Predicting cancer cells’ response to a plant-derived agent is critical for the drug discovery process. Recently transcriptomes advancements have provided an opportunity to identify regulatory signatures to predict drug activity. Here in this study, a combination of meta-analysis and machine learning models have been used to determine regulatory signatures focusing on differentially expressed transcription factors (TFs) of herbal components on cancer cells. In order to increase the size of the dataset, six datasets were combined in a meta-analysis from studies that had evaluated the gene expression in cancer cell lines before and after herbal extract treatments. Then, categorical feature analysis based on the machine learning methods was applied to examine transcription factors in order to find the best signature/pattern capable of discriminating between control and treated groups. It was found that this integrative approach could recognize the combination of TFs as predictive biomarkers. It was observed that the random forest (RF) model produced the best combination rules, including AIP/TFE3/VGLL4/ID1 and AIP/ZNF7/DXO with the highest modulating capacity. As the RF algorithm combines the output of many trees to set up an ultimate model, its predictive rules are more accurate and reproducible than other trees. The discovered regulatory signature suggests an effective procedure to figure out the efficacy of investigational herbal compounds on particular cells in the drug discovery process. Plants are an important source of novel pharmacologically active compounds with many novel drugs. Approximately 25% of natural-based medicines were directly or indirectly derived from medicinal herbs . For insDetecting new and innovative drugs from natural resources remains a complicated, time-consuming, and expensive project ,4. RecenAmong the genetic factors, TFs play a crucial role in several cellular functions, such as cell growth and development, response to changes in their internal and external environment, and control of cell cycle and carcinogenesis. TFs bind to the promoter region of genes and regulate the transcription of DNA fragments to RNA messenger. Their primary responsibility is governing gene expression in the correct cell at the precise time .Regarding the cancer area, transcriptional dysregulation triggers many disorders leading to tumor progression and drug resistance acquisition . Such dyEffective computational methods and a substantial number of samples are required to identify predictive biomarkers. Such biomarkers help to evaluate the drug effectiveness, which in turn helps reduce time and money spent on curing diseases ,15. AdvaHowever, exploring biomarker genes using individual transcriptomic studies is challenging due to low replications, low data repeatability, and significant prediction error. Meta-analysis is a way to deal with these deficiencies by gathering as much relevant data as possible from a range of available experiments . MergingAdditionally, the availability of transcriptomic datasets in gene expression databases rendered a significant opportunity to apply machine learning models to predict drug activity. Recently, supervised machine learning models are frequently being employed on the omics data to identify ‘druggable’ genetic targets and drug response-predictive biomarkers ,19. SupeA decision tree is an efficient method for pattern discovery analysis in data mining algorithms . It creaIn this study, machine learning methods, namely here decision tree (DT), gradient boosted tree (GBT), and RF, have been applied to analyze multiple datasets, which are combined in a meta-analysis from studies that evaluate gene expression in multiple cancer cell lines before and after different herbal extract treatments, focusing on differentially expressed transcription factors in the meta-analysis. Transcription factors were then used to train and build models to predict whether or not a sample belonged to the control or the treated group and identify a signature/pattern of transcription factors capable of discriminating between the groups.The presented flowchart in https://www.ncbi.nlm.nih.gov/sra accessed on 1 May 2020), EMBL-EBI (https://www.ebi.ac.uk/ena accessed on 1 May 2020), and DRA–DDBJ (https://www.ddbj.nig.ac.jp/dra/index-e.html accessed on 1 May 2020) were used as repositories of high-throughput expression. Datasets in connection with the effect of herbal compounds on inhibiting cancer cells were gathered for meta-analysis. Our target studies contained 88 samples: 36 control and 52 treated. Details of experiments are as follows: PCa treated via Wedelia chinensis extract (WCE)  (GEO accession number GSE99820), HCT116, SW480, SW620, HT29, and RKO cell lines treated via oligomeric proanthocyanidins (OPC) from grape seeds and grape seed extract (GSE)  (GEO accession number GSE109607), A549 cell line treated via jinfukang (JFK)  (accession number ERP015444), MCF-7 cell line treated via compound kushen injection (CKI)  (GEO accession number GSE78512), LNCaP and PC-3 cell lines treated via Sulforaphane (SFN)  (GEO accession number GSE48812), SK-BR-3, MCF-7, and MDA-MB-231 cell lines treated via shikonin  (GEO accession number GSE100687). Other details about the collected datasets are represented in Three databases including SRA–NCBI . Briefly, after finding the raw reads’ quality, those with low quality were trimmed off. Then, high-quality short reads were mapped into the human reference genome (hg19) using the following criteria: mismatch cost = 2, insertion cost = 3, deletion cost = 3, length fraction = 80%, and similarity fraction = 80%. RPKM index (reads per kilobase of transcript per million mapped reads) was reported as expression estimations for every gene and used as inputs for meta-analysis. Meta-analysis was implemented in R program (version 3.6.0) using the Meta-Seq package (version 1.22.1). This package uses NOISeq to detect genes that are differentially expressed. The number of reads is often different depending on the studies, and this generally influences statistical tests. NOISeq is almost not affected by the number of reads and helps overcome the read size effect bias [ect bias .Next, the overall statistical significance was calculated using Fisher’s probability test, and the identified genes were named meta-genes.The obtained meta-genes were classified based on their function . Finally, the TFs introduced by meta-analysis (named as meta-TFs) were considered for the following analysis.http://david.abcc.ncifcrf.gov/home.jsp accessed on 10 October 2020) was used to obtain a complete set of the biological importance of meta-TFs. This database employs the p-value and Benjamini methods to determine the significance of pathways of input TFs.DAVID classification system  and 88 records (samples) belonging to treatment and control categories (label variable) was prepared. Then, GBT, DT, and RF models were run on the dataset by rapidminer software (RapidMiner 9.7). DT and RF models contained two different criteria, including accuracy and gain ratio.In this study, trees were constructed using a ten-fold cross-validation algorithm to assess the models’ performance for predicting the correct class. To conduct ten-fold cross-validation, the dataset was partitioned into ten equal size sub-samples. The first nine samples were used as training sub-samples, and the last one was employed as unseen data. The cross-validation procedure was repeated ten times, and the average of the series was computed by dividing the percentage of accurate predictions over the total examples. Finally, accuracy, AUC, ROC, sensitivity, specificity, precision, recall, F measure, and classification error of models were determined.p-value presenting the absence of publication bias. All analyses were performed with the Comprehensive Meta-Analysis 2.2 software.Meta-analysis of individual signature genes was employed by combining the RPKM index for each gene. Effect size (mean difference between the RPKM in the treated vs. untreated control samples) was calculated separately for each of the 13 datasets. Fixed-effect and random-effect models were applied to determine overexpression of biosignature TFs in response to herbal compounds in carcinogenic cells. Additionally, the 95% confidence intervals (CI) were estimated. Positive and negative mean difference values showed upper and lower levels of gene expression. The forest plot was plotted to compare the mean differences of predictive genes in each independent dataset and overall effects for the selected genes. In addition, to determine whether prediction bias existed among different datasets, both Begg and Mazumdar rank correlation test and Egger’s test of the intercept were employed to examine the prediction bias on the summary estimates ,35. Non-For external validation to examine the effectiveness of the predictive TFs on the new herbal-derived compound, independent samples of treated and non-traded from an experiment with GEO accession of GSE40069 were selected. The original study was planned to investigate the effect of genistein on hepcidin expression in human hepatocytes . GenisteThere was limited research available that assessed the effects of herbal compounds on cancer inhibition. Among the available datasets/data/studies in the transcriptomic database, six RNA-Seq datasets were selected.p-value in any original studies, possibly due to the effect of inadequate replication on DEGs identification in single studies  and 52 post-administration (treatment) samples. Meta-analysis was implemented based on joining the RPKM index of 58,175 genes at 13 levels. As a result, 6992 meta-genes were upregulated differentially , while no significant down-regulated genes were detected. Interestingly, 6180 meta-genes never revealed a significant dies see .p-value of 0.05. In comparison to the independent studies, the meta-analysis showed many TFs (438) that only were significantly different following this approach  terms and fold enrichment through the DAVID classification system using enriched A. In terenriched B. Regardenriched C.  were run on the TF dataset. Among the trained models, only RF trees (gain ratio and accuracy) could predict TF signatures correctly. The AUC, sensitivity, specificity, accuracy, precision, recall, F measure, and classification error criteria for RF models are presented below.As presented in The sensitivity percentages of RF_accuracy and RF_gain ratio were 80 and 76.3, respectively . This shRF_Accuracy model showed higher specificity in comparison with RF_gain ratio model. The specificity percentages of RF_Accuracy and RF_gain ratio were 85.7 and 76.3 respectively . This evThe percentage of accuracy belonging to RF_accuracy was 81.8%, while the accuracy computed for the RF_gain ratio was 76.1% . TherefoFor TF prediction, the gained precisions of RF models were 79.9% and 74.27 % . Then, 7In the prediction of TFs, the percentage of recall for the RF_accuracy model was 93.97% and for the RF_gain ratio was 86.85% . Based oThe F measure criteria were 84.85% and 78.78 % for RF_accuracy and RF_gain ratio, respectively . This isThe classification errors for the RF_gain ratio and RF_accuracy were 25.3% and 18.17%. .RF_accuracy showed the best area under ROC curve in predicting the true-positive rate versus false-positive rate, supporting it as a strong model for TF signatures prediction .More details of different decision tree models in predicting transcription factor signatures were presented in As shown in p < 0.001) (see p ≤ 0.01) except ID1 (p = 0.210). For most of the datasets, when analyzed individually, the genes seem not to reach significant over-expression, but when the meta-analysis of the datasets was performed, statistical significance was obtained. This proves the meta-analysis’s power in providing statistically significant results. In addition, Begg and Mazumdar rank correlation test and Egger’s test of the intercept showed that no obvious prediction bias existed in the individual meta-analysis of genes  see . The Ranof genes . It indiA dataset corresponding to purely one herbal compound that was not involved in differential analysis and pattern recognition used to examine the effectiveness of the predictive TFs on new herbal medicine. Results showed the regulatory signature, derived from meta-analysis and machine learning combination, were repeatable when a new and independent experiment was utilized for its validation .Considering the medicinal importance of plant-derived compounds, the development of new anti-cancer drugs based on plants is still the primary strategy in the modern drug discovery era . IdentifMeta-analysis is a way to combine the results of independent studies, which increases the sample size and improves the results’ generalizability and statistical capability . AdditioThe meta-analysis was able to successfully identify 6992 upregulated DEGs which included 479 TFs. Based on the biological process term from GO results, the identified meta-TFs were significantly involved in regulating RNA polymerase II. As all meta-TFs were upregulated during herbal-compound treatments, the expression of genes involved in controlling transcription from RNA polymerase II promoter was required. In addition, according to GO annotation for molecular function, a large number of meta-TFs classified into the binding category, which is because of TFs’ ability in binding to the promoter region.Based on categorical feature analysis, the best patterns were obtained through the RF_accuracy model. The AIP/TFE3/VGLL4/ID1 pattern was upregulated in treated cases, whereas AIP/ZNF7/DXO pattern was enhanced in control samples. Previous studies showed that the RF model offers the most reliable accuracy in numerous scientific fields of study in recent years . That isKip1, p18Ink4c, and Rb. Raitila et al. used murine models and in vitro studies to investigate the anti-tumor role of AIP. Based on their results, the proliferation of pituitary cells was decreased in AIP overexpressed samples. By contrast, in AIP knock-down samples, cell proliferation was increased [In this study, AIP was identified as the most important biomarker. AIP is an immunophilin-like protein ubiquitously expressed in the cytoplasm . AIP conncreased . These fncreased ,45. Incrncreased ,46. It hncreased .TFE3, the second gene belonging to the treated rule, is a master transcriptional regulator of several biological processes, including autophagy, inflammatory process, and the unfolded protein response . Ample eAnother biomarker found by the treated rule was VGLL4. Vestigial like family member 4 is a transcriptional cofactor from the VGLL family. In contrast to other VGLL family members, VGLL4 works as a novel tumor suppressor through cooperating with TEAD transcription factors . A consiID1 (Inhibitor of differentiation/DNA binding 1) is an oncogenic protein. However, recently, it has been suggested that this gene may play a role in increasing drug sensitivity of non-small cell lung cancer (NSCLC). Upregulation of ID1 was connected with helpful prognoses for patients administered with adjuvant paclitaxel plus cisplatin after surgery. Tan et al. used murine orthotropic lung carcinoma models with or without stable ID1 overexpression. The murine models were treated with gefitinib. The results showed that upregulation of ID1 in gefitinib-treated NSCLC cells induced necroptosis. Hence, they concluded that ID1 could elevate NSCLC cells’ sensitivity to gefitinib .According to the obtained tree in Few studies have investigated the biological role of DXO (Decapping and exoribonuclease protein) in cancers. DXO regulates several processes linked to mRNA 5′-end capping, including decapping, pyrophosphohydrolase, deNADding, and 5′–3′ exoribonuclease activities. It has been revealed that cell proliferation was increased by DXO downregulation and destabilizing cyclin D1 mRNA in bladder cancer. However, genes controlled by this transcription factor have not yet been identified, indicating that more research is required to be performed .metaSeq package of R, which is a robust method against read-size effect and also uses TMM normalization [There are some limitations in this study, including a limited number of available studies on the effectiveness of plant-derived compounds on cancers. We employed an integrated approach of meta-analysis and machine learning to aggregate the different datasets from different cell lines. The following strategies were employed to minimize the batch effects: (1) RPKM index was used as expression measurement of genes in meta-analysis. RPKM normalizes the counts of the mapped reads to a gene in respect to the transcript length and the sequencing depth. Consequently, expression measurements across different genes and different experiments were comparable . (2) Alllization .This study was designed to discover a core set of transcription factors responding to different herbal compounds in various carcinogenic cells. First, 479 differentially upregulated TFs were detected by meta-analysis. Then, the best combination of TFs/features that accurately discriminated herbal-treated samples from untreated ones was determined via categorical feature analysis. The results showed that the machine learning method combined with meta-analysis successfully identified general TFs responding to herbal-derived compounds. RF model with accuracy criterion performed better in mining important transcription factors. This tree also provided a more effective and reproducible bio-signature. The reason is that the RF algorithm merges the yield of various random trees to generate the final result. The ability of random feature selection makes RF considerably more accurate in comparison to other models. As a result, the identified biomarkers in this study might be ideal candidates to distinguish whether an investigational new herbal compound is effective on a particular cell or not. In addition, reported results in this article confirm machine-based prediction’s capability in finding the relation between important transcription factors.


The concept of depth induces an ordering from centre outwards in multivariate data. Most depth definitions are unfeasible for dimensions larger than three or four, but the Modified Band Depth (MBD) is a notable exception that has proven to be a valuable tool in the analysis of high-dimensional gene expression data. This depth definition relates the centrality of each individual to its  inclusion in all possible bands formed by elements of the data set. We assess (dis)similarity between pairs of observations by accounting for such bands and constructing binary matrices associated to each pair. From these, contingency tables are calculated and used to derive standard similarity indices. Our approach is computationally efficient and can be applied to bands formed by any number of observations from the data set. We have evaluated the performance of several band-based similarity indices with respect to that of other classical distances in standard classification and clustering tasks in a variety of simulated and real data sets. However, the use of the method is not restricted to these, the extension to other similarity coefficients being straightforward. Our experiments show the benefits of our technique, with some of the selected indices outperforming, among others, the Euclidean distance. Quantification of gene expression data has become a benchmark in biomedical research for the study of numerous diseases. High-throughput techniques have been developed to monitor the level of expression of thousands of genes simultaneously across different samples. The potential heterogeneity of such samples is enormous, as they might include a variety of tissues of origin, cell types or diseases, as well as diverse time points or experimental conditions, such as stress, treatments or viral infections. This typically makes gene expression profiles very irregular and complex.4. Nevertheless, classical classification and clustering methods for their analysis are also widely extended, as they have permitted major advances such as identification of novel tumor subtypes7, prediction of gene functions9 or discrimination of patients with good or poor prognosis11. There are many studies that address the problem of identifying the most suitable technique for gene expression data among the plethora of possibilities , which is, indeed, a difficult issue. But this is just the first step.The nature of this type of data has raised tremendous statistical interest as it can be used to gain insight into human genetics by revealing hidden patterns or by providing accurate predicting models. Biological information about genes and samples can be incorporated into the data in the form of labels or “annotations” and used at some point of the analysis. If they are used from the beginning, the technique is said to be a supervised one, whereas in unsupervised approaches, they are ignored and just used to interpret the results. Many supervised and unsupervised methodologies have been tailored for the exploration of gene expression datam-dimensional vector space, where m is the number of samples, or considering each sample as a point in an n-dimensional vector space, where n is the number of genes. In either case, this perspective allows using concepts and techniques from linear algebra for the analysis of gene expression data. In particular, the resemblance between two genes, for instance, can be stated in terms of a certain dissimilarity measure 18).Most classification and clustering techniques have in common their need for quantifying pattern proximity, according to a certain criterion. The conventional approach consists in considering each gene as a point in an 19) commonly yield different results on the same expression data and the identification of the most suitable dissimilarity for a particular problem is still an open question.A well known example of dissimilarity is the Euclidean distance—indeed a metric—which is commonly used in every research discipline when the patterns to be compared have continuous features. However, this distance assumes a clean (noise-free) experimental vector space, which is an overly stringent premise for gene expression data. Another popular choice for quantitative data is the Pearson correlation distance, which has the drawback of presuming a linear relationship between similar vectors. These and many other distances in the literature . Though they are widely used in other applications, many of them have arisen in the ecology and geology fields for presence-absence data, where 1 designates a present attribute and 0 an absent one. There are several studies that compare similarity indices , but they commonly report different conclusions. This has motivated the general acceptance that the performance of similarity coefficients is also data-specific.On the other hand, other types of data, like qualitative or binary data, can be found in the gene expression context, with 1s representing expressed genes and 0s corresponding to not expressed ones, for instance. In this situation, there are better dissimilarity measures that account for their singular nature. In particular, numerous pairwise similarity indices have been proposed in the literature for binary data .As a consequence, new ways of measuring proximity between pairs of objects (of diverse nature) have been further investigated in different contexts similarity measure for multivariate quantitative observations by “transferring” quantitative information in the (gene or sample) vector space into a different space of binary matrices. This new space encodes the relative position of each original vector with respect to the other observations, which can be used to assess how similar profiles are.d binary vectors to each d-dimensional data point by borrowing certain elements from the finite-dimensional version of the Modified Band Depth (MBD)34: the bands defined by two or more distinct elements in the data set. The MBD is a particular definition of multivariate depth, a concept conceived to generalise univariate order statistics, ranks and medians to higher dimensions, that allows ordering multivariate data from center outwards.Our methodology consists in first associating 37 among others), but most of them have the drawback of being unfeasible for dimensions larger than 3 or 4. In contrast, the MBD is specially suitable for high dimensional observations and has been used in the DS and TAD methods38 to successfully classify gene expression data or in the BRIk algorithm39 to initialise k-means by making a smart use of the information conveyed by the data.A number of depth notions can be found in the literature  or exclusion (0) inside it. In this way, each d-dimensional observation has an associated binary matrix of size With a philosophy similar to that ofd contingency tables—one for each coordinate—can be computed for each pair of data points, and used to evaluate, coordinate by coordinate, how “resembling” they are, according to some conventional index. In our proposal, the computational cost of evaluating all From the columns of these matrices, a collection of Though there is a manifest interest in thoroughly examining the implications of choosing one of the many different similarity coefficients found in the literature in the analysis of gene expression data, here we just aim at illustrating the usefulness of the information summarised in the band-based matrices associated to each observation and its applicability to different techniques.42, k-means43 or Partitioning Around the Medoids (PAM)44, by identifying which ones are suitable for the analysis of gene expression data.With this in mind, we propose to use—some—typical (dis)similarity measures for binary data as an alternative to classical distances for multivariate data in benchmark algorithms such as k Nearest Neighbours (kNN)The rest of the manuscript is organised as follows. The Methods Section includes a detailed description of the proposed theoretical methodology and its efficient implementation to derive different (dis)similarity measures. The Results Section evaluates the use of such measures in classification and clustering tasks when compared to standard distances, for both synthetic and real data. The Discussion Section summarises the main findings and enhances the potential of the proposed band-based indices for the analysis of gene expression data. Final conclusions are reported in the last section.precision and recall.In the context of binary data, given two objects mentclass2pt{minimnd-dimensional observations j-band determined by the j distinct elements d-dimensional interval delimited by the minimum and maximum of their coordinates, in the form j-bands in the cases where it is irrelevant to identify which elements define them, we simplify the notation by letting j-bands On the other hand, given a sample of j-bands are the basic elements for defining the MBD. This depth notion computes and averages the proportion of coordinates of element j-bands, with j, we can associate to each p, k) element equals 1 if k-th  interval determined by the p-th j-band in k-th column represents the number of bands in k-th interval contains Such k-th interval contains the k-th coordinate of both points k-th column of this matrix is denoted by In a similar way, given the pair of distinct elements Simple matching (SM) coefficient: Introduced by23, it provides the simplest similarity measure as the proportion of matching binary attributes, both present and absent, of items Jaccard (J) coefficient: It was introduced in20 and is one of the most frequently used indices. In fact, the dissimilarity index derived from it is a metric. It counts the number of matching present attributes and compares it to the number of attributes that are present at least at one of the objects: Simpson (S) coefficient: This popular index was introduced in22 to measure set similarity as the proportion of the smallest set included in the largest one. It is often referred to as Lennon’s coefficient45, and corresponds to the largest of proportions Forbes (F) coefficient: It was derived in the ecology field as the observed-to-expected ratio of the number of species in two random samples21. Despite its good general properties, this index has been routinely excluded from studies and discussions on similarity indices. Nevertheless, it has recently raised interest47 and it has been shown to outperform other indices in several situations. The corresponding formula is: Dice (D) coefficient: Presented in48, and also known as SAnderberg (A) coefficient: It was used in24. It is another variation of the Jaccard coefficient that gives double weight to mismatching attributes: Ochiai (O) coefficient: This index is described in49. It represents the geometric mean of 27) it has outperformed other indices. Its expression is: Russell and Rao (RR) coefficient: Introduced in50, it represents the proportion of positive matching attributes: All these coefficients are symmetric and non-negative similarity indices. When an index 28. Also, note that Some of the most widely used similarity indices for binary data are described below.j used to define the bands, we can associate to each data point Given the multivariate, quantitative data set under study mentclass2pt{minimmentclass2pt{minimThe key idea behind our method is the following. We expect that two observations whose coordinates are simultaneously embedded in many bands are more similar than those with coordinates located in few bands at the same time. Note that such similarity is dependent on the data set, i.e., two given elements will have different similarities when the other elements in the set change, in the same way the depth of a point is modified when the data set is altered.k and for each j, with Next, for each coordinate mentclass2pt{minimj, it is possible to summarise the information contained in these d tables in two different manners: a) by adding corresponding values in all the tables and plugging the results in the chosen index, or b) by computing the chosen index component-wise and averaging across coordinates. To keep track of similarities between At this point, for a given J, if we denote by k, j), the band-based similarity measure that we proposed is simply the average of the coordinate-wise indices across the different values of j:Finally, for a given J increases. To overcome this problem, current applications of the MBD52 use the value J34. However, the effective construction of the j-bands formed by j distinct observations from the sample becomes intensive as n increases, even for A direct implementation of expressions  and 2)  2) to co53 provided an implementation of k-th column of To speed up computations, Torrente et al.k with k-th coordinate of k-th coordinates of those pairs. The complement principle states thatFor a fixed j-tuples from the sample such that the k-th coordinate of k-th coordinates of the j elements:J, asj-tuples containing the k-th coordinate of both points. Thus we writeThis principle can also be used to count the number of j-band and each binary matrix (red blocks), we can obtain the numbers These calculations reveal the shortcut in the method workflow displayed in Fig. Finally, we describe how to examine the efficiency of the proposed dissimilarities indices in different standard techniques to be run on either simulated models or real data (see the Results section).k Nearest Neighbours (kNN) algorithm42, a simple, widely-used rule that has proven to be very powerful. Though the Euclidean distance is the dissimilarity measure most frequently used with this method we also assess it when using other classical distances, namely: the Manhattan distance, the Minkowski distance, for k of nearest neighbours used in the simulations was selected by the rule of thumb of setting k equal to the largest odd integer smaller than or equal to the square root of the number of training samples. The only exception to this is considered in one of the real datasets, where two of the classes are very small and the selection of k according to such a rule resulted in high classification error rates. Ties were broken by choosing the group containing the neighbour closest to the sample to be classified .For artificial data, we generated 1; that is, for each partition, we permuted the cluster labels and selected the minimum proportion of samples with labels disagreeing with the true ones. In addition to this, we computed the adjusted Rand index (ARI)55 to assess the quality of the clustering output by comparing the labels provided by the PAM algorithm to the real ones.Additionally, we have selected PAM, a deterministic method, to illustrate the performance of the dissimilarities indices in clustering artificial data with respect to the distances mentioned above. For each dissimilarity measure, the partitions produced by the algorithm were compared to the true ones using the clustering error rate described inFor real data, we considered complete-linkage hierarchical clustering instead, as the biological results yielded by this method are easier to visualise and interpret.All these methods were used as implemented in the statistical R environment (http://www.R-project.org), in packages class, cluster and stats, respectively.For the simulated data, we considered normally distributed models with different dimension and number of groups, as detailed in Table J on the classification performance. To that end, we considered We evaluated the performance of kNN with the band-based similarity indices with respect to that resulting from the use of the standard distances. However, prior to such a comparison, we carried out an analysis of the influence of the value of For each of these models, we generated 200 training sets with 100 observations in each group, and 200 independent test sets with 25 elements in each class. We computed the classification error rate in each test set.J increases, in agreement with the ordering induced by the MBD in the data, for different values of J34. This is also true even for values of J that have not been previously reported in the literature. Also, we can establish a rather persistent ranking of these indices for the selected models. Clearly, S is the best option, followed by O, whereas RR and A stand on the opposite side. However, there is a slight trend to deteriorate the performance, which is more remarkable for some indices than for others. According to these results, hereinafter in the comparison with classical distances we will report only results corresponding to Supplementary Figure Model 1: It is taken from56 to mimic simple gene expression data and consists of two overlapping groups of points with 100 independent variables, drawn from normal distributions s identical components Here, most of the methods have an appropriate behaviour; see Fig. Model 2: It also has two groups, now with 10 independent variables; the last five ones are noise. The standard deviation of a single noise variable is increased an amount When Models 3–5: These are models simulated by MixSim, a method proposed by57 that allows generating Gaussian mixture models with fixed levels of overlap between groups (in terms of probability). The respective number of groups are 3, 4 and 5; the respective number of variables are 15, 25 and 35 whereas the respective average overlaps are 0.01, 0.005 and 0.002.These models are difficult scenarios for classification. The error rates are higher than in the previous cases and their distributions have larger interquartile ranges and contain many outliers  clustering approach, we kept the 200 most variable genes and clustered the samples, for which we knew the group labels . We do not report here the results corresponding to the use of PAM, because it produced poor results in all the cases when the number of clusters was set equal to the number of classes. This is a common situation in gene expression data when samples are clustered. Therefore, instead of PAM, we used hierarchical clustering in conjunction with heatmaps, as these can be visually examined.Lymphoma data set. It is a subset of the set presented in6; it contains 62 samples from three of the most prevalent lymphoid malignancies: 42 diffuse large B-cell lymphomas (DLBCL), 9 follicular lymphomas (FL) and 11 chronic lymphocytic leukemias (CLL).Figure On the other hand, after filtering the genes for clustering, we computed, for each pair of samples, the band-based dissimilarity measures, as well as the other classical distances. We used these to build a hierarchical tree using complete linkage.We illustrate some of the results produced by the different indices in Fig. J, but misplaces 2 DLBCL cells, one in each of the other groups. On the other hand, the band-based index O, as well as indices A .Figure J: both main branches in the tree contain normal and cancerous samples, but the second one contains two neater sub-branches, roughly corresponding to both classes. An analogous behaviour can be observed for most of the other methods, as illustrated in Fig. The use of S results in a similar situation, regardless of the value of Leukemia data set. This data set was made publicly available in61, and contains the gene expression levels in 72 leukemia samples, 25 of which were AML and 47 were ALL; additionally, 38 ALL samples originated from B-cells and 9 from T-cells. After the preprocessing steps, the expression matrix included 3571 genes and 72 samples. We first analysed the data considering two classes, AML and ALL, and next, also distinguishing the three groups.The results corresponding to classification are illustrated in Fig. With respect to clustering, the top dendrogram in Fig. Results corresponding to alternative band-based indices and classical distances are shown in Fig. Next Generation Sequencing (NGS) provides more accurate data than microarrays, though their analysis is more challenging and complex. In this section, we additionally test the performance of the proposed indices with two NGS data sets.Pan-cancer data set. This is a subset of the PANCAN data set62, downloaded from the UCR Time Series Classification Archive63. It contains the RNA-Seq gene expression levels of 20531 genes, measured by Illumina HiSeq platform and monitored in 801 tissue samples, randomly withdrawn from the original standardised dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR) set. Alignments were performed on the GRCh38 reference genome; read counts were measured on a gene level using HTSeq and normalised using the Fragments Per Kilobase of transcript per Million mapped reads (FPKM). The set comprises five types tumor: breast (BRCA), kidney (KIRC), colon (COAD), lung (LUAD) and prostate (PRAD) carcinomas. The groups are very distinct and easy to separate, as previously reported .This leads to excellent classification results with the kNN algorithm. The Minkovski distances are capable of correctly classifying all the samples in 25% of the iterations, though their median (corresponding to 1 misclassified element) matches that of S. The other band-based indices, except for RR, have a slightly worse performance, but better than that of the Pearson, Manhattan and Euclidean distances.On the other hand, due to the massive size of the data set, in Fig. J.None of the classical distances is capable of getting better results  and fresh frozen (FF), which affect nucleic acid extraction. The experimental design defines 9 known distinct classes indicated in Table k, the classification error rates in kNN are remarkably higher than in previous data sets. This is because some of the groups are very small and do not have enough representatives to get the majority vote. In such a case, average error rates are in the order of 0.25. Therefore, we show in Fig. First, as mentioned before, if we stick to the given rule for selecting 60.Next, we built the heatmaps and hierarchical trees associated to the clustering procedure; see Fig. Interestingly, the S and O indices, shown in the middle and right panels of Fig. Figure It is worth noting that since the class labels are known, it is possible to filter the genes using the B/W criterion also for clustering, but using the most variable ones suits the unsupervised philosophy better. Nevertheless, we repeated the analyses selecting the genes with that criterion. The results, in terms of structure recovery, were very similar to the ones previously discussed.In summary, we conclude from the previous results that, even though not all the dissimilarity coefficients based on bands have an appropriate behaviour for every (simulated) dataset, when it comes to efficiently assessing biological differences between samples they reveal as suitable alternatives, especially in clustering tasks. In particular, the Simpson and the Ochiai indices are, globally, the best options: they have consistently good or the best performance across our variety of experiments.Our findings from the Results section suggest that the proposed technique for constructing a similarity or dissimilarity measure for quantitative data is effective for analysing gene expression data. However, it is clear that there are always scenarios where a particular dissimilarity/distance measure is not the best option, as it has been illustrated in this study.J, the largest number of different observations considered to form the j-bands. This is computationally feasible due to the efficiency of the proposed method. Our experiments show, as expected, that the results produced for different values of J are very similar, but sometimes with a tendency to deteriorate as J increases, a fact that has not been previously reported. Therefore, it is enough to set J equal to 2 or 3 to get satisfactory results fast. We can rank the band-based dissimilarity indices that we have considered from best to worst and identify which of them have systematically a bad behaviour (or worse than the competitors) in the analysed simulated data sets, i.e., with distributions that are spherical or with ellipsoidal weighted components. These indices are the RR and A measures, which seem less suitable for classifying or clustering this kind of data; this is consistent to what has been reported in several studies with respect to RR. Surprisingly, despite being one of the most popular indices, J does not perform as well as expected. Instead, S is clearly the best option in most of the tested data sets, as it achieves lower classification/clustering error rates and higher ARIs than every other dissimilarity measure, including, for instance, the benchmark Euclidean distance.We have first evaluated the performance of these indices in relation to In the case of analysing gene expression data, the differences among the band-based methods are much smaller as most of them retrieve the underlying biological structure. The variety of analised data sets illustrates several situations where some of the band-based indices outperform the classical distances, especially for clustering tasks, earning their right to be considered a suitable measure of similarity in the gene expression context. As an example, the ability to cluster together samples from the same tissue of origin but stored under different conditions, or to better arrange samples according to the disease status, appears to be of particular interest. As opposed to the simulated Gaussian data case, the Jaccard index has a more relevant behaviour in this context.Different applications such as pattern recognition or classification of multivariate data require a similarity measure In this work we propose a similarity/dissimilarity measure for continuous data based on binary features associated to the original observations. To that end, we make use of the idea underlying in the construction of the MBD, a data depth notion that has proven useful and efficient in the analysis of complex data, like gene expression data. More precisely, for each coordinate of a given observation we build a binary vector that helps count how many bands in the sample contain such coordinate. These vectors allow constructing contingency tables as in the presence-absence context, which become the input of standard similarity indices. The computation of the contingency tables is very efficient as it is possible to determine the corresponding values without evaluating each band. Instead, we use a combinatorial approach after reordering the columns of the data in an increasing way.We have used this strategy for analysing a collection of simulated and real gene expression data sets using supervised and unsupervised classification. In summary, it is apparent that the binary matrices constructed from the bands keep track of similarities appropriately, though not all indices make a sound use of this information in every scenario. For simulated data sets, with (Gaussian) spherical or with ellipsoidal weighted-component distributions, the Simpson index is the best choice, followed by the Ochiai index. On the contrary, for more complex real gene expression data, additional band-based indices  come to the fore as better alternatives than the widely-used Euclidean, Manhattan or Pearson distances. In some cases, S and O are not the best options, yet their performance is good enough to be considered as relevant choices. The different Minkovski distances are the most appropriate ones for classification tasks in these data sets, but are outperformed by S and O in clustering. In brief, S and O are sound options that produce competitive, sometimes complementary, outputs. Therefore, a joint analysis using them is suggested.In either case, the technique presented in this study to construct a (dis)similarity measure based on bands from the MBD notion reveals as a suitable alternative, which can be extended to any of the multiple similarity indices defined for binary data and thus applied to quantitative data.Supplementary Information.


The effects of confounding factors on gene expression analysis have been extensively studied following the introduction of high-throughput microarrays and subsequently RNA sequencing. In contrast, there is a lack of equivalent analysis and tools for RNA splicing. Here we first assess the effect of confounders on both expression and splicing quantifications in two large public RNA-Seq datasets . We show quantification of splicing variations are affected at least as much as those of gene expression, revealing unwanted sources of variations in both datasets. Next, we develop MOCCASIN, a method to correct the effect of both known and unknown confounders on RNA splicing quantification and demonstrate MOCCASIN’s effectiveness on both synthetic and real data. Code, synthetic and corrected datasets are all made available as resources. Confounding factors on gene expression analysis can be analyzed by several existing tools. Here the authors develop an algorithm called MOCCASIN to correct the effect of known and unknown confounders on RNA splicing quantification. RNA-Seq is an experimental technique that quantifies the relative abundance of RNA molecules in a sample through sequencing of short RNA fragments. By mapping those sequences to a transcriptome or an annotated genome, researchers can quantify expression levels and alternative splicing of genes. RNA-Seq is commonly used in a variety of analysis tasks like the identification of differentially expressed genes or isoforms between two or more conditions; the identification of genes and/or samples that cluster together by gene expression or RNA splicing variations; and quantitative trait loci analysis to identify genetic variants associated with changes in expression (eQTL) or splicing (sQTL).2 or processing batch4. These factors are either already known prior to starting the analysis task , or unknown . Generally, such confounders  affect the variability of the data and, depending on the relationship to the biological signal of interest, can lead to inflated rates of false positives and false negatives. Fortunately, for gene expression analysis, there are well-established methods to remove or account for both known and unknown confounding factors, such as limma5, ComBat6, RUV8, and surrogate variable analysis (svaseq)10. All of these tools have been applied in many studies and are highly cited.The results of such analyses can be greatly affected by unwanted factors such as sequencing lane11, SALMON12, and Kallisto13 estimate some form of normalized transcript expression (TE). These estimates can then be corrected for confounder effects using the methods listed above. Other methods, such as MISO14 and BANDITS15, estimate relative transcript usage (TU), i.e., the fraction of transcripts from each gene’s isoform. In contrast, many other methods focus on “local” splicing changes, quantifying expression at the exon levels , or the relative usage of specific RNA segments or splice junctions. This latter approach, described in more details below, involves quantifying the percent spliced in (PSI) for local splice variations (LSVs), or AS events. AS events commonly captured by these methods include exon skipping (where PSI captures the fraction of isoforms including or excluding an exon) and alternative 3′/5′ splice sites. Notably, PSI-based quantification of AS events has been the main approach in the RNA splicing field and is also the focus of the work presented here. Some of the reasons for this focus on PSI quantification in the RNA splicing field include the ability to more accurately and easily quantify local AS events from junction spanning RNA sequencing reads, the ability to capture complex and un-annotated (de-novo) splice variants which are particularly relevant for disease studies, and the ability to validate and manipulate such AS events using RT-PCR and mini-gene reporter assays17.For the quantification of alternative splicing (AS), a variety of methods and approaches exist. Some methods quantify whole transcripts, usually by assuming a known transcriptome. Many methods, such as RSEMHowever, in contrast to the common usage of PSI quantification methods for the study of RNA splicing, there is a clear lack of tools for modeling known and unknown confounding factors in PSI-based splicing analysis. We suspect this scarcity of tools reflects a general lack of awareness of the effect of confounders on RNA splicing quantifications. Specifically, we were not able to find any previous work that quantitatively assessed the effect of confounders on splicing analysis and compared it to the effect on gene expression.One challenge with correcting for confounders in RNA splicing analysis based on PSI estimates, is that the tools designed for correcting expression estimates are not well suited for this task. Local splicing variations (LSV) are typically quantified from junction reads, a subset of exon-mapped reads spanning across introns. When junction reads from an exon map to two or more alternative splice sites up/downstream, the percent splice inclusion (PSI) of each of these splice sites is quantified as the ratio of junction reads mapping to that splice site over the total number of junctions reads mapped to all splice sites at that locus. Similarly, between samples or groups of samples, the delta PSI (dPSI) quantifies the extent of changes in PSI, or differential splicing. Consequently, alternative splicing (AS) quantification is distinct from that of gene or transcript expression: rather than real values or log fold change, PSI values are in the range of 0 to 1, or −1 to +1 for dPSI. LSV PSI quantification also suffers from different biases and challenges than those typical in expression estimates. For example, in typical RNA-Seq experiments many LSVs suffer from low coverage in terms of junction spanning reads, which in turn limits the ability to quantify changes in PSI associated with those. On the other hand, issues such as effective transcript length or 3′ bias in read coverage do not play a major role in PSI quantification as those can benefit from the built in normalization between the reads associated with an LSV’s junctions.18 quantified variations in intron cluster usage using LeafCutter19 and then applied fastQTL20 to the data matrix. This analysis implicitly assumes gaussian distributions, standardizes the data, and then corrects for confounders such as age or gender. However, not only do PSI values not follow a Gaussian distribution, but the corrected values are commonly negative or greater than one, losing their interpretation as splicing fractions. Furthermore, these pipelines use only point estimates for PSI, losing read coverage information which is crucial for controlling false positives in differential splicing analysis21. Finally, while some software allow users to specify confounders for differential splicing analysis22, we are not aware of any tool that is able to correct for both known and unknown confounders, outputting corrected junction spanning reads for both supervised  and unsupervised  splicing analysis tasks.Most previous work involving PSI-based splicing analysis and confounding factors utilized pipelines originally built for QTL analysis. For example, Raj et al.23 though we show that in MOCCASIN’s model formulation a simple linear model performs well. Next, each junction’s read rates are fit, using vanilla ordinary least squares regression (OLSR) to a linear combination of factors, which may include both known and unknown (learned) factors. The unwanted factors of variations are then removed, leaving corrected read rate per junction which are then renormalized and scaled back to the original coverage level.In order to address the lack of tools for correcting confounders in RNA-Seq based splicing analysis we developed MOCCASIN (Modeling Confounding Factors Affecting Splicing Quantification) algorithm, which operates by jointly adjusting the estimated read-rate for the junctions in each LSV. At its core, the MOCCASIN model is notably simple: read rates across samples are all scaled to the median of the total read rates across all junctions in the matching LSV. As a result, adjusting a junction’s read rates across samples adjusts its respective PSI across samples, up to a scaling factor. Optionally, read rates can be log scaled to capture log linear effects which have theoretical support24 LSV quantification (see Methods), but it also implements a general API so other PSI quantification algorithms can easily employ it. We evaluate MOCCASIN’s performance on both synthetic and real datasets under a variety of settings, demonstrating that it works effectively to eliminate false positives and recover true biological signals diminished due to confounders.The simplicity of the MOCCASIN model allows it to be highly efficient in both memory and compute time, while still being able to effectively correct the effect of confounders on splicing quantification from RNA-Seq experiments. MOCCASIN is implemented in python as an open source package. MOCCASIN takes advantage of specific features characteristic to MAJIQ’s25, each with hundreds of samples. The ENCODE dataset comprises 236 and 238 shRNA knockdown experiments performed in K562 and HepG2 cell lines, respectively, with many of the knockdown experiments targeting RNA binding proteins. The ENCODE experiments were done in 61 distinct batches, encompassing a total of 122 control and 978 knockdown RNA-Seq samples. The TARGET dataset comprises samples from 250 and 329 pediatric patients with B and T Cell of origin acute lymphoblastic leukemias (ALL), respectively. Samples were taken at primary diagnosis and/or after relapse and sequenced in multiple batches. See First, we set out to assess the effect of confounders on both expression and splicing analysis in two large and highly used datasets, TARGET  and ENCODER2 (see “Methods”) compared to 6.7% and 39.8% for expression. Indeed, Van Nostrand et al. noted such batch effects for ENCODE26, but this effect was not quantified and we are unaware of a previous publication reporting batch effect associated with the sequencing machine for the TARGET dataset. Importantly, batch effects are not restricted to such large consortium data. We observed similar results for mouse tissue samples previously used to study expression batch effects by Peixoto et al4.  and variables of interest . In addition, MOCCASIN also allows users to specify a number of unknown factors of variations to be learned from the data using a factor analysis approach similar to that used by RUV7 (see “Methods”). The set of read rates Rmk are scaled across all samples; fit to the variables of wanted and unwanted variations using OLSR; adjusted to remove the unwanted sources of variations; then scaled back to the original total read rate per LSV in each sample such that MOCCASIN outputs a “cleaned” read rates matrix (see methods for more details). The resulting corrected read rates matrix can then be fed into any downstream algorithm involving tasks such as PSI quantification for clustering analysis or detection of differentially spliced events. Specifically, MOCCASIN was designed to work in conjunction with MAJIQ but other algorithms for PSI or dPSI quantifications can also be used via MOCCASIN’s API.In order to address the need for correcting confounding factors in splicing analysis we developed MOCCASIN. Briefly, MOCCASIN input consists of two elements. One is a matrix 27 to simulate a total of 16 real samples of RNA-Seq from mouse tissues28 (Aorta and Cerebellum). Simulating real samples allowed us to capture realistic expression levels and variability between samples in and between the original tissue groups (see “Methods”). After the original samples were simulated, we introduced a synthetic batch effect to half of the samples denoted as batch B. Specifically, To assess the effectiveness of MOCCASIN we first created a “realistic” synthetic data, using BEERSG = 20% and C = 60%) compared to the ground truth unperturbed data, either before MOCCASIN (blue), or after MOCCASIN where we vary the total number of input samples from 4 (purple) to 8 (brown), 12 (yellow), or 16 (orange). For example, we see that the number of LSVs with dPSI > 0.2 drops from 1229,  to 345-151  i.e., up to 88% reduction in the number of highly perturbed LSVs.Figure y-axis) as a function of the difference’s significance . Here, with G = 20% and C = 60%, we see the batch injected data (blue) compared to the “ground truth” data before batch injection (green) causes inflation of both batch-associated differences  > = 95%). This threshold is commonly used to detect splicing changes that are considered biologically significant, of high confidence, and can be validated using orthogonal approaches such as RT-PCR. Figure C = 2% or even C = 10% have little effect on the false positive rate (FPR) with respect to the original batch signal . Subsequently, applying MOCCASIN (orange) effectively controls for the batch effect and perhaps most importantly maintains an empirical FDR of 6–8%, close to the Bayesian estimate of 95% confidence. We also found that using known batch covariates or treating those as unknown and letting MOCCASIN discover and correct them performed similarly (gray), and this result was not sensitive to the number of unknown confounders used , and again by tissue after MOCCASIN is applied (right). Similar results are observed when MOCCASIN is configured to identify an unknown confounder  in batches 9 and 36, respectively, of which only 773 were identified in both batches , and each batch consists of RNA-Seq experiments in one of two cell lines (HepG2 and K562). Every KD experiment had 2 biological replicates within the same batch, and each batch had its own two biologicial replicate controls (the matching cell line without any KD perturbation). After some experimentation we converged to a procedure where MOCCASIN was run separately for each cell type, removing variation due to the 29 and 32 batch identifiers in the HepG2 and K562 samples, respectively. The result of this correction procedure was that variance explained by batch dropped from 46% as shown in Fig. hes Fig. , left. Ahes Fig. , right tThe TARGET dataset introduced another set of challenges for correcting the effect of confounding variables on PSI quantifications. First, we identified a strong batch effect associated with the sequencing platform (HiSeq 2000 vs 2500), which to the best of our knowledge was not reported before. Our initial analysis was successful in controlling for the unwanted variation associated with the sequencing platform, dropping the amount of variance explained by it, from 15.9% to 0.4%  transformation of the read rates did not improve accuracy 27. For all BEERS simulations we used the same GENCODE release M21 gene models30 for transcript quantification, and the sequence from build GRCm38.p6 of the mouse reference genome (downloaded from the GENCODE website). Briefly, we directly converted the table of TPM values for each input sample into a BEERS feature quantification file so we could simulate RNA-Seq data with the same expression distributions as the input samples. Next, we used BEERS to simulate 30 M paired end RNA-Seq reads for each input sample, with uniform coverage across the length of each expressed transcript and no intronic expression. BEERS introduced polymorphisms and errors into the resulting data with the following parameters: substitution frequency = 0.001, indel frequency = 0.001, error rate = 0.005. The BEERS simulator generated FASTQ files containing the simulated read pairs, a listing of the true alignments and source transcript for each read pair, and the true expression counts for each transcript in the simulated sample.The mouse tissue simulated data are based on the real mouse cerebellum and aorta data from Zhang et al.C% Change in Isoform TPM” and correspondingly increasing the TPM of the randomly selected transcript, thus maintaining the overall TPM of the gene and not breaking the definition of TPM . The “C% Change in Isoform TPM” factors included 2%, 10%, and 60%. In addition to introducing three different levels of percent changes in isoform TPMs, we also varied the percent of genes batch-effected. We introduced batch effects to G = 0%, 2%, 5%, or 20% of all genes with protein coding transcripts.Batch effects were introduced to the transcript level TPMs of four aorta and four cerebellum samples. The first four of the aorta samples and the first four of the cerebellum samples were defined as “batch 1”. The last four of the aorta samples and the last four of the cerebellum samples were defined as “batch 2.” Batch effect perturbations were only ever introduced to the batch 2 samples and batch effects were restricted to genes with at least two protein coding transcripts and at least one transcript with ≥10 reads per kilobase of transcript length in every sample. The procedure to introduce batch effects is as follows: first, the most abundant protein coding transcript per gene was identified as the transcript having the maximum over all transcripts of the minimum reads per kilobase over all samples. This definition ensures the selected transcript is not zero in any sample. Then, for a given gene a batch effect was introduced by (1) selecting a transcript uniformly at random (excluding the most abundant transcript) and (2) reducing TPM of the most abundant transcript by a factor of “R is a matrix such that each entry Rmk represents the read rate for a splice junction C and variables-of-interest/non-confounders V. In the case when the known confounders are supplemented with additionally learned factors of unwanted variation (see below) the columns of C are partitioned into known N and unknown U confounders.MOCCASIN adjusts read rates representing evidence for RNA splicing events in order to remove confounding variation. The two main inputs provided by the user are the read rates matrix (dependent variables) and the design matrix which lists the confounding factors and covariates. The read rate input data 31. Hence, the junctions represented in the read rate table are assumed to be grouped into LSVs, with a separate data structure given as input and identifying each junction row m with its matching LSV 19), under the assumption that the input elements represent read rates of entities  that belong to groups such that the relative fraction of each such entity  need to be adjusted.MOCCASIN considers the fundamental splicing unit of interest to be a local splicing variation (LSV), where each LSV refers to a collection of splice junctions with either a common source or target exonk, MOCCASIN first computes the total read rates over junctions in the LSV F over the read rate For each LSV m;N, U, V respectively; and F. The simpler default function is unity F(x) = x which translates to a linear model over the scaled read rates. The second is a smoothed towards zero log (STZL) transformation:Where 23. Similar to arcsinh used for gene expression, variance stabilization32 STZL avoids exploding negative values for low read rates but unlike arcsinh does not cause variable deviation from the theoretically desirable log transformation for high read rates. See Supplementary Fig. The STZL transformation has some theoretically desirable properties. It allows for a log linear model where the effect of confounder has a multiplicative effect on read rates, inline with recently suggested models for elements affecting PSIC(x) = x if x > 0 otherwise C(x) = 0 is a simple clipping function to avoid too low (negative) read rates after confounders effect removal. We found clipping to affect approximately 7% of the junctions in our tests and it did not seem to have a significant effect on the model accuracy compared to non-clipped junctions . To account for that, MOCCASIN’s output includes a list of all the LSV it corrected y/n.17.When MOCCASIN is applied to MAJIQ’s output, every LSV has multiple read rate estimates for each of its associated junctions. MOCCASIN is then applied to each of those read rates estimates independently, resulting in a set of corrected read rates per junction, per LSV. These sets are combined together by the MAJIQ algorithm to output posterior probabilities for PSI and dPSI as described previously7 - See supplementary methods for details.In order to learn unknown confounders, denoted by the matrix U above, we utilize a procedure similar to the one used by RUVFinally, a few additional elements of the MOCCASIN algorithm are worth noting:Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Genome-wide association studies (GWAS) that link genotype to phenotype represent an effective means to associate an individual genetic background with a disease or trait. However, single-omics data only provide limited information on biological mechanisms, and it is necessary to improve the accuracy for predicting the biological association between genotype and phenotype by integrating multi-omics data. Typically, gene expression data are integrated to analyze the effect of single nucleotide polymorphisms (SNPs) on phenotype. Such multi-omics data integration mainly follows two approaches: multi-staged analysis and meta-dimensional analysis, which respectively ignore intra-omics and inter-omics associations. Moreover, both approaches require omics data from a single sample set, and the large feature set of SNPs necessitates a large sample size for model establishment, but it is difficult to obtain multi-omics data from a single, large sample set.To address this problem, we propose a method of genotype-phenotype association based on multi-omics data from small samples. The workflow of this method includes clustering genes using a protein-protein interaction network and gene expression data, screening gene clusters with group lasso, obtaining SNP clusters corresponding to the selected gene clusters through expression quantitative trait locus data, integrating SNP clusters and corresponding gene clusters and phenotypes into three-layer network blocks, analyzing and predicting based on each block, and obtaining the final prediction by taking the average.We compare this method to others using two datasets and find that our method shows better results in both cases. Our method can effectively solve the prediction problem in multi-omics data of small sample, and provide valuable resources for further studies on the fusion of more omics data.The online version contains supplementary material available at 10.1186/s12864-021-07867-w. An important goal of current genetics is to establish global functional associations between genotype and phenotype, the so-called genotype-phenotype map . The stuDue to limitations at the single-omics level, it is necessary to integrate multi-omics data to more accurately predict the biological associations between genotype and phenotype . Such daWhen using a multi-omics biological network to explore genotype-phenotype association, it is generally believed that the difference in phenotypic traits is primarily attributable to the cumulative effect of omics. For example, SNPs lead to changes in gene expression, which in turn affect protein expression and ultimately cause diseases . Such laMeta-dimensional analysis has the three approaches of concatenation-based, transformation-based, and single-omics model-based integration. In concatenation-based integration, the features of omics data are integrated through machine learning to form a more comprehensive input matrix, through which a prediction model is established . In tranFurthermore, genotype-phenotype association by multi-omics requires omics data from a single sample set, and because of the large feature set of SNPs, both multi-omics analysis methods require larger sample sizes for model construction. Acquisition of clinical data can be hampered by patient privacy protection and specific data requirements of various institutions. Therefore, public clinical data do not meet the needs of multi-omics data integration methods in terms of sample size and number of omics. We propose a method of genotype-phenotype association for multi-omics data based on a small sample size and large feature set. This method offers the following innovations. a) It solves the problem of ineffective regression due to a large feature set and small sample size in three-layer networks. b) It considers intra-omics associations to improve prediction accuracy. c) It analyzes the biological pathway associations across omics layers to clarify the biological significance. d) It accounts for tissue specificity, because each tissue of a multicellular organism has distinguishing characteristics due to the tissue-specific expression of genes that confer unique morphological structures and physiological functions on various tissues , 22. e) Two datasets derived from the Gene Expression Omnibus (GEO) database were used to verify the effectiveness of our method . GSE3335The PPI network data were derived from PICKLE (Protein InteraCtion KnowLedgebasE) , which aThe data were preprocessed for consistent nomenclature of SNPs and genes in data of various types. Data with more than 10% missing values were removed from SNPs, data with less than 10% missing values were imputed with the most frequent value or the mean value, and only SNP data with minor allele frequency (MAF) greater than 0.1 were used.There is currently no typical method of genotype–phenotype association for data with a small sample size and a large number of features. We compared the following methods to verify the feasibility of our approach. In comparison experiments, the method developed in this paper was named GSPLS (Group lasso and SPLS model). The detailed implementation process of this method can be viewed in the section “Methods” and the Supplementary Information.1) The method GGLM  differs from GSPLS in that multiple regression rather than SPLS is used to associate SNP layers with gene layers, which allows us to verify the effect of gene-gene association while considering intra-omics association.πtbr in this method . These two parameters serve the Stability Selection part. However, due to our small sample size, Stability Selection cannot be carried out, so we test NETAM without the benefits of stability selection, where lasso and L1-regularized logistic regression are employed with 5-fold cross-validation.2) The method NETAM  involves3) The method mixOmics  involvesOur method was compared to the above three approaches on GSE33356 and GSE114269 datasets. Because it was a dichotomous classification, receiver operating characteristic curves were used for comparison and explanation. Figure As shown in Fig. In this experiment, we found that the results of the last three methods were unstable with small sample sizes. Overall, GSPLS was superior to GGLM, mixOmics, and NETAM with small sample sizes.Through the selection of gene clustering and corresponding SNP clusters, we not only grouped the sample features, but also screened the grouped features through Group Lasso, so that the feature quantity contained in each block was sharply reduced, which not only reduced the sample size required by our algorithm, but also reduced the computation time. We tested the four methods GSPLS, GGLM, NETAM and mixOmics on GSE33356 data with 10 repeated experiments respectively on a PC with Intel Core i7-10510U processors, 16 Gb RAM, and NVIDIA GeForce MX330 video card. The computation time of GSPLS was 66.213 ± 3.905 s, GGLM was 138.527 ± 12.149 s, NETAM was 712.141 ± 89.491 s and mixOmics was 47.065 ± 4.135 s. Although the GSPLS time is longer than the mixOmics time, the mixOmics cannot reflect the pathway correlation. In the study of the SNP-gene-phenotype pathway, our method is the most time-saving.N > 200, but GSPLS only needs dozens of samples.In the study of three-way association analysis among genotypes, gene traits and phenotypes, large number of samples are often needed. For example, NETAM  showed sN = 80, 60, 40, 30, 20, from the data set GSE33356, and N = 48,40,30,20, from the data set GSE114269 (Because this data set contains only 48 sample sizes). The positive and negative sample sizes are guaranteed to be equal during extraction. When sample size is less than 20, Group Lasso could not achieve effective regression for these two data sets. Therefore, the minimum sample size we choose is 20. In Fig. To validate the effect of sample size, we randomly select samples We analyzed the generated pathways to further demonstrate the effectiveness of our method. The weight of the edge between each gene and relevant SNP in the cluster was obtained by SPLS, and the weight of the edge connecting disease and the relevant gene was obtained by group lasso. The two layers of weights were multiplied to yield the weight value of a pathway , which we name Pathway Score (PS) to reflect its importance.Using GSE33356 as an example, we ranked the generated associations by PS, and aligned the top 50 pathways with the lung cancer-associated SNPs and genes in the PhenoScanner database , which cAs shown in Fig. We addressed genotype–phenotype association based on multi-omics data for small samples. In the case of data with numerous features, there are two commonly used approaches: dimension reduction and feature selection. However, the former may lead to the inability to analyze features before dimension reduction. Therefore, sparsification and existing associations were used to screen and reduce the number of features. The greatest contribution of this paper is the ability to group genes into clusters according to their network characteristics while significantly reducing the number of features in each cluster. The clustering and effective screening of gene networks drastically reduces the number of features of the three-layer network, making it suitable for application to data with small sample sizes.Data is a key in multi-omics studies. However, the following two conditions generally occur to the multi-omics data. One is that with the increase of the types of omics data, the multi-omics data is not easy to obtain and the data sample size is not large. In this paper, such problems can be solved in the case of small samples, the advantage is that it can reflect the association relationship of each omics, it makes the biological mechanism clearer, but the accuracy is not high. The other is that when the sample size is large, the number of omics types is often small, such as GWAS data. Although GWAS analysis has a certain accuracy, it is generally analyzed by statistical methods without understanding the internal biological mechanism. How to achieve the analysis effect of multi-omics data, when we use the data with sufficient sample size and insufficient omics types. This will be the direction of our next research.Our method has obvious advantages, but the data rely on the PPI network and eQTL association. Therefore, only associations in an SNP/CNV-gene-phenotype three-layer network can be processed, rather than any three omics, such as SNP, methylation, and phenotype.When clustering with SPICi, although we can select the ranges of the three clustering hyperparameters according to certain criteria, unknown factors still influence the selection of range limits, and this method is highly sensitive to the choice of hyperparameters. We also attempted to replace SPICi clustering with fuzzy clustering, which showed results similar to sampling multiple gene clusters with replacement and was expected to achieve better clustering. However, fuzzy clustering requires specification of parameters, including cluster number. Inappropriate initial parameters may affect clustering accuracy, and the real-time performance of the algorithm can be compromised by a large sample dataset and feature set. These problems must be addressed in future studies.In summary, in this study we used SPICi and Group Lasso methods to perform gene cluster and screen, obtained the SNP corresponding to gene in each cluster through eQTL data, established the association relationship between SNP and gene by SPLS, and formed block together with phenotype. Finally, these blocks are integrated by taking the average. Compare with state-of-the-art methods, our method achieved better results on GSE33356 and GSE114269 datasets, and it consumes the least time. To validate the effect of sample size, we randomly select samples from these two datasets, we found that our method was effective for the sample size greater than 20. And we ranked the generated associations by PS, SNPs or genes in 6 of the top 10 pathways were present in PhenoScanner, as were those in 10 of the top 20 pathways. Collectively, our method can effectively solve the prediction problem in multi-omics data of small sample, and provide valuable resources for further studies on the fusion of more omics data.Multi-staged analysis ignores intra-omics correlations and requires a large sample size, whereas meta-dimensional analysis ignores inter-omics associations. To address this issue, we propose an approach to genotype-phenotype association for small samples based on multi-omics data. The model is illustrated in Fig. Diseases are not caused by the effects of one or more genes –32, but Clustering algorithms play an important role in the analysis of biological networks and can reveal their functional modules. Most clustering algorithms perform well on biological networks of moderate size, but they become impractical on larger networks due to low speed or high network complexity. According to the analysis of Jiang et al. for each clustering methods including Markov cluster (MCL) , moleculThe minimum cluster size is used to determine the inclusion/exclusion of a cluster by comparing the number of genes involved in it. If the number of elements in a cluster is greater than the minimum cluster size, then the cluster is included. If the minimum cluster size is too small, then it fails to capture the associations between genes, and the opposite may result in the inappropriate deletion of clusters. According to tests on different datasets, the minimum cluster size was set to the interval of , 14.G = , for any vertex u and connecting vertex set S ⊂ V, support is defined asIn an undirected graph u, where wu, v is the weight of any edge  ∈ E. If the support of a vertex is less than the minimum support threshold, then the vertex is discarded. The weight of an edge is represented by the Pearson correlation coefficient of two vertex vectors; a coefficient can be positive or negative, and coefficients can cancel each other during the calculation of support. Therefore, the absolute value of the Pearson correlation coefficient was used, so wu, v ∈  and Y(n × q) of independent and dependent variables, respectively. PLS regression extracts principal components t1 and u1 from X and Y, respectively. For regression analysis, t1 and u1 should carry as much information as possible of the data they represent. After extraction of the first set of principal components t1 and u1, PLS performs regression of X on t1 as well as Y on u1. If the regression equations are sufficiently accurate, then the algorithm is terminated. Otherwise, a second round of component extraction is performed using the residual information after removing the variance of X and Y explained by t1 and u1, respectively. This process is repeated until satisfactory accuracy is achieved. SPLS combines the advantages of principal component analysis, canonical correlation, and linear regression, and effectively solves the problems of ineffective regression and multicollinearity among features due to fewer samples than features. In order to assess the impact and usefulness of the single steps, GSE33356 is used as an example data to analyze the intermediate results of each step in the Supplementary Information.Gene clusters closely related to a disease can be selected by group lasso , 40. The, …, xp} , the staAdditional file 1.


Tumor‐educated platelets (TEPs) may enable blood‐based cancer diagnosis. This study aimed to identify diagnostic TEPs genes involved in carcinogenesis.The TEPs differentially expressed genes (DEGs) between healthy samples and early/advanced cancer samples were obtained using bioinformatics. Gene ontology (GO) analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis were used to identify the pathways and functional annotation of TEPs DEGs. Protein‐protein interaction of these TEPs DEGs was analyzed based on the STRING database and visualized by Cytoscape software. The correlation analysis and diagnostic analysis were performed to evaluate the diagnostic value of TEPs mRNAs expression for early/advanced cancers. Quantitative real‐time PCR (qRT‐PCR) was applied to validate the role of DEGs in cancers.TEPs mRNAs were mostly involved in protein binding, extracellular matrix, and cellular protein metabolic process. RSL24D1 was negatively correlated to early‐stage cancers compared to healthy controls and may be potentially used for early cancer diagnosis. In addition, HPSE, IFI27, LGALS3BP, CRYM, HBD, COL6A3, LAMB2, and IFITM3 showed an upward trend in the expression from early to advanced cancer stages. Moreover, ARL2, FCGR2A, and KLHDC8B were positively associated with advanced, metastatic cancers compared to healthy controls. Among the 12 selected DEGs, the expression of 7 DEGs, including RSL24D1, IFI27, CRYM, HBD, IFITM3, FCGR2A, and KLHDC8B, were verified by the qRT‐PCR method.This study suggests that the 7‐gene TEPs liquid‐biopsy biomarkers may be used for cancer diagnosis and monitoring. Receiving operating characteristic (ROC) curves show the relative diagnostic performance of TEPs RSL24D1, HPSE, IFI27, LGALS3BP, CRYM, HBD, COL6A3, LAMB2, IFITM3, ARL2, FCGR2A, and KLHDC8B have predictive validation value for cancers. The array data GSE68086 includes 285 platelet samples covering 55 healthy samples, 39 early localized cancer samples, and 191 advanced metastatic cancer samples. The GSE89843 database includes 636 samples covering 234 healthy samples, 55 early‐localized cancer samples, and 345 metastatic cancer samples. In their study, advanced, metastatic cancer or metastatic cancer patients were defined as cancer patients with clear metastasis from the primary tumor to distant organ(s).2.2p‐value  < 0.05 were considered to be differentially expressed in platelets collected from early/metastatic cancer samples and healthy samples. R software was then quoted to obtain the heatmaps and volcano plots about differential expression of genes in TEPs.EdgeR package was used to identify the differential expression of genes by linear modeling. Genes with FC (fold change) > 1 and adj 2.3http://www.david.niaid.nih.gov) is a database that is applied for annotation, visualization, and integrated discovery.p‐value < 0.1Gene ontology (GO) analysis was used to reveal the function of genes and gene products in many organisms.2.4https://string‐db.org/) database aims to collect and integrate the information, which represents all functional interactions between the expressed proteins through strengthening known and predicted protein‐protein association data among plenty of organisms.The Search Tool for the Retrieval of Interacting Genes . 19 CRC patients, 16 NSCLC patients, and four healthy volunteers were recruited for the studies. The details are described in Table 2.6g for 8 min. Platelets were washed with CGS buffer . Total RNA was extracted from washed platelet from patients or healthy volunteers using TRIzol reagent , and the RNA concentration was determined using a NanoDrop‐2000 Spectrophotometer (Thermo Fisher Scientific). Then, the RNA was reverse transcribed to cDNA using RevertAid First Strand cDNA synthesis kit  according to the manufacturer's instruction. Quantitative real‐time PCR (qRT‐PCR) was performed using primers specific for RSL24D1, HPSE, IFI27, LGALS3BP, CRYM, HBD, COL6A3, LAMB2, IFITM3, ARL2, FCGR2A, KLHDC8B, and GAPDH on a LightCycler 96 instrument . Primer sequences were as follows:Washed platelets were prepared from whole blood. All procedures were done at room temperature. PRP was separated by centrifugation of blood at 200 KLHDC8B‐ forward, 5′‐GACACTGCTGAGACACTGGACATG‐3′;KLHDC8B‐ reverse, 5′‐TCATCCACACCACCCACCACTAG‐3′;FCGR2A‐ forward, 5′‐CCAGGAGGACTCTGTGACTCTGAC‐3′;FCGR2A‐ reverse, 5′‐GCTGCGTGTGGGTGGGAATG‐3′;ARL2‐ forward, 5′‐GAACTACTTTGAGAGCACCGAT‐3′;ARL2‐ reverse, 5′‐ATTAGCAAAGATGAGGAGGGTT‐3′;IFITM3‐ forward, 5′‐CTTCTTCTCTCCTGTCAACAGT‐3′;IFITM3‐ reverse, 5′‐GTTCATGAAGAGGGTGTTGAAC‐3′;LAMB2‐ forward, 5′‐TGGTGCTAGAGATGTTTAGTGG‐3′;LAMB2‐ reverse, 5′‐TCAGAACTCAGTGAACCTTGAG‐3′;COL6A3‐ forward, 5′‐CATGTTCTCCTTGGACACCTAC‐3′;COL6A3‐ reverse, 5′‐GTAGCGAATCTCGTCACTAGAA‐3′;HBD‐ forward, 5′‐AGAGGTTCTTTGAGTCCTTTGG‐3′;HBD‐ reverse, 5′‐CTCACTCAGCTGAGAAAAAGTG‐3′;CRYM‐ forward, 5′‐TGCCCTGAAGGAGTCTGGAGATG‐3′;CRYM‐ reverse, 5′‐AACTGTGTCTTCCACTGCCATTCC‐3′;LGALS3BP‐ forward, 5′‐CAATGGTACTTCTACTCCCGAA‐3′;LGALS3BP‐ reverse, 5′‐GAACTGTAGGCAGAGCTTCTC‐3′;IFI27‐ forward, 5′‐TCACTGGGAGCAACTGGACTCTC‐3′;IFI27‐ reverse, 5′‐TCGCAATGACAGCCGCAATGG‐3′;HPSE‐ forward, 5′‐AGTGGATGAAAACTTCGATCCT‐3′;HPSE‐ reverse, 5′‐CAGTGTTTGTGCAATGAAGGTA‐3′;RSL24D1‐ forward, 5′‐ CTGGTAAAGAGCTTACAGTGGA‐3′;RSL24D1‐ reverse, 5′‐TCTTCAACTCTCTTCATCGCAT‐3′;GAPDH‐ forward, 5′‐GGACCTGACCTGCCGTCTAG‐3′;GAPDH ‐ reverse5′‐GTAGCCCAGGATGCCCTTGA‐3′.2.7p < 0.05. Different levels of significance were indicated as *p < 0.05, **p < 0.01, and ***p < 0.001.SPSS version 23.0 software  and GraphPad Prism version 8 software was used to perform statistical analysis. Spearman correlation coefficient was used to assess the correlation between the average gene expression and that of the sample group for identifying genes whose expression change goes up or down strictly monotonically with respect to the group. The Mann–Whitney test was then applied to identify the differential expressed genes among the different stages. Receiving operating characteristic (ROC) curve analysis was used to evaluate the discriminatory power of the combinations. Data were shown as median ±interquartile range. Differences were considered significant at 33.1n = 14), colorectal cancer , hepatobiliary cancer , non‐small cell lung carcinoma , and pancreatic cancer . The advanced, metastatic cancer patient cohort consisted of six tumor types, including BrCa (n = 25), CRC (n = 36), glioblastoma , HBC (n = 10), NSCLC (n = 56), and PAAD (n = 24) . We verified 2593 commonly altered differential mRNAs from the above platelet RNA‐sequencing datasets; among them, 669 were consistently up‐regulated, and 1924 were consistently down‐regulated  Figure . There w) Figure  and S2. d Figure .3.2n = 234), individuals without reported cancers but with inflammatory conditions (n = 143), early‐localized (n = 57) or metastatic NSCLC cancer (n = 345), respectively. In this study, 636 samples were selected for further analysis, excluding individuals with inflammation  ≥ 1 as the cut‐off criterion, we extracted 3905 (1060 up‐regulated and 2845 down‐regulated) versus 164 (117 up‐regulated and 47 down‐regulated) DEGs from localized tumor educated platelets as compared to platelet samples of healthy controls, and 3059 (854 up‐regulated and 2205 down‐regulated) versus 49 (39 up‐regulated and 10 down‐regulated) DEGs from metastatic tumor educated platelets in the two datasets, respectively Figure . To furt3.4To explore the underlying mechanism and signaling pathways of these enriched DEGs, DAVID, and KEGG were performed to acquire functional and pathway enrichment analysis. GO (gene ontology) analysis was employed to functionally annotate the differentially expressed platelet RNAs. The most significant output of GO analysis was related to protein binding, extracellular matrix, cellular protein metabolic process, mitochondrial outer membrane, and innate immune response in the mucosa Figure . For bioCell components showed that DEGs were enriched in extracellular exosome, extracellular region, proteinaceous extracellular matrix, extracellular matrix, mitochondrial outer membrane, and extracellular space Figure . By usin3.5r = 0.359, p < 0.001), SAMD14 , LRRC75A , CD69 , RSL24D1 , ZNF667 , IARS , and HERC3  were positively or negatively correlated with the early pan‐cancer based on the data from GSE68086 , SAMD14 , ALAS2 , C4orf48 , PARP10 , EHBP1L1 , SSBP4 , LRRC75A , CD69 , RSL24D1 , ZNF667 , PPT1 , IARS , and HERC3  were positively or negatively correlated with the early NSCLC cancer based on the data from GSE89843 , LRRC75A‐AS1 , CD69 , RSL24D1 , and HERC3  had a diagnostic value based on the data from GSE68086 , ALAS2 , EHBP1L1 , LRRC75A‐AS1  and RSL24D1  had a diagnostic value based on the data from GSE89843 HPSE, IFI27, LGALS3BP, CRYM, WASF1, HBD, COL6A3, PRSS50, LAMB2, LTF, TPM2, TYMP, NELL2, SLC38A1, and IFITM3 were differentially expressed in both localized and metastatic cancer educated platelets as compared to the platelets of healthy controls. By using the Spearman correlation coefficient, we found that in addition to TYMP, the expression levels of the remaining 14 TEPs DEGs were positively or negatively related to the pan‐cancer stage based on the data from GSE68086 Figure , showing3.7p < 0.001), KLHDC8B , MAOB , ZNF346 , ARL2 , MMP1 , and MS4A1  had the diagnostic value based on the data from GSE68086 , KLHDC8B , IGFBP2 , and ARL2  had the diagnostic value for metastatic NSCLC cancer . We then explored the mechanisms of changes in TEPs RNA profiles by using bioinformatics analysis, correlation analysis, diagnostic analysis, and qRT‐PCR. Seven TEPs mRNAs were discovered in our study: RSL24D1, IFI27, CRYM, HBD, IFITM3, FCGR2A, and KLHDC8B in TEPs, which are mainly enriched in protein binding, extracellular matrix, and metabolic process, and may be used for cancer diagnosis.In this study, we downloaded the next‐generation sequencing datasets, GSE68086 (285 samples) and GSE89843 (636 samples), from GEO (Blood samples for liquid biopsy include circulating tumor cells (CTCs), cell‐free nucleic acids, exosomes , and TEPs.In this study, 7 TEPs mRNAs were verified, including RSL24D1, IFI27, CRYM, HBD, IFITM3, FCGR2A, and KLHDC8B, which may be used for cancer detection. RSL24D  encodes ribosome biogenesis protein RLP24 involved in the biogenesis of the 60S ribosomal subunit, ensuring the docking of GTPBP4/NOG1 to pre‐60S particles. RSL24D1 is related to hypercholesterolemia and children's chronic kidney disease (CKD).FCGR2A and KLHDC8B in TEPs were positively related to metastatic cancers in comparison with healthy controls and had potential diagnostic significance for metastatic cancers with a sensitivity of 61.8%, 59.7%, and a specificity of 89.1%, 83.6%, respectively. Previous studies reported that FCGR2A regulates cancer growth, cancer invasion and has an important role in tumor recurrence.IFI27, CRYM, HBD, and IFITM3 in TEPs were positively related to the pan‐cancer stage and essential for diagnosing cancers with a sensitivity of 59.1%, 57.8%, 54.3%, and 60.9%, and a specificity of 90.9%, 89.1%, 72.7%, and 94.5%, respectively. Ketimine reductase mu‐crystallin and hemoglobin subunit delta encoded by CRYM and HBD, respectively, did not seem to be associated with tumor progression. IFI27  is involved in tumor apoptosis signaling pathways, including type‐I interferon‐induced apoptosis and TNFSF10‐induced apoptosis,According to the GO term analysis and KEGG pathway analysis, we found that TEPs mRNAs were correlated with protein binding, extracellular matrix, cellular protein metabolic process, mitochondrial outer membrane, and innate immune response in the mucosa, as well as enriched metabolic process, mostly glycine, serine, and threonine metabolism. As one of the ten cancer hallmarks, metabolic abnormalities are mutually causal with tumor tumorigenesis.The authors declare no conflict of interest.Fig S1‐S7Click here for additional data file.Table S1Click here for additional data file.Table S2Click here for additional data file.Table S3Click here for additional data file.Table S4Click here for additional data file.Table S5Click here for additional data file.Table S6Click here for additional data file.Table S7Click here for additional data file.Table S8Click here for additional data file.