RNA sequencing (RNA-Seq) has been widely applied in oncology for monitoring transcriptome changes. However, the emerging problem that high variation of gene expression levels caused by tumor heterogeneity may affect the reproducibility of differential expression (DE) results has rarely been studied. Here, we investigated the reproducibility of DE results for any given number of biological replicates between 3 and 24 and explored why a great many differentially expressed genes (DEGs) were not reproducible.Our findings demonstrate that poor reproducibility of DE results exists not only for small sample sizes, but also for relatively large sample sizes. Quite a few of the DEGs detected are specific to the samples in use, rather than genuinely differentially expressed under different conditions. Poor reproducibility of DE results is mainly caused by high variation of gene expression levels for the same gene in different samples. Even though biological variation may account for much of the high variation of gene expression levels, the effect of outlier count data also needs to be treated seriously, as outlier data severely interfere with DE analysis.High heterogeneity exists not only in tumor tissue samples of each cancer type studied, but also in normal samples. High heterogeneity leads to poor reproducibility of DEGs, undermining generalization of differential expression results. Therefore, it is necessary to use large sample sizes (at least 10 if possible) in RNA-Seq experimental designs to reduce the impact of biological variability and DE results should be interpreted cautiously unless soundly validated.The online version contains supplementary material available at 10.1186/s40246-021-00308-5. RNA-Seq has become an indispensable tool for transcriptome-wide analysis of differential gene expression in oncology to elucidate the mechanism of tumorigenesis and metastasis –3. Due tExtensive genetic intertumoral and intratumoral heterogeneity has long been recognized –14. HighScientists of a biotechnology firm had tried to confirm published preclinical research findings related to their research, but they were shocked to find that the best-known scientific findings from cancer biology were confirmed in only 6 cases out of 53 , 16. PooNormally, it is arduous for researchers to verify their own or other people’s findings due to the difficulty of sampling and limited budget. However, with the help of public large-scale projects which have plenty of samples, such as the Cancer Genome Atlas (TCGA) , reproduIn this work, the raw RNA-Seq count data for the three cancer types that have the most samples, namely breast cancer (BRCA), kidney renal clear cell carcinoma (KIRC), and lung adenocarcinoma (LUAD), were obtained from TCGA database. First, we investigated the reproducibility of DE results among the four repeated differential expression analysis, each using totally different samples, for any given number of biological replicates between 3 and 24. Then, we investigated the detection power depending on the number of biological replicates. Finally, we explored why a great many DEGs were not reproducible. All DE analyses were performed using edgeR . The edgAs shown in Fig. As is shown in Fig. Both the number of common DEGs and the overlap rate increase with the elevated number of biological replicates, but the increasing rate slows down after around 10 biological replicates. For all three cancer types studied, the overlap rates among four repeats are all below 40% for the maximum number of biological replicates, and the percentage drops to below 10% for 3 biological replicates, which implies that the DE results for relatively large sample sizes are not reliable, and the reliability of DE results for small sample sizes are even poorer.As it is difficult to choose one repeat to represent the four repeats for any given number of biological replicates, the common DEGs (intersection) and all detected DEGs (union) of the four repeats were used to calculate the power and intersection/union ratio .As shown in Fig. The low power of DEG detection for small sample sizes can also be seen from the three curves in Fig. As shown in Fig. As can be seen in Fig. The results above demonstrate that a large number of DEGs for one repeat are not DEGs for another and these DEGs are referred to as non-common DEGs in this paper. Although non-common DEGs have also been found in previous literatures , the cauIBSP, SGCG, DCT, APCDD1, and DPP6 were identified as DEGs only in repeat III, while SLC16A3, CDH23, FOXJ1, FGF10, and BMP5 were identified as DEGs only in repeat II. The %CV, Log2FC, and false discovery rate (FDR) values for the 10 non-common DEGs in KIRC and LUAD are shown in Supplementary Table STen non-common DEGs in repeat II and repeat III for 10 biological replicates in BRCA were used as examples to illustrate the phenomenon, as shown in Table −7 when they are DEGs, which definitely means that they are, statistically, significant DEGs between the tumor and normal group, even if a threshold of 0.0001 is applied to control the FDR. Even so, the ten genes are not identified as DEGs in the other repeat. The result indicates that statistically significant DEGs are not as reliable as commonly believed.As shown in Table 2FC and FDR and sometimes may even lead to opposite results. The high variation of expression levels for the same gene in different samples may be the main cause of the poor reproducibility of DE results.In order to explore the reasons behind the non-common DEGs, we analyzed the dispersion of normalized read counts of these genes for some clues. As shown in Table 2FC. By checking the Log2FC values of the 3079 common DEGs between repeat II and III, we found that 35 of them (about 1.14%) also show opposite regulating trends, which indicates that the common DEGs are not reliable either.In addition, we noticed in Table It is clearly shown by the boxplots in Fig. Saccharomyces cerevisiae [The results above are based on the RNA-Seq data of human tumor and adjacent normal samples. Nevertheless, the issue of low reliability of DEGs for very small sample sizes has also been found in studies using RNA-Seq data from mouse , 29, Sacrevisiae , and tomThe maximum number of biological replicates studied here is already much larger than recommended in the literatures , 10, butAs shown in Table The authenticity of outlier counts is beyond the scope of this research. Nevertheless, figuring out whether the extreme counts are true or not is the prerequisite to properly deal with them. The popular edgeR  takes raSince low technical variation is one of RNA-Seq’s potential advantages –7, most One goal of DE analysis in cancer research by RNA-Seq is to identify and catalog expression of new or alternative transcripts between tumor and normal tissues, which is essential for understanding the mechanism of tumorigenesis and developing effective therapies. Apparently, given the high heterogeneity of tumor and normal samples, it is hard to achieve that goal using small sample sizes, let alone with no biological replicates. Moreover, as demonstrated by our findings, incorporating a relatively larger sample size than recommended for RNA-Seq experiments in previous literatures , 27 doesIn conclusion, both tumor tissue samples and normal tissue samples show high heterogeneity. DE results of small sample sizes are more susceptible to heterogeneity, compared with those of large sample sizes. As a result, reproducibility of DE results and DEG detection power for small sample sizes are far lower than those for large sample sizes. Even if large sample sizes are utilized, a large proportion of the detected DEGs are irreproducible. Therefore, large sample sizes (at least 10 if possible) should be considered in RNA-Seq experimental designs to reduce the interfering effect of sample heterogeneity and DEGs of interest should be validated before making generalized statements.Similarly, since it is difficult to distinguish which DEGs are specific to the samples in use and which are common to the studied populations, DE results from published RNA-Seq literatures, especially those with very small sample sizes or no biological replicates, should be consulted with caution. With regard to the reproducibility crisis which is particularly severe in cancer biology , 16 and Raw RNA-Seq read count data for all available BRCA, KIRC, and LUAD tumors and available adjacent normal tissues were downloaded from The Cancer Genome Atlas (TCGA) database. To ensure sample consistency, data from metastatic or formalin-fixed paraffin-embedded tissue samples , 33, as n. Although there are algorithms specially developed for DE analysis of RNA-Seq data without biological replicates [n was set at 3 for each cancer type.This work was designed to investigate the evolution of reproducibility of DE results and the detection power depending on the number of biological replicates plicates –36, the n, four sets of n tumor samples and n normal samples were randomly chosen without replacement from datasets of each cancer type to simulate four different experimental repeats, which were denoted as repeat I, II, III, and IV, respectively, for ease of description. For any given n, samples in the four repeats were all different. Limited by the number of normal samples in BRCA, KIRC, and LUAD datasets , the maximum n for BRCA, KIRC, and LUAD was accordingly set at 24, 18, and 14, respectively. The sampling process was shown in Supplementary Figure SFor each value of M values (TMM) normalization was performed to normalize the counts among the different samples [filterByExpr function as recommended in the user’s guide. Genes were marked as DEGs if the absolute value of log2 transformed fold change (log2FC) ≥ 1 and the false discovery rate (FDR) < 0.05.All DE analyses were done with R software (version 3.5.3) and the edgeR package   to the total number of DEGs of the corresponding repeats. For instance, the total number of DEGs identified in repeat II and III for 10 biological replicates in BRCA was 6528, 3079 of which are common to both of the two repeats; therefore, the overlap rate of the DE results for the two repeats was 47.17%. The overlap rate of DE results for any two, three, or four repeats for a given n was computed in the same way as exemplified above. Overlap rate was calculated using VENNY (version 2.1.0)  Venny; an interactive tool for comparing lists with Venn's diagrams. https://bioinfogp.cnb.csic.es/tools/venny/index.html).As described above, four repeated DE analyses were performed for each number of biological replicates n, but the number of DEGs and DEG composition of the four lists are quite different, so it is difficult to choose one of the lists as a representative. Therefore, the intersection  and the union  for any given n were used for power analysis. Note that the power was defined as the ability for a given sample size to detect “true” DEGs. Obviously, we needed a reference list of “true” DEGs. As is generally accepted that results obtained using larger sample sizes are more robust, the intersection and union of the maximum n for each cancer type were used as references.We have four lists of DEGs for each n to the number of DEGs in the corresponding referential intersection (or union). The ratio of the number of DEGs in the union to that in the intersection for any given n was also calculated and marked as union/intersection.The power was calculated by the ratio of the number of DEGs in the intersection (or union) for any given The non-common DEGs, as opposed to the common DEGs, were the DEGs that could be identified in one repeat, but not in another. To explore the cause of non-common DEGs, we selected 10 non-common DEGs from the DEG lists of repeat II and repeat III for 10 biological replicates in BRCA dataset and analyzed the characteristics of raw count data of these genes. Although the number of DEGs was close between the two repeats, about 53% of the DEGs were non-common DEGs.. The percent coefficient of variation (%CV) of normalized read counts in both tumor and normal groups of repeat II and repeat III was calculated for each non-common DEG. Similarly, dispersion of normalized read counts was analyzed and displayed in boxplots using IBM SPSS Statistics (19.0). The read count dispersion analyses for the non-common DEGs in KIRC and LUAD were conducted in the same way as in BRCA.In order to eliminate the interference of different sequencing depth, TMM normalized read counts were used for analysisAdditional file 1: Supplementary Table S1. Detailed %CV, Log2FC, and FDR values for the 10 non-common DEGs in KIRCAdditional file 2: Supplementary Table S2. Detailed %CV, Log2FC, and FDR values for the 10 non-common DEGs in LUADAdditional file 3: Supplementary Figure S1. Dispersion of normalized read counts for the 10 non-common genes in KIRC. Mild outliers  and extreme outliers  are shown. The number beside the marker shows the normalized count value of the point. RII and RIII refer to repeat II and repeat III, respectively. Capital letters “T” and “N” represent the tumor group and the normal group, respectively. IQR indicates the interquartile range.Additional file 4: Supplementary Figure S2. Dispersion of normalized read counts for the 10 non-common genes in LUAD. Mild outliers  and extreme outliers  are shown. The number beside the marker shows the normalized count value of the point. RII and RIII refer to repeat II and repeat III, respectively. Capital letters “T” and “N” represent the tumor group and the normal group, respectively. IQR indicates the interquartile range.Additional file 5: Supplementary Figure S3. Diagram of experimental design for the BRCA dataset. The processes of sampling and analysis for the KIRC and LUAD datasets were the same as that of BRCA. The numbers of tumor tissue samples and adjacent normal tissue samples for KIRC were 526 and 72, respectively, while the two numbers were 509 and 58, respectively, for LUAD. Restrained by the number of normal tissue samples which was far less than the number of tumor samples for each cancer type, the maximum number of biological replicates for BRCA, KIRC, and LUAD was accordingly set at 24, 18, and 14, respectively.


In recent years, to investigate challenging bioinformatics problems, the utilization of multiple genomic and proteomic sources has become immensely popular among researchers. One such issue is feature or gene selection and identifying relevant and non-redundant marker genes from high dimensional gene expression data sets. In that context, designing an efficient feature selection algorithm exploiting knowledge from multiple potential biological resources may be an effective way to understand the spectrum of cancer or other diseases with applications in specific epidemiology for a particular population.select. Three important resources of biological data  along with gene expression values are collectively utilized to design two different views. UMVMO-select aims to reduce gene space without/minimally compromising the sample classification efficiency and determines relevant and non-redundant gene markers from three cancer gene expression benchmark data sets.In the current article, we design the feature selection and marker gene detection as a multi-view multi-objective clustering problem. Regarding that, we propose an Unsupervised Multi-View Multi-Objective clustering-based gene selection approach called UMVMO-A thorough comparative analysis has been performed with five clustering and nine existing feature selection methods with respect to several internal and external validity metrics. Obtained results reveal the supremacy of the proposed method. Reported results are also validated through a proper biological significance test and heatmap plotting. Sample classification is one such gene expression data analysis technique that lets researchers identify categories of unknown diseased samples based on expression levels of genes. However, with the ever-increasing newly discovered biological data, handling the high-dimensional gene expression data sets has become a genuine problem which seeks a potential solution. The gene expression data set consists of expression values of thousands of genes, where only a small fraction of them shows a strong correlation with the targeted phenotypes. Therefore, as a popular solution of onality’ , in the onality’ –4 have b-markers –7, and tOwing to the nature of the problem, the biological data can be interpreted in different ways. In short, biological data are often multi-faceted or have multiple views. For example, characteristics of genes for a particular genome can be obtained from—(1) gene expression data in the form of expression levels; (2) Gene Ontology (GO) expressing semantic functionalities; 3) Protein–Protein-Interaction Network (PPIN) in the form of functional interactions between corresponding protein molecules; (4) protein sequence data in the form of encoded structural information of corresponding protein molecules and, etc. Although in recent years, several clustering-based feature-selection strategies  Protein–, 9 have select). As the underlying optimization strategy of the proposed algorithm, the Archived Multi-Objective Simulated Annealing (AMOSA) [Recent advances of single-view clustering methods applied to complex biological data sets have proven the superiority of their multi-objective versions over single-objective counterparts , 15. Henselect algorithm seems valid to identify optimal clustering solutions.Simulated annealing (SA) is a popular optimization algorithm that follows the principle of annealing metallurgy—a process involving heating and controlled cooling of a material to increase its crystals’ size and reduce their defects. To follow the annealing process of a metal, at first, the temperature is increased   , and Child ALL.1We conduct experiments on three benchmark cancer gene expression data sets; Finally, sample classification is carried out through multi-objective clustering for both original and reduced gene-spaces. A thorough comparative analysis with other existing single-objective/multi-objective single-view/multi-view feature selection algorithms has been carried out.From the overall literature survey we did, it is clear that most of the existing gene selection algorithms have followed single-view approaches. Less attention has been provided on developing multi-view gene-selection strategies. Observing the above-mentioned limitation in the existing works, in the current paper, we propose an unsupervised multi-view multi-objective gene selection approach called UMVMO-Prostate cancer data set contains gene expression values of both cancerous prostate tissues and normal prostate tissues. Originally this data set has 12,533 genes and 102 tissue samples. Among 102, the number of prostate tumor tissue samples is 52, and the rest 50 are normal tissue samples. On the other side, Diffuse large B-cell lymphomas (DLBCL) and follicular lymphomas (FL) are two B-cell lineage malignancies with very different clinical presentations, natural histories, and response to therapy. A total of 7070 number of genes and 77 number of samples are there. Out of 77 samples, 58 are of type DLBCL, and rest 19 are of type FL. Child ALL or GSE412 data set includes gene expression information on 110 childhood acute lymphoblastic leukemia samples over 8,280 genes. The samples are divided into two classes based on changes in gene expression before and after treatment, regardless of the type of treatment used. Out of 110, 50 samples are taken before any therapy, and rest 60 samples are taken after therapy.In the current work, all three chosen data sets are available in : www.biolab.si/supp/bi-cancer/projections/info/. The First of all, the necessary pre-processing has been carried out on all data sets. The log transformation is performed on expression values, and after that, samples are normalized to variance 1 and mean 0. Once the data sets are pre-processed, next, for each of them, two views are generated. As mentioned before, views are represented in the form of dissimilarity matrix view-2, the proposed Prostate cancer data set, out of 12,533 genes, 11,669 genes get mapped to significant GO-terms. The total number of mapped significant GO-terms . Also, for Child ALL data set, 6890 number of genes out of 8280 gets mapped to their corresponding GO-terms and the number of significant GO-terms is 2118 . For all data sets, only mapped genes further take part in the gene selection process. The obtained annotation data and GO-tree are utilized for computing GO-based multi-factored similarity according to Eq. Homo Sapiens or H. Sapiens organism is downloaded from HitPredict [https://www.uniprot.org/. The protein IDs are used to retrieve their interacting proteins and associated statistics from downloaded H. Sapiens PPIN. For the last factor of the For generating view-2 or Prostate cancer, DLBCL, and Child ALL data sets, the dimensions of view-2 or Next, according to Eq. view-1, pairwise correlation distance , the single-view single-objective clustering method developed in our previous work on gene selection [We compare the performance of our proposed method for gene clustering with five other alternative clustering techniques, which are single-view versions of the proposed algorithm (unsupervised multi-objective clustering (UMC) with election , and PAMselect in sample classification is compared with nine other existing gene selection and sample classification methods. These are the approach of Acharya et al. [T-test [Also, the efficiency of selected genes by our algorithm UMVMO-a et al. , Wilcoxoa et al. , T-test  [T-test , graph-t [T-test  and it’s [T-test , the app [T-test , and fea [T-test .Prostate cancer data set, before selecting candidate genes, the ensembled clustering solution’s quality is measured through the Silhouette index and compared with other single-objective/single-view clustering techniques. We execute our algorithm 5 times, so for this data set select, we perform GO enrichment analysis on their gene-clusters using the GO tool. The outcome of the biological significance test on random two clusters of the best ensembled solution  has been tabulated in Table After performing our proposed multi-view multi-objective clustering on the mentclass2pt{minimProstate cancer data set are reported in Table Next, the centers of the best ensembled solution (with respect to Silhouette index) are extracted. Hence, the reduced set of most relevant and non-redundant genes  among all nine feature selection techniques. Regarding sensitivity, our algorithm outperforms all other reported methods except MRMR [We also compare obtained classes of samples with their original class levels, and the performance has been evaluated through four external validity measures, which are reported in Table Prostate cancer, the obtained results for the DLBCL data set are also analyzed thoroughly. Here also we choose Similar to mentclass2pt{minimIn Table Child ALL is also reported in the current manuscript. Here we choose the value of ‘The outcome of the comparative study for mentclass2pt{minimChild ALL data set. Also, with the reduced gene space, the Silhouette and DB index values corresponding to the sample clustering solution are better  than the original gene space. The Dunn index value is found almost the same (0.7131) for both original and reduced gene space. The comparative sample classification outcome of this data set is also shown in Table DLBCL data set, here also with respect to Sensitivity, F-score, and Classification accuracy, our method performs better than other existing nine feature selection techniques . For Specificity, we observed MRMR [From the results reported in Table better 0.39, 0.721ved MRMR  performe‘omic’ data sources and objective measures on proposed UMVMO-select, we perform experiments with different sets of views and objective functions. Table bold) of the table is the sample classification accuracy while considered all three objective functions as the original algorithm does. It is evident from the reported results that the combination of three objective functions in UMVMO-select made it performing best compared to lesser objectives selection.To investigate the effect of different ‘omic’ data sources in produced views and reported the obtained sample classification accuracy. As view-1, we always select gene expression (GE) data. For view-2, we choose different combinations of data sources. Please note, here we have choose all three objective functions as our original algorithm does. The last row (marked as bold) of the table reports the results considering all data sources. According to reported results in this table, the proposed approach’s superiority proves the importance of considering multiple ‘omic’ sources to design views.Similarly, in Table Prostate cancer data set, the reason behind choosing DLBCL data set, we observe the set of gene markers saturates after Child ALL data set, we choose Prostate cancer data set, gene 41288_at (CALM1) and 32243_g_at (CRYAB) have been also reported in [As we have seen before, for each of the chosen data sets, ‘mentclass2pt{minimorted in .Table 9I_at (SLC25A6) and 33614_at  in [_at (HPN) and 41504_s_at (MAF) have been reported in [DLBCL data set we observe, X02152_at (LDHA) and M25753_at (CCNB1) have been identified by [_at (FH) has been reported in [Child ALL data set, 41117_s_at (SLC9A3R2) and 33069_f_at (UGT2B15) have been reported in [_at (BNIP1) and 34757_at (PARP2) were identified by [Again, gene 40435orted in . Similar_at SLC5A6 and 3red cell indicates a high expression value, whereas the green cell represents a lower value of an expression. The black cell indicates an average expression value. To be differentially expressed, a marker gene is needed to be either up-regulated  or down-regulated  for each tissue sample of the respective tumor class. In Fig. Prostate cancer data set, the obtained marker genes are either up-regulated or down-regulated for most of the samples over both classes. For example, gene 32243_g_at (CRYAB), 41288_at (CALM1), 37639_at (HPN), and 41504_s_at (MAF) are up-regulated . On the counterpart, 40435_at (SLC25A6), 33614_at , and 1657_at (PTPRR) are down-regulated . Similarly, if we study the heatmap of DLBCL data set in Fig. _at (LDHA), M25753_at (CCNB1), and M16336_s_at (ENO1) are up-regulated (high expression in DLBCL and low expression in FL) and U59309_at (FH) is down-regulated (high expression for FL and low expression for DLBCL). For Child ALL data set, as we can observe from Fig. _s_at, 33069_f_at, 37226_at, 34757_at are down-regulated  class and low expression values in ‘after therapy’ (after Th) class). On the other side, gene marker 32659_at and 39221_at are up-regulated  class and high expression values in ‘after therapy’ (after Th) class).Every row of a heat map represents each of the chosen gene-markers from the corresponding data set. Each cell of the heat map represents the expression level of a gene for the corresponding sample. The select, which intuitively identifies gene markers from the chosen cancer gene expression data sets. Multiple ‘omic’ data sources like gene expression, GO, PPIN, and protein sequence are amalgamated to build two views. Experiments are carried out on three cancer gene expression data sets; Prostate cancer, DLBCL, and Child ALL. From the thorough comparative analysis with existing feature selection algorithms and several validation tests, we observe that our proposed method reduces the original gene space significantly and improves the sample classification accuracy. From the obtained experimental outcomes, we also observed that incorporating more relevant data sources in designing views increases the overall efficiency of the multi-view clustering approach. Therefore, in the future, more views can be identified based on other genomic/proteomic resources [In the current article, we propose an unsupervised multi-view multi-objective gene selection approach called UMVMO-esources –44, and esources , essentiselect.In this section, at first, we describe the development mechanism of both views in detail. Next, we elaborate on different steps of the proposed UMVMO-‘omic’ data sources are treated as two different views. The first view is the gene dissimilarity network based on pair-wise correlation distance [Two gene-gene dissimilarity networks developed utilizing multiple distance  between mentclass2pt{minimview-1 is represented as two-dimensional distance matrix a and b can be defined as follows [a and b are correlated to each other, i.e., exact linear dependency exists, then a and b or correlation distance. All entries of the Mathematically, suppose  follows .1\documeFor building the second view, at first, we propose an integrated gene-gene dissimilarity measure Multi-factored protein–protein similarity based on GO annotation data Functional similarity between proteins based on the confidence of association in PPIN Protein sequence-based similarity utilizing Basic Local Alignment Search Tool (BLAST) based bitscore The multi-factored gene similarity measure [To design  measure  capturesLet rding to , the mul measure , Shen’s  measure , and nor measure  respectiUtilizing Eq.  measure  and \docAgain let, the corresponding gene-product or protein of gene  in PPIN  is definApart from GO and PPIN, the proposed  average  of two BThe protein sequence alignment-based similarity between protein view-2, dissimilarity matrix select algorithm for gene selection.For select algorithm comprises of nine essential steps, which are described with details in this section. The flowchart of the overall technique is shown in Fig. The UMVMO-Step 1: Encoding scheme and initializing solutionsStructure of encoded solution Our proposed algorithm starts with initializing ‘view-1 and view-2, two separate vectors of centers  ratio [R) ratio The samples of chosen gene expression data sets belong to either class 1 , we perform a basic unsupervised multi-objective clustering [select.Please note that our proposed UMVMO-ustering  on samplAverage pairwise correlation distanceselect aim not only to be relevant but non-redundant as well. To identify the set of non-redundant genes, a second objective function has been designed, i.e., average pairwise correlation distance [view-1 or The selected genes by proposed UMVMO-distance  between distance  between Agreement Index (AI)view-1 and view-2   applied a et al. , though Step 5: Forming non-dominatingArchiveStep 1 and Step 2, then according to Step 3, all three objective functions are calculated for them. Next, these solutions are perturbed once (according to Step 4). Similar to original solutions, objective functions are calculated for their corresponding perturbed solutions too. In this step, UMVMO-select aims to identify non-dominated solutions. According to the concept of underlying optimization strategy AMOSA [Archive is maintained. These non-dominated solutions are also called Pareto-optimal solutions, and the front made by them is also known as Pareto-front [Once all of ‘gy AMOSA , to storto-front .Archive, the domination status between ‘Archive made of non-dominated solutions . After that, objective function values of n-pt are calculated (according to Step 3), and domination status between c-pt and n-pt along with rest solutions in Archive is checked. According to [a and b is defined as follows.Archive along with c-pt and the n-pt are used. Next, based on the domination status of the n-pt and the c-pt along with Archive member, three different cases can arise. Based on these cases, different strategies are adopted to update c-pt and Archive members. The overall pseudo-code of the optimization strategy (AMOSA), followed by our proposed algorithm, is shown in Fig. Archive is considered as the final optimized set containing non-dominated solutions or the final Pareto-front.At this step, our proposed algorithm follows the optimization strategy of AMOSA . Accordirding to , the amoStep 7: Ensemble operation on the final Pareto frontselect performs an ensemble operation on consensus solutions  who fails in the validation test is discarded. Rest consensus solutions are ensembled to produce the final solution, according to If all Pareto front solutions are biologically significant, but not the ensembled one then, the consensus solution having maximum Silhouette index value  is choseselect.If neither ensembled nor any Pareto front solutions are biologically significant, then the input parameters are changed through the sensitivity analysis (discussed in the “Results” section) to re-execute UMVMO-After validating the obtained solution, the encoded cluster centers are extracted as candidate genes (features) of reduced gene-space. Suppose Once the final ensembled clustering solution is obtained, we perform a biological significance test on the obtained clustering solution before extracting the candidate genes. The GO enrichment analysis—presented by the GO toolStep 9: Identifying gene markers from multiple runs of proposed algorithmselect ‘We execute the proposed algorithm UMVMO-


P value < 0.001 (log-rank test)) than UICC tumor, node, metastasis (TNM) staging and microsatellite instability status are identified and validated using DNA methylation-specific PCR. The MethCORR approach is general, and may be similarly successful for other cancer types.Transcriptional characterization and classification has potential to resolve the inter-tumor heterogeneity of colorectal cancer and improve patient management. Yet, robust transcriptional profiling is difficult using formalin-fixed, paraffin-embedded (FFPE) samples, which complicates testing in clinical and archival material. We present MethCORR, an approach that allows uniform molecular characterization and classification of fresh-frozen and FFPE samples. MethCORR identifies genome-wide correlations between RNA expression and DNA methylation in fresh-frozen samples. This information is used to infer gene expression information in FFPE samples from their methylation profiles. MethCORR is here applied to methylation profiles from 877 fresh-frozen/FFPE samples and comparative analysis identifies the same two subtypes in four independent cohorts. Furthermore, subtype-specific prognostic biomarkers that better predicts relapse-free survival (HR = 2.66, 95%CI [1.67–4.22],  Molecular analysis of archival formalin-fixed clinical tissues can be difficult. Here, researchers have developed MethCORR, an approach that infers gene expression from DNA methylation data and use the approach for molecular characterization and prognostication of colorectal cancer using archival samples. Despite a continuous refinement of the UICC tumor, node, metastasis (TNM) staging system to measure disease extent and define prognosis, the disease outcome still varies considerably even for patients with the same tumor stage. Therefore, new factors that can more precisely stratify patients into different risk categories are clearly warranted4. Consensus molecular subtype (CMS) classification stratifies CRC into four subtypes CMS 1–4 with distinct biology and histopathological features2. Still, the CMS taxonomy itself has limited prognostic power for therapeutic decision-making5. To address this, we previously combined transcriptional subtyping with subtype-specific prognostic biomarkers to improve prognostication beyond TNM staging in retrospective cohorts3. This indicated a clinical potential of using molecular classification and subtype-specific biomarkers as a complement to TNM staging for prognostication. Furthermore, it highlighted the importance of archived tumor material for biomarker discovery and pre-clinical validation.Recent attempts to resolve CRC heterogeneity and improve prognostication include molecular subclassification and characterization based on transcriptional profiling9. The clinical popularity of FFPE tissue is unlikely to change as it forms the basis for histopathological diagnoses and is a convenient, cost-effective preservation method. For wide utility, strategies for molecular classification, characterization, and prognostication should therefore be compatible with FFPE tissue.The strategies for transcriptional classification and subtype-specific prognostication were developed by, and still primarily rely on, profiling high-quality RNA purified from fresh-frozen (FF) tissue. However, high-quality RNA is often not recovered from the formalin-fixed, paraffin-embedded (FFPE) tissue that is routinely archived in the clinic. This can preclude confident transcriptional profiling and hereby complicate clinical testing of molecular classification and exploratory analysis in well-annotated, archival FFPE material11 and enzymatic strategies for DNA repair have greatly improved the analysis of FFPE DNA15. A strategy for robust analysis of clinical and archival FFPE samples could involve DNA methylation as highly concordant DNA methylation profiles are produced from matched FF and FFPE tissues when using the Illumina Infinium HumanMethylation Beadchip technology17. In addition, many biological traits, such as RNA expression and cell-type identity, are associated with specific and robust DNA methylation patterns in the genome19. This suggests that both gene expression and cell-type information may be extracted from DNA methylation profiles of FFPE samples and used for molecular classification and prognostication, as an alternative to RNA profiling. Furthermore, conversion of methylation profiles into a gene-centric expression format would allow molecular analysis of FF and FFPE samples using the plethora of bioinformatics tools, databases, and signatures established for RNA expression analysis.Strategies based on DNA rather than RNA profiling may be a way forward. DNA is considered less sensitive to degradation than RNA in FFPE samples22. We hypothesize that genome-wide correlation analysis will identify far more associations and that these will include both functional gene-regulatory interactions and indirect associations e.g. between cell-type-specific RNA expression and cell-type-specific DNA methylation. We here show that MethCORR, independent of whether the methylomes were produced from FFPE or FF tissues, allows expression information to be inferred for a large number of genes . Consequently, MethCORR enables a plethora of molecular analyses to be performed on otherwise difficult-to-analyze FFPE tissues e.g. tumor characterization, tumor classification, and interpretation of expression signatures to derive DNA methylation-based biomarkers. Hereby MethCORR also provides a path for improved, subtype-specific prognostication of CRC using clinical FFPE samples.Motivated by this, we here develop MethCORR, an approach, which identifies genome-wide correlations between gene expression and DNA methylation and use this to obtain gene expression and cell-type information in independent samples from their DNA methylation profiles. In homogenous cell preparations, associations between gene expression and DNA methylation have been observed only for a small fraction of genes when analyzing local promoters, gene bodies, or nearby enhancersβ-values) from 394 FF CRC samples of The Cancer Genome Atlas (TCGA) Project, denoted the COREAD cohort  in which genome-wide correlation analysis was performed independently and one validation set  overlapping in the two discovery sets for 17,776 of 20,530 genes  and confirmed these for 11,222 genes in the validation set ; Supplementary Data R2 = 0.76 (range = 0.62–0.82); Fig. R2 = 0.92 (range = 0.87–0.95); COREAD median R2 = 0.74 (range = 0.55–0.82); Fig. R2 between cohorts was caused by differences in RNA quantification methods rather than the MethCORR approach. In support, comparative analysis of COREAD validation samples using normalized RNA expression data from the UCSC XENA database24 and the National Cancer Institute (NCI) genomic database commons (GDC)25 confirmed that MethCORR iRNA-RNA correlations were not lower than if applying two different RNA normalization strategies to the same samples  and CRC intrinsic subtype  predictions when using the measured RNA or iRNA expression as input Fig. .27. Indeed, samples with the lowest correlation between measured RNA and iRNA expression had significantly lower RNA quality than high correlation samples  test; Supplementary Fig. 11. In agreement, analysis of nine COREAD samples with available RNA sequencing and 450K methylation profiles from matched FF and FFPE tissues identified higher intra-sample R2’s between FF RNA sequencing and FFPE iRNA profiles (median R2 = 0.91 (range: 0.80–0.94)) than between FF and FFPE RNA-sequencing profiles (median R2 = 0.7 (range: 0.63–0.87); P value < 0.001, WRS test; Fig. R2 = 0.98 (range: 0.98–1.00); Table R2 = 0.96 (range: 0.94–0.98); Supplementary Fig. 17. Additional evidence came from principal component analysis (PCA). Here samples clustered according to preservation method when analyzing FF and FFPE RNA-sequencing profiles together, whereas samples clustered more according to patient ID when analyzing RNA profiles of FF samples together with iRNA or MCS profiles of FFPE samples  tCollectively, this showed that MethCORR expression measures (MCSs and iRNAs) can be inferred from DNA methylation for a large number of genes, even when methylation data are based on FFPE tissue.1. Two MethCORR subtypes, CRC1 and CRC2, were identified in all four cohorts put Fig.  or when put Fig. . Indeed,put Fig. . Furtherput Fig. . Similarput Fig. . Despiteput Fig. .Collectively, these results demonstrate that MethCORR allows uniform discovery and characterization of biologically relevant CRC subtypes in FF and FFPE samples using well-established bioinformatics tools.18; Supplementary Fig. CD3 Epsilon (CD3E) gene overlapped with T-cell specific DNase I hypersensitive sites and DNA methylation patterns characteristic of T-cells  and epithelial cellular adhesion molecule (EPCAM) overlapped with patterns characteristic of stromal cells/fibroblasts and intestinal epithelial cells, respectively . This showed that analysis of expression-correlated CpGs help identify clusters of co-expressed genes and link them to particular cell types via comparison to cell-type-specific DNA methylation profiles.By analysis of expression-correlated CpGs in the MethCORR matrix, we found that most CpGs were not located on the same chromosome as the gene they correlate with  and near-identical to a MethCORR map comparing serrated/MSI and conventional adenocarcinomas from the 450K methylation dataset GSE6806031 . Similar results were obtained when the map was overlain with MethCORR interpretation of a transcriptional gene set defining serrated vs. conventional CRC  and stem cells, or undergo EMTern Fig. . Three Tern Fig. . This suern Fig.  consiste. 2 Fig. . Instead. 2 Fig. : Two reg 33 Fig. . A third 33 Fig. . Finally 35 Fig.  and GSEA 35 Fig. . Collect 35 Fig. .36, ColoGuideEx37, Oncotype DX38, ColoPrint39, and Tian et al.40 showed that MCSs for almost all stromal transcripts were positively correlated with the median MCS for all signatures  expression41  expression  assays. In addition, a QMSP assay targeting the HNF4A promoter was included for CRC subtyping; HNF4A is upregulated in CRC2  would expectably be robust. By contrast, RNA profiling is highly affected by RNA degradation26 and the RNA quality obtainable from FFPE is often compromised9. In agreement, tumor samples with the lowest correlation between iRNA and measured RNA expression had lower RNA quality scores than samples with high correlations, whereas 450K methylation data quality did not differ  were acquired via the UCSC XENA Public Data Hubs3. The FFPE1 cohort encompasses CRC samples from the prospective study COLOFOL56 collected at hospitals in the central region of Jutland, Denmark. None of the patients received neoadjuvant therapy. The tumors were histologically classified and staged according to the UICC TNM staging system. Cancer cell percentage was evaluated individually by two trained researchers, and when necessary, tumor biopsies were macroscopically trimmed to enrich the fraction of neoplastic cells. The SYSCOL and COLOFOL study was conducted in accordance with Danish law and is approved by local institutional review boards and ethical committees and written informed consent was obtained from all patients. The FFPE2 cohort (IDIBELL) encompasses 56 samples collected at Medical Oncology Service of ICO Badalona-Germans Trias i Pujol Research Institute (IGTP), Spain. None of the patients received neoadjuvant therapy. The tumors were histologically classified and staged according to the UICC TNM staging system. Cancer cell percentage was evaluated individually by two trained researchers, and when necessary, tumor biopsies were macroscopically trimmed to enrich the fraction of neoplastic cells. Patients were followed according to the national clinical guidelines and written informed consent was obtained from all patients. Clinical information regarding the COREAD, SYSCOL, COLOFOL, and IDIBELL cohort samples is presented in Supplementary Table The SYSCOL and FFPE1 cohorts were acquired from the CRC biobank at the Department of Molecular Medicine, Aarhus University Hospital, Denmark. SYSCOL samples were collected at hospitals in the central region of Jutland, Denmark from 1999–2013FF tumors from the SYSCOL cohort were macrodissected to enrich the fraction of neoplastic cells and DNA was extracted from serial cryosections using the Puregene DNA purification kit (Gentra Systems). Integrity of the genomic DNA from FF samples was assessed by 1.3% agarose gel analysis and only samples containing a high molecular weight smear (~50 KDa) were analyzed further. Bisulfite (BS) conversion of 600 ng DNA of each sample was performed according to the manufacturer’s recommendations for the Illumina Infinium Assay (EZ DNA methylation kit. Zymo Research. Cat. No. D5004). Next, DNA methylation profiling was performed using Infinium HumanMethylation450 BeadChip technology , as described by the manufacturer.FFPE tumors from the COLOFOL FFPE1 cohort were macrodissected to enrich the fraction of neoplastic cells, DNA was extracted using the QIAamp DNA FFPE Tissue kit (Qiagen) and all samples passed the Infinium FFPE quality control . For methylation profiling 500 ng DNA underwent FFPE DNA restoration  after BS conversion and profiling was performed using Infinium HumanMethylationEPIC BeadChip technology , as described by the manufacturer.β-values for each CpG site on the BeadChip were derived using the ChAMP R-package57 using the champ.import and champ.norm functions.FFPE tumors from the IDIBELL FFPE2 cohort were macrodissected to enrich the fraction of neoplastic cells. DNA was extracted using the QIAamp DNA FFPE Tissue kit (Qiagen) and all samples passed the Infinium FFPE quality control . For methylation profiling 250–500 ng DNA underwent FFPE DNA restoration  after BS-conversion and profiling was performed using the Infinium HumanMethylation450 BeadChip technology  as described by the manufacturer. For both the SYSCOL, FFPE1, and FFPE2 cohort the methylation 24 [https://xena.ucsc.edu/public-hubs/] and the GDC Data Portal25 [https://portal.gdc.cancer.gov/] as normalized DNA methylation β-values. Missing β-values were imputed using the R-package Impute58. All DNA methylation measurements were performed once for each distinct sample.HM-450K DNA methylation profiles of the COREAD samples were acquired from the UCSC XENA Public Data Hubs59) and estimating fragments per kilobase of exon per million fragments mapped (FPKM) values for Ensembl genes using Cufflink .FF tumors from the SYSCOL cohort were macrodissected to enrich the fraction of neoplastic cells and total RNA from serial cryosections were extracted using the RNeasy Mini Kit (Qiagen). RNA integrity was assessed using the Agilent RNA 6000 Nano Kit on an Agilent 2100 Bioanalyzer and >98% of analyzed samples had a RNA integrity number (RIN) > 6. Paired end mRNA sequencing was performed using 500 ng total RNA for library preparation with the TruSeq RNA Sample Prep Kit v2 and the TruSeq SBS Kit v3 was used for sequencing aiming for a minimum of 40 Million reads per sample. Sequencing reads were mapped to the human genome issue HG19 (hg19) using the Tophat2 mapper (Tophat: v2.0.1024 [https://xena.ucsc.edu/public-hubs/] as log2(FPKM + 1) normalized RNA expression values for 20,530 genes and via the GDC Data Portal25 [https://portal.gdc.cancer.gov/] as FPKM normalized RNA expression values for 60,483 transcripts. During comparison of RNA-sequencing data from nine matched FF and FFPE samples, only data originating from the same TCGA source center (indicated in Supplementary Data RNA-sequencing profiles for the COREAD samples were acquired from the UCSC XENA Public Data Hubs2(FPKM + 1) normalized RNA expression values for all available 20,530 RNAs and DNA methylation β-values for the 396,065 CpGs, where β-values were provided by the XENA Public Data Hubs24. This analysis generated the COREAD MethCORR matrix (Supplementary Data 3 (Supplementary Data 25 (Supplementary Data The MethCORR development strategy was independently applied in three CRC datasets of paired RNA expression and DNA methylation data (Supplementary Data 2(FPKM + 1)) and the DNA methylation β-value of each CpG site were calculated independently in discovery sets 1 and 2 using the publicly available R function “cor”. All non-significant correlation pairs were discarded . The remaining expression-correlated CpGs were ranked by their Spearman’s rho in each discovery set and next by their rank sum within discovery sets 1 and 2 to identify top common expression-correlated CpGs. From these lists of ranked CpGs specific for each RNA, we selected up to 100 CpGs whose methylation β-value most negatively or positively correlated with its expression resulting in lists of ≤200 RNA expression-correlated CpGs for each RNA (depending on the number of expression-correlated CpGs in the ranked lists). To ensure analysis robustness, especially in FFPE samples, we excluded all CpG sites that had a detection P value > 0.05 (ChAMP package57) in ≥5% of samples in either the SYSCOL, FFPE1, or FFPE2 cohort. Top ranking CpGs for all analyzed genes for the TCGA COREAD cohort (datasets acquired via the UCSC XENA Public Data Hubs) can be found in Supplementary Data The CRC cohort was divided in two discovery sets , whereas a third set was reserved for independent validation  to calculate a MCS for all genes with both positively and negatively expression-correlated CpGs using the formula:For each sample we used the methylation β-values of any relevant 450K CRC data set of choice.The MCS formula calculates the average methylation value of the expression-correlated CpG sites specific for each gene. Unless otherwise indicated, the COREAD MethCORR matrix encompassing expression-correlated CpGs for 11,222 genes (Supplementary Data B0 + B1 × MCS) and polynomial regression models . The Caret R-package61 was used to perform modeling by 10 × 10-fold cross validation and we used the average RMSE to select the best model for each gene. As performances were highly similar for simple linear and polynomial models for most genes, we only selected polynomial models if a ≥5% relative decrease in RMSE values were observed over simple linear models. Model performances were independently validated in validation set 3 (Supplementary Data R2 > 0.16 in both the discovery (set 1 + 2) and validation (set 3)) were regarded as MethCORR genes and included in the MethCORR matrix . Only CpGs with negatively expression-correlated CpGs from the MethCORR matrix  were used for identifying the overlap given that inclusion of all expression-correlated CpGs in a single map would complicate interpretation as genes with opposite expression-correlation to DNA methylation would cluster together. Genes with no significant CpG overlap to other genes are not included in the graphical representation of the MethCORR map for visual simplicity. For interpretation, the MethCORR map was overlain with several data types including external DNA methylation data, transcriptionally defined marker genes, gene sets, and signatures. To visualize these diverse data types using the MethCORR map, four types of scores were established as follows:The MethCORR map for the COREAD cohort was created by clustering MethCORR genes according to their overlap in expression-correlated CpGs using Cytoscape V3.2.0z-scores (Δmedian MCS z-score) was used to visualize differences between subtypes encompassing multiple samples  whereas MCS z-scores were used for visualization of differences between individual samples within a cohort. MCS z-scores were calculated for each gene within each investigated cohort by subtracting the cohort mean from an individual sample MCS and dividing the difference by the cohort standard deviation. E.g. for analysis of inter-tumor heterogeneity, MCS z-scores were calculated for each gene within the whole COREAD FF1 cohort. For analysis of the cellular composition of the TME cluster, MCS z-scores were calculated from a collection of cell types with available 450K analysis downloaded from either Marmal-aid64, Gene Expression Omnibus (GEO)65, or Array express .For DNA methylation datasets (450K/EPIC arrays), MCSs were first calculated for all samples and two types of scores were used for map visualization. The difference in median MCS upreg. − cMCSdownreg.) for each gene. For visualization, MethCORR map gene nodes were colored according to these MCS z-scores, ΔMCS z-scores, cMCS, and ΔcMCS as indicated in the text. For map visualization of published prognostic signatures, cMCS were calculated for the five general (non subtype-specific) signatures , as they are single lists of RNAs associated with poor prognosis CRC . For the CRC subtype-specific SSC prognosis and CIN prognosis signatures ΔcMCS were calculated, as they are complex signatures encompassing lists of RNAs with high and low expression in aggressive CRC3.For transcriptionally defined marker genes, gene sets, and signatures, two types of scores were used for map visualization depending on the data format. For simple gene sets and RNA signatures, defined by only one gene list , a correlation to median MCS (cMCS) was calculated for each MethCORR gene. The cMCSs were calculated as the average Pearson correlation between the median MCS of the gene set and the MCS of each MethCORR gene within the FF1, FF2, FFPE1, and FFPE2 cohorts. For complex gene sets/signatures, defined by two gene lists , a correlation to median MCS difference score (ΔcMCS) was instead calculated for each MethCORR gene. The ΔcMCSs were calculated by subtracting the cMCSs for the downregulated gene set from the cMCSs for the upregulated gene set  using pairwise comparisons of MCSs and the following settings: num. marker genes = 50, number permutations for Fisher’s statistics = 1000, weighted score type = no, null distribution = each. A false discovery rate (FDR) P value < 0.05 was used as significance cutoff (provided by the Submap software68).NMF consensus clustering was performed using the R-package NMF2. RNA expression or iRNA expression were used as input, as indicated in the text. CRIS classification was performed using the R-package CRISclassifier provided by Isella et al.4 using RNA expression or iRNA expression as input, as indicated in the text.CMS classification was performed with the R-package CMSclassifier using the single sample method and nearest CMS as predicted subtype48. Genes with stroma scores >0.5 were considered stromal genes, whereas genes with stroma scores <0.1 were considered epithelial cancer genes. For the COREAD cohort, gene- and sample-specific CIN scores were established from the gene-level copy-number data  available at the UCSC XENA Public Data Hubs24. The gene CIN scores were defined for each gene as the standard deviation of the GISTIC2 copy-numbers of all samples within the COREAD cohort. The sample CIN scores were defined as the standard deviation of GISTIC copy-number scores across all genes within a sample calculated for each sample within the COREAD cohort. For non-COREAD cohort samples (without GISTIC2 data) CIN scores were derived from copy-number data extracted from the HM-450K/EPIC methylome BeadChips using the champ.CNA module of the ChAMP R-package57. Here, the sample CIN score was defined as the mean interquartile range of the copy-numbers for all chromosomal segments (seg.mean) covered by at least 25 Illumina probes (num. probes). The sample DNA methylation score for each sample was defined as the 40th percentile of DNA methylation β-values of all CpG sites common to all four CRC cohorts. ESTIMATE Stroma scores and Immune scores were calculated using the R-package ESTIMATE69 using default parameters and MCSs as input. Household gene status was defined as genes included in the list of housekeeping genes70 available at [https://www.tau.ac.il/~elieis/HKG/].Stroma scores for each gene (fraction of reads of murine origin) was acquired from Isella et al.29 using default settings. Genes were pre-ranked according to their Spearman correlation of their MCS to CRC1 subtype status and gene sets were considered significantly up- or downregulated for FDR q values < 0.05 (provided by the GSEA software29). The Molecular Signatures Database (MsigDB) gene set collection v6.1 was used with the addition of custom gene sets (Supplementary Table Pre-ranked GSEA was performed using the GSEA 3.0 tool71 [https://www.proteinatlas.org/]. The following antibody and tissue sections were chosen (available from v8.proteinatlas.org): ACTA2 , PDPN , CD3E , HNF4A .Immunohistochemical stainings of CRC tissue sections were acquired from the Human Protein Atlas64, GEO65, or Array express72. Median MCSs were calculated for similar sample types, such as technical replicates prior to analysis , 0.2 µl of 25 pmol/µl forward and reverse primer, 0.1 µl of 17 pmol/µl hydrolysis probe, 0.125 µl TEMPase hot start DNA polymerase, 0.4 µl of 12.5 pmol/µl dNTP mix, 0.475 µl H2O, and 2 µl of 2.5 ng/µl BS treated DNA template. QMSP reactions were mixed in MicroAmp Optical 384 Well reaction Plates (Applied Biosystem) and run on the ViiA™ 7 Real-Time PCR system (Applied Biosystems) with the following PCR program: denaturation at 95 °C for 10 min, followed by 40 cycles at 95 °C for 15 s, and 60 °C for 1 min. The ViiA7TM software (Applied Biosystems) was used for evaluation of the fluorescence signals and the ΔCT was calculated by the use of the reference gene AluC4A. Subtyping was performed using ΔCTHNF4A as a marker for CRC2 and the average of ΔCTCD3E and ΔCTACTA2 as a marker for CRC1 . Samples with a ΔCTHNF4A/(ΔCTCD3E +  ΔCTACTA2)average ratio <0.85 were defined as CRC2 samples. CT values were measured three times for each sample .QMSP was performed using DNA primers specific for unmethylated 28 and pre-ranked GSEA29 a FDR-corrected P value < 0.05 was considered significant and P values were provided by the corresponding software. During gene list enrichment analysis an adjusted P value < 0.05 was considered significant (provided by the Enrichr software30). During eFORGE analysis a Q-value < 0.05 was considered significant (provided by the eFORGE software). RFS analysis was performed in UICC TNM stage II–III samples with good clinical annotation and follow-up  software. Significance was evaluated by log-rank test of equality. Cox proportional hazards regression analysis was used to assess the impact of MethCORR risk groups, TNM stage, and MSI status on RFS. The proportional hazard assumption was tested by a global test of the Schoenfeld residuals.Unless otherwise noted, statistical significance of differences between groups was determined using a non-parametric WRS test. During Submap analysisFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Reporting Summary


Precision medicine for breast cancer relies on biomarkers to select therapies. However, the reliability of biomarkers drawn from gene expression arrays has been questioned and calls for reassessment, in particular for large datasets. We revisit widely used data-normalization procedures and evaluate differences in outcome in order to pinpoint the most reliable reprocessing methods biomarkers can be based upon. We generated a database of 3753 breast cancer patients out of 38 studies by downloading and curating patient samples from NCBI-GEO. As gene-expression biomarkers, we select the assessment of receptor status and breast cancer subtype classification. Each normalization procedure is applied separately, and biomarkers are then evaluated for each patient. Differences between normalization pipelines are quantified as percentages of patients having outcomes different for each pipeline. Some normalization procedures lead to quite consistent biomarkers, differing only in 1-2% of patients. Other normalization procedures—some of them have been used in many clinical studies—end up with distrusting discrepancies (10% and more). A good deal of doubt regarding the reliability of microarrays may root in the haphazard application of inadequate preprocessing pipelines. Several modes of batch corrections are evaluated regarding a possible improvement of receptor prediction from gene expression versus the golden standard of immunohistochemistry. Finally, we nominate those normalization methods yielding consistent and trustable results. Adequate bioinformatics data preprocessing is key and crucial for any subsequent statistics to arrive at trustable results. We conclude with a suggestion for future bioinformatics development to further increase the reliability of cancer biomarkers. Gene expression microarrays have been widely used to derive biosignatures for survival prediction  and alloth, 2019), and reuse of this “big data treasure” has become not only a promising opportunity but also a significant challenge: Data from different studies not only pose additional questions about normalization but also show batch effects to be considered and properly adjusted .Distances and correlations are sound mathematical concepts. However, for precision medicine, it is even more relevant if biomarkers result stable and reproducible: Do biomarkers, as determined from a given array , lead to unique consequences for a given patient, irrespective of the particular pipeline being used? To clarify, we compute two test sets of predictive biomarkers:A”, compare H”), see Regarding (1), we predicted receptor status, yielding one out of 3 classes—positive, negative, or unknown—for each of the receptors (estrogen (ER), progesterone (PGR), and HER2. For a given comparison (say “Regarding (2), patients are divided in either 4 classes  or in 5 classes . For each class, a specific treatment is adequate and misclassification may significantly reduce survival chances or unnecessarily increase unwanted side effects. The difficulty and yet focal importance of adequate preprocessing of data has been deplored in a seminal paper : “The geFirst, all arrays are processed by each of the two pipelines to be compared. Then, for each array (out of 3753), all biomarkers are evaluated to see how much these predictions differ due to preprocessing via different pipelines. For each biomarker, we give percentages of patients for which this biomarker would change , we present a small selection to highlight differences of particular interest regarding approach, performance, stability, or results. More comparisons are shown in the supplementary material.Secondly, and maybe even more interestingly, we evaluate the performance of single pipelines in predicting receptor status against the golden standard IHC. Since some “ground truth” is at hand in this case, we are able to compute “percent misclassification” as a measure of performance.RMA in R implementation, being the most widely used pipeline, appears in many of our comparisons and evaluations. On a visual basis, all possible pairwise comparisons are summarized as heat map in In the following detailed description, we let—for ease of reference—comments immediately follow the corresponding results, rather than relegating them into the discussion section.A” in As the first pipeline, we define MAS5 applied to single studies, using the R implementation (pipeline token “MAS5 R Single”). As the second pipeline, we define PLIER using the Bioconductor R implementation (token “PLIER R Single”), see Results differ substantially: MAS5 yields a distribution centered at larger expression values (panel a) and is little affected by study-specific bias, panel (c). PLIER shows considerable variation within studies, comparable to variation between studies, panel (c). This is in contrast to all other pipelines, see below. We may interpret this as follows: PLIER preserves biologic variation within studies in such a way that it is not dominated by batch effects between studies. In contrast, MAS5 gets rid of all batch effects but at the same time might be prone to extinct also biological variation. Numerical results for correlation distances are given in A”–“N”, e.g. “A” standing for the comparison “MAS5 R Single” versus “Plier R Single”, see q0.15 to q0.85). Smaller values indicate more similar results. Note that these quantiles describe specific points in the distributions shown in panels (b) of Pairwise comparisons between data processing pipelines via distance distribution statistics. Pairwise comparisons of pipelines are labelled by “Both, MAS5 and PLIER, use mm-probes designed by Affymetrix to remove hybridization bias. After all, this was considered the stronghold of Affymetrix in paving the way towards absolute measurement of gene expression. This is most obvious in Bland-Altman plots, see B”): We find systematic differences in absolute values, and these differences also depend on the mean in a suspicious way, as clearly visible in Figure RMA was established to overcome MAS5's subtraction of mismatch intensities, considered unrealistically large. This came with the cost of replacing probe-specific bias correction by a blunt estimate. In fact, RMA deviates significantly from MAS5  within the triple {MAS5, RMA, PLIER}, are in bold entries  is shown here for ease of reference.) to contrast them from comparisons anticipated in the table here, but described later.Now we turn to the stability of biomarker predictions as pipelines are swapped, see columns “ication”  and Coheication” . The firB”) yield fairly discordant receptor status (8.1% misclassification on average). In contrast, mm-probes disregarded by RMA does not seem to severely harm , ending up with relatively low misclassification . Table Interestingly, despite excellently looking BA-plots Figure , MAS5 anA”–“N”) between pipelines, see Pairwise comparisons , GcrmaRGlobal and GcrmaRSingleCombat , PLIER and RmaRSingle  as well as between PlierRSingle and GcrmaRSingle . Note that in particular the first biomarker of “genefu”, “smgene”, proofs excessively sensible to swaps “C” and “E”, yielding discrepancies up to almost 60%.A”–“N”, see We evaluate six biomarkers computed by algorithms within the R-package “genefu”: scmgene, scmod1, and scmod2 assign subtypes out of 4 classes, whereas the other 3 assign subtypes out of 5 classes, see column “#”. For each specific comparison  of the same algorithm should be expected to yield equal results. However, for GCRMA, we find distinct differences, see section 7.4 in supplementary material.To the best of our knowledge, all normalization methods mentioned above have originally been devised for application to single studies (in this work labelled by token “single”). If multiple studies were to be analyzed, in many cases, “meta-studies” were performed : SeparatAn approach intentionally reducing study bias in multistudy normalization has been undertaken by IRON. It first generates a “reference chip” from all arrays to be analyzed and then normalizes all chips according to that reference. IRON is thus designed to handle multiple studies as one single batch.A similar but even more general approach is realized by “frozen RMA” (token “fRMA”): It computes a reference profile of gene expression from samples of the respective platform in GEO, and based on that performs RMA , separatAs an additional alternative, we follow the proposal to normalize samples from several studies collectively , 56 as iThe same holds for GCRMA: It almost completely eliminates study-bias, in addition to its merits regarding probe-specific bias reduction.Note that fRMA is exceptional: To the best of our knowledge, it is the only normalization method yielding unique results for a given chip, i.e., “fRMA Sinlge” is in effect identical to “fRMA Global”.H” in q0.80. Pairwise comparisons of normalized values are shown in We first investigate the consequences of applying RMA not only to single studies but simultaneously to a whole bunch of studies: The comparison “RMA M Single” versus “RMA M global” is labelled “Unfortunately, however, it was technically impossible for us to normalize all 3753 samples jointly in R, seemingly due to weaknesses in software design, rendering “RmaRGlobal” unfeasible. The cause of this problem was a memory leak (‘memory not mapped'). Even when executing Bioconductor rma on a machine with excessively larger memory (256 GB), the same type of error occurred. We performed bench marks with increasing numbers of samples and found an approximate limit of 2K samples. This number is to be interpreted with caution, since input (CEL) files vary in size (even for the same chip), depending on the format. Moreover, the same error occurs with any of the derivative procedures  based on the same C++ sources. For machine specifications, see also caption to Table For global RMA, we instead had to retreat to MATLAB, presuming the bioinformatics community up to now could not benefit from global RMA normalization of large joint studies .While the R-implementation of RMA  cannot normalize our set of samples in a single batch, the R-implementation of GCRMA, surprisingly, can. We have to note, however, that careful setting of parameters is mandatory.We compare the pipelines “GCRMA R global” with “GCRMA Matlab global” and include “GCRMA R Single” as reference for existing study-specific bias, see Efforts to extend RMA by design towards multistudy normalization resulted in and fRMA  and IRONFrozen RMA (fRMA) allows one to analyze samples individually or in small batches and then combine the data for analysis. This is achieved by drawing on publicly available databases to estimate probe-specific effects and variances (frozen estimates). These frozen parameters are used to normalize any new array. The fRMA is particularly useful when it is not feasible to preprocess all of the data simultaneously.FRMA and IRON base normalization on unified expression profiles and drastically reduce study bias, see J” in J” of The differences between IRON global and fRMA regarding individual probe sets are shown in K” in Differences between fRMA and GCRMA seem more pronounced: Comparison of single values shows distinct trends, most probably reflecting probe-dependent bias correction by GCRMA, see q0.80 exhibits fairly the same variability within studies as seen between studies. This feature of PLIER hints to poor performance regarding biomarkers, if single pipelines are evaluated (see chapter 3.6).PLIER, being implemented in R, for technical reasons cannot handle all 3753 samples in one bunch. But even when normalizing single studies (token “PLIER R Single”), PLIER seems to suppress batch effects drastically, see panel (c) in Up to now, we have compared pairs of pipelines, now we evaluate single pipelines: Following normalization via each pipeline, logistic regression is trained as described in our previous work , using aAs the first procedure to eliminate batch effects after normalization, we considered ComBat , being iSVA with ViBatch = {ER, GSE} was trained on 3014 samples (with IHC-ER estimates available), with the ER declared as the only variable of interest: SVA estimates 9 surrogate variables when applied to data normalized for single studies (RmaMSingle), see As a second procedure to eliminate batch effects after normalization, we considered surrogate variable analysis (SVA), , 42, 43.Based on that, we applied fSVA and obtained batch-corrected corrected expression values for all 3753 samples. Our odds-prediction algorithm , 50 was (2) Next, we trained SVA with ViBatch = {ER, HER2, GSE} on 2291 samples (with IHC-ER and -HER2 available), and the IHC-estimate of ER and HER2 as variables of interest. We did not include PGR as variable of interest since it is physiologically highly correlated with ER, and SVA—by design—should not be fed with models including correlated variables. HER2 is but a variable of interest, uncorrelated with both others. SVA estimates 5 and 9 surrogate variables for RmaMSingle- and RmaMGlobal-normalized data, respectively, see Table 6After global normalization , SVA estimated 8 surrogate variables to be relevant. Based on that, fSVA was applied to all 3753 samples, and the odds-prediction performed. Again misclassification increased, but only slightly—from 9.7% to 10.0%, see Table 6Applying fSVA after RmaMSingle again worsens prediction (10.7% to 15.0% misclassification on average). As opposed to this, fSVA after RmaMGlobal slightly improves prediction on average (9.8% to 9.5%). It is noteworthy that PGR performs exceptionally weak in this setting: misclassification rises from 14.1% to 24.2% for fSVA after RmaMSingle. And even after RmaMGlobal, some worsening is noticeable (from 12.5% to 13.4%).(3) SVA was trained with ViBatch = {ER, PGR, HER2, GSE}, i.e., the maximum model, on 1825 samples with all 3 IHC estimates available. After RmaMSingle, it estimated 8 surrogate variables, and after fSVA the overall performance of the odds-prediction method worsened even more, from 10.7% to 25.1%. Not even PGR-prediction itself benefitted from including PGR as variable of interest. It rather worsened (24.2% to 27.5% misclassification).We suspected that the bad performance of PGR-prediction might root in the fact that it was not declared a variable of interest—despite good reasons for that (its correlation with ER). To clarify, we performed an additional testAfter global normalization, SVA estimated 10 surrogate variables, and fSVA yielded a marginal improvement in classification (decreases misclassification from 9.7% to 9.5% on average)Batch correction was performed alternatively with ComBat and also with surrogate variable analysis (SVA), in two modes. #SurrVar gives the number of surrogate variables, as estimated by the SVA-package. Values show percent misclassification of hormone receptors as predicted by the odds-model (single cutoff) versus IHC. Note that the single cutoff creates to 2 levels . Each mode of SVA was performed after both, RMA-normalization GSE-wise (RmaMSingle) and globally over all GSEs in one bunch . Note that different numbers of samples for training root in the fact that ER, PGR, and HER2 were available only for respective subsets of samples (# samples). GSE is, of course, always known. For ease of reference, we repeat here the results for RmaMSingle RmaMGlobal (bold entries) already shown in Performing batch correction (ComBat or SVA) after single RMA normalization increases the percentage of misclassified patients from 10.7 to 15–27%, depending on the particular mode of batch correction, see Table 6Performing SVA after global RMA normalization leaves the percentage of misclassified patients almost unchanged (9.5 to 10.0%), as compared to 9.7% with global RMA normalization, see Table 6Above results indicate for the prediction of receptor status out of 2 genes (per receptor):We conclude that “global normalization” is to be preferred against “single normalization plus batch correction”, at least for the prediction models we have evaluated. We have to formulate our conclusion with caution, since it may be possible that batch correction (ComBat and SVA) provides significant benefit for the establishment of new markers , as indicated in the literature. It is important to note that our markers are very “small” (just two genes for each receptor), and our conclusion pertains to such cases only. As opposed, batch corrections are designed to correct whole expression profiles, across many genes. Hence “broad markers”—involving many genes—may well benefit.Further research will be necessary to exactly specify the domain for the beneficial application of batch corrections. For the markers considered here, batch corrections  did not prove very helpful and we hence focus on comparing normalization methods against each other in the following conclusions.R- and MATLAB- implementations of RMA differ very little, as compared to the vast difference of both (compared) to MAS5. We conclude that differences seen in statistical measures do not necessarily corrupt biomarker usability.R is a free and powerful software framework and hence most widely used for statistical analysis. However, numerous bioinformatics algorithms have also been implemented in MATLAB, a commercial and fairly costly platform. We have compared the results of the same algorithms, implemented in R and MATLAB, respectively.RMA, fRMA, or IRON  orGCRMA and PLIER (performing probe-specific bias correction)What truly remains to be selected is between two flavors:RMA in R has limitations for large datasets. These can de facto only be handled in MATLAB—the latter being not really an option for the majority of users due to costs.IRON and fRMA do not differ a lot. For making a choice, we remind that IRON computes a reference chip out of those chips submitted—and hence results for a given sample always depend on the remaining set of samples submitted. Opposed to this works fRMA, it draws from information out of GEO, irrespective of particular data submitted. Hence, we prefer fRMA against IRON.q0.80: PLIER seemingly eliminates most of the study bias, see q0.80 fails to reveal any study bias, receptor status diagnostics is worse than with any other pipeline considered. Hence, PLIER cannot be the pipeline to conclusively suggest.Both, GCRMA and PLIER, compute probe-specific affinity estimates—not from all of GEO but rather from the set of samples submitted for analysis. Unfortunately, PLIER suffers from its inability to handle a large number of samples . This precludes global normalization of all studies, and one has to retreat to normalization of single studies. Despite this drawback, results look promising, as long as one considers nothing but GCRMA also reduces probe-specific bias and should therefore be a promising candidate in a final selection.K”) and compare each of these two against the dinosaur “RMA R single”, being the pipeline most frequently used up to now throughout the community, see K”, M” and “N” of In our final lap, we display the “derby” fRMA against “GCRMA R global” (comparison “2(expression), see K” in Tables q0.80-quantiles reflect a very concordant dependence on samples, see GCRMA subtracts an estimate of positive bias in the hybridization of GC-pairs, as already seen in the normalization of single studies; hence, the GCRMA profile is shifted to the left with respect to fRMA (which performs a blunt correction similar to RMA). “Shapes of profiles” are similar, however: fRMA and GCRMA exhibit a shoulder between 6 and 8 of logq0.80 (The difference between common practice (RmaRSingle) and one of the best choices broadly available today (fRMA) is of special interest: The distance seen in q0.80  correspoM” in M” in Differences regarding biomarkers are ambiguous: Little impact is seen for receptor status prediction  is substantial. Differences in average values indicate a major discrepancy, i.e., a deficiency in at least one of both methods. While profiles are very similar  and “GcrmaRGlobal”  is far from what deems optimum according to our analysis: “GcrmaRGlobal” or fRMA.Regarding receptor prediction (gene + cogene), the new pipelines (fRMA and GCRMA) have about equal distances towards each other as they both have to the most common practice, RMA singleRegarding breast cancer subtypes, the novel methods agree much closer than each of them agrees with RMA singleAs final showdown, we end up with the “derby”, “GcrmaRGlobal” against fRMA on a probe-set basis, already shown in We may conclude that common practice, i.e., RmaSingle, should be seriously questioned when applied in marker development involving multiple genes.The robustness of biomarkers indicates a somewhat different ranking of “distances” between pipelines—as compared to statistical parameters (correlation distance). Both groups of biomarkers seem most sensitive to corrections of bias and background, performed differently by each pipeline.We conclude that any software intended to compute biomarkers has to be individually calibrated: The particular normalization pipeline used during calibration has to be applied also in the prediction phase. During calibration, we encounter the difficulty that the ground truth, i.e., the true subtype of a patient, is not known for sure (in most cases). Among all pipelines described, several make sense and can be recommended, from others one should rather refrain and some even show peculiar results. In the following, we provide a summary.To start with, MAS5, although fine regarding concept, yields unfavorable distributions of expression values, precluding reasonable subsequent analysis.“RMA R Single”, seemingly the “dinosaur” pipeline most frequently used in the bioinformatics community, leaves significant batch effects uncorrected, see Figure ComBat produces dubious outliers and devastates biomarker precision, hence, cannot be recommended.RMA performs well, if performed globally over all samples. This is feasible either in its variant fRMA or in MATLAB implementation . Both pipelines render subsequent batch corrections unnecessary.L”) seem to advocate for ComBat, biomarkers may be deteriorated (see L”). We conclude that markers with few genes (as investigated in this work) do not really benefit from batch correction: Performing batch correction after single normalization worsen results, see Combining multiple studies raises the question of batch correction. Even if statistical measures for single expression values (see The situation may be totally different for multigene markers, where batch correction may allow for significant improvements. More research seems necessary to develop specific criteria for the usefulness of batch correction in deriving small and broad markers from the same set of studies.GCRMA yields more usable distribution profiles than RMA does. Beware of differences in results between implementations, however (see Figure As a final conclusion, we end up with fRMA and “GCRMA R global” as top pipelines, both rendering subsequent batch correction unnecessary. fRMA has the unique advantage to normalize additional samples in a fairly standardized way: dependence on other samples previously normalized is usually negligible.GCRMA performs probe-specific bias correction and seemingly yields more realistic distributions of expression values than RMA does. Even though both implementations can normalize multiple studies, results for single samples still depend on the “community of all other samples” normalized. This specific drawback has been overcome by fRMA. What hence would be desirable is a version of GCRMA with frozen reference. For the majority of users, an implementation in R would be preferable. However, we have to remind that results from R are seriously questioned by those from MATLAB, and quality assurance seems a must for any such new implementation. More research also seems desirable on the conditions for applicability and benefit of batch corrections, ComBat, and SVA.


Eucommia ulmoides leaves (EUL) and rapidly authenticate its geographical origins, 187 samples from 13 provinces in China were systematically investigated using three data fusion strategies  combined with two discrimination model algorithms . RF models constructed by high-level data fusion with different modes of different spectral data  were most suitable for identifying EULs from different geographical origins. The accuracy rates of calibration and validation set were 92.86% and 93.44%, respectively. In addition, climate parameters were systematically investigated the cluster difference in our study. Some interesting and novel information could be found from the clustering tree diagram of hierarchical cluster analysis. The Xinjiang Autonomous Region (Region 5) located in the high latitude area was the only region in the middle temperate zone of all sample collection areas in which the samples belonged to an individual class no matter their distance in the tree diagram. The samples were from a relatively high elevation in the Shennongjia Forest District in Hubei Province (>1200 m), which is the main difference from the samples from Xiangyang City (78 m). Thus, the sample clusters from region 9 are different from the sample clusters from other regions. The results would provide a reference for further research to those samples from the special cluster.To explore the influences of different cultivated areas on the chemical profiles of  Eucommia ulmoides Oliver is the single species of the genus Eucommia, which in turn is the only genus in the family Eucommiaceae. This plant has been applied as a tonic herb in China since ancient times  and leaves (Eucommiae Folium) as mentioned in the Chinese Pharmacopoeia  performance liquid chromatography coupled with diode array detector or mass spectrometry is the common tool for analyzing of chemical composition and for determination of EUL’s relative content . EssentiWolfiporia cocos; the inner parts had better quality consistency, which was affected by the main factor, i.e., the epidermis’ poor resistance to the external environment. Hippophaë rhamnoides. The strong adaptive capability to a changeable environment condition makes E. ulmoides a widespread species in China, with large scope latitude (N24.5°–N41.5°) and longitude (E76°–E126°) and high drop altitude   . VariousWe selected infrared spectroscopy (MIR and NIR) rather than other expensive techniques to examine chemical profiles for the geographical authentication of EUL samples from 13 provinces in China. A multi-spectral information fusion study of EUL has not been conducted thus far. Therefore, we intended to combine both NIR and MIR spectral information to demonstrate the feasibility of discriminate EUL geographic origins based on RF and PLS-DA algorithms and data fusion strategy and to make a preliminary evaluation of the influence of the growth environment on the accumulation of EUL chemical components. Our results can be useful for determining the geographical traceability of EUL products in the market and have potential broader applications to the management of the safety tracking of food or health products.A total of 187 specimens of EULs were collected from 13 provinces of China (23 different sites with varied growth environments) from 25 May 2017 to 10 June in 2017 . To reduce human operation error, each sample powder (1.0 ± 0.05 g) was weighed by electronic balance  and placed inside a uniform clean glass vessel for scanning. Prior to each scan, laboratory air (H2O and CO2) spectrum was recorded as background absorption and automatically deducted to eliminate the interference of air information. Constant conditions (25°C/30% RH) were controlled in order to maintain the consistency of the experiment’s operation environment. Each sample was measured in triplicate. The obtained spectra were then analyzed by SIMCA-P+ 14.1 , a data processing software, and averages were obtained prior to further analysis.Sample powders were scanned using an FT-NIR spectrometer  equipped with a diffused reflection accessory. The detection wavenumber ranges were 10,000–4,000 cm−1 range with a resolution of 4 cm−1 and a total of 32 scans. A stainless-steel circular ring was placed on the reflection diamond to obtain a constant layer thickness for each determination. A pressure tower is placed on the top of the circular ring (PerkinElmer Inc. micrometric pressure device). When the sample spectrum was obtained, each sample powder was placed into the circular ring hole, after which the pressure tower was rotated to press the powder tightly until a consistent pressure (131 ± 1 bar) was obtained to achieve reproducible results. After each measurement, the surface of ATR crystal, circular ring and apex of pressure tower were individually wiped with a lint-free tissue containing a combination of alcohol and deionized water. The next sample was detected when the equipment was dry to avoiding mutual interference between the samples. The temperature and humidity of the laboratory remained the same as in the FT-NIR spectroscopy analysis. Each sample was measured in triplicate, these obtained spectra were then analyzed by SIMCA-P+ 14.1.Fourier transform mid-infrared spectrometer  with deuterated triglycine sulfate (DTGS) detector equipped with an attenuated total reflectance (ATR) mode  was used to collect the sample spectra. Each spectrum was conducted in the 4,000–650 cm+ 14.1. Some spectral regions were removed prior to chemometric analysis because of the interference information . Thus, each sample finally consisted of ~4,145 preprocessed data points , and data matrices were used for further chemometric analysis.The collected ATR-FT-MIR spectra had to undergo advanced ATR correction and transformed transmittance into absorbance by OMNIC 9.2 . Raw spectra data contained vast noise and interference information. Different preprocessing methods were applied to optimize the dataset for systematic noise reduction and baseline correction . Multiplhttp://www.resdc.cn/DOI/doi.aspx?DOIid=39). Meteorological background data set was based on the meteorological data of 1,915 stations in China. After sorting and inspection, the original database was formed. It included the monthly precipitation and monthly average temperature of each site. Then, the annual average temperature, annual average precipitation, and ≥10°C accumulated temperature were calculated based on the site data. The annual average temperature, annual average precipitation, ≥10°C accumulated temperature, and moisture index (Thornthwaite method) spatial distribution data set with a spatial resolution of 500 m × 500 m are interpolated by the inverse distance weighted average method. More than 60% of China’s regions are mountainous. Thus, the meteorological indicators in mountainous areas are largely affected by the topography. The data set was corrected by a 1:100 million digital elevation model (DEM). The DEM correction was carried out for annual average precipitation and ≥10°C accumulated temperature based on the temperature decrease rate of 0.6°C for each 100 m rise in altitude. The locations of the sampling points in the meteorological background dataset and the soil type map were all drawn by using ArcGIS 10.0 , and the data of the sampling points were extracted by the extraction analysis tool under the arc toolbox of ArcGIS.For the exploratory analysis, geographical and climate information were used to assess possible relations of chemical accumulation. Meteorological background data set and soil types for the sampling sites of EULs were downloaded from the Resource and Environment Data Cloud Platform, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences  transforms raw data into a set of linearly independent representations of each dimension through linear transformation, which can be used to extract the main feature components of the data and turn the high- dimension data into low dimension data. Through the extracted principal components, the data were transformed into a new coordinate system, and the correlation between the samples and the variables can be seen, thereby allowing the visual analysis of the samples’ classification trend .In addition to the linear dimensionality reduction algorithm of PCA, a nonparametric and nonlinear algorithm of t-distributed stochastic neighbor embedding (t-SNE) also exists. t-SNE finds the law in the data by identifying the observed patterns based on the similarity of data points with multiple features. By reducing the tendency of gathering the points in the center of the map, this algorithm can lead to obvious and better visualizations .Different from the above 2D reduction algorithms, hierarchical cluster analysis (HCA) determines the similarity between data points of each category and all data points by determining the distance between them. A small distance results in high similarity. The two closest data points or categories were combined to generate a clustering tree. Samples with similar chemical profiles were clustered into one group, whereas the samples with larger differences were divided into different groups according to the theory of hierarchical clustering algorithm .By comparing the visualization results of the above mentioned three algorithms, we initially explored the differences between the effects of weather conditions and altitudes of different geographical ranges on the chemical composition of EUL. PCA and t-SNE were completed by MATLAB R2017a . HCA was performed by IBM SPSS Statistics 20.0 .X), which are used to predict which class the unknown sample belongs to. In the calculation, the observed X matrix was transformed into a set of several intermediate linear latent variables (LVs). The first n LVs were selected according to the maximum eigenvalue >1. For the establishment of PLS-DA classification models, the dataset was split into two subsets, i.e., the ratio of the calibration set and validation set was 2:1, by Kennard-Stone algorithm. At the same time, the model was constructed using a calibration set with 7-fold cross validation. The veracity of a prediction model was evaluated in terms of some statistical parameters including R2(X), Q2(Y), root mean square error of estimation (RMSEE) and root mean square error of cross validation (RMSECV). R2(X) indicates the cumulative interpretation ability and Q2(Y) indicates the prediction ability of the well-established model. Furthermore, the permutation test was calculated to validate the fitting degree of the PLS-DA model based on the results of R2-intercept and Q2-intercept. The permutation test of each category model was conducted with 20 iterations. Except for the steps of the Kennard-Stone algorithm that were calculated by MATLAB R2017a, the rest of the operation was performed on the SIMCA-P+ 14.1 software.PLS-DA is a variant classifier of partial least squares regression algorithm. PLS-DA model shows the relationship among the variable matrices  error. Based on OOB error, the parameters  of the model needed to be optimized to enhance the performance. The values of best ntree are used for selection of mtry on the basis of the lowest OOB error. Finally, we exported the confusion matrix, and calibrated and verified votes for each sample. For high-level data fusion, a single spectra matrix requires the creation of a new spectral matrix by the selected importance variables. The permutation accuracy importance shows a strong preference for the discontinuous variable , specificity (SPE), precious (PRE), and efficiency (EFF) of the calibration set and the validation set. The values of these four conceptions were further calculated by true positive (TP), false positive (FP), true negative (TN), and false negative (FN). TP and TN represent the correctly identified samples of positive and negative classes, respectively. On the contrary, FP and FN represent the incorrectly identified samples of positive and negative classes, respectively.Four parameters were calculated synergistically to evaluate of model performance. Amongst these parameters, sensitivity (true positive rate) displays the fraction of samples belonging to the defined class, which is correctly accepted by the category. Specificity indicates the true negative rate and samples not belonging to the specified class that is rejected by the modelled class. Efficiency is a summarizing parameter that concerns both sensitivity and specificity. Precision is the ratio between accepted samples in specified samples and totally accepted samples of the model in the calibration or validation set.To avoid the influence of different magnitudes, the dataset was normalized in the range of . Data fusion techniques were used for coalescing information from different instruments to obtain a more accurate and holistic description. It could offset the deficiency among different analytical instruments . In otheLow-level data fusion, raw data from different instruments are directly spliced according to the sample number, and each sample achieves a new fingerprint for further analysis . However-1 was shown on the upper left corner. The wideband at 8,295 cm-1 is the second overtone of C–H stretching vibrations of the CH3 and CH2 groups. An obvious peak at 6,881 cm-1 was the first overtone with O–H stretching, whereas the weak absorbance at 5775 cm-1 was C–H stretching of R–OHCH3. The sharpest peat at 5,172 cm-1 is O–H stretching and OH deformation of H2O. The bands at 5,000–4,000 cm-1 were the C–H stretching and C–O group frequencies of carbohydrates and C–H stretching and deformation group frequencies of polysaccharide. Intuitively, an absorbance difference was observed among different geographical origins except for the band at 4,584 cm-1, which indicated the sample spectra from Guizhou and Anhui provinces. The whole peak explanation of NIR spectra is summarized in For a better interpretation of the peak alignment, the averaged FT-NIR spectra of 13 collection sites were stacked in 2, the spectra variables could reflect most of the chemical structure information including polysaccharide, amide, lipids and flavones. The detailed peak alignments are summarized in -1 is incorporated with the C═O stretching of flavones. A total of 36 flavonoids have been reported in E. ulmoides  distribution in China and samples cluster results based on the climatic conditions are shown in Wolfporia extensa sclerotia analyzed using mid-infrared spectra combined with the same HCA method. In addition, P<0.05) compared with the <3,000-meter group.To display more detailed visualization results, these samples from 23 collection sites were analyzed with HCA method, because each sample has precise classification with a direct measurement distance , Hunan Provincial Innovation Foundation for Postgraduate (Grant No. CX2018B725) and Natural Science Research Project of Jishou University (Grant No. Jdy1856 and Grant No. Jdx17020).This work was supported by the National Natural Science Foundation of China (Grant No. 31960323), Special Funds Project for Central Government Guides Local Science and Technology Development (Grant No. 2018CT5012), National & Local United Engineering Laboratory of Integrative Utilization Technology of The authors declare that the research was conducted in the absence of any commercial or financial relationships.