Incorporating safetydata early in the drug discovery pipelineis key to reducing costly lead candidate failures. For a single drugdevelopment project, we estimate that several thousand samples perday require screening (<10 s per acquisition). While chromatography-basedmetabolomics has proven value at predicting toxicity from metabolicbiomarker profiles, it lacks sufficiently high sample throughput.Acoustic mist ionization mass spectrometry (AMI–MS) is an atmosphericpressure ionization approach that can measure metabolites directlyfrom 384-well plates with unparalleled speed. We sought to implementa signal processing and data analysis workflow to produce high-qualityAMI–MS metabolomics data and to demonstrate its applicationto drug safety screening. An existing direct infusion mass spectrometryworkflow was adapted, extended, optimized, and tested, utilizing threeAMI–MS data sets acquired from technical and biological replicatesof metabolite standards and HepG2 cell lysates and a toxicity study.Driven by criteria to minimize variance and maximize feature counts,an algorithm to extract the pulsed scan data was designed; parametersfor signal-to-noise-ratio, replicate filter, sample filter, missingvalue filter, and RSD filter were all optimized; normalization andbatch correction strategies were adapted; and cell phenotype filteringwas implemented to exclude high cytotoxicity samples. The workflowwas demonstrated using a highly replicated HepG2 toxicity data set,comprising 2772 samples from exposures to 16 drugs across 9 concentrationsand generated in under 5 h, revealing metabolic phenotypes and individualmetabolite changes that characterize specific modes of action. ThisAMI–MS workflow opens the door to ultrahigh-throughput metabolomicsscreening, increasing the rate of sample analysis by approximately2 orders of magnitude. Being able to incorporate high-value on- and off-targetsafety data into decision making, early in the drug discovery pipeline,is key to reducing this attrition. One approach to safety screeningis to measure a defined panel of molecular key event biomarkers thatcan predict specific modes of action (MoA’s) and/or adverseoutcomes (AOs).5 While several metabolic biomarkers of toxicity areknown,6 the optimal composition of a metabolicbiomarker panel that can predict multiple MoA’s has yet tobe discovered. Instead, omics technologies are attempting to addressthis knowledge gap, particularly via their ability to detect off-targettoxicities. Metabolomics provides the most downstream molecular phenotypeof the omics approaches and has been used to identify early responsebiomarkers that are indicative of AOs. A significant bottleneck, however,is the relatively low sample throughput of metabolomics analyses,typically requiring chromatographic separation and mass spectrometricdetection to analyze ca. 100 samples per day, per mass spectrometer.7 In contrast, for a single drug development project,we estimate the need to analyze several thousands of samples per day,equivalent to substantially less than 10 s per metabolomics analysis.If achievable, ultrahigh-throughput (UHT) metabolomics may enablea step change in our ability to generate high-impact safety data forhundreds of drug candidates per week, early in development, and mayalso provide vital mechanistic insights into the pathways involvedin toxicity.Safety concerns arethe major cause of drug candidate failuresin both preclinical and Phase 1 trials.9 and has been utilized extensively in toxicology.10 Othersystems such as the Agilent RapidFire offer improved throughput of7 s per sample for enzyme screening12 and <1min per sample for untargeted metabolomics13 but are yet to be widely adopted in toxicology. Surface analysismass spectrometry approaches such as matrix-assisted laser desorptionionization (MALDI) and desorption electrospray ionization (DESI) cansample at rates of ca. 1 Hz, directly from extracts spotted onto microarraysfor mass spectrometry.14 Such approacheshave been applied to single-cell metabolomics;16 however, these techniques suffer increased sample preparation timeassociated with matrix application (MALDI only) as well as suppressionof the small-molecule m/z range, caused by the appliedmatrix17 and solvent clusters in MALDIand DESI, respectively.Several analytical systems are available for high-throughputmetabolomics,which can screen hundreds of putatively annotated metabolites, althoughthese systems are not intended to definitively identify metabolitesnor to fully characterize cellular or biofluid metabolomes. For example,nanoelectrospray ionization direct infusion mass spectrometry (nESI–DIMS)offers a relatively rapid analysis in untargeted metabolomics studies with high analytical sensitivity3 from 3500 cells/samplewith just 0.002% carryover.4 More recently,AMI–MS has been shown to simultaneously detect many biologicallyrelevant metabolites to infer MoA’s in treated MCF7 cell extractswith comparable throughput and sensitivity as the targeted assays.18Acoustic mist ionization mass spectrometry(AMI–MS) hasproven capable of measuring the deacetylation of acetylated humanhistone deacetylase (HDAC), in the presence of a library of HDAC inhibitorsand HDAC enzyme, at a rate of 100,000 samples/day (anticipated limitof 3 samples/s)4 Application of AMI–MSto metabolomics could open a door to UHT screening, with applicationsin several fields, including toxicology.The atmospheric pressure ionization techniqueachieves this usingan echo acoustic transducer and charging cone to generate a mist ofnanoliter-sized charged droplets that are guided through an ion transferline to a mass spectrometer.To deploy AMI–MSmetabolomics in a drug safety screeningprogram, specifically to adapt it to the principles and experimentaldesigns for both high-throughput screening and metabolomics, it requiresextensive optimization of the data generation, signal processing,and analysis. The overall aim of this study was to implement a signalprocessing and data analysis workflow to ensure that high-qualityAMI–MS data can be produced and to demonstrate its applicationto a UHT metabolomics screening study in drug toxicology. Specifically,the first objective was to deploy and optimize a multistep signalprocessing workflow that addressed the following challenges: (i) definingon- and off-scans in the polarity switching cycle of AMI–MSin order to extract meaningful real signals, (ii) filtering relativelylow intensity, sparse, but reliable signals of the intracellular originfrom noise, (iii) addressing intensity corrections through batch correctionbetween sample plates and normalization across all samples, and (iv)processing the metabolic phenotypes of the samples consistently, eventhough they can range from healthy controls to completely cytotoxic,via parallel fluorescence imaging measurements and “cell phenotypefiltering”. The second objective was to highlight the novelapplication of UHT AMI–MS metabolomics to toxicity screeningthrough the rapid collection of HegG2 metabolic phenotypes in a highlyreplicated exposure study with 16 drugs each at 9 concentrations,using a 384-well plate format throughout exposures, sample preparation,and data generation.Supporting Information for further information). The samplesof standards each contained a mixture of eight stable isotope-labeledand eight unlabelled metabolite standards in 50 μL of 25:75(v/v) methanol/water . The technical replicates and biological controls comprised extractsof HepG2 cells that had been cultured for 48 h in bulk and then aliquotedacross eight 384-well analysis plates or cultured in individual wells(seeded at 3500 cells/well) across three plates, respectively, washedand then lysed with methanol and aliquoted into analysis plates (see Supporting Information). The toxicity study sampleswere derived as for the biological controls; negative control samples[dimethyl sulfoxide (DMSO)-controls] remained untreated, but the remainingsamples were additionally treated for 24 h with each of the 16 drugs(Table S2) at nine half-log concentrationsfrom 31.6 nM to 316 μM, and the positive control samples (death-controls)were formed through treatment with 316 μM chlorpromazine.Four AMI–MS metabolomicsdata sets were generated from eitherstandards or extracts of HepG2 C3A cells to address each of the objectivesdescribed above, all prepared in 384-well plates. The sample setsare referred to throughout this paper as “standards”,“technical replicates”, “biological controls”,and “toxicity study” samples  and weremade up to a final volume of 50 μL of 25:75 (v/v) methanol/water.“Cell imaging” data were generated for the toxicitystudy by staining the cellular DNA bound to the culturing plates followingcell lysis, with Hoechst 33342 and analyzing using a fluorescencemicroscope CellInsight (Thermo Fisher Scientific). This cell countwas used as a proxy for the number of live cells in the sample priorto cell lysis, as the majority of apoptotic and dead cells were removedby the washing steps (see Supporting Information).The study included 4 In brief, the acoustic wave wasprovided using an Echo 555 liquid handler  positioned beneath an XY-stage (Labcyte custom-made) holding the384-well plate. A charge cone with a high-voltage power supply (RIGOL)and a heated transfer tube held above the plate formed and guided the ions into the Xevo G2-XSquadrupole time-of-flight mass spectrometer (Waters). The ionizationprocess inferred particular demands on the signal processing. Specifically,the charging cone (+3 kV) placed above each test well (in the 384-wellplate) induced an accumulation of negative charge on the liquid surface.Each time an acoustic wave was pulsed through the sample (at 1400Hz), a mist of nL-sized droplets, carrying the negative charge, wasgenerated. To discharge the well and circumvent charge build up, thecharging cone polarity was inverted after every 10 nL package of sampledispensed. From the total of 100 nL dispensed per sample, five packagesof negative ions were measured at the mass detector, which we referto as “on-scan” data. Typically, the first package ofdroplets from each sample was fired using the “off”polarity (an “off-scan”) to settle the meniscus; hence,the desired signals were from the second, fourth, and so forth ionpackages. The droplet mist was drawn into a heated capillary (250°C), which along with a nitrogen cone gas flow of 50 L/h, aideddesolvation of the sample as it transited into the ion source (100°C). The mass detector scanned for 80 ms with an interscan delayof 14 ms across a mass range from 50 to 1200 Da. This enabled an averageof 3 scans per 10 nL ion package (and polarity switch), hence a totalof 15 on-scans per sample.AMI–MS instrumentconfiguration and methods were as previously described by Sinclairet al.http://proteowizard.sourceforge.net/) before being processed using AMIMSpy , a new software package that has been derived from DIMSpy , the latter being developed over the last decade specifically toprocess direct infusion mass spectrometry metabolomics data.8Implementingand optimizing a data-processing workflow involved sequentially consideringeach of the components outlined in Figure S1). For the unusual case of only twoconsecutive on-scans ,the single scan with the highest intensity was extracted. To undertakethis comparison of extraction methods, first, an automatable decisionthreshold to assign a scan as “on” or “off”was required of the intensities of these features. Two factors were observed toinfluence the signal quality, first the switching of the charge conepolarity that generated on- and off-scan data and second our observationof “edge effects”, that is, reduced signal intensityin the first and/or last on-scan due to the temporal proximity ofthe polarity switch 2a. Consem/z featureand sample-filtering capabilitiesoriginally developed to separate signal from noise in nESI–DIMSdata8 were implemented and the relevantparameters were optimized for the novel AMI–MS metabolomicsscan data. First, any features with an SNR  less than a threshold value were discarded, termed“SNR filtering”; then, only those features present inat least a threshold percentage  of thetechnical replicate analyses of each sample were retained, termed“replicate filtering”; and only those features in atleast a threshold percentage  of all samplesin the study were kept, termed “sample filtering”. Missingintensity values occurred in the resulting data matrix, as occursroutinely in metabolomics data sets,20 sothe percentage of missing values per sample was calculated to determinea threshold value (optimized in study) above which the sample spectrumwas regarded as low quality and discarded. These parameter optimizationprocedures used the biological control data set to best representa metabolomics study. The final feature filtering step calculatedthe RSD of the intensity of each remaining feature across all samplesand discarded those that exceeded a threshold . This “RSD filter” was based on the technicalreplicate data set.Extensive 21 and the unusually large number of samples and384-well plates comprising an AMI–MS study. First, the intra-and interplate intensity variations were characterized based on themeasurement of the standards across the technical replicate data setand through principal component analysis (PCA) of distinct metabolicphenotypes in the toxicity data set. Then, a suitable two-step algorithmwas implemented for effective normalization and batch correction ofthe data. In step one, probabilistic quotient normalization (PQN)22 was applied to each plate individually, usinga plate-specific reference spectrum  to ensure it was representative of the sample space.Next, the intensity of each m/z featurewas batch-corrected by dividing the intensity values by a plate-specificcoefficient for the given feature .Intensity correctionalgorithms were applied to the AMI–MS data to enable reliablecomparisons of samples given both the inherent nature of systematicintra- and interplate variation in mass spectrometry studiesSamples exhibiting high celldeath in the HepG2 toxicity study (and therefore metabolic phenotypesthat convey little about a drug’s MoA) were filtered from themetabolomics data matrix using a “cell phenotype filter”,which flagged  any exposure time/concentration drug treatmentthat induced a cell count below an optimized threshold value. Thedetermination of those treatments that were cytotoxic was derivedfrom the cell imaging data set collected from the lysis plates ofthe toxicity study, the optimized filter was then assessed by comparingthe PCA of the filtered and unfiltered data from the toxicity study.Table S3 anddiscussed within the paper, were all applied to the toxicity dataset using the associated cell imaging data, AMIMSpy and R v4.0. Theonly exception was that the RSD filter could not be applied to thetoxicity data set as intrastudy quality control (QC) samples werenot available due to constraints imposed by the automated extractionof 384-well plates. The MoA’s of the drugs in the toxicitystudy were investigated using PCA and Uniform Manifold Approximationand Projection (UMAP) analysis, and the concentration response ofsignificant metabolites, determined from PCA loadings and analysisof variance (ANOVA), was visualized. Processing the toxicity dataset was completed in <3 h utilizing 4 CPU cores in parallel and16 GB RAM (Intel i7 CPU 3.40 GHz).The optimizedprocessing steps, listed in Following manual interrogationof the standard data set, an SNR threshold of 15 was selected to distinguishon- from off-scans, since the majority of features in the off-scanshad SNR < 15 and the majority of features in the on-scans wereabove this threshold.Figure S3) identified that allowingthree of these high intensity features within off-scans was optimal,giving a sensitivity of 0.91 (detecting on-scans as “on”)and specificity of 0.98 (off-scans as “off”).Some features in the off-scans, however,had higher SNR , so a threshold number of such features (SNR > 15) that couldbe present within off-scans also had to be determined for more robustlabeling of the scan types. A receiver operating characteristic (ROC)curve  and the “on-scans-no-edge” method.Next, the three methods to extract the raw scan data were compared,with the evaluation based on which the method detected the metabolitestandards in the most replicate scans and which yielded the lowestRSD values of the intensities of these standards , providing nojustification to deviate from the well-accepted norm of SNR > 3toretain a signal.23 The optimal threshold for the“replicate filter” was 50% , which on average required features to be detected in aminimum of 3 out of 6 replicate scans per sample, based upon an analysisof the mean feature count per sample . This value is lowerthan that used in nESI–DIMS, suggesting a less stable productionof ions in AMI–MS related to nanoelectrospray ionization.8 Next, the effect of the “sample filter”on feature count was investigated , showing the expected decrease in feature count as the percentagethreshold increased, and a value of 50% was selected based on a balancebetween not reducing the feature count too severely while attemptingto retain only reliable, repeatedly observed features. Overall, usingthis combination of SNR, replicate, and sample-filtering thresholds,the feature count for the AMI–MS biological control data setwas an order of magnitude lower than that routinely achieved usingnESI–DIMS. However, a major cause of this apparently lowersensitivity of AMI–MS is the very low biomass of HepG2 cellsbeing investigated, estimated at 3500 cells per well of a 384-wellplate. In contrast, nESI–DIMS metabolomics studies have moretypically analyzed ca. 100-fold greater biomass, helping to explainthe apparent differences in sensitivity.8 The critical question is whether the AMI–MS-detectable metabolome,using a 384-well plate format, yields sufficient toxicological insightsto achieve its screening mission, discussed below. Next, to filterout low-quality samples, the number of missing values per sample wasassessed. A threshold of 50% was selected as this retained the majority(92%) of the biological control samples  while removing > 50% of the suspected noisefeatures, shown by the distributions in Supporting Information) were removed by other components of the multistepfiltering workflow that were purposely not applied for this RSD filteroptimization.Theeffects of multiple parameters associated with stigated S4, provistigated S4, provi25 Even after this normalization, the intraplate variance is greaterthan that with traditional approaches, for example, Kirwan et al.reported a median RSD of <11.5% using nESI–MS metabolomics.21 However, AMI–MS metabolomics is proposedas a tool for UHT screening and it is not expected to match the analyticalreproducibility of lower-throughput approaches. To characterize theinterplate variance, the intensities of the internal standards inthe technical replicate data set were tracked across eight plates(Supporting Information); for example, the averageintensity of citrate IS varied significantly across the plates, indicatingthat interplate variability needed to be addressed. The need to applya batch correction algorithm was reinforced by PCA of the technicalreplicate data set , in whichsamples predominantly clustered based on the study plate. After applyingthe batch correction algorithm, the intensity variation decreased(Supporting Information) and the PCA analysiswas no longer dominated by interplate variance along the first principalcomponent . The batch correctionalgorithm was also applied to a subset of the HepG2 toxicity studydata set containing just the DMSO-control and death-control samples,two groups of samples that would be predicted to have distinct metabolicphenotypes, followed by PCA analysis. To minimize theintensity variance of features across replicate samples, PQN was applied,which reduced the median RSD from 29.4 to 24.8% in the technical replicatedata set and is in line with metabolomics best practices.data set S10, in wdata set S10, in wFigure S11). This threshold also retained all the DMSO-control samplesand >80% of the drug-treated HepG2 samples, only rejecting samplesthat exhibited high cytotoxicity  support thehypothesis that the major source of variance switches from cytotoxicityto a perturbation relating to MoA. This loading data also provideinsights into the MoA of tamoxifen, discussed below. In contrast,deferoxamine showed no cytotoxic effects and the concentration responsewas evident without the need to employ cell phenotype filtering toreject any high-cytotoxicity samples .A traditional toxico-metabolomicsstudy typically requires an initial dose range-finding experimentto determine a small number of exposure concentrations for a subsequentsmaller-scale omics investigation. This is due to the relatively lowthroughput and high costs associated with traditional metabolomicsapproaches. In contrast, UHT AMI–MS allows metabolomics tobe readily applied to a large number of samples across a wide concentrationrange without an initial range-finding experiment. However, this time-and cost-saving benefit brings a new challenge of how to process widelydiffering metabolic phenotypes, from baseline metabolism to high cytotoxicity.We sought to remove the cytotoxic samples using a cell phenotype filterwith a threshold of 602 cells, which corresponded to the 95th quantileof counts in the death-control samples S11. ThisTable S3), except for the RSD filter, due to the lack of intrastudyQC samples. Traditionally, these QC samples are generated by poolingan aliquot of every biological sample in a metabolomics study,10 which was not feasible here given the sole useof automated extraction methods, although in future studies, we recommenda strategy that is developed to generate and use intrastudy QC samplesin AMI–MS. Following cell phenotype filtering, we first conductedan analysis using UMAP on only the highest noncytotoxic concentrationsof each drug. This untargeted profiling revealed four distinct clustersof samples  pertaining to (i) DMSO-controls and drugs eliciting minimal metabolicresponses, (ii) bosentan and deferoxamine, (iii) carbonyl cyanide3-chlorophenyl hydrazon (CCCP), and (iv) a large cluster of drugsinducing a similar, significant metabolic effect on the right of theplot. The molecular perturbations driving each cluster were determinedby conducting further analyses of each drug separately, focusing onmetabolite features with high PCA loadings  and based on univariate analysis (ANOVA) of individualmetabolites across noncytotoxic concentrations . To illustrate the findings, 26 The gray-shaded region in 27 While this approach to data generation prevents robustbiochemical inferences of toxicity mechanisms, this is not our proposedrole for AMI–MS metabolomics. Instead, this UHT platform wouldbe used to screen vast numbers of samples and utilize untargeted profilingto discover the relatedness of their metabolic phenotypes to drugsof known toxicity mechanisms. Traditional metabolomics approacheswith higher metabolic specificity could be used to analyze selectedcell extracts, depending on the study objectives.The AMI–MSmetabolomics workflow was tested using the HepG2toxicity study data set, comprising 2772 samples distributed acrossnine 384-well plates, representing the metabolic responses to 16 drugs.All steps of the workflow 1 were ap samples S14 perta samples S14 perta samples S14 pertaThis study introduces signal-processing stepsspecific to AMI–MSdata and adapts and optimizes traditional processing algorithms froman nESI–MS workflow, for this novel UHT metabolomics platform.We demonstrate that rejecting “off” and “edge”scans in AMI–MS data sets greatly improves data quality andthat traditional noise-filtering approaches are compatible with this data type, following optimization. The implementationof normalization and batch correction algorithms had to be adapted,primarily due to the lack of intrastudy QC samples from the fullyautomated extraction methodology used in the AMI–MS experiment.Additionally, we implemented a data-dependent cell phenotype filterto aid the interpretation of noncytotoxic AMI–MS metabolicmeasurements. During optimizing the parameters in the AMI–MSworkflow, we reported somewhat lower feature counts, increased technicalvariance, and lower confidence in metabolite annotations comparedwith the current state-of-the-art direct infusion mass spectrometrymetabolomics approaches. However, despite these limitations of AMI–MS,we have demonstrated its unprecedented benefit of ultrahigh sampleanalysis, with HepG2 metabolomics data generated from a highly replicatedexposure to 16 drugs across 9 concentrations—amounting to almost3000 samples—in under 5 h. Analyses of these data using univariateand multivariate methods revealed metabolic changes consistent withthe known drug-induced hepatotoxicity. This suggests that AMI–MSmetabolomics could be deployed as an effective UHT metabolic screeningtool in toxicology and other fields, analyzing ca. 2 orders of magnitudemore samples within a given time period relative to the existing methods.


Imprecision and biases inherited in current cancer detection innovations hamper their applications at population level. Here, we employ deep learning neural networks and noncoding RNA biomarkers to develop an accurate cancer detection system to detect multiple cancer types. Our system binarily classifies 26 common cancers vs. normal with >96% AUC, and it can become a practical cancer screening system at population level.Detecting cancers at early stages can dramatically reduce mortality rates. Therefore, practical cancer screening at the population level is needed. To develop a comprehensive detection system to classify multiple cancer types, we integrated an artificial intelligence deep learning neural network and noncoding RNA biomarkers selected from massive data. Our system can accurately detect cancer vs. healthy objects with 96.3% of AUC of ROC (Area Under Curve of a Receiver Operating Characteristic curve), and it surprisingly reaches 78.77% of AUC when validated by real-world raw data from a completely independent data set. Even validating with raw exosome data from blood, our system can reach 72% of AUC. Moreover, our system significantly outperforms conventional machine learning models, such as random forest. Intriguingly, with no more than six biomarkers, our approach can easily discriminate any individual cancer type vs. normal with 99% to 100% AUC. Furthermore, a comprehensive marker panel can simultaneously multi-classify common cancers with a stable 82.15% accuracy rate for heterogeneous cancerous tissues and conditions. This detection system provides a promising practical framework for automatic cancer screening at population level. Key points: (1) We developed a practical cancer screening system, which is simple, accurate, affordable, and easy to operate. (2) Our system binarily classify cancers vs. normal with >96% AUC. (3) In total, 26 individual cancer types can be easily detected by our system with 99 to 100% AUC. (4) The system can detect multiple cancer types simultaneously with >82% accuracy. The application of modern scientific advances to cancer therapy have dramatically expanded cancer patients’ life expectancy ,2,3,4. OAdvancements in high-throughput technologies, such as microarray and sequencing, offer rich resources to understand gene alterations associated with cancer markers ,12. ThouRecently, numerous approaches have been proposed for cancer screening. Three remarkable innovations are promising. The first of these is circulating tumor DNA (ctDNA) detection, which measures bloodstream DNA released from dead tumor cells , but theThe core challenge when developing a practical system is to find a set of biomarkers that are endogenous for all cancers. Because countless factors  contribute to cancer phenotypes ,23,24,25Artificial intelligence methods have recently been applied to improve prediction accuracy ,31,32,33In this study, we employed NN and universal noncoding RNA biomarkers for all 26 cancer types ,20 to deAll data downloads, processing, computations and graphing were performed in Linux by using Python 3.8 and R 3.6. TensorFlow 2.4.0 and Scikit-learn 0.24.0 were used for deep learning neural networks.The primary schematic of this study included TCGA data downloads, biomarker selection, NN model building, and prediction and validation A.All data were downloaded from TCGA, as previously described . Brieflyhttps://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-5423/, accessed on 28 August 2021). This data set contains 27 cancer types (1209 samples) and normal control (150 samples)  format were downloaded from The International Cancer Genome project deposited in ArrayExpress (#E-MTAB-5423) (samples) C. Validasamples) D. To generate a practical system, we used the data directly from the real world in TPM (Transcripts Per Million) data format for all computational processes in this entire study without any model-based normalization and filtering. TCGA gene expression data were normalized to TPM (Transcripts Per Million) and the raw downloaded TPM data for independent validations were also directly applied to validate our models without any filtering and normalization. Three independent machine-learning sets were prepared in this study, including test, validation, and training. These three groups were randomly split from a total 9057 samples. The test set takes 20% (1812) of the 9057 samples for independently measuring final accuracy and AUC. Another 20% (1449) of the remaining samples (7245) was set for validation, and the remaining 5796 for training.This sample splitting scheme to generate training, validation and test data sets was also applied to independent validation and math model comparison. https://www.gencodegenes.org/, accessed on 19 January 2021) were analyzed and used in this study, as in our previous study [https://combai.org/software/survival/, accessed on 29 November 2021). Briefly, normal samples were not involved in these biomarker selections. We only used cancer samples to generate biomarkers associated with death, so these biomarkers were actually the deadliest markers. The stability selection was applied to all feature selections, in which samples were randomly split into m subgroups (m ≥ 2) and preliminary biomarkers were selected in each subgroups [https://combai.org/ai/cancerdetection/, accessed on 29 November 2021).All noncoding RNAs defined by gencode . For multi-cancer classifiers, six hidden layers with 240 units for each layer were set . Activation was set to relu for the hidden layer. Adam was used as a model optimizer.To avoid over-fitting, we set dropout (0.1) for each model layer for all models in this study. For binary classifications, a NN model with three layers was built, including one input layer with 30 units, one hidden layer with 60 units and an output layer .Programming codes for all NN models and running examples are available on our project website .Final summary AUCs were drawn by using ggplot2 in R. Complete result plots and data are available on this project website  to binarily classify cancer vs. normal. In total, 8425 cancer and 632 normal samples measured by TCGA were used , and thus the whole system was run for 30 epochs to estimate the prediction accuracy.Here, we used these 56 noncoding RNAs as biomarkers and employed NNs from Keras Sequential library using TensorFlow v2.4.1 with one hidden layer , the accuracy of training and validation both reached 0.95, and the AUC reached 0.934 , the accuracy of both training and validation reached 0.96, and the AUC stabilized at 0.963  and normal controls . This indicates that our system is robust in the real world.Validation 1 features much more variation and outliers than the TCGA data . MoreoveSimilarly, we used validation 2 from an exosome database containing 12 cancer types D to testhttps://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html, accessed on 29 November 2021), while TD is a non-parametric supervised machine learning algorithm that learns simple decision rules from training data features to make predictions . To compare the performance of our model with other mathematical models, we ran an independent test and measured the AUCs for three models, neural network (NN), random forest (RF), and decision trees (TD). RF is a supervised machine learning approach that randomly selects sub-samples to create trees and uses an average of tree prediction votes to predict unknown samples  and systematically run these three models with the same training and test data to make the results comparable. The AUC plot showed that the AUC of our NN model was significantly higher than the other two models . With 10 biomarkers, our NN model reached an AUC of 0.9, while RF and TD only achieved 0.84 and 0.74, respectively. In addition, our NN model could reach up to 0.96 of AUC, but RF and TD never went beyond 0.87 and 0.75, respectively , in which the losses for training and validation were close to 0 and 0.1, respectively, and the AUC reached 100% for the test data set for OV . The subsequent challenge in cancer screening is to simultaneously detect all 26 specific cancer types. This requires a comprehensive biomarker panel and a practical mathematical model for multiple classifiers. We split the samples into training, validation, and test sets, but only the training sets were used to select biomarkers. To avoid sampling biases and to examine our system’s robustness, we randomly generated eight training data sets independently . For each training set, we inserted the stability selection into a support vector machine implemented in feature selection using Scikit-learn (0.24) and selected a panel of noncoding RNA biomarkers ranked according to their highest frequency score . Based on multiple cancer types and the biomarker panel, we built a complex NN model with six hidden layers . When the biomarker number accumulated to 50, the accuracy and loss for both training and validation achieved >0.75 and the loss declined to ~1. The accuracy and loss for training and validation reached >0.8 and <1.0 for 100 accumulated biomarkers .We examined the performance of each biomarker panel independently. During the first run, when 25 accumulated biomarkers were applied, the accuracy for both training and validation reached >0.6 and the loss reduced to <2, respectively  and the accuracy data were dependent on the sampling, instead of the stable accuracy of our system. Therefore, our system can stabilize at 82.15% with 178 biomarkers while simultaneously detecting all 26 cancer types.These eight training sets resulted in different test set accuracies when the biomarker number accumulated to 300 . Our NN model should be trained with many large and diverse data sets. In addition, the parameters of our NN model should be optimized when diverse data are available, although optimizing NN parameters is challenging because of its large number of parameters. Furthermore, the question of how to unify the protocol of cancer sample collections and non-coding RNA measurements remains to be investigated.


Single-cell RNA sequencing (scRNA-seq) has enabled the unbiased, high-throughput quantification of gene expression specific to cell types and states. With the cost of scRNA-seq decreasing and techniques for sample multiplexing improving, population-scale scRNA-seq, and thus single-cell expression quantitative trait locus (sc-eQTL) mapping, is increasingly feasible. Mapping of sc-eQTL provides additional resolution to study the regulatory role of common genetic variants on gene expression across a plethora of cell types and states and promises to improve our understanding of genetic regulation across tissues in both health and disease.While previously established methods for bulk eQTL mapping can, in principle, be applied to sc-eQTL mapping, there are a number of open questions about how best to process scRNA-seq data and adapt bulk methods to optimize sc-eQTL mapping. Here, we evaluate the role of different normalization and aggregation strategies, covariate adjustment techniques, and multiple testing correction methods to establish best practice guidelines. We use both real and simulated datasets across single-cell technologies to systematically assess the impact of these different statistical approaches.We provide recommendations for future single-cell eQTL studies that can yield up to twice as many eQTL discoveries as default approaches ported from bulk studies.The online version contains supplementary material available at 10.1186/s13059-021-02407-x. Expression quantitative trait locus  is now an established technique and can be deployed at population scale, across many individuals, by exploiting multiplexed experimental designs and using appropriate demultiplexing tools –10. The As scRNA-seq in large sample sizes becomes feasible, it is important to establish “best practices” and benchmark approaches for the design and analysis of genetic studies using single-cell data. Current efforts in this space have primarily focused on the experimental design of single-cell eQTL  single-cell and bulk expression data from a homogenous cell population  to Traditional bulk eQTL are germline genetic variants that are associated with differences in gene expression between donors, where the gene expression values represent the summary of a gene’s expression across all cells in the tissue sample. In order to use traditional bulk eQTL mapping methods for single-cell eQTL mapping, we first need to aggregate the multiple measurements  from each donor to obtain bulk-like measurements. Here, we explore different aggregation methods Fig. b. In parGiven that single-cell RNA sequencing allows for pooling of multiple donors into single sequencing runs , and for post hoc grouping of cells (for instance into cell types), we considered a second set of aggregation approaches, namely aggregating not only at the donor level but also for each individual sequencing run , we performed single-cell-level normalization using scran  implemenWe tested these approaches on two empirical datasets and two simulated datasets. The main analyses were performed on expression data gathered from iPSCs, for which we have bulk RNA sequencing  and SmarWe first focused on the single-cell data from Cuomo et al. , from whcis- expression quantitative trait loci (eQTL) using an LMM as implemented in LIMIX [cis-eQTL map .We aggregated the single-cell information as described above Fig. b and we in LIMIX , consideWe identified between 776 and 1835 genes with at least one eQTL  vs d-mean (1305); dr-median (1337) vs d-mean (776); dr-sum (1463) vs d-sum (1174)). Next, our results indicate that mean aggregation  outperforms sum aggregation , and median aggregation performs worst in all cases (1337 eGenes). As well as finding the lowest number of eGenes, the median methods are also responsible for the low intersection of HVGs from 20,545 to 12,720, due to cells with low read counts. When dropping median from the comparison the increase from d-mean to dr-mean is even higher  and a-bulk n = 526), to assess the replication of the iPSC sc-eQTL mapping results in bulk data  and sum (p.adj = 7.3 × 10-11) aggregation, regardless of aggregation level  = 0.022, p = 0.97; Fig. −4) and mean performing better than median (p.adj = 0.041) and sum (p.adj = 7.5 × 10−5). These results corroborate findings from the empirical data analysis showing that mean aggregation after single-cell level normalization performs better than sum aggregation followed by bulk-normalization methods. Further, by comparing the mathematical properties of the eQTL correctly detected by each aggregation method, we can tell that median aggregation tended to miss eQTL for genes with high variance and low mean expression, while sum aggregation tended to miss eQTL with small effect sizes . To ensure the simulations reflected real data, expression statistics from the iPSC Smart-Seq2 dataset were used to estimate key parameters used in the simulations ; second, we subsampled to a fixed number of cells  per individual. In both cases, we observed a high number of eGenes . However, the replication rates were very low, ranging from 10 to 15% when considering the two approaches and the two sets of bulk results . An LMM is often used for “repeated measures” analyses—where multiple measurements are made from the same subject—in preference to classical approaches such as repeated measures ANOVA . Thus, wresults “”, suggesTo further confirm this result, we tested direct single-cell eQTL mapping on one of the simulated datasets. Compared to dr-mean aggregation, using all available cells or 10 cells per donor reduced eQTL detection power by 56% and 67% and increased the empirical FDR from 0.07 to 0.68 and 0.36, respectively  in single-cell expression quantifications by including a (second) random effect in our linear mixed model that captures sampling effects  increase in eGene discovery than was observed in the iPSC Smart-Seq2 results  random effect accounting for sampling variation on the power to detect sc-eQTL, we added the SV term when mapping eQTL on the simulated scRNA-seq data. For both Smart-Seq2 and 10X simulations, adding either 1/#cells or 1/#reads as an additional random effect resulted in an increase in the number of true eQTL detected  on an external set of test statistics [Given that the discovery power of eQTL is heavily dependent on the number of donors for which genetic and expression data is available, and large bulk eQTL studies are available, we set out to leverage bulk data to increase discovery power when mapping sc-atistics , 42 and To assess the impact of using cFDR, we used the dr-mean iPSC results and the eQTL statistics of the m-bulk samples to condition our FDR estimates on and used a-bulk samples to test the replication rates  the lymphocyte eQTL summary statistics from GTEx (v7) and (2) the meta-tissue eQTL results by GTEx (v7). For the meta-tissue results, we assessed all different meta-analysis p values . Lastly the lympThe previous sections describe how to optimize individual steps during a sc-eQTL mapping workflow, including steps specific to single-cell aggregation and normalization, statistical modeling, and multiple testing correction. Here we describe our combined optimized sc-eQTL mapping workflow and showcase the total increase in power possible when combining the individual findings. We directly compared the eGene discovery results from a default workflow using d-mean aggregation (baseline) to dr-mean with an additional random effect to capture sampling variation and an improved multiple testing strategy applied. Again, we focused here on the iPSC data to be able to assess replication in bulk.Overall, we observe a 142.7% increase in eGenes detected when comparing the d-mean aggregation with Storey Q-based multiple testing correction (1402 eGenes) versus the optimized eQTL mapping approach, specifically dr-mean with additional random effect and cFDR multiple testing  work best even though only 20 (23%) of the Smart-Seq2 study samples and 25 (14%) for the 10X study samples were assayed in more than one run. We expect that these improvements are also down to improved normalization as well as improved covariate correction. Finally, we tested median aggregation, but given the sparsity of the single-cell data this method drastically decreases the number of genes that can be assessed for eQTL mapping. Therefore, the use of median aggregation is not a sensible method in most single-cell applications.Next, we assessed the effects of both covariate and sampling variation correction approaches. First, the effect of the different methods to identify covariates, i.e., data driven factors that capture unwanted variation in eQTL mapping, is ambiguous. Adding covariate factors of any method increases eGene discovery power substantially, but the results for the different tools when using the optimal number of factors for that tool are broadly comparable both in terms of discovery and number of replicated effects. Correcting for 15 PCs yielded the most eGenes at a high replication rate. Importantly, the bulk eQTL results we use to assess replication were mapped after correcting the bulk RNA-seq data using 20 PCs, which might bias the replication for the sc-eQTL mapping to be more similar to the bulk PCA-based results. However, regardless of the replication rate in bulk, PCA correction yielded the highest number of eGenes and so remains a sensible “default” choice for defining covariates capturing unwanted variation. Second, including sampling variation as a random effect in the models results in an increase in eGene discovery power on both Smart-Seq2 and 10X datasets, especially when using the 1/#cell-based random effect.Finally, we explored the use of bulk eQTL mapping statistics to guide our multiple testing correction using the conditional false discovery rate (cFDR) in the implementation by Liley et al. We observe that the cFDR method gives a marked increase in the identified eGenes (between 21% and 40% increase), both when matched bulk data is available and when meta-tissue eQTL summary statistics are used. However, using the larger iPSC dataset, we found cFDR had limited impact in terms of replication rates of the eQTL. While cFDR allows us to guide the multiple testing, potential cell type-specific effects that are borderline significant might not be “boosted” in power by cFDR if those cell types are not present or lowly represented in the bulk references. We did, however, observe that all the effects found using standard FDR methods  were also deemed significant using cFDR, so no potential effects were lost by using the guided FDR approach. We explored the use of cFDR for other single-cell studies, but more tests are needed for mixed cell types to find the optimal way to use cFDR in sc-eQTL studies.This study demonstrates that optimizing the sc-eQTL mapping workflow can increase eGene discovery power substantially. However, our conclusions come with some caveats; (1) simply discovering more eGenes does not necessarily mean that an approach is better, as false positives could arise due to data processing decisions; (2) bulk eQTL are a powerful, but not perfect, gold standard for assessing truth, as biases in bulk eQTL mapping may be replicated in sc-eQTL mapping analyses; (3) while our simulation framework uses parameters estimated from empirical data to resemble real population-scale scRNA-seq data, all simulation frameworks have limitations; (4) single-cell processing steps such as expression quantification, cell-level QC, and cell-type annotation could affect eQTL mapping performance, but best practices for these steps are likely to be dataset and technology specific rather than specific for sc-eQTL mapping, and were thus not assessed here. Also, the methods we explored here are based on current standards for bulk eQTL mapping. Though we find that directly using single-cell expression information does not yield trustworthy sc-eQTL, there is room for methodological improvements to better use the replicated structure of single-cell expression data and low counts in each cell.In this paper, we discussed the impact of various normalization, aggregation, and covariate correction procedures as well as multiple testing correction in the context of single-cell eQTL mapping power. We conclude that following methodological choices are currently optimal: scran normalization; mean aggregation of expression across cells from one donor (and sequencing run/batch if relevant); including principal components as covariates in the LMM; including a random effect capturing sampling variation in the LMM; and accounting for multiple testing by using the conditional false discovery rate. Together our optimized sc-eQTL mapping workflow increases eGene discovery by more than a factor two in our empirical data. These findings should guide the community as more population-scale scRNA sequencing data becomes available and as groups like the single-cell eQTLGen consortium are establishing sc-QTL studies on a massive scale.To optimize processing and workflows for eQTL mapping in single cells we focus on iPSC cells, as iPSCs have a homogeneous and stable expression profile  and becaThe Cuomo et al.  study deFirst, we quantified gene expression levels in line with the bulk study. We mapped RNA-seq reads to the genome hg19) using STAR   and four donors that were outliers in the genotype space (based on projection with the 1000G project data) were excluded.After QC and selection, the number of cells per donor varied from 5 to 383 iPSCs per donor (mean = 84.8) and the number of reads per donor varied from 1 to 104 million (mean = 42 million). For reference, the average number of reads per donor in our bulk data is 44 million, but the range is much smaller  and time-point (day 11 of iPSC differentiation towards dopaminergic neurons) from Jerber et al , using eSufficient single-cell data (≥ 5 cells) were available for 174 individual cell lines from 174 donors. The number of cells per donor ranged from 7 to 7782 (mean = 831.4). The total number of reads per donor ranged from 0.02 to 114 million reads (mean = 9.7).To assess the replication of 10X FFP eQTLs, we leveraged the brain summary statistics available from the GTEx study (v7) . Given thttps://github.com/bmvdgeijn/WASP/tree/master/CHT). To replicate the eQTL results, we used the WASP ASE counter to count genic reads overlapping phased variants linked to the eQTL SNP. For the gene counting, we use a processed gene annotation file that contains merged overlapping exons from the same gene and removed regions that are present in multiple genes as recommended by WASP. After counting, we used the bias corrections implemented in WASP to correct the read counts and used WASP to quantify ASE. Importantly, WASP was used as provided, meaning that no specific optimizations for single-cell ASE counting were used. Given the lower read counts in single-cell data, we could not replicate many eQTL results; however, the allelic expression ratios overlap the eQTL signs to a large extent. To correct for multiple testing in the ASE analysis, we used the Storey Q method over the relevant top eQTL per gene.In addition to bulk eQTLs results, we assessed allele-specific expression (ASE) levels as a second method of replication. We leveraged the WASP (version: 0.3.4) pipeline to quantify ASE on both the single-cell datasets  . First, i = yi + (yi × Gi × w), where Gi is the minor allele dosage of the eSNP and w is the eQTL effect size. To ensure realistic simulations, parameters were estimated from real data. Single-cell parameters defining the distribution of library sizes (gamma), common BCV (inverse chi-squared), and dropout rates (logistic) were estimated from the empirical single-cell count data from the donor with the most cells . Population parameters defining the distributions of gene mean expression (gamma) and variance in expression (gamma binned by mean expression) were estimated from the mean aggregated expression levels for all genes from either iPSC Smart-Seq2 or FPP 10X. Finally, eQTL parameters defining the distribution of the eQTL effect sizes (gamma) were estimated from the most significant eQTL hit for each gene from bulk iPSC data for eSNPs with MAF > 0.1. The eQTL effects were assigned randomly to 35% (n = 439) of the genes being simulated with eSNPs having a MAF > 0.1 and being within 100 kb of their eGenes. The number of cells to simulate per donor was sampled from a gamma distribution estimated from the number of cells per donor from the empirical data. Data was simulated for genes from chromosome 2 (n = 1255) for 87 individuals in the British cohort for the Smart-Seq2 simulations and for 173 individuals from the European cohorts from 1000Genomes [Two types of population-scale scRNA-seq datasets (iPSC Smart-Seq2 and FPP 10X) were simulated using splatPop  from spl0Genomes . For theTo mimic the experimental design of the empirical datasets, cells were simulated in batches with 5 (iPSC Smart-Seq2) or 24 FPP 10X) individuals per batch, where 28% (iPSC Smart-Seq2) or 14% (FPP 10X) of individuals were replicated in two batches, reflecting the pooling sizes and replication rates in the empirical data. Batch effect sizes were sampled from log-Normal distributions with location = 0.001 and scale = 0.12 (iPSC and FPP) and with the splatPop parameter similarity.scale = 5 (iPSC Smart-Seq2) or similarity.scale = 22 (FPP 10X) set to reflect the batch effects and relationship between samples observed in the real data  if the simulated eSNP was among the significant eSNPs or it was considered a false negative (FN). Note that the FN includes eGenes with no significant eSNPs (type 1) and eGenes with significant eSNPs, but for which the correct eSNP was not significant (type 2). Each simulated gene not assigned an eQTL effect was considered a true negative (TN) if it had no significant eSNPs and a false positive (FP) if it had a significant hit. Performance metrics include power (TP / (TP + FN)), empirical FDR (FP / (FP + TP)), and the Pearson’s correlation between simulated eQTL effect sizes and estimated effect sizes . Statistical tests were performed in Rv4.0.3 and results are described in detail in Additional file Single-cell normalization and log2 transformation of normalized counts-per-million were performed on raw single-cell counts using scran version: 1.14.1)  with siz.14.1  wiIn all cases, aggregation was performed at two levels of batch Tables  and 2. FHighly variable genes (HVGs) were defined as the genes in the top two quartiles based on their squared coefficient of variation (CV^2 = variance / mean^2) calculated across all cells of each different cell-type. In this manner, we identified 21,592 HVGs for the iPSC Smart-Seq2 dataset, and 16,369 for the FPP 10X dataset.cis-eQTL mapping, we followed Cuomo et al. [For o et al.  and adopo et al. . The expcis regulatory variant on the expression of one gene, thus accounting for global batch effects in our LMMs will not remove the signal for eQTL. Unless specified otherwise, in our sc-eQTL mapping, we included the first 20 principal components calculated on the relevant expression value aggregation/normalization in the model as fixed effect covariates. However, in order to assess the impact of variations in the types and numbers of covariates included, we also considered alternative methods; first, we considered Probabilistic Estimation of Expression Residuals (PEER (version: 1.3) [To adjust for experimental batch effects across samples, we included covariates as fixed effects in our LMM models to correct for both known and hidden sources of unwanted variation. These covariates  usually affect the expression of many genes and therefore are detectable in the principal components of expression. Furthermore, global batch effects are orthogonal to the effects of a single on: 1.3) , a methoon: 1.3) , 40). Whon: 1.3) , 40). Thhttps://github.com/PMBio/peer/) was run using the R implementation with the number of factors set to 30 and default parameters. MOFA v2 (https://biofam.github.io/MOFA2/) was run using the R implementation, for n = 25 factors. Both default parameters  and parameters with the sparsity constraints removed  were used.All covariates  were calculated using the relevant aggregated expression values at the level of donor or donor-sequencing run, for all genes tested . PCA was computed using the R function prcomp. PEER (https://www.scvi-tools.org/en/stable/user_guide/notebooks/linear_decoder.html) but changed it to estimate 25 factors over 1000 epochs with 400 warmup epochs. After training, we extracted the LDVAE factors and aggregated the factors over the cells by taking the mean per donor or donor run.Finally, we included a recent approach using variational autoencoders: linearly decoded variational autoencoder  ). In conIn order to account for possible population substructure in the sample considered as well as, importantly, for repeated observations for the same donor , we include in the linear mixed model a random effect modeling such structure :K is a random variable such that uK ~ N, where K is a kinship matrix, specifically a SNP-based identity by descent (IBD) matrix estimated using PLINK (version 1.9) [where Y is the expression of the gene considered , covs are the expression covariates (described above); SNP is the vector of allele values for the variant being tested; uion 1.9) , and ε iImportantly, when considering “dr” aggregation methods (as well as the direct single cell eQTL mapping), this kinship matrix K is expanded to reflect the repeated samples .Additionally, we tested the effect of the inclusion of a noise term in the LMM that accounts for the variable number of cells used to calculate aggregate expression measures, which has been shown to improve eQTL discovery power . Here weK and ε are defined as above; uncells is a random variable such that uncells ~ N), where diag(1/ncells) is a diagonal matrix with the inverse of the number of cells per donor as the diagonal terms and, analogously, unreads ~ N).where Y, covs, SNP, uFor dr-mean, since we have replicate measurements for each donor (across sequencing runs), we always need to account for population structure (and replicated structure), by including the (expanded) kinship matrix as a random effect. As such, we test the following models:w, between 0 and 1) to incorporate the relative weight of the two random effect term matrices, e.g., w × 1/ncells + (1 − w) × K. We optimized w using a grid search per gene , where Multiple testing correction for the eQTL results was performed in two steps, as is common practice in eQTL studies , 47. Firi} and Q = {Qi} are two sets of random variables, representing the observed p values from the internal  and external  hypothesis tests, respectively. Traditionally the cFDR is approximated with the empirical joint cumulative distribution function , ...} that defines the region where H0 will be rejected. However, this approach does not explicitly control FDR. Here we apply an adjusted estimator of cFDR proposed and described in detail by Liley and Wallace [i, qi), the adjusted estimator adds randomly chosen points  to the original p value pair space . Then the “transformed” p value is calculated as the probability that the randomly chosen points had a more extreme cFDR than the point of interest . Ultimately, with this approach, we are able to define a better  H0 rejection region than what would be defined by strict thresholds for P and Q, and thus better integrate the external information into the statistical test.Conditional FDR is a method to condition or transform the p value from one hypothesis test using an external set of test statistics. For a series of hypothesis tests , P = {Psee Fig. a and is  Wallace , which wPerformance of sc-eQTL mapping was measured using two metrics: number of significant sc-eQTL  and replication with bulk eQTL. A sc-eQTL  was considered to replicate a-bulk eQTL if it passed two criteria: (1) the gene-SNP pair was significant in the bulk test at FDR < 10% and (2) the direction of the effect was consistent between the sc-eQTL and the bulk eQTL.Note that a small percentage (~ 1%) of non-replicated sc-eQTL in bulk were due to the gene and/or the SNP of interest not being assessed in the bulk eQTL results.Additional file 1: Table S1-S4 & S6-S16 and Fig S1-S15. Supplementary tables and figures including legends.Additional file 2: Table S5. Simulation Smart-Seq2 & 10X QTL mapping results.Additional file 3. Review history.


This approach allows for the natural integration of multi-layer representations of data in a given network from which one derives clusters via a hierarchical clustering approach. In this study, we applied the methodology to multi-omics data from the two largest breast cancer studies. The resultant clusters showed significantly different survival rates in Kaplan-Meier analysis in both datasets. CIBERSORT scores were compared among the identified clusters. Out of the 22 CIBERSORT immune cell types, 9 were commonly significantly different in both datasets, suggesting the difference of tumor immune microenvironment in the clusters. vWCluster can aggregate multi-omics data represented as a vectorial form in a network with multiple layers, taking into account the concordant effect of heterogeneous data, and further identify subgroups of tumors in terms of mortality.In this paper, we present a network-based clustering method, called  The clustering results are shown in The clustering results were compared with PAM50 subtypes. The associations were assessed using a chi-squared test, resulting in p < 0.0001 as shown in For the four clusters identified, 22 CIBERSORT immune cell types were compared using the one-way ANOVA test. Fifteen immune cell types were statistically significantly different among the four clusters. The most significant immune cell type was M0 macrophages with p = 1.51E-08 .Nine immune cell types were significantly different in both METABRIC and TCGA studies: memory B cells, resting memory CD4 T cells, follicular helper T cells, monocytes, M0 macrophages, M1 Macrophages, resting dendritic cells, activated dendritic cells, and resting mast cells . Three iThe clustering performance of vWCluster was compared with that of SNF using gene expression and CNV data for METABRIC and gene expression, CNV, and methylation data for TCGA breast cancer. Kaplan-Meier analysis for the resulting four clusters from SNF yielded statistical significance for METABRIC with a log-rank p < 0.0001, but for TCGA breast cancer, the log-rank test was statistically insignificant with p = 0.13 .We also compared vWCluster with SNF for five clusters. For METABRIC, both methods resulted in significant log-rank p-values with p < 0.0001. For TCGA breast cancer, vWCluster resulted in statistical significance with p = 0.013, which was slightly worse than that of four clusters, whereas the p-value of SNF for five clusters remained statistically insignificant with p = 0.0884.The treatment of multi-omic biological data in a vector-valued manner may provide new insights for understanding the biological mechanisms of cancer biology, using complementary information offered by individual omics types. vWCluster is a data analysis methodology based on OMT theory, which enables the integration of multi-omics data in a vector-valued form, represented by multiple layers in a network. The Wasserstein distance computed on the vector-valued data was further employed to identify cancer subtypes. We applied this method to the two largest breast cancer studies, METABRIC and TCGA. The clusters identified showed significantly different survival rates in both studies.vWCluster identified cluster 4 as a poor survival group in both METABRIC and TCGA breast cancer studies and cluster 2 and cluster 1 as a good survival group in METABRIC and TCGA breast cancer, respectively. In both studies, the poor survival group was enriched for basal subtype and the good survival group was enriched for Luminal A subtype. This is consistent with the clinical findings that in general, the triple-negative/basal-like subtype has a poor prognosis  while thet al. [CIBERSORT scores, consisting of 22 immune cell types, were further compared among the identified clusters. CIBERSORT employs gene expression profiles from a set of 547 genes to predict 22 immune cell types, using support vector regression . ANOVA tet al.  investigKaplan-Meier analysis was performed for intrinsic molecular subtypes in the METABRIC and TCGA breast cancer studies . As in Ket al. [et al. [Prior to this study, Chen et al.  and Ryu  [et al.  introducWe proposed a multi-omics data integration and clustering method, called vWCluster, based on the vector-valued Wasserstein distance. In this method, individual omics types represented as multiple layers in a network can be efficiently integrated, considering the biological interactions of biomarkers and providing complementary biological information. The formulation of vWCluster treats the data vectorially, which potentially minimizes information loss. vWCluster is flexible and applicable to the integration of multi-modal data including imaging and genomic data, which is a research direction we plan to explore in the future.S1 Fig(PDF)Click here for additional data file.


A transcriptome‐wide association study (TWAS) attempts to identify disease associated genes by imputing gene expression into a genome‐wide association study (GWAS) using an expression quantitative trait loci (eQTL) data set and then testing for associations with a trait of interest. Regulatory processes may be shared across related tissues and one natural extension of TWAS is harnessing cross‐tissue correlation in gene expression to improve prediction accuracy. Here, we studied multi‐tissue extensions of lasso regression and random forests (RF), joint lasso and RF‐MTL (multi‐task learning RF), respectively. We found that, on our chosen eQTL data set, multi‐tissue methods were generally more accurate than their single‐tissue counterparts, with RF‐MTL performing the best. Simulations showed that these benefits generally translated into more associated genes identified, although highlighted that joint lasso had a tendency to erroneously identify genes in one tissue if there existed an eQTL signal for that gene in another. Applying the four methods to a type 1 diabetes GWAS, we found that multi‐tissue methods found more unique associated genes for most of the tissues considered. We conclude that multi‐tissue methods are competitive and, for some cell types, superior to single‐tissue approaches and hold much promise for TWAS studies. Only probes that have at least one cell type with a nominally associated cis‐SNP  depending on how similar these subgroups are with respect to a given dissimilarity measure.Joint lasso is a type of linear regularized regression that handles multiple data sets simultaneously by estimating different regression coefficients for different tissues while encouraging coefficients of similar tissues to be closer. This is done by introducing an extra regularization term penalizing difference between coefficients of different subgroups  via external fivefold cross‐validation and for the general penalty parameter λ via an in‐built cv.glmnet internal 10‐fold cross‐validation described above . The sequence of γ values was taken as in the authors' example code (http://fhm-chicas-code.lancs.ac.uk/dondelin/SubgroupFusionPrediction). For any probe and two tissues i and j we set group‐specific penalty τij to ρij/maxk≠l{ρkl}, where ρij is the correlation between expression in tissues i and jin the Fairfax data set. However, in Dondelinger and Mukherjee . We used the ranger function in the ranger R package to fit RF.RF is an ensemble tree‐based nonparametric method and requires relatively little tuning: the optimal number of trees is determined by assessing out of bag error as the forest is grown (we grew 500 trees which was sufficient for convergence) while it has been suggested that regulating depth of the trees  has limited benefits . This way, the size of the training data was increased and subsets corresponding to different tissues could be separated or pulled together (via tree branching) depending on their dissimilarity or similarity, respectively. For genes with highly correlated expression values across different cell types, the id variable tends to be less important , the whole data set being treated as homogeneous. For genes exhibiting less or no correlation across different cell types, the id variable would split samples into different subsets forcing them into separate end nodes.The id variable was available for splitting at each iteration of the RF algorithm For RF‐MTL, the pooled approach should cater for situations when the underlying sub‐data sets have a varying degree of similarity. Pooling completely homogeneous  datasets, should not adversely affect performance as the tissue id variable, although available as a splitting variable at every split, does not have to be used if it does not help reduce residual variance for a given tree. Strong differences between sub‐groups, on the other hand, should be handled by the use of the tissue id variable at various splits, effectively separating samples into homogeneous subsets. Thus arguing, we of course assume that similarities/dissimilarities between different subgroups are reflected in similarities/dissimilarities of their respective distributions over features.2.1.5Models were trained on a training set and evaluated on a test set, comprising roughly 70% and 30% of the data, respectively. To avoid information leaking in the MTL set‐up, all samples from the same individual were designated to either the training or the test set.R2 as a measure of predictive accuracy of different models. For a predictive model f, R2 is informally known as the “proportion of the variance explained” by f and is defined as:f(xi) is prediction at point xi, y¯ is sample mean of outcome y, σˆ2 is y's sample variance and MSE is mean square error. Note that the above fraction is a measure of how well f does compared to the “base” constant model g(xi)=y¯, ∀i. One would expect a “good” model to have small MSE compared to σˆ2, and hence larger R2. Conversely, a “bad” model would have a larger MSE and smaller R2, with a truly hopeless model performing en par with a constant mean predictive function. Note also that, while the phrase “proportion of variance explained” suggests a value of R2 in the interval , in reality the definition above does not put any such restriction on R2. Indeed, a heavily overfitting model, or that trained and tested on data coming from vastly different distributions, can produce large negative R2 values.We used m1 and m2, trained and validated on the same data sets with respective R2R,R2Rm12 and Rm22, we say that m1 has an advantage over m2 if Rm12>0 and Rm12>Rm22. This advantage is quantified by Rm12−max{0,Rm22}. The average advantage of m1 over m2 is calculated over a set of regression problems to which both methods are applied and m1 has an advantage over m2. In essence, the average advantage indicates by how much on average method m1 is more accurate than method m2 for problems, where m1 does outperform m2.For two methods, 2.2We assessed the performance of each eQTL prediction method for TWAS in a simulation framework. Within each simulation, we simulated separate eQTL and GWAS data sets. For each data set, we first sampled independently 400 pairs of haplotypes from the 1000 Genomes EUR subset to generate genotype data, and sampled causal variants independently from among the SNPs.∑iβijGi, where i indexes causal variants, j indexes traits, and βij is the effect size of variant i on trait j and Gi the genotype vector at variant i. To avoid too many simulations with small beta and nonsignificant effects, βi was sampled as the maximum of five Gaussians with variance 0.04. The first expression trait was assigned as the trait to be tested via TWAS, and the remainder as additional “background” expression traits. Each expression trait was regressed against all SNPs, and the simulation retained if the minimum p value over all SNPs and expression traits was less than 10−7.For the eQTL (GWAS) data sets, 5 (1) quantitative traits were simulated, respectively, as Gaussian variables with variance 1 and mean 1.learn a predictive model in the eQTL data set2.predict values for the first expression trait into the GWAS data set3.test association between the GWAS trait and the predicted expression trait in the GWAS data set using linear regressionWe conducted TWAS with each of the four methods described above, following the steps:p value from this test.and retained the The aim of TWAS is to associate genes and diseases. Although association can be thought necessary for causation, it is not sufficient , and selected the PCs accounting for at least 80% of the variation, or the first six PCs, whichever number is the smallest.To reduce the degrees of freedom of the test, proportionality testing works by first finding principal components (PCs) of the genotype matrix accounting for the majority of the variation , and then regressing the two traits on these PCs. Finally, a null hypothesis that the two sets of coefficients are proportional  is tested (at 0.05 significance level). To reduce the number of PCs used, we only used SNPs with GWAS or eQTL p value, pf. We assessed TWAS performance according to the proportion of simulations that gave a TWAS p < .05, before and after filtering at fp < .05.We ran proportional filtering on each simulated data set, and stored its 2.3predicted gene expression for GWAS individuals. Note that for the joint lasso and the RF‐MTL methods, only one model is needed for each probe, rather than one model for each probe/cell pair. To obtain predictions for a particular cell type, genotypic data was fed to the model together with the id variable indicating which tissue type we would like a prediction for. We then tested for association between the imputed expression levels and the disease status of the individuals in the GWAS data set, to see which probes/genes are differentially expressed. We used the Cochran‐Armitage test  as a cell‐probe‐method triplet for which predicted expression has a significant fold change, that is, an FDR‐adjusted Cochran‐Armitage test p < .05.To compare performance of the predictive methods in a real‐world data set, we retrained the models on the whole eQTL data (as opposed to 70% training set) and used them to impute (predict) gene expression into a large T1D GWAS cohort .We then passed all the TWAS‐significant hits through the proportionality filter, described above. Thirteen out of 224 TWAS‐significant probe–cell pairs (corresponding to six probes) did not have enough SNPs with sufficiently small 33.1We started by assessing single‐tissue models. Among the linear methods, ridge regression strictly underperformed compared to lasso and elastic net which performed similarly to each other, with lasso slightly preferred Figure , suggestR2 but lasso fails to produce a useful model (negative R2).RF outperformed lasso in the overwhelming majority of regressions with mean advantage , resulting in the same number of regressions (each able to predict expression for five cell types).We compared MTL extensions of lasso and RF to each other and to the reference models fitted on individual tissue types (STL). We considered the same 4288 probes for which at least one cell type has a nominally associated Joint lasso outperforms standard lasso in the absolute majority of cases Figure . However3.3To assess the performance of the four methods as part of the complete two‐stage TWAS procedure, we simulated GWAS‐trait and gene expression data for five cell types under several genetic causal scenarios. Generally, when colocalised GWAS and eQTL signals were simulated, multi‐trait methods outperformed single‐trait methods when eQTL variants were shared between the test and background expression traits, and single‐trait methods performed slightly better when there was no sharing, though the difference was more pronounced in the former versus the latter ; see Figure p<10−5) in this study, have been robustly associated with T1D in other studies, including CLECL1  and searching for associated diseases, excluding genetic association data from the data types included to avoid circular reasoning. We ranked the diseases listed according to their relevance p value, and found that the RF‐based gene lists ranked more obviously T1D‐related diseases higher than lasso‐based gene lists  produces biased solutions (unlike standard linear regression) with the resulting coefficients biased towards zero, accepting this cost to generate predictions with lower variance. Random forest, on the other hand, produces a low‐bias model but higher variance predictions . Thus, we might expect them to also be differentially expressed between cases and controls in a hypothetical study in which expression is measured directly. Therefore, we suggest such genes might be considered as biomarkers rather than red herrings. Even TWAS‐hits passing colocalisation tests can be validated only through practical lab‐based experiments.In this study, we demonstrated the applicability of nonlinear and multi‐tissue methods in the TWAS framework. Both real data and simulation studies showed, in particular, that RF is at least as competitive and, for some tissue types, superior to lasso. Similarly, RF‐MTL is superior to RF for some tissue combinations, whilst joint lasso identifies more unique SP‐hits than lasso for all the tissue types. Our results highlight the potential to exploit multiple tissue‐eQTL studies in TWAS but we expect this to be most useful when tissues are closely related, so that information may be legitimately borrowed between tissues.R using glmnet for lasso and elastic net, ranger for RF and RF‐MTL, and fuser and bespoke helper functions https://github.com/stas-g/fuser_helper for the joint lasso. coloc package was used for the post‐hoc colocalisation analysis. All simulation code is available from https://github.com/chr1swallace/twas-sims.All analysis was done in Supporting information.Click here for additional data file.Supporting information.Click here for additional data file.Supporting information.Click here for additional data file.Supporting information.Click here for additional data file.Supporting information.Click here for additional data file.Supporting information.Click here for additional data file.Supporting information.Click here for additional data file.Supporting information.Click here for additional data file.Supporting information.Click here for additional data file.Supporting information.Click here for additional data file.


Colorectal cancer (CRC) carcinogenesis is generally the result of the sequential mutation and deletion of various genes; this is known as the normal mucosa–adenoma–carcinoma sequence. The aim of this study was to develop a predictor-classifier during the “adenoma-carcinoma” sequence using microarray gene expression profiles of primary CRC, adenoma, and normal colon epithelial tissues. Four gene expression profiles from the Gene Expression Omnibus database, containing 465 samples , were preprocessed to identify differentially expressed genes (DEGs) between adenoma tissue and primary CRC. The feature selection procedure, using the sequential Boruta algorithm and Stepwise Regression, determined 56 highly important genes. K-Means methods showed that, using the selected 56 DEGs, the three groups were clearly separate. The classification was performed with machine learning algorithms such as Linear Model (LM), Random Forest (RF), k-Nearest Neighbors (k-NN), and Artificial Neural Network (ANN). The best classification method in terms of accuracy (88.06 ± 0.70) and AUC (92.04 ± 0.47) was k-NN. To confirm the relevance of the predictive models, we applied the four models on a validation cohort: the k-NN model remained the best model in terms of performance, with 91.11% accuracy. Among the 56 DEGs, we identified 17 genes with an ascending or descending trend through the normal mucosa–adenoma–carcinoma sequence. Moreover, using the survival information of the TCGA database, we selected six DEGs related to patient prognosis . The six-gene-based classifier described in the current study could be used as a potential biomarker for the early diagnosis of CRC. Colorectal cancer (CRC) is the third most common malignancy among adults and the second leading cause of cancer-related death worldwide . CRC devAdvances in omics technologies have helped to better understand the molecular mechanisms that discriminate normal and tumor tissues at gene, mRNA, and protein levels. Genome-wide gene expression profiling analyses of large numbers of multiple CRC tumor specimens have enabled the classification of four consensus molecular sequences (CMS1–4) with a distinct biology and gene expression patterns, and subtypes characterized by mixed transition features . A compaThe aim of the present study was to develop a predictor-classifier of the stages during the “adenoma-carcinoma” sequence using microarray gene expression profiles of primary CRC, adenoma, and normal colon epithelial tissues. Our study is based on the generation of a decision support system to evaluate the existence of a relationship between the gene expression in the three groups. Our hypothesis is that gene expression could accurately predict not only the presence of the disease, but also its severity (Adenoma or CRC).https://www.ncbi.nlm.nih.gov/geo, accessed on 24 May 2021) [GSE100179: based on GPL17586 Affymetrix Human Transcriptome Array 2.0 platform . Biopsy samples were collected from 20 healthy colon biopsies (Control), 20 colorectal adenoma (Adenoma) and 20 colorectal cancer (CRC) tissues during routine colonoscopy ;GSE117606: based on GPL25373 Affymetrix HT HG-U133+ PM Array Plate. FFPE samples derived from patients with adenoma, tumor, and adjacent tissues. This set contained 71 CRC, 62 Adenoma, and 65 Control samples ;GSE4183: based on GPL570 Affymetrix Human Genome U133 Plus 2.0 Array. Total RNA was extracted, amplified and biotinylated from frozen colonic biopsies of 15 patients with CRC, 15 with Adenoma, and 8 healthy Controls [GSE71187: based on GPL6480 Agilent-014850 Whole Human Genome Microarray 4x44K G4112F . This set contained 99 CRC, 58 Adenoma, and 12 Control samples [We analyzed raw microarray data in four different datasets downloaded from the Gene Expression Omnibus database  :GSE10017In short, the merged dataset consists of 465 samples divided into three cohorts: 105 samples as Healthy Controls; 155 samples in the Adenoma Group; 205 samples in the CRC Group.Data preprocessing;Differential Expression Analysis;Feature Selection;Unsupervised learning: Cluster analysis;Supervised learning: Comparison of different classifiers;Test of the most performing learning model.To evaluate a possible relation between the gene expression in the three groups, we proposed an approach based on six main steps, shown in https://www.r-project.org/, accessed on 24 May 2021) [To read and preprocess raw data, different packages in the framework R (ay 2021)  were useay 2021)  package.ay 2021) , which iay 2021)  package.t-test, included in the ‘limma’ [Differential expression analysis was performed to find genes that are differentially expressed (DEG) in different conditions. To study DEGs, an unpaired  ‘limma’  package  ‘limma’ . In thisStarting from the list of DEGs for all comparisons, we performed a feature selection procedure to select a subset of relevant features for the construction of the model. To select the best DEGs on which to develop a gene-based classifier for the adenoma–CRC evolution, we based the feature selection on the sequential use of two algorithms: firstly Boruta  and thenFollowing the feature selection procedure, we studied the dataset homogeneity through an unsupervised learning method. This approach permits associations and patterns among the set of input variables to be identified, verifying whether gene expression is able to characterize the three groups. Here, we used the clustering procedure for the unsupervised learning method. In detail, we performed the clustering procedure in three steps, as follows: (1) Optimal cluster number k evaluation: Silhouette , Within This procedure had been implemented using the ‘factoextra’  package Simultaneous to the clustering analysis, we implemented Supervised Learning methods, analyzing the training data that produce an inferred function, which can be used for mapping new examples. We compared the performances of four different classifier algorithms: Linear Model (LM), Random Forest (RF), k-Nearest Neighbors (k-NN), and Artificial Neural Network (ANN). They had been fed with the features selected by the Stepwise Regression algorithm. We started with a linear hypothesis and then applied three different machine learning algorithms based on different fundamental units, decision trees for RF, and artificial neuron for ANN. In order to build a robust classifier, we randomly chose 90 observables (about 20% of the entire dataset), 30 for each class, and used them as validation set. The four predictors were built on the remaining 375 observables.The classification models had been implemented using basic R package for LM, ‘randomForest’  package The robustness of the classifier was verified by performing a cross-validation technique. Specifically, we applied k-fold cross-validation that randomly partitioned the original sample into k equal-sized subsamples. On the k subsamples, a single subsample was retained as the validation datum for testing the model, and the remaining k-1 subsamples are used as training data. The cross-validation process was then repeated several times, with each of the k subsamples being used once as the validation datum. The four models had been compared by applying a common five-fold cross-validation, repeated 300 times in order to avoid overfitting and to evaluate the model stability. The validation set was used to confirm the transferability and goodness of the classifier.The performances of the classification model were assessed with the following parameters:The Area Under the Curve (AUC) is the measure of the ability of a classifier to distinguish between classes and is used as a summary of the ROC curve, estimated by the ‘multiclass.roc’ function from the ‘pROC’  package iTP, iTN, iFP, iFN as true positives, true negatives, false positives, and false negatives, respectively, in a classification problem with N classes, the accuracy for the class i is defined as:Accuracy is the proportion of true results among the total number of cases examined. We defined The total accuracy is the average of all class values; it was evaluated by the function ‘Accuracy’ from the ‘ML metrics’  package Sensitivity is the fraction of the total amount of relevant observations that were actually retrieved:Precision is the fraction of relevant observations among the retrieved observations:F1 score is the harmonic mean of precision and sensitivity: ‘caret’  package In order to detect significant differences among the four algorithms, the Kruskal–Wallis test  was applhttps://www.gsea-msigdb.org/gsea/index.jsp, accessed on 24 May 2021) using the hallmark gene sets of the Molecular Signature Database gene set version 7.4 [https://biit.cs.ut.ee/gprofiler/gost, accessed on 24 May 2021) [Gene Set Enrichment Analysis (GESA) was performed with GSEA software version 4.1.0 (sion 7.4 . Phenotyay 2021) .Before using the feature selection algorithms, we performed differential expression analysis to screen the dataset. According to the cut-off criteria |log2FC| ≥ 0.263 and (FDR) < 0.05, there were 11,530 genes identified as differentially expressed, of which 7794 were in the comparison CRC vs. Controls, 7434 were in the comparison Adenoma vs. Controls, and 7825 were in the comparison CRC vs. Adenoma.Subsequently, wrapper methods were implemented for a more accurate selection. The first selection was done through the Boruta algorithm, which identified 240 important features. Then, Stepwise Regression was applied to the Boruta selected features. A total of 56 DEGs were selected as the optimal genes discriminating the three groups .We firstly evaluated the optimal cluster number k through three different methods: Silhouette, Within Cluster Sum of Squares, Gap Statistic Method. For all the algorithms used, the optimal number of clusters was three . Then, fThis separateness was also confirmed by displaying the correlations among the selected 56 DEGs using the K-Means method .Finally, we evaluated the correct assignment of the samples in each cohort, comparing the cluster label assigned by the K-Means algorithm to the true label of the sample. As shown in F1 Score . A five-fold cross-validation analysis repeated 300 times was computed. Then, we compared the performance of the LM, RF, k-NN, and ANN models based on Accuracy, AUC, Sensitivity, Precision, and F1 Score .Classification performances of each model are summarized in The best classification method in terms of accuracy (88.06 ± 0.70) and AUC (92.04 ± 0.47) was k-NN. RF and ANN methods showed similar values, while the LM model was the least-performing method. In addition, we evaluated the consistency of the prediction of all four classifiers by means of pairwise contingency tables . InteresMoreover, the Kruskall–Wallis test computed to study potential significant differences among the tested methodologies revealed no significant differences.F1 score values proved that the best performance was the k-NN model.Finally, to confirm the relevance of the predictive models, we applied the four models on the validation cohorts composed of 90 samples, 30 randomly selected subjects for each group. As shown in Functional enrichment analysis on 56 DEGs with GSEA (FDR < 0.05) showed that DEGs were enriched in the following hallmarks: epithelial mesenchymal transition, hypoxia, angiogenesis, hedgehog signaling, IL2/STAT5 signaling, KRAS signaling .Moreover, Gene Ontology analysis for cellular component showed that the most significant were enriched in cell junctions and microvillus membranes .In order to find the most significant biomarkers able to discriminate adenoma status from CRC, 56 DEGs were further filtered. The dataset was firstly normalized between 0 and 1; then, mean expression values in the three groups were evaluated for each gene. We identified the genes with a changed expression in a stepwise manner during the normal-adenoma–carcinoma sequence. Starting from 56 DEGs, we selected 17 genes . Then, wCRC is characterized by variations in the molecular profile during the disease progression . PreviouIn recent years, the application of machine learning algorithms has provided new insight into early cancer detection ,40,41. IMachine learning algorithms, namely Linear Model (LM), Random Forest (RF), k-Nearest Neighbors (k-NN), and Artificial Neural Network (ANN), were also used to classify the samples. The k-NN model was the best performing method in terms of accuracy and AUC. The other three provide a good performance, although RF and ANN provide a better performance compared to LM.Similarly, the predictor-classifier demonstrated a high accuracy in the validation sets. In fact, the k-NN model still remained the best model in terms of performance, with an accuracy of 91.11%, and its accuracy was pretty balanced for the three classes. These results suggest that the classifier is robust. Thus, our study may establish a basis for further research into the early diagnosis of CRC.Functional enrichment analysis on 56 DEGs revealed that they are involved in the epithelial mesenchymal transition, hypoxia, angiogenesis, hedgehog signaling, IL2/STAT5 signaling, KRAS signaling. Moreover, Gene Ontology analysis for cellular components showed that the most significant were enriched in cell junctions and microvillus membranes.Among the 56 DEGs, we identified 17 genes with an ascending or descending trend through the normal mucosa–adenoma–carcinoma sequence. In addition, using the survival information of the TCGA database, we selected six DEGs related to patient prognosis. A functional review of the selected genes  demonstrated that all of them are reported to be related to the pathogenesis of CRC. In scavenger receptor class A, member 5 (SCARA5) is a tumor suppressor gene that was downregulated in many cancer types, including CRC ,43,44. PThis study indicates novel potential targets for the early diagnosis of CRC. Nevertheless, it has several limitations. Firstly, the study was based on a small sample size; future studies are needed to validate and improve the predictor-classifier in a larger multicenter prospective patient cohort. Secondly, data in the GEO database were derived from different experimental platforms, and although internal standardization has been performed, the results could be heterogeneous. Thirdly, our results were based on tissue samples, which still remains an invasive procedure. Future investigations are needed to demonstrate whether the identified genes could be detected in blood to allow a non-invasive diagnosis and prognosis of the disease.In conclusion, we identified six DEGs involved in the normal colorectal mucosa–adenoma–carcinoma sequence associated with CRC patients’ prognosis. Our results demonstrate the robust diagnostic performance of the gene-based classifier in the training and validation cohorts, confirming its potential clinical value. These findings may help to elucidate the molecular mechanisms involved in the onset and development of CRC, providing the basis for the identification of potential biomarkers for early diagnosis and of new therapeutic targets.