ecological environmental factors become a research hotspot, affect the quality of Chinese medicinal materials and the source of the formation of authentic medicinal materials. EULs like a warm and humid climate and a sunny environment and are resistant to severe cold. It can be cultivated in most areas of my country and has strong adaptability. There is no strict selection of soil, but the soil layer of deep, loose, fertile, moist, and well-drained loam is best. The growth rate of the http://worldclim.org/). In this study, the climate data such as average temperature, precipitation, solar radiation, maximum temperature, and minimum temperature were analyzed and evaluated. The 19 bioclimatic indicators of environmental indicators were listed in Additional file The bioclimatic indicators were selected from the world climate-global climate database  and regions (B and b) of E. ulmoides leaves based on PLS-DA model. Figure S5. The regions and drying methods discrimination strategy of E. ulmoides leaves based on ResNet. Figure S6. The distribution of average climatic data of 16 regions. Figure S7. The correlation plot based on 16 regions and 19 climatic data with the p-value falls below the 0.05 (A and D), 0.001 (B and E), and 0.0001 (C and F) level of significance.


Distance functions are fundamental for evaluating the differences between gene expression profiles. Such a function would output a low value if the profiles are strongly correlated—either negatively or positively—and vice versa. One popular distance function is the absolute correlation distance, P-value In this work, we propose The online version contains supplementary material available at 10.1186/s12859-023-05161-y. In biological data analysis, we regularly evaluate the similarity between two gene expression profiles. For example, when identifying gene expression patterns across different conditions, when clustering genes of similar functions , 2, whenSeveral distance functions are commonly adopted to evaluate the similarity—the most prominent one being the absolute correlation distance. The function regards positive correlation and negative correlation equally, giving a value close to zero to highly correlated profiles (whether positively or negatively correlated), and a value of one to uncorrelated profiles. More precisely, the absolute correlation distance is defined as in WGCNA , and in in WGCNA . Howeverin WGCNA , 8. For d which (1) satisfies the triangle inequality and (2) has metric [A distance measure a metric . Researca metric . A measumentclasspt{minimaa metric  and DBSCa metric , can expa metric . The samVariants of the absolute correlation distance are not the only distance measures used in gene expression analysis that violate the triangle inequality. Prasad et al.  compiledmentclass2pt{minimuated in .Fig. 2ARIn this work, we propose an alternative, There are previous works that discuss the gene expression distance in clustering. Priness et al.  applied We compared the performance of medoids) . For \doex (ARI) , while tP-valueP-valueP-value. P-valueP-value Our results show ssolved” . That maX and Y are expression profiles, and The original absolute correlation distance n-dimensional Euclidean space, First consider the case where X and Y.For data from a sample, the Pearson correlation can be calculated as follows,X and Y. We can rewrite this asSince the Pearson correlation is invariant under shifting and positive scaling of the two variables, this implies that Therefore, we have the modified Pearson distanceWe prove the triangle inequality of We compared our modified absolute Pearson correlation distances profiles  and 35 dprofiles , as wellprofiles –31 sets k, according to the benchmark. We evaluated the clustering result by examining how consistent the clusters are with the benchmarks in terms of ARI [For the sample clustering, we selected the number of clusters, s of ARI . A greatk is the number of clusters, N is the total number of samples, k which corresponds to the peak or at least an abrupt elbow on the line-plot of For the gene clustering, we evaluated clustering performance by gene functional analysis . The numCHindex)  as folloP-value < 0.05, denoted as P-value than that of P-value than that of P-value of the same GO term acquired by After obtaining the clusters, we performed GO enrichment for each generated cluster with R package –35. For n samples in total, we bootstrapped 100 times. For each time, we randomly selected n samples with replacement from the original dataset, and performed clustering on the resampled data to get a resulting partition j in k in i, we calculated the Jaccard similarity, i. We counted the number of times the class To test the robustness of clusters with different distance measures, we performed bootstrap experiments on the 35 microarray datasets for clustering cancer samples, and investigated the “dissolved”  event foi and j in one dataset, we counted the number of times We also investigated sample pairs for robustness. We selected sample pairs that are clustered together to see whether they are consistently clustered together across multiple runs, in which case, the result for the sample pair is robust. Similarly, we examined sample pairs that are not clustered together to see if they are consistently placed in different classes. For each sample pair The squared correlation distance mentclass2pt{minimk set as the number of unique labels in each dataset. We evaluated the performance by ARI [P-value We compared the performance of ous work . The same by ARI , which mmentclass2pt{minimk by P-value .From step 2 to step 52, there exist different ranks in the distance of pairs in two distance experiments. However, the pairs of rank 1 are the same, showing that both In the sample clustering experiment, due to the scarcity in the number of pairs (the maximum number of samples in a single dataset is 248 among datasets in this sample clustering experiment), the difference in ARI only occurred in 4 out of 35 datasets. In the gene clustering experiment, the boosted number of pairs enlarged the differences in the dendrogram, hence the partition is different in all 16 time-series datasets.We compared the methods’ robustness with bootstrap experiments in clustering cancer samples on 35 microarray datasets. This is done by examining the number of sample pairs that are consistently clustered across 20 iterations. In each iteration, we resampled 100 times for each dataset. For PAM, Figure For the hierarchical clustering, we examined all the internal nodes for the number of times that the classes dissolved for each dataset. Figure We further examined the performance of clustering based on n neuron , mouse rn neuron , blood in neuron , airway n neuron , human ln neuron , mESC [3n neuron  and mousn neuron . The clun neuron , and dim see Fig . That maThe absolute correlation distance equality .mentclass2pt{minimAdditional file 1:  The proof of


The exact pathogenesis of autism spectrum disorder (ASD) is still unclear, yet some potential mechanisms may not have been evaluated before. Cuproptosis is a novel form of regulated cell death reported this year, and no study has reported the relationship between ASD and cuproptosis. This study aimed to identify ASD in suspected patients early using machine learning models based on biomarkers of the cuproptosis pathway. We collected gene expression profiles from brain samples from ASD model mice and blood samples from humans with ASD, selected crucial genes in the cuproptosis signaling pathway, and then analysed these genes with different machine learning models. The accuracy, sensitivity, specificity, and areas under the receiver operating characteristic curves of the machine learning models were estimated in the training, internal validation, and external validation cohorts. Differences between models were determined with Bonferroni’s test. The results of screening with the Boruta algorithm showed that FDX1, DLAT, LIAS, and ATP7B were crucial genes in the cuproptosis signaling pathway for ASD. All selected genes and corresponding proteins were also expressed in the human brain. The k-nearest neighbor, support vector machine and random forest models could identify approximately 72% of patients with ASD. The artificial neural network (ANN) model was the most suitable for the present data because the accuracy, sensitivity, and specificity were 0.90, 1.00, and 0.80, respectively, in the external validation cohort. Thus, we first report the prediction of ASD in suspected patients with machine learning methods based on crucial biomarkers in the cuproptosis signaling pathway, and these findings may contribute to investigations of the potential pathogenesis and early identification of ASD. Autism spectrum disorder (ASD) is defined as a group of neurodevelopmental psychiatric disorders characterized by deficits in social interactions, interpersonal communications, and repetitive and stereotyped behaviors and can accompany other disorders, such as intellectual and language disorders . AlthougThe genetic influence of autism is complex and possibly related to environmental factors . ASD hasScience by Tsvetkov et al. showed that intracellular copper (Cu) induced a novel form of cell death  blocks cuproptosis by chelating intracellular Cu. In addition, lipoic acid synthetase (LIAS) decreases cell sensitivity to cuproptosis by blocking the lipoylation of proteins. Solute carrier family 31 member 1 (SLC31A1) and ATPase copper transporting beta (ATP7B) affect cuproptosis sensitivity by regulating the level of intracellular Cu+. However, no study has revealed the relationship between ASD and crucial genes for cuproptosis thus far.A recent study published in ll death , named cPredicting the incidence of disease has been a challenging task in the past. In recent years, the development of machine learning methods has allowed us to envision a future of improved health care through the investigation of biomedical profiles and patient datasets . A recenHence, we aimed to investigate some novel biomarkers in the cuproptosis signaling pathway for ASD through the use of machine learning algorithms. To support our goals, we collected gene expression profiles from brain tissue samples from ASD model mice and peripheral blood samples from humans with ASD. Then, we selected crucial genes in the cuproptosis signaling pathway for ASD and verified these features with different machine learning algorithms.p-value was < 0.01 between arrays, the corresponding gene was considered a DEG.The gene expression data of ASD mouse brain samples were obtained from the Gene Expression Omnibus (GEO) database (GSE72149 and GSE81501). The gene expression data of peripheral blood samples from 20 children with ASD and 20 healthy control children were also obtained (GSE26415). All genes in the expression profiles were annotated as unique gene symbols, and expression values were transformed by log2. Then, expression values were normalized with the “limma” package in R software to achieve consistency and comparability between arrays. The differentially expressed genes (DEGs) were screened by the “limma” package according to a previous study . If the We selected six crucial genes of cuproptosis regulation reported as candidate biomarkers in a previous study, including FDX1, DLAT, LIAS, GSH, ATP7B, and SLC31A1 . SelecteThe FDX1, DLAT, LIAS, GSH, ATP7B, and SLC31A1 expression data were evaluated by the Boruta algorithm. The Boruta method, which has shown reasonable reliability for feature selection in many fields, and is considered one of the most powerful algorithms for analyzing large data sets –14. ThisWe next divided the gene expression data of ASD mice into a training cohort (70%) and an internal validation cohort (30%), and the peripheral blood gene expression profiles of humans with ASD were used as an external validation cohort.1 This database maps all human proteins in cells, tissues, and organs using an integration of various omics technologies, including antibody-based imaging, mass spectrometry-based proteomics, transcriptomics, and systems biology . biology . This da biology –19. The The risk factor genes in the cuproptosis signaling pathway screened with the Boruta algorithm were verified by five frequently used machine learning methods, including k-nearest neighbor (KNN), naive Bayesian (NB), support vector machine (SVM) with polynomial kernel, random forest (RF), and artificial neural network (ANN). All five machine learning models were trained in the training cohort and verified in the internal validation cohort and external validation cohort.k-nearest neighbor performs classification by assigning a point to the class that is most prevalent out of the k points closest to it . The k pNaive Bayesian is conducted based on Bayes’ theorem and finds the probability that an input with some features belongs to a certain class . NB was Support vector machine performs input data as feature vectors and calculates them in a space with the same dimensionality, divides the data points into two categories, and finally selects the optimal hyperplane . SVM wasRandom forest is made up of decision trees with slight differences. RF can classify input data into the most common classifications based on constituent decision trees . The optThe ANN was made up of several layers of neurons and could loosely mimic the learning method in human brains . The numThe true condition was set to ASD or control in different cohorts. The prediction accuracy and its 95% confidence interval (CI) and kappa statistic values were calculated in the training, internal validation, and external validation cohorts for all models. For repeatability, a fixed seed number was set before cross validation. Receiver operating characteristic (ROC) curves were plotted for the internal validation cohort and external validation cohort, and the area under the curve (AUC) was calculated to examine the performance of different machine learning models.The “resamples” package in R was used to analyse and visualize the performance of each model after cross validation. Differences between paired machine learning methods were determined with Bonferroni’s test .Twenty brain expression datasets of ASD and control mice (10 each) were collected from GSE72149 and GSE81501. Twenty peripheral blood gene expression profiles of children with ASD and 20 age- and sex-matched peripheral blood gene expression profiles of healthy controls were collected from GSE26415. The flowchart of the data analysis is shown in The selected expression arrays in each cohort and crucial genes in the cuproptosis signaling pathway were visualized with a heatmap . The resThe expression profiles of humans with ASD were obtained from blood; however, whether these risk genes are expressed in the human brain is still unclear. Based on Human Protein Atlas immunofluorescence analysis, FDX1, and DLAT were located in mitochondria, ATP7B was expressed in the Golgi apparatus, and LIAS could be detected in mitochondria and the nucleoplasm . In addiThe optimized k value was set as 11 . In the The results showed that the accuracy of NB was 0.64 , the sensitivity was 0.95, and the specificity was 0.36 in the training dataset. The accuracy was 0.56  and 0.55  in the internal validation cohort and external validation cohort, respectively. The sensitivity of the internal validation cohort and external validation cohort was 1.00, but the specificity was zero in the internal validation cohort and only 0.1 in the external validation cohort . The AUCThe number of support vectors was 25 with the best SVM model in the present study, and the performance of the SVM is shown in The RF was performed with an optimized tree number . The accWe first trained the ANN model in the training cohort . After 4p < 0.05) (p > 0.05), we considered ANN to be the most suitable model for ASD prediction because of the high accuracy, sensitivity, specificity, and AUC, especially in the external validation cohort.We evaluated the different machine learning models with the “resamples” function in R software after cross validation. The 95% CIs of the accuracy and kappa values after cross validation in each model are visualized in  < 0.05) . AlthougThe prevalence of ASD has risen from 2 to 4 in 1,000 population to around 1% in large-scale population surveys . In clinCuproptosis is a new form of programmed cell death that is unlike apoptosis, pyroptosis, necroptosis, and ferroptosis . We seleOver the last 3 years, the Boruta algorithm has been used in many fields for feature selection, and it has shown reliability and stability with different evaluation methods –31. We aA previous study found that zinc-copper rhythmicity was disrupted in children with ASD . HoweverDLAT was another crucial risk gene in the cuproptosis signaling pathway for ASD identified by Boruta analysis in the present study. DLAT was specifically related to depression and anxiety in a chronic mild stress rat model . 6-PhospLipoic acid synthetase is a protein target of lipoylation, and LIAS mutation has been described as being related to a defect in mitochondrial energy metabolism . In the The brain expression level of ATP7B was lower than that of other crucial genes based on the Human Protein Atlas; ATP7B plays an essential role in human physiology in the brain and liver. The deletion of ATP7B in cells and animals could decrease copper toxicity in Wilson’s disease . Copper Although four crucial genes in the cuproptosis signaling pathway were screened, their power to predict ASD in suspected patients still needs to be investigated.We next employed five machine learning methods for testing. The results showed that the accuracy of KNN, SVM, and RF was approximately 70% and up to 90% with the ANN model in the external validation cohort. Previous studies also show SVM, KNN, and RF have a decent prediction value for ASD , 45. Whip < 0.05). Some other studies have also found that the performance of NB was poor in comparison to other methods  was identified as the most suitable method for ASD prediction in the present study. For developing the DrugMiner web tool, Jamali et al. found that ANN outperformed NB, KNN, RF, and SVM . In addiThus, detecting the expression levels of FDX1, DLAT, LIAS, and ATP7B in blood could predict the risk of ASD with ANN. These four risk factor genes could also be developed as microarrays for clinical examination. Future basic and experimental studies could also investigate the underlying pathophysiological mechanisms of the risk genes for ASD screened in the present study.Furthermore, there are some limitations to the present study. The current study has a limited number of samples. The results need to be validated in a large sample size. Additionally, a prospective cohort study would be needed to detect the conclusions. However, we provided reliable machine learning methods, and four genes in the cuproptosis pathway that may be crucial for identifying mechanisms in autistic children.In the present study, on the basis of the results of screening with the Boruta algorithm, we selected FDX1, DLAT, LIAS, and ATP7B as crucial genes in the cuproptosis signaling pathway for ASD. The crucial risk genes were expressed in the brains of not only mice but also humans. ANN was the most suitable model for ASD prediction in the present study. We first reported that biomarkers in the cuproptosis-related signaling pathway had good power to predict ASD in suspected patients through different machine learning methods, which indicated that the cuproptosis signaling pathway may play a crucial role in ASD. The findings of the present study could contribute to the early identification of ASD in children and provide novel inspirations for investigations of the causes and treatments of ASD.https://www.ncbi.nlm.nih.gov/gds with the accession numbers GSE72149, GSE81501, and GSE26415.Publicly available datasets were analyzed in this study. This data can be found here: YZ: conceptualization, methodology, software, data curation, and writing—original draft preparation. JG: visualization, investigation, supervision, software, validation, writing—reviewing and editing, and required funding. Both authors read and approved the manuscript.


In the global effort to discover biomarkers for cancer prognosis, prediction tools have become essential resources. TCR (T cell receptor) repertoires contain important features that differentiate healthy controls from cancer patients or differentiate outcomes for patients being treated with different drugs. Considering, tools that can easily and quickly generate and identify important features out of TCR repertoire data and build accurate classifiers to predict future outcomes are essential.This paper introduces GENTLE (GENerator of T cell receptor repertoire features for machine LEarning): an open-source, user-friendly web-application tool that allows TCR repertoire researchers to discover important features; to create classifier models and evaluate them with metrics; and to quickly generate visualizations for data interpretations. We performed a case study with repertoires of TRegs (regulatory T cells) and TConvs  from healthy controls versus patients with breast cancer. We showed that diversity features were able to distinguish between the groups. Moreover, the classifiers built with these features could correctly classify samples from the TRegs repertoire when trained with the TConvs repertoire, and from the TConvs repertoire when trained with the TRegs repertoire.https://github.com/dhiego22/gentle and https://share.streamlit.io/dhiego22/gentle/main/gentle.py.The paper walks through installing and using GENTLE and presents a case study and results to demonstrate the application’s utility. GENTLE is geared towards any researcher working with TCR repertoire data and aims to discover predictive features from these data and build accurate classifiers. GENTLE is available on The online version contains supplementary material available at 10.1186/s12859-023-05155-w. TCRsque TCRs . This prque TCRs . The betque TCRs .Many features of the repertoire, such as diversity, clonality, motifs, and network presentation, are often used as metrics to distinguish between cancer patients who may respond to a certain drug or simply between sick and healthy individuals –8. TableRecent technological breakthroughs have advanced the entire field . In tandIn summary, we have developed GENTLE, the first tool to offer a machine learning pipeline for TCR repertoire cancer data: it enables the generation of features, normalization methods, feature selection algorithms, classifiers construction, and evaluation metrics for internal and external validation.https://github.com/dhiego22/gentle; the README documentation is succinct and clear for users, and the program can be easily installed with virtualenv or docker.The code for GENTLE was written in Python 3 and can be run on version 3.9 or higher. We used Streamlit to construct the GUI , Pandas for data manipulation, Plotly for data visualization, and scikit-learn for many machine learning algorithms . StreamlThe input into GENTLE is a.csv file format. For files that surpass the maximum size supported (200 MB), the.csv file can be zipped and uploaded in a zip format. The file must be a dataframe in which the rows represent the samples of the experiment, and the columns represent the TCRs with one additional column representing the label of each sample . The values should represent the counts of the TCRs in the samples. We provide examples of the input data in the Github repository.Diversity metric calculates popular diversity measurements widely used in ecology such as richness, Shannon, Simpson, inverse Simpson, Pielou, one minus Pielou, hill numbers and Gini indices. The Network metric will use the TCR sequences as nodes and calculate a Levenshtein distance of two, according to [Motif metric calculates the frequency of contiguous letters specifically, 2-mers, 3-mers and 4-mers. Finally, GENTLE gives the option to use six different dimensionality reduction methods: PCA  [After uploading the data, four options will appear in the sidebar; these options are the feature dimensions to be analyzed. It is important to emphasize that when uploading a different dataframe, one should always erase the cache from the options menu in the top-right corner of the screen. The rding to , to creanalysis) , t-SNE  , UMAP  , ICA  , SVD  , and ISOnalysis) . In addinalysis) .GENTLE provides four feature selection methods: Pearson’s correlation, Ridge, XGBoost, and mRMR. Upon selecting the methods, a dataframe will be created with a rank of the features with the greatest predictive power, according to each method, where zero means the feature was not selected by the method; one means the feature was selected as the most predictive; two, the second-most predictive; and so on. It is important to emphasize that Pearson's method considers only one feature at a time when defining its predictive power; this way, the two most predictive features can be so correlated that their combination may not improve a classifier's predictive power when trained together. In contrast, the other methods will consider the combination of the features, which means the two most predictive features are the combination of two features that will produce the most predictive classifier if trained together. In addition, for visualization purposes, one can choose two featuresto display a 2D scatter plot, or three featuresto display a rotating 3D scatter plot.For the next step, one can perform the classification and validate the predictive power of the selected features. Four classifiers can be chosen: GNB (Gaussian Naive Bayes), LDA , LR (Logistic Regression), and DT (Decision tree). A radar plot will be generated, representing the five main scoring methods for classifiers: accuracy, precision, recall, F1, and AUC (Area Under Curve) ROC (Receiver Operating Characteristics) curve.Finally, one can upload another dataset for external validation purposes. This ultimate step produces a confusion matrix and a radar plot with accuracy, precision, recall, F1-score and AUC ROC scoring methods as explained above.Each dataframe generated can be downloaded (in.csv file format), along with the networks, the charts (in.png image format), and the classifier model (in pickle format). The networks created can also be visualized. GENTLE can be used for educational purposes due to its user-friendly interface and simplicity. This tool is particularly useful in providing fast feedback when analyzing a TCR repertoire and its features. Figure Pearson’s correlation is a widely applied strategy to find the features most related to a target and to eliminate any redundancies between features . It provNaive Bayes could classify between malignant and benign breast cancer using the Breast Cancer Wisconsin Data Set, providing fast and accurate results [Linear Discriminant Analysis is widely used in the biomedical field to separate groups according to disease and response to treatments. It is an efficient approach to dealing with the small sample size problem [Logistic Regression was applied to calculate the log-likelihood ratio of radiological response to anti-PD1 (programmed cell death protein 1) therapy TCR repertoire data of patients with metastatic melanoma [Decision Tree was the base method used to investigate features in 15 patients with NSCLC  using a combination of exome, transcriptome, and TCR repertoire data [To build the classifiers, we chose four methods based on their transparency, speed, and capacity to compete with sophisticated methods  and degree of relevance in the literature. In terms of transparency, we are referring to internal processes used and the various weighted factors that remain unknown, commonly known as a ‘black box’ algorithm.  results . Linear  problem . Logistimelanoma . Decisioire data .In summary, the feature selection methods and the classifier models can tackle diverse issues, perform rapidly, and deliver competitive and accurate results compared to other state-of-the-art approaches.http://bioinfo.life.hust.edu.cn/TCRdb/#/download (access the project PRJNA297261). The website contains seven TCR repertoire projects, but only two of them contain at least two conditions: ‘Healthy’ or ‘Breast Cancer’. From these two projects, the immunoSEQ20 project is unbalanced, as it contains 60 ‘Breast Cancer’ samples and only three ‘Healthy’ samples. Considering that, we decided to move forward with the PRJNA297261 project, which was balanced. Notice that the dataset here is only used for demonstration purposes and not for its medical/biological merit. We did not produce this dataset. This database provides preprocessed dataframes that require less processing to fit GENTLE’s input. The original project labels each sample with the condition of ‘Breast Cancer’ or ‘Healthy’ tissues; the project also labels each sample with the cell type of ‘TRegs’ or ‘TConvs’. We made available on Github the script in which we processed the data extracted from the website, turning it into a dataframe that serves as input for GENTLE. This script splits the original project (PRJNA297261) into two dataframes; one with only the TRegs samples, and the other one with only the TConvs samples. Each dataframe labels its samples as ‘Healthy’ or ‘Breast Cancer’. The processed data is summarized in Table To perform a case study workflow, we used a public dataset from the TCRdb that can be accessed from the link In this case study, we identified the features that can separate the healthy from the sick samples in both datasets, and used them to build classifiers. To avoid overfitting, we limited the number of features according to the minimum number of samples found in each category minus one . When bux and y-axis of each scatter plot were chosen based on the feature selection method’s choice for the most predictive features. Both Simpson and Shannon indices from the diversity features were able to separate the healthy and sick samples  using the TConvs dataset, we could construct scatter plots that portray a separation between the healthy and the sick samples. The features in the les Fig. A, whereiles Fig. B, in whiles Fig. C; both fles Fig. D. The feThe TRegs dataset could not distinguish the healthy from the sick samples as the TConvs did. The only feature that perfectly separated the samples was the Shannon index Fig. E. Again,It is important to emphasize that the feature selection algorithms chose the features shown in each scatter plot as the ones with the highest predictive power. Many features from the diversity dimension (omitted here) could distinguish the ‘Healthy’ and ‘Breast Cancer’ samples from the TConvs dataset, while even the most predictive features from the network, motif, and dimension reduction had difficulty distinguishing the samples from the TRegs dataset.Based on the exploratory analysis we performed : Linux, Windows, Mac. Programming language: Python 3.9 + . Other requirements: Streamlit, Plotly, Pandas, Sckit-learn. License: MIT License. Any restrictions to use by non-academics: None.Project name: GENTLE. Project home page: Additional file 1. A Walkthrough of GENTLE.Additional file 2. Diversity metrics, network metrics, dimensionality reduction, classifiers and scoring metrics.