Although different quality controls have been applied at different stages of the sample preparation and data analysis to ensure both reproducibility and reliability of RNA-seq results, there are still limitations and bias on the detectability for certain differentially expressed genes (DEGs). Whether the transcriptional dynamics of a gene can be captured accurately depends on experimental design/operation and the following data analysis processes. The workflow of subsequent data processing, such as reads alignment, transcript quantification, normalization, and statistical methods for ultimate identification of DEGs can influence the accuracy and sensitivity of DEGs analysis, producing a certain number of false-positivity or false-negativity. Machine learning (ML) is a multidisciplinary field that employs computer science, artificial intelligence, computational statistics and information theory to construct algorithms that can learn from existing data sets and to make predictions on new data set. ML–based differential network analysis has been applied to predict stress-responsive genes through learning the patterns of 32 expression characteristics of known stress-related genes. In addition, the epigenetic regulation plays critical roles in gene expression, therefore, DNA and histone methylation data has been shown to be powerful for ML-based model for prediction of gene expression in many systems, including lung cancer cells. Therefore, it is promising that ML-based methods could help to identify the DEGs that are not identified by traditional RNA-seq method.We identified the top 23 most informative features through assessing the performance of three different feature selection algorithms combined with five different classification methods on training and testing data sets. By comprehensive comparison, we found that the model based on InfoGain feature selection and Logistic Regression classification is powerful for DEGs prediction. Moreover, the power and performance of ML-based prediction was validated by the prediction on ethylene regulated gene expression and the following qRT-PCR.Our study shows that the combination of ML-based method with RNA-seq greatly improves the sensitivity of DEGs identification.The online version of this article (10.1186/s12864-018-4932-2) contains supplementary material, which is available to authorized users. Differentially expressed genes (DEGs) have been widely used to understand not only gene function but also the molecular mechanisms underlying different biological processes. A number of methods have been developed to analyze differential gene expression, such as Real-time reverse transcription PCR (qRT-PCR) , cDNA miAlthough different quality controls have been applied at different stages of the sample preparation and data analysis to ensure both reproducibility and reliability of RNA-seq results, there are still limitations or bias on the detectability for certain DEGs –11. WhetMachine learning (ML) is a multidisciplinary field that employs computer science, artificial intelligence, computational statistics and information theory to construct algorithms that can learn from existing data sets and to make predictions on new data set . It is iArabidopsis, and a linear signal transduction pathway has been proposed [Arabidopsis etiolated seedlings [CONSTITUTIVE TRIPLE RESPONSE 1 (CTR1) [EIN3-BINDING F BOX PROTEIN 2 (EBF2) [ETHYLENE RESPONSE 2 (ETR2) [EIN3-BINDING F BOX PROTEIN 1 (EBF1) [Take plant response to ethylene as an example: ethylene is a small volatile hydrocarbon gas and regulates a wide variety of developmental processes and stress responses in plant cells. Signal transduction of ethylene has been studied for more than 2 decades, mainly with the model plant proposed , 26. Etheedlings , 27, 28,1 (CTR1) , EIN3-BI2 (EBF2) , ETHYLEN2 (ETR2)  etc. How1 (EBF1) , WRKY 251 (EBF1) , WRK 26 1 (EBF1)  were notArabidopsis seedlings of Col-0 and ein2–5, we tested the performance of ML-based identification of DEGs in response to ethylene. In brief, 468 features were collected from histone H3K9Ac, H3K14Ac and H3K23Ac ChIP-seq data in Col-0 and ein2–5 mutant seedlings that treated with or without 4 h of ethylene gas. We then identified the top 23 most informative features through assessing the performance of three different feature selection algorithms combined with five different classification methods on training and testing data sets. By comprehensive comparison, we determined that the model based on InfoGain feature selection and Logistic Regression classification is powerful and robust for DEGs prediction. Moreover, the power and performance of ML-based prediction on the expression of ethylene regulated gene were evaluated by qRT-PCR. Taken all together, our study shows that the combination of ML-based method with RNA-seq significantly improved the sensitivity of DEGs identification.Here, by using epigenomics and transcriptomics data from 3-day-old etiolated Arabidopsis seeds were surface-sterilized in 50% bleach with 0.01% Triton X-100 for 15 min and washed five times with sterile, doubly distilled H2O before plating on MS medium . After 3–4 days of cold (4 °C) treatment, the plates were wrapped in foil and kept in at 24 °C in an incubator before the phenotypes of seedlings were analyzed. For propagation, seedlings were transferred from plates to soil (Pro-mix-HP) and grown to maturity at 22 °C under 16-h light/8-h dark cycles. Ethylene treatment of Arabidopsis seedlings was performed by growth of seedlings on MS plates in air-tight containers in the dark supplied with either a flow of hydrocarbon-free air  or hydrocarbon-free air with 10 ppm (ppm) ethylene as previously described [escribed .Arabidopsis Col-0 and ein2–5 etiolated seedlings treated with air or 4 h ethylene gas were downloaded from NCBI GEO under GSE77396 [Arabidopsis genome (TAIR10) [Raw ChIP-seq data associated to histone H3K9Ac, H3K14Ac and H3K23Ac from GSE77396  and GSE9GSE77396 . The raw(TAIR10)  and uniq(TAIR10) , 37. For(TAIR10) , 34, 38.ein2–5 seedlings. Following the initial is a K started number representing the specific histone H3 acetylation marker: K9 for H3K9Ac, K14 for K3K14Ac, K23 for K3K23Ac. Next to the histone marker is the represent the treatment or comparison of histone modification: A for air treatment, C for ethylene (C2H4) treatment, diff for the difference of the reads between them were divided by the average of the two [The aligned reads were intersected with the relevant segments of the transcript including upstream of the transcription start site (TSS) (TSS1500 and TSS200), downstream of the TSS (TSS + 200), UTR region (UTR5 and UTR3), first and last exon/intron, exon/intron body, single exon/intron, full transcripts and full length of gene Fig. , using t the two , and logPeaks significantly enriched in ChIP-seq tags were identified by Model-based Analysis for ChIP-Seq  as previously described . The nea2H4 treated sample/Read density in air treated sample)) represents log2-transformed fold changes of enrichment intensities at each peak region [P ≤ 0.05 were used to select differentially enriched peaks as done previously [Differential peaks between air and ethylene treatment were identified using the MAnorm method . For thik region –42. Thuseviously , 34. Theeviously , 28, 34.RNA-seq raw data were downloaded from NCBI GEO under GSE77396 , 43. RawWeka 3 data mining software  was usedIn detail, important features were first identified with three feature selection algorithms: Information Gain (InfoGain) ; CorrelaTotal RNA was extracted using a Qiagen Plant Total RNA Kit (Sigma) from 3-day-etiolated seedlings treated with air or 4 h ethylene gas. First-strand cDNA was synthesized using Invitrogen Superscript III First-Strand cDNA Synthesis Kit. PCR reactions were performed in a total volume of 20 µL, containing 2 µL each 5-mM primer and 10 µL SYBR Green PCR Supermix in triplicate on a Roche 96 Thermal cycler according to the manufacturer’s instruction. The cycling program comprised an initial denaturation step at 95 °C for 10 min, followed by 50 cycles of 95 °C for 10 s, 60 °C for 10 s, and 72 °C for 20 s. All qRT-PCR values were normalized using the cycle threshold value corresponding to the reference gene. The relative expression levels of the target gene were calculated using 2(−Delta Delta C(T)) method . The seqein2–5 etiolated seedlings treated with or without ethylene gas were used to extract the features. Totally, we collected 468 features which can be divided into two categories  [EIN3-BINDING F BOX PROTEIN 2 (EBF2) [ETHYLENE RESPONSE 2 (ETR2) [EIN3-BINDING F BOX PROTEIN 1 (EBF1) [WRKY 25 [WRKY 26 [YUCCA6 [WEAK ETHYLENE INSENSITIVE 8 (WEI8/CKRC1) has been reported to be induced by ethylene [Among 2600 genes predicted by InfoGain and Logistic Regression in Col-0, 742 genes were well-known ethylene regulated, including 1 (CTR1) , EIN3-BI2 (EBF2) , and ETH2 (ETR2)   , WRKY 25[WRKY 25 , WRKY 26[WRKY 26  and YUCC [YUCCA6  that werethylene .Fig. 4AnGene ontology (GO) enrichment analysis revealed that the group of predicted up-regulated genes was enriched with genes involved in response to ethylene  and ethylene down-regulated genes (n = 1508), and analyzed the signals of H3K9Ac, H3K14Ac and H3K23Ac associated with them. In the absence of ethylene, peak breadths of H3K9Ac were larger in the predicted ethylene up-regulated genes than in down-regulated genes, but no difference were detected for H3K14Ac and H3K23Ac  for two human cell lines: lymphoblastoid cells (Gm12878) and Hela S3 cells. We collected genome-wide histone modification for H3K4me3, H3K9Ac and H3K27me3, as well as differentially expressed genes from RNA-seq data between these two cell lines. Totally, we collected 114 features which included histone modification on different gene segments and histone peak-associated features, as mentioned before. After F measure evaluation for three feature selection algorithms , the top 46 features , the precision greater than 0.9995 was used to select predicted candidate genes  and SET DOMAIN GROUP 711 RNAi plants (SDG711RNAi). We collected genome-wide histone modifications for H3K4me3 and H3K27me3, and differentially expressed genes from RNA-seq data between these two rice lines. Totally, 84 features were collected and the top 25 features  for two rice samples : wild tyArabidopsis [Although remarkable power of RNA-seq has been achieved in the past few years, currently available methods leave rooms for improvement in terms of sensitivity and efficiency that are greatly affected by experimental design/operation and the following data analysis processes , 67, 68.bidopsis  or cancebidopsis . In thisTo validate our prediction, we examined the expression of the newly identified genes by qRT-PCR. Up to 60% of newly predicted genes were confirmed to be regulated by ethylene Additional file 2:Figure S1. RNA-seq quality detection of differential regulated genes in air and ethylene treatment. Figure S2. The F measure performance curves of all the feature selection methods on the training data when the number of the selected features is the top 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100%. Figure S3. Evaluation of predicted genes. Figure S4. Evaluation in human cells. Figure S5. Evaluation in rice. (PDF 1302 kb)


Data normalization is a crucial step in the gene expression analysis as it ensures the validity of its downstream analyses. Although many metrics have been designed to evaluate the existing normalization methods, different metrics or different datasets by the same metric yield inconsistent results, particularly for the single-cell RNA sequencing (scRNA-seq) data. The worst situations could be that one method evaluated as the best by one metric is evaluated as the poorest by another metric, or one method evaluated as the best using one dataset is evaluated as the poorest using another dataset. Here raises an open question: principles need to be established to guide the evaluation of normalization methods. In this study, we propose a principle that one normalization method evaluated as the best by one metric should also be evaluated as the best by another metric (the consistency of metrics) and one method evaluated as the best using scRNA-seq data should also be evaluated as the best using bulk RNA-seq data or microarray data (the consistency of datasets). Then, we designed a new metric named Area Under normalized CV threshold Curve (AUCVC) and applied it with another metric mSCC to evaluate 14 commonly used normalization methods using both scRNA-seq data and bulk RNA-seq data, satisfying the consistency of metrics and the consistency of datasets. Our findings paved the way to guide future studies in the normalization of gene expression data with its evaluation. The raw gene expression data, normalization methods, and evaluation metrics used in this study have been included in an R package named NormExpression. NormExpression provides a framework and a fast and simple way for researchers to select the best method for the normalization of their gene expression data based on the evaluation of different methods (particularly some data-driven methods or their own methods) in the principle of the consistency of metrics and the consistency of datasets. Global gene expression analysis provides quantitative information about the population of RNA species in cells and tissues . High-thBasically, two classes of methods are available to normalize gene expression data using global normalization factors. They are the control-based normalization and the average-bulk normalization. The former class of methods assumes the total expression level summed over a pre-specified group of genes is approximately the same across all the samples. The latter class of methods assumes most genes are not significantly Differentially Expressed (DE) across all the samples. The control-based normalization often uses RNA from a group of internal control genes  or external spike-in RNA , while Although many metrics have been designed to evaluate the relative success of these methods, different metrics or different datasets yield inconsistent evaluation results. Here raises another open question: principles need to be established to guide the evaluation of normalization methods. ∗ in this study. It has been confirmed that all the modifications do not change the evaluation results.In total, 14 normalization methods have been evaluated in this study. They are Housekeeping Genes (HG7), External RNA Control Consortium (ERCC), Total Read Number (TN), Total Read Count (TC), Cellular RNA (CR), Nuclear RNA (NR), median of the ratios of observed counts (DESeq), Relative Log Expression (RLE), UQ, Trimmed Mean of M values (TMM), Total Ubiquitous (TU), Network Centrality Scaling (NCS), Evolution Strategy (ES), and SCnorm . Currently, most methods with a few exceptions  are used to normalize a raw gene expression matrix (n samples by m genes) by multiplying a global normalization factor to each of its columns, yielding a normalized gene expression matrix . In diffIn the previous study, The non-zero ratio cutoffs  from 0.2 to 0.9 for scRNA663 and from 0.7 to 1 for bkRNA18 were used to produce AUCVCs of all methods . Among 1The evaluation results using both scRNA663 and bkRNA18 showed consistency (the consistency of datasets) that all methods except HG7, TN and SCnorm were classified into three groups, based on their AUCVC values sorted in descending order . The firThe evaluation results  by mSCC To further test our principle, we searched other performance metrics in the published papers. The Bioconductor package scone  providesThe raw gene expression data, normalization methods  and evaluation metrics (AUCVC and mSCC) have been included in the R package NormExpression. The data process in this study is provided in detail , TN, TC, CR, NR, DESeq (RLE), UQ, and TMM. TN, NCS, ES, and SCnorm are not used in the second mode, as the evaluation results of TN and SCnorm cannot achieve consistency, and NCS and ES have similar performances to TU but are much more time consuming. In the third mode, AUCVC and mSCC are used to select the best method from TU and at least 10 normalization methods. The normalization with simple evaluation determines the best method based on AUCVC values, while the normalization with complete evaluation determines the best method in the principle of the consistency of metrics and the consistency of datasets. As a result of a complete evaluation, the tables of AUCVC and mSCC  are requIn a previous study (SRA: SRP113436), 831 single-cell samples and 18 bulk samples had been sequenced using the Smart-seq2 scRNA-seq protocol. In this study, we built a scRNA-seq dataset including 663 single cells from colon tumor tissues and 10 single cells from distal tissues (>10 cm) as control. The data of 166 single-cell samples were removed, as each of them contained NR less than 100,000 reads. The data of two single-cell samples were removed, as each of them contained simulated ERCC RNA less than 0 reads. We also built a bulk RNA-seq dataset including nine samples from colon tumor tissues and nine samples from distal tissues. The cleaning and quality control of both scRNA-seq and bulk RNA-seq data were performed using the pipeline Fastq_clean  that wasThe library size methods  use the gene expression level summed over total genes in a sample as the library size to calculate the normalization factor. HG7, ERCC and TU use the gene expression level summed over these pre-selected genes in a sample as the pseudo library size (see section “Results”). NR only counts reads which can be aligned to nuclear genomes, while CR counts reads which can be aligned to both nuclear and mitochondrial genomes. TC counts reads which can be aligned to 92 ERCC RNAs, nuclear and mitochondrial genomes (TC = CR + ERCC). TN uses the number of all reads which can be aligned to 92 ERCC RNAs, nuclear and mitochondrial genomes. The pre-selected genes used by HG7, ERCC and TU are seven housekeeping genes, 92 ERCC RNAs and the ubiquitous genes (described below), respectively. Seven genes  in HG7 had been used to achieve the best evaluation result among those using all possible combinations of tested housekeeping genes in the previous study by A gene is defined as uniform when the Coefficient of Variation  of its expression values across all samples is not more than a cutoff . To deteUbiquitous genes are defined as the intersection of a trimmed sets of all samples . This trIn the previous study, x-axis). As a high or a low normalized CV cutoff produces more false or less true uniform genes, it is reasonable to consider the overall performance of each method at various cutoff settings instead of that at one specific cutoff setting. In Formula 1 and 2, symbols have the same meanings as those in ∗ does not count zero elements in each sample.AUCVC  is creatSG conceived this project. SG and JR supervised this project. ZW, WL, and XJ performed the programming. ZW and HJ analyzed the data. HW prepared all the figures and The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Gene shaving (GS) is an essential and challenging tools for biomedical researchers due to the large number of genes in human genome and the complex nature of biological networks. Most GS methods are not applicable to non-linear and multi-view data sets. While the kernel based methods can overcome these problems, a well-founded positive definite kernel based GS method has yet to be proposed for biomedical data analysis.Since the kernel based methods on genomic information can improve the prediction of diseases, here we proposed a noble method, “kernel based gene shaving” which is based on the influence function of kernel canonical correlation analysis. To investigate the performance of the proposed method in comparison to state-of-the-art-method in gene saving, we analyzed extensive simulated and real microarray gene expression data set. The performance metrics including true positive rate, true negative rate, false positive rate, false negative rate, misclassification error rate, the false discovery rate and area under curves were computed for each methods. In colon cancer data analysis, the proposed method identified a significant subsets of 210 genes out of 2000 genes and suggestive superior performance compared with other methods. The proposed method can be applied to the study of other disease process where two view data is a common task.We addressed the challenge of finding unique kernel based GS methods by using the influence function of kernel canonical correlation analysis. The proposed method has shown to have better performance than state-of-the-art-methods in gene saving and has identified many more significant gene interactions, suggesting that genes function in a concerted effort in colon cancer. In similar biomedical data analysis, kernel based methods could be applied to select a potential subset of genes. The positive definite kernel based methods can overcome the non-linearity problem and improve the prediction process. Gene shaving (GS), to identify significant subsets of the genes, is an important research area in the analysis of DNA microarray gene expression data for biomedical discovery. GS methods aim to remove redundant and irrelevant genes so that performing in supervised learning will be more accurate , 2. It lThe incorporation of various statistical machine learning approaches into genomic analysis is a rather recent area of study. Since large-scale microarray data presents significant challenges for the statistical data analysis, in addition the classical approaches, there is a need for an advanced method. The kernel methods (methods based on positive definite kernel) are the appropriate tools to deal with such data set that map data from a high dimensional space to a feature space using a nonlinear feature map. The main advantage of these methods is to combine statistics and geometry in an effective way –9. As a Nowadays, IF based methods  have been used to detect an influence observation. IF is used to find a set of vectors that have much greater effect on the estimator of the parameter . A visuaThe contribution of this paper is three-fold. First, we address the IF of kernel CCA. Second, we use the distribution based methods to confirm the influential observations. Finally, the proposed method is applied to identify a set of genes in both synthesized and gene expression data. The accuracy of the proposed method shows superior performance compared to the the state-of- the-art-method in gene saving based on the area under curves (AUC). In colon cancer data analysis, we used the proposed method to identify genes and perform pathway analysis  and gene-gene interaction networks. We found that identified genes function in a concerted effort and have biological relevance to colon cancer. In addition, the selected genes based classification is superior than selected genes by other methods as well as classification using all genes. For any biomedical data analysis, the proposed method could be applied to select a potential subset of genes.The remainder of the paper is organized as follows. In the materials and methods section, we provide a brief review of positive definite kernel, kernel CCA and IF of kernel CCA. The utility of the proposed method is demonstrated by both simulated and real data analysis from an colon cancer study in the experimental results section. In the discussion section, we also summarize our findings and give a perspective for future research.k is associated with a Hilbert space f(X) is f with k defined on a space k)ij is positive semi-definite. To transform data for extracting nonlinear features, the mapping Φ(X) = k, which is a function of the first argument. This map is called the f feature map, and the vector Φ(X) in Φ(⋅) [In kernel methods, a nonlinear feature map is defined by positive definite kernel. It is known that a positive definite kernel e kernel . For any of Φ(⋅) –9.X and Y with two functions in the RKHS, fX(X) and fY(Y) isfX(⋅) and fY(⋅) are determined up to scale.Kernel CCA has been proposed as a nonlinear extension of linear CCA . ResearcFXY, by taking the inner product with elements or “parameters” in the RKHS, we have features kX and kY are the associated kernel functions for MX = CKXC and MY = CKYC, where 1n is the vector with n ones. The empirical estimate of aX and aY are the directions of X and Y, respectively.Using a finite sample, we are able to estimate the desired functions. Given an i.i.d sample, Since 1974, the IF plays an important role for detecting outlying multivariate observations in statistical analysis. The IF can usually be defined on first order approximation for estimators of parameters in a multivariate population which indicates where in the n-dimensional space of observations. The observed vectors should have a large effect on the value of the estimator of the parameter. For a sample of observation vectors, we can define the IF based on empirical distribution (EIF) to find set of these vectors that have much greater effect on the estimator. This vector is called set of outline vector . In manyX, Y) having the distribution FXY and the j-th kernel CC (ρj) and kernel CVs (fjX(X) and fjX(Y)), the influence functions of kernel CC at Z′ =  is given byUsing the idea of IF of the linear PCA, the kernel PCA, and the linear CCA, the IF of kernel CCA has been proposed by Alam et al., . To definear PCA , 25, thenear PCA , and thenear PCA .FnXY. The EIF of kernel CC at  for all points  is defined asLet Using the above result, we can identify a set of observations based on its influence values. To demonstrate, we proposed a noble method, with application to DNA microarray gene expression data. This novel method can be applied to the study any disease processes, where two-view data analysis is a common task. The proposed approach consists of two basic parts: a step that aims to calculate influence value of each gene and a step that aims to determine the outline gene. For the first step, we use EIF in X is very high, this might allow for more complexity in the function class than what we could measure and assess otherwise. It has limitation of linearity. Using a polynomial kernel it is possible to use the higher order correlation between the data in the different purposes. But, due to the finite bounded degree such kernel will not provide us with guarantees for a good dependency measure. In addition both liner and polynomial kernels are non-robust.In kernel based learning, choosing a suitable kernel is key for favorable results. Most of unsupervised kernel methods suffer from the problem of kernel choice. The liner kernel is just used the underlying Euclidean space to define the similarity measure. Whenever the dimensionality of the input space, X into an infinite dimensional space. The Gaussian kernel is defined as:The Gaussian kernel, is a radial basis function kernels that maps This most applicable kernel in kernel methods has a number of theoretical properties  . In thisThe assumption of kernel methods (methods based on positive definite kernel) is that the data should be a non-empty set. The kernel methods are independent of the dimensions. Its allow us to construct spaces of functions on an arbitrary set with the appropriate structure of a Hilbert space. By the reproducing property, computing the inner product on RKHS is easy and the computational cost only depends on the sample size. It is true that kernel methods may have computational issues for very large data set in handling Gram matrices of sample size. However, recent developments on approximation methods such as random Fourier features enables us to apply kernel methods to data size of millions.While the proposed approach is designed for two view data set, we compare its performance against other relevant algorithms in univariate data or multivariate data (one view data) set only, since a two view data comparison is not feasible. To demonstrate the performance of the proposed method in a comparison, we examine four popular gene selection methods: T-test, significance analysis of microarrays (SAM), Linear Models for Microarray and RNA-Seq Data (LIMMA) and principal components to identify outliers (PCout) , 30–32. We have used both simulated and real microarray gene expression data set of colon cancer . To compk = 2 groups, we considered gene expression profiles from both normal distribution and t-distribution. We also considered data set of both small-and-large-sample cases with different percentages of differently expressed (DE) genes.To investigate the performance of the proposed method in comparison with four popular methods as mentioned above with xijk, i is the expression of the ith gene for the jth samples in k group, μik is the mean of all expressions of ith gene in the kth group and ϵijk is the random error which usually follows a normal distribution with mean zero and variance σ2.We used a one-way ANOVA model to generate simulated data sets from normal distributionk = 2 groups, we generated 100 data sets using 100 times of simulations for both small (n1 = n2 = 3) and large (n1 = n2 = 15) sample cases using μi1, μi2) ∈  and σ2 = 0.1, accordingly. Each data set for each case represented the gene expression profiles of G = 1000 genes, with n = (n1+ n2) samples. The proportions of DE gene (pDEG) were set to 0.02 and 0.06 for each of the 100 data sets. We computed average values of different performance measures such as TPR, TNR, FPR, FNR, MER, FDR and AUC based on 20 and 60 estimated DE genes by five methods  for each of 100 data sets. To investigate the performance of the proposed method in a comparison of other four popular methods as early mentioned for We also investigated the performance of the proposed method in a comparison of other four methods for non-normal case. Accordingly we generated 100 simulated data sets from t-distribution with 10 degrees of freedom. We set the mean and variance as previously mentioned. We estimated different performance measures such as TPR, TNR, FPR, FNR, MER, FDR and AUC based on 20 estimated DE genes by four methods for each of 100 data sets. The average values of performance measures are summarized in The data consists of expression levels of 2000 genes obtained from a microarray study on 62 colon tissue samples collected from colon-cancer patients . Among tp-values are 75, 214, 5.71, 0.473 for p ≤ 8.22 × 10−15, respectively. This network of genes has significantly more interactions than expected, which indicates that they may function in a concerted effort.Genes do not function alone; rather, they interact with each other. When genes share a similar set of gene ontology (GO), they are more likely to be involved with similar biological mechanisms. To verify this, we extracted the GO of biological process categories and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotations of 210 genes detected by proposed KCCA using Database for Annotation, Visualization and Integrated Discovery (DAVID) . The GO The proposed method can be applied to the study of other disease process, where two view data is a common task. To confirm, we have applied the proposed method to another real data set: RNA-sequence study for osteoporosis risk (Source: Tulane Center of Bioinformaties and Genomics). The details of the data and the results are provided in supplementary material, In addition, the data set was used to classify the colon cancer patients from the healthy controls via the PCOut and the proposed feature extraction techniques (CCOut and KCCOut) and followed by the two classifiers (the k-nearest neighbors (KNN) and liner support vector machine (SVM)). For the proposed approach, we considered the features 31, 133 and 210 that have influence effects using the PCout, the CCOut and the KCCOut, respectively. The PCOut, CCOut, and KCCOut serve as a feature extraction tool based on which the classifier is used to separate patients from healthy controls. Kernel based machine learning methods are vital for the biomedical data analysis. The kernel based methods provide more powerful and reproducible outputs, while the interpretation of the results remain challenging. In this paper, the influence function of the kernel CCA based gene shaving method is proposed. The performance of the proposed method was evaluated on both simulated and real data set. The extensive simulation studies show the power gained by the proposed method relative to the alternative methods. The utility of the proposed method is to further demonstrate its application to analyze cancer microarray data, e.g. colon cancer microarray data. According to the influence values, the proposed method is able to rank the influence of a gene, and the genes are identified to be highly related to disease. Using an distribution based outlier detection method, the proposed method extracts 210 genes out of 2000 genes, which are considered to have a significant impact on the patients. Incorporating biological knowledge information  can provide additional evidence for the results. By conducting GO, pathway analysis, and network analysis including visualization, we find evidence that the selected genes have significant influence on the manifestation of colon cancer disease and can serve as a distinct feature for the stratification of colon cancer patients from the healthy controls. This novel method can be applicable to the study of other disease processes including cancer, where gene shaving is a common task.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S1 File(PDF)Click here for additional data file.


We propose a new classification method based on a model where the data is marginally negative binomial but dependent, thereby incorporating the dependence known to be present between measurements from different genes. The method, called qtQDA, works by first performing a quantile transformation (qt) then applying Gaussian quadratic discriminant analysis (QDA) using regularized covariance matrix estimates. We show that qtQDA has excellent performance when applied to real data sets and has advantages over some existing approaches. An R package implementing the method is also available on  Classification on the basis of gene expression data has the potential to become an important part of modern medicine, for example, for disease diagnosis and personalization of treatment. For example, consider breast cancer. This is a heterogeneous disease consisting of several distinct types, with each type being characterized, not necessarily by its morphological or clinical characteristics, but by its molecular characteristics, thereby making it difficult to diagnose the particular type affecting a patient . Moreovecounts: for each gene we obtain a non-negative integer count which quantifies the gene’s expression level; roughly speaking, the larger the count the higher the level of expression.There are many ways of measuring gene expression. One common approach, due to its numerous advantages, is RNA-sequencing (RNA-seq) which measures gene expression across the whole genome simultaneously see . RNA-seqindependent.Several approaches have been proposed for classifying RNA-seq data. General machine learning approaches have been investigated, for example, support vector machines (SVMs) and k-Nearest Neighbour (kNN) classifiers, and general regression approaches have also been applied, for example, logistic regression see . Others However, this independence assumption is very unrealistic, since genes are typically involved in networks and pathways, implying that a particular gene’s expression level is likely to be correlated with the expression level of other genes. Moreover, some have argued, for example, qtQDA implementing the method is available on https://github.com/goknurginer/qtQDA.In this article we propose a new classification method for RNA-seq data based on a model where the counts are marginally negative binomial but dependent. Like previous work, we use the multivariate normal distribution for classification, where each class is assumed to have its own covariance matrix. However, our approach has two key differences: (1) instead of modeling log-transformed counts, we model quantile transformed counts; and (2) we use a novel application of a powerful regularization technique for covariance matrix estimation. We call the method qtQDA: quantile transformed (qt) quadratic discriminant analysis (QDA). We demonstrate the performance of the method by applying it to several real data sets, showing that it performs better than, or on par with, existing methods. qtQDA has advantages over some existing approaches, and an R package K distinct classes on the basis of m genes . Let kth class where i. Like others, for example, NBLDA and the method of i, respectively ( is generated by the following process:Zk), where Zk).Let ith component of Xk)} has distribution function G  is more nearly uniformly distributed than kF(X) itself (zscoreNBinom function in the R package edgeR (see below).We now turn to how the model above is used for classification. Suppose we observe ) itself . The trax* is from the kth class then z*k) distribution. Thus, by Bayes theorem, the posterior probability that x* belongs to the kth class isk is the prior probability that Pr(y* = k), and kf is the densityz*k).The dispersion Σk, we begin by calculating the standard estimate:m ≈ n or m > n). In such situations this standard covariance matrix estimate is known to perform poorly . This probability can either be specified by the user, for example, if epidemiological knowledge is available, or estimated directly from the training data usingK classes.The final parameter needed for classification with Bayes theorem m genes for classification: (1) we filter genes with low expression across all samples; (2) for each remaining gene we perform a likelihood ratio test (LRT) to test for genes differentially expressed between groups; (3) a list of genes is made, sorted by LRT statistic; (4) finally, the top m genes from this list is used for classification. As with negative binomial parameter estimation, this strategy is implemented using edgeR. Others have adopted essentially the same gene selection strategy, for example, NBLDA, SQDA, and the method of Lastly, we turn to the question of which genes to use for classification. When RNA-seq is performed we typically obtain data on more than 20,000 genes. A vast number of these genes, however, will not be informative for the purposes to distinguishing between different classes. We therefore employ the following simple strategy for selecting Cervical cancer data (see data see . This coProstate cancer data (see data see . This coHapMap data (see data see . The datTo assess the performance of qtQDA we apply it to three publicly available data sets:e1071)SVM (e1071)kNN (glmnet)Logistic regression (PoiClaClu)PLDA (http://www.comp.hkbu.edu.hk/xwan/NBLDA.R)NBLDA (MLSeq)voomDLDA (SQDA)SQDA (These data sets are very common in the RNA-seq classification literature see . Using tℓ1) penalty in the log-likelihood function which thus overcomes many of the problems with logistic regression in high-dimensional settings , and both methods were applied to log transformed counts. We apply all methods as recommended in their documentation and any “tuning” parameters were chosen with the cross-validation tools provided in the corresponding software package or chosen with our own cross-validation. The Gaussian copula method of For logistic regression, we use the GLMnet method proposed in ings see  and encom = 100, 200, 300, 500, 700 genes, where these genes are selected using the procedure detailed in the previous section.For evaluation, we estimated the true error rate, that is, the rate at which false classifications are made, using the following bootstrap procedure: (1) each data set is randomly divided into two parts, one part consisting of 70% of the data, put aside for training the classifier, and one part consisting of 30% of the data, used as a test set to apply the trained classifier from which an error rate is recorded; (2) this is repeated 1,000 times and the error rates from each iteration is averaged to produce an estimate of the true error rate. This is the same procedure used by Results are shown in Early investigations into classification with gene expression data from microarrays, for example, Our method has two key advantages. Firstly, unlike some approaches , qtQDA does not have any “tuning” parameters which need to be chosen with cross-validation, thus making it more straightforward to apply in practice. Secondly, in comparison to approaches which take gene dependence into account, for example, SQDA and the method of only those genes which are informative for distinguishing between classes. Thus, future research will aim at developing a sparse version of qtQDA, involving some level of regularization for features, that is, identifying less informative features and reducing their influence to zero . A sparse qtQDA may also help address problem (3) above, the answer to which has practical advantages, for example, knowing which subset of genes need to be measured for effective classification, and theoretical advantages, for example, obtaining insight into the underlying biological process driving the disease (or subclass) in question. A sparse qtQDA may also deliver a further bonus: it may lead to a better answer to problem (2), that is, to even better disease classifications.As edgeR methodology for negative binomial parameter estimation, to parametrize the quantile transformation, and by using the powerful corpcor methodology for regularized covariance matrix estimation, so that effective QDA can be performed on the transformed data. We have shown that, when applied to real data sets, the classifier has excellent performance in comparison to other methods, and has two key advantages which makes it easy to apply in practice: (1) it does not have any tuning parameters; and (2) it is computationally very fast. An R package called qtQDA implementing the method is also available on https://github.com/goknurginer/qtQDA.We have proposed a new classification method for RNA-seq data based on a model where the data is marginally negative binomial but dependent, thereby incorporating dependence between genes. The method works by first performing a quantile transformation then applying Gaussian QDA, where each class is assumed to have its own covariance matrix. The classifier is trained by using the sophisticated


The application of gene expression data to the diagnosis and classification of cancer has become a hot issue in the field of cancer classification. Gene expression data usually contains a large number of tumor-free data and has the characteristics of high dimensions. In order to select determinant genes related to breast cancer from the initial gene expression data, we propose a new feature selection method, namely, support vector machine based on recursive feature elimination and parameter optimization (SVM-RFE-PO). The grid search (GS) algorithm, the particle swarm optimization (PSO) algorithm, and the genetic algorithm (GA) are applied to search the optimal parameters in the feature selection process. Herein, the new feature selection method contains three kinds of algorithms: support vector machine based on recursive feature elimination and grid search (SVM-RFE-GS), support vector machine based on recursive feature elimination and particle swarm optimization (SVM-RFE-PSO), and support vector machine based on recursive feature elimination and genetic algorithm (SVM-RFE-GA). Then the selected optimal feature subsets are used to train the SVM classifier for cancer classification. We also use random forest feature selection (RFFS), random forest feature selection and grid search (RFFS-GS), and minimal redundancy maximal relevance (mRMR) algorithm as feature selection methods to compare the effects of the SVM-RFE-PO algorithm. The results showed that the feature subset obtained by feature selection using SVM-RFE-PSO algorithm results has a better prediction performance of Area Under Curve (AUC) in the testing data set. This algorithm not only is time-saving, but also is capable of extracting more representative and useful genes. Cancer has become the main cause of morbidity and mortality worldwide, due to population growth, aging, and the spread of risk factors such as tobacco, obesity, and infection, which will worsen in the next decade, in which breast cancer is the most common cancer, especially in women –8. At prA genetic disease may produce or cause one or more mutations; this phenomenon also reflects the characteristics of the real biological system module . AnalysiIn this paper, based on traditional gene selection algorithms, we proposed a model combined with grid search method and heuristic algorithms, namely, genetic algorithm and particle swarm algorithm, to search for the best parameters for the gene selection algorithms. The important genes within breast cancer gene expression data extracted from SVM-RFE-GS, SVM-RFE-PSO, and SVM-RFE-GA algorithms were regarded as the final features, and samples with these features were fed into classifier to train it. By comparing the classification accuracy of the above three algorithms, it could be concluded that the SVM-RFE-PSO algorithm could be better applied to select genes from breast cancer gene expression data than traditional SVM-RFE algorithm, and the SVM-RFE-PSO algorithm can select fewer gene features but can extract more information effectively. We also used RFFS, RFFS-GS, and mRMR algorithm for comparative experiments in feature selection and found that the SVM-RFE-PSO algorithm is still better than other algorithms. In addition, the classification accuracy using SVM-RFE-PSO algorithm is the best one. This method can also be used for more feature selection problems. p value can usually be calculated for each gene by statistical methods such as T-test, which is commonly used in differential gene expression testing. It evaluates whether a gene is differentially expressed in two samples by combining variable data between samples. However, due to the limited amount of experimental samples, the estimation of overall variance is not rather accurate. So the test performance of statistical test is reduced. Furthermore, the false positive rate (FPR) would increase significantly if the statistical test is repeated. In order to control the number of the FPR, we need to test the p value for multiple tests to increase the threshold.Cancer gene expression data are highly dimensionally characterized, so it is important to filter out differential genes in the sample data. p values by controlling the FDR value. The steps to filter out the differential genes are as follows. First, we calculate p value for each gene. Then we calculate the value of FDR. Finally, we use the FDR error control method to make multiple hypothesis tests for p value. The adjusted p value, q-value, is obtained by FDR control. The smaller the q-value, the more obvious the difference between the genes is, so we should try to choose the smaller q-value gene as the differential gene.The false discovery rate (FDR) error control method was proposed by Benjamin and Hochberg in 1995  to deterω generated by the SVM during training, removes a signature attribute with the smallest ranking coefficient in each iteration, and finally obtains the decreasing order of all signature attributes. Fan Zhang et al.  = 0.(4) Initialize the classification accuracy of each iteration in 10-fold cross-validation (5) For(i in 1:10)Create a classifier by running random forest on S.Perform predictor on the test data set for classification.Acc.Compare classification results with observations and calculate TLMean=TLMean + Acc[i]/10.Compute TLMax < = Acc[i]).If(TLMax = Acc[i].Then Sort.Sort features by variable importance and save as TGMax < = TLMean)(6) If Remove the feature with the lowest importance score from (3) Output ResultTGMax.(1) Output global highest classification accuracy FGSort.(2) Output the global maximum classification accuracy corresponding to the feature set Minimum redundancy maximum redundancy (mRMR) is a filter type feature selection method, mainly by maximizing the correlation between the features and the classified variables, and minimizing the correlation between features to get the best feature set. mRMR algorithm guarantees maximum correlation while removing redundant features. In the obtained feature set, there is a great difference between the features, and the correlation with the target variables is also very large , 36.Our goal is to select a set of influential features by using the mRMR algorithm. The remaining question is how to determine the best number of features. Since the mechanism of removing potential redundancy from potential features is not considered in the incremental selection, we need to refine the results of incremental selection based on the idea of mRMR. In the first stage, we use mRMR algorithm to find candidate feature sets. In the second stage, we use more complex mechanisms to search the feature subset from the candidate feature set .Ω. The best characteristic number of candidate set (expressed as n∗) is determined in Ω.In order to select candidate feature sets, we calculated a large number of cross-validation classification errors and found a relatively stable small error range. This range is called S1 ⊂ S2 ⋯ ⊂Sn.(1) Use mRMR incremental selection to select n (a present large number) sequential features from the input X. This leads to n sequential feature sets S1, ⋯, Sn, (1 ≤ k ≤ n) to find the range of k, which is called Ω; the corresponding  error ek is always small .(2) Compare the feature sets of n sequence e∗ = minek within Ω. The best size of the candidate feature set is n∗, which is selected as the smallest k and responds to e∗.(3) Find the smallest classification error Grid search is similar to the exhaustion method. It is to find all the combinations and experiments. During the experiment, we need to cross-validate. Taking five-level cross-validation as an example, for each possible combination of parameters, one-fifth of the sample is used for testing, and the others are used for training. Average after five cycles, the final set of parameters with the smallest cross-validation error is the optimal parameter pair we want.Unlike the exhaustive search, heuristic search means that the search in the state space evaluates the position of each search to get the best position and then searches from this position to the target. Some parts of information generate inferences for calculations. Particle swarm optimization algorithm (PSO) is a kind of heuristic algorithm based on swarm intelligence, which originated from the research of bird predation behavior; the basic idea of the algorithm is through collaboration and information sharing between individual groups to find the optimal solution . The genFor SVM, there are many kernel functions to choose, and different kernel functions correspond to different feature maps, so the SVM hyperplane also has different abilities and characteristics. In this paper, the data used have the characteristics of small sample, high dimension, and nonlinearity, so the RBF kernel function is selected in this experiment. The RBF kernel function contains two parameters c and g, so the above algorithms, namely, GS, PSO, and GA were used to find the optimal parameter pair  in the process of building SVM classifier with cross-validation.max-features. Increasing the max-features generally improves the performance of the model, but at the same time it reduces the diversity of individual trees and slows down the algorithm. The number of trees in a random forest, n-estimators, is also important for the performance of random forests. When there are many decision trees in a random forest, both the space and time complexity would be greater. This paper uses the grid search method to search for the best parameters  of the model, which we called RFFS-GS algorithm.For random forest, the choice of random forest parameters is critical to performance. For example, a random forest allows a single decision tree to use the maximum number of features, which we called Generally, the method initializes the required set of features into the entire set of genes, and at each iteration, the feature with the smallest ordering coefficient is removed and the rest of the features are then retrained to obtain a new ordering coefficient. By iteratively executing this process, a feature ordering table can be obtained using feature selection method combined with parameter optimization models. Different parameter optimization models are used to find the best parameters for different feature selection methods. The sorted list defines a number of nested feature subsets to train feature selection models, and the prediction accuracies of these subsets were used to evaluate the advantages and disadvantages of these subsets so as to obtain the optimal subset of features.It should be noted that each of the features listed above is used separately and may not necessarily result in the best classification performance of the SVM classifier, but the combination of multiple features will enable the classifier to obtain the best classification performance. Therefore, the proposed gene selection algorithms can choose a complementary feature combination.Support vector machine is often used in two-class classification tasks, its basic model is to find the best separating hyperplane on the feature space, so that the positive and negative sample intervals on the training set are the largest.The basic idea of support vector machine is as follows: firstly, search for the optimal hyperplane of two types of samples in the original sample space under the linearly separable samples. The classification hyperplane is established by SVM, which can ensure the classification accuracy and maximize the margin on both sides of the hyperplane including the maximization of interval, so as to realize the optimal classification of linear separable problems.D = {, ,…, }, yi ∈ {−1, +1}, i = 1,2,…, n, n, represents the sample size. Thus, the objective function can be defined asGiven the set of training samples on a feature space γ is the geometric interval. Additionally, we also need to meet some conditions; according to the definition of interval we can see the following:yi(ωTxi + b) ≥ 1, i = 1,…, N. In this case, we can introduce slack variables, that is, to allow mis-divided samples, and the optimal hyperplane obtained at this time becomes the generalized classification hyperplane. So after we add a relaxation term to the equation, the equation could becomeεi is greater than zero. Introducing the error penalty coefficient C, the generalized optimal classification surface problem can be further evolved to the minimum of the following functions under the above constraints:where ϕ(xi)ϕ(xj) in the input space. It is an implicit mapping of the input space to a high dimensional space. It does not need to explicitly give that mapping, and ϕ(xi)ϕ(xj) can be calculated in the input space. In this way, the optimization problem becomes as follows:When the original problem is converted into a duality problem, the original formulas becomeGene expression data contains DNA microarray data and RNA-seq data. Analysis of microarray data helps clarify biological mechanisms and push drugs toward a more predictable future. Compared to hybridization-based microarray technology, RNA-seq has a larger range of expression levels, and more information is detected. In this paper, DNA microarray data and RNA-seq data were used for research.https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE16443 with minor modification for employed program. The data set includes 130 sample data containing 67 breast cancer samples and 63 control samples. After we randomly divided the 130 samples into two groups, we obtained 32 health samples and 33 cancer samples in the training set and 31 health samples and 34 cancer samples in the testing set. The division of data sets can be seen from The DNA microarray data used in the experiment is peripheral blood data with the accession number GSE16443 under the public database GEO platform. All of them were taken with URL http://portal.gdc.cancer.gov/ with minor modification. The data set includes 1178 sample data containing 1080 cancer samples and 98 health samples. We randomly divided the data set into a training set and a test set. The training set and the test set each contain 540 cancer samples and 49 health samples. The division of data sets can be seen from The RNA-seq data used in the experiment is RNA-seq data of breast cancer through the TCGA database, which were taken with URL  q-value distribution of GEO platform data is shown in the  q-value <0.1 has no genes, so q-value <0.2 is selected as the differential gene after screening.For DNA microarray data, we calculated the FDR q-value using the package “Q-value” of R; theFor RNA-seq data, we used the 'edgeR' package of R to calculate the FDR q-value. We chose a significance screening filter q-value<0.001.When using the SVM algorithm for classification directly, we use all the differential genes screened as feature subsets. After using the feature selection algorithm, we can get an ordered set and then we loop through the genes in the ordered set as feature subsets and select the subset with the best classification effect and the fewer features as the final feature subset. When we get the final subset of features, we use the SVM classifier for classification.From Tables In There are three main methods of parameter optimization, in which the grid optimization algorithm is the most common one. Similar to the exhaustive search, grid search attempts all possible  pairs of values and then cross-validates to find the  pairs with the highest cross-validation accuracy as the optimal parameter.Although the highest classification accuracy could be obtained in the cross-validation, since using grid search included the global optimal solution, it may be time-consuming to find the best parameters c and g in a larger range. Using the heuristic algorithm, all the parameters could be traversed in the grid for the global optimal solution.By using the heuristic algorithm PSO and GA algorithm to optimize the parameters, the optimal parameters are applied to the classification of cancer. Using these two algorithms, we can get the optimized parameters in the feature elimination stage and get the optimal classifier, so as to get the parameter w to eliminate features. Therefore, we call the feature elimination algorithm SVM-RFE-PSO and SVM-RFE-GA, respectively. We use these two algorithms to get the feature subset and classify them by SVM classifier and compare their advantages and disadvantages through classification results.As shown in Tables The RFFS and mRMR algorithms are often used for feature selection and both can get a sorted subset in the feature selection process. RFFS-GS optimizes the parameters of the RFFS algorithm by using the grid optimization method. The method of parameter optimization often achieves better results. In this paper, we use these three methods to select features of two data sets and verify their effects by classification. In From the above, we can conclude that the SVM-RFE-PO algorithms perform better on GEO data set. And on TCGA data set, SVM-RFE-GS algorithm and SVM-RFE-PSO algorithm also have a good performance. After we compare the AUC obtained by the SVM-RFE-PSO, RFFS, RFFS-GS, and mRMR algorithms of the two data sets, in For gene microarray data, there have been many related feature selection methods. The eight gene names and their IDs are obtained by using the SVM-RFE-PSO algorithm on GEO data set which are shown in As can be seen from  q-value <0.1. When we perform differential gene filtering, errors may occur. The TCGA data set used in this paper is based on the gene expression data obtained by RNA-seq technology. The number of samples is larger, the number of differential genes is more, and the difference between genes is more obvious.The GEO data set used in this paper is the gene expression data obtained based on microarray technology, and the number of samples in this data set is small. When the number of samples is small, the increase and decrease of the sample will significantly affect the accuracy of the algorithm. The GEO data set has fewer differentially expressed genes, and the differences between genes are not obvious. There are no genes withPrior to RNA-seq technology, microarray technology was the mainstream technology for studying gene expression profiles. However, when quantifying gene expression, microarray technology is limited to gene levels. RNA-seq high-throughput sequencing technology is now commonly used in biology. RNA-seq can be used for unspecified genes and subtypes of any species, allowing genome-wide analysis of any species, while microarray technology relies on prior information to quantify known genes. In addition, RNA-seq has very low background noise and high sensitivity, while having a larger detection range. The study found that the RNA-seq technology detected that the number of differentially expressed genes was about twice that of the microarray technology.The genes expressed by cells in different environments are different, and RNA-seq can provide real-time expression of genes rather than information fixed in the genome. RNA-seq can compare changes in expression profiles of tumors in different drugs and treatments and provide more information than changes in genome exomes. Therefore, genes screened using RNA-seq data may have higher confidence, and the use of RNA-seq gene expression data will have more significance for future research on cancer. q-value less than 0.2 were selected as the characteristic genes on GEO data set and 159 genes with q-value less than 0.001 were selected on TCGA data set, which were used in the SVM-RFE algorithm. As a result, the effect of SVM-RFE method could be further improved. Therefore, we use the GS algorithm, the PSO algorithm, and the GA algorithm, respectively, to search the SVM kernel parameters in the feature elimination stage and obtain the feature subsets. We also use RFFS, RFFS-GS, and mRMR algorithm for comparative experiments in feature selection. It is observed from the results that SVM-RFE-PO emerges as a potentially dominant feature extraction technique for gene expression data classification. The SVM-RFE-PSO algorithm is able to extract the optimal discriminative feature information gene from the expression data. We use these characteristic information genes to study the relationship between genes and cancer. This has an extremely important role in the in-depth discovery and understanding of the disease mechanism and in the improvement of the clinical diagnostic accuracy of the disease. In the future, we will explore more feature extraction algorithms to achieve more accurate feature screening.We developed an integrated method in the early detection of breast cancer. In view of the high dimension of gene expression data, some methods were selected to reduce the dimension. First of all, statistical methods and FDR error control method were employed to screen genes. After FDR control, 56 genes with


RNAs during the preliminary stages of breast carcinogenesis is poorly understood. In this study, we performed RNA sequencing to identify long noncoding RNA expression profiles associated with early‐stage breast cancer. RNA sequencing was performed on six invasive ductal carcinoma (IDC) tissues along with paired normal tissue samples, seven ductal carcinoma in situ tissues, and five apparently normal breast tissues. We identified 375 differentially expressed lncRNAs (DElncRNAs) in IDC tissues compared to paired normal tissues. Antisense transcripts (~ 58%) were the largest subtype among DElncRNAs. About 20% of the 375 DElncRNAs were supported by typical split readings leveraging their detection confidence. Validation was performed in n = 52 IDC and paired normal tissue by qRT‐PCR for the identified targets . We evaluated the prognostic significance of DElncRNAs based on TCGA datasets and report that overexpression of FAM83H‐AS1 was associated with patient poor survival. We confirmed that the downregulation of ADAMTS9‐AS2 in breast cancer was due to promoter hypermethylation through in vitro silencing experiments and pyrosequencing.Breast cancer is the most common malignancy among women, with the highest incidence rate worldwide. Dysregulation of long noncoding  DCISin situductal carcinoma IDCinvasive ductal carcinomalincRNAlong intergenic noncoding RNAlncRNAlong noncoding RNAsPCAprincipal component analysisPCCPearson′s correlation coefficientTNBCtriple‐negative breast cancers1et al., in situ (DCIS) and invasive ductal carcinoma (IDC). Understanding the mechanism of breast carcinogenesis at genetic and transcriptional level can aid in characterization of DCIS or early‐stage IDC tumors. Gene expression signatures are used to classify IDC subtypes of hormone receptor‐positive (estrogen and progesterone receptors), that is, luminal A and B, and hormone receptor‐negative HER2 and basal‐like  with highest mortality rates , paired normal , DCIS (n = 7), and apparently normal (n = 5). Validation cohort of IDC (n = 52) and corresponding paired normal tissue were used to gauge candidate lncRNAs. The clinicopathological features of patients in the discovery and validation cohort are detailed in Table The study cohort includes patients diagnosed and treated for breast cancer at Cancer Institute (WIA), Chennai, Tamil Nadu, India. These patients were histologically confirmed of invasive ductal carcinoma (IDC—stage I–II A) and DCIS. Apparently, normal breast tissues were obtained from patients undergoing surgery for breast conditions other than malignancy. Samples having > 70% for cancer cells following histopathological examination were included in the study. Paired normal and apparently normal tissues completely free of tumor cells were selected and kept frozen (−80 °C) until further processing. Total RNA sequencing was done for 24 samples, that is, tumor , which includes an on‐column DNase treatment. The quality and quantity of total RNA was evaluated through 2100 Bioanalyzer . Ribosomal RNA was depleted , and cDNA library was prepared using Illumina TruSeq Stranded Total RNA Library Prep Kit. The library profile was verified using 2100 Bioanalyzer (Agilent Technologies). Subsequent RNA sequencing of cDNA libraries with paired‐end reads (2 × 100 bps reads) was performed according to the standard Illumina protocol using HiSeq 2500 sequencing platform.2.3fastqc | > 1 and q‐value  < 0.1. Expression profile of lncRNAs from TCGA breast cancer dataset  was used for survival analysis  was used to determine linear correlation between mRNA and lncRNA expression profiles using 2.52ΔCt method (ΔCt = ΔCt target gene‐ΔCt reference gene).Total RNA of 500 ng was used for preparing cDNA libraries using QuantiTect Reverse Transcription Kit . Gene expression was estimated by QuantStudio 12K Flex Real‐Time PCR System using TaqMan™ gene expression assays  containing primers and probes specific for lncRNA and GAPDH. The expression values were calculated using the 2.6Expression of ADAMTS9‐AS2 was evaluated in MDAMB‐231 and MCF7 cells. The cells were cultured in Dulbecco's modified Eagle's medium with 10% FBS at 37 °C. Knock‐down was carried out using Lipofectamine 3000 , siRNA targeting DNMT1  with cells maintained in Opti‐MEM (Life Technologies) during and after transfection. Transfected cells were collected after 48 and 72 h for total RNA and DNA isolation.2.7Hs_AC132007.1_01_PM  with primers spanning ADAMTS9‐AS2 promoter region. The amplified fragment was sequenced using pyromark Q48 Autoprep  and analyzed by pyromark q24 software v 2.0.7.Genomic DNA was extracted from tissues and cultured MDAMB‐231 and MCF7 cells using NucleoSpin Kit . About 500 ng of DNA was used for bisulfite treatment following manufacturer's protocol of EZ DNA Methylation‐Gold Kit . Bisulfite‐treated DNA was amplified using inventoried PyroMark CpG assay 2.8graphpad prism7  was used for evaluating qRT‐PCR gene expression data. Student's t‐test was used for pairwise analysis of tumor and paired normal samples. Welch correction was done if significant difference in variance was observed and Wilcoxon rank sum test was applied whenever non‐Gaussian distribution was followed.33.1RNA sequencing resulted in generation of ~ 89 million reads per sample with ~ 87.24% alignment against human genome build Hg38. We identified ~ 2 689 lncRNAs and ~ 18 132 mRNAs with ≥ 15 reads in at least three samples per cohort Table . In agreet al., et al., et al., We observed antisense RNAs (asRNA) and long intergenic noncoding RNAs (lincRNAs) to be the major lncRNA subtypes differentially expressed among these four groups. asRNAs accounted for 58.9% of total DElncRNAs in IDC compared to paired normal and 55.3% compared to apparently normal samples Fig. E–F. WDR83.2Dysregulated lncRNAs with evidence of ≥ 2 junction reads in each comparison groups were further investigated Fig. F–I. We i3.3n = 52 early‐stage IDC samples  for validation using TaqMan™ gene expression assays in les Fig. A–D. We o3.4et al. (n = 52) were done using pyrosequencing. We observed a nearly two‐fold (1.9) increase in methylation levels (P < 0.0001) in the promoter region (+879 to +929 bp from TSS) of tumor samples compared to paired normal samples  and 2.32‐fold (P < 0.001) in MDAMB‐231 and MCF7, respectively  and MCF‐7 cells  Comparative histogram represents relatively lower expression of lncRNAs (blue bars) compared to protein coding genes (grey bars) based on raw read count profile of apparent normal samples (n = 5) (B) Comparative histogram represents relatively lower expression of lncRNAs (green bars) compared to protein coding genes (grey bars) based on raw read count profile of paired normal samples (n = 6) (C) Comparative histogram represents relatively lower expression of lncRNAs (yellow bars) compared to protein coding genes (grey bars) based on raw read count profile of DCIS samples (n = 7) (D) Comparative histogram represents relatively lower expression of lncRNAs (red bars) compared to protein coding genes (grey bars) based on raw read count profile of IDC samples (n = 6) (E) PCA using normalized read counts of protein coding genes. Color legend. Apparent normal samples: Yellow, DCIS samples: Purple, Paired normal samples: Green, IDC samples: Red (F) Venn diagram depicting comparison of differential expression analysis group IDC vs. paired normal, IDC vs. apparent normal and DCIS vs. apparent normal samples (G) Venn diagram depicting comparison of differential expression analysis group IDC vs. paired normal and IDC vs. apparent normal samples (H) Venn diagram depicting comparison of differential expression analysis group DCIS vs. apparent normal and IDC vs. paired normal samples (I) Venn diagram depicting comparison of differential expression analysis group IDC vs. apparent normal and DCIS vs. apparent normal samples.Click here for additional data file.Fig. S2. Summary of lncRNA expression profile in IDC vs. DCIS. (A) Volcano plot representing expression pattern in IDC vs. DCIS (B) Heatmap depicting expression trend of differentially expressed gene in IDC vs. DCIS.Click here for additional data file.Fig. S3. LncRNA expression profile in various molecular subtype of breast cancer obtained from TCGA dataset using TANRIC platform (A) RP11‐161M6.2 (B) ADAMTS9‐AS2 (C) CTB‐92J24.3 (D) CTB‐131K11.1 (E) EPB41L4A‐AS1 (F) FAM83H‐AS1 (G) LINC01614 (H) MIAT (I) RP11‐295M3.4 (J) RP11‐490M8.1 (K) ST8SIA6‐AS1 (L) WDFY3‐AS2.Click here for additional data file.Fig. S4. Expression levels of deregulated lncRNAs in various combination of receptors  positivity in TCGA dataset (A) ER+ or PR+ or Her2+ (B) ER+ or PR+ along with Her2− (C) ER+ and PR− and Her2+ (D) ER− or PR− along with Her2+ (E) Molecular subtype stratification of validation cohort; Red background: Upregulated lncRNAs and Blue background: Downregulated lncRNAs.Click here for additional data file.Fig. S5. High confidence interaction network (score: 0.7) representing differentially expressed mRNA that are known to co‐express with each other as per String analysis and with lncRNA with Pearson correlation coefficient ≥ 0.9 (A) RP11‐142C4.6 (B) RAMP2‐AS2 (C) RP11‐701H24.4 (D) PSMB8‐AS1.Click here for additional data file.Fig. S6. High confidence interaction network (score: 0.7) representing differentially expressed mRNA that are known to co‐express with each other as per String analysis and with lncRNA with Pearson correlation coefficient ≥ 0.9 with TINCR.Click here for additional data file.Fig. S7. High confidence interaction network (score: 0.7) representing differentially expressed mRNA that are known to co‐express with each other as per String analysis and with lncRNA with Pearson correlation coefficient ≤ −0.9 with LINC01359.Click here for additional data file.Table S1. List of clinicopathological features of patients’ tissue samples used in discovery and validation cohort in the study.Click here for additional data file.Table S2. Read alignment statistics and number of genes identified in different samples.Click here for additional data file.Table S3. Complete list of DElncRNAs identified to be differentially expressed in IDC (T) vs. paired normal (N) samples with adjusted P‐values < 0.1 in this study along with normalized read counts from individual samples.Click here for additional data file.Table S4. Complete list of DElncRNAs identified to be differentially expressed in IDC (T) vs. apparent normal (APN) with adjusted P‐values < 0.1 in this study along with normalized read counts from individual samples.Click here for additional data file.Table S5. Complete list of DElncRNAs identified to be differentially expressed in DCIS vs. apparent normal (APN) with adjusted P‐values < 0.1 in this study along with normalized read counts from individual samples.Click here for additional data file.Table S6. Complete list of DElncRNAs identified to be differentially expressed in IDC (T) vs. DCIS with adjusted P‐values < 0.1 in this study along with normalized read counts from individual samples.Click here for additional data file.Table S7. Complete list of dysregulated mRNA co‐expressed with dysregulated lncRNAs supported by split reads in IDC vs. paired normal with Pearson correlation coefficient (PCC) ≥ 0.9.Click here for additional data file.Table S8. Complete list of dysregulated mRNA co‐expressed with dysregulated lncRNAs supported by split reads in IDC vs. apparent normal with Pearson correlation coefficient (PCC) ≥ 0.9.Click here for additional data file.Table S9. Complete list of dysregulated mRNA co‐expressed with dysregulated lncRNAs supported by split reads in DCIS vs. apparent normal with Pearson correlation coefficient (PCC) ≥ 0.9.Click here for additional data file.Table S10. Complete list of dysregulated mRNA co‐expressed with dysregulated lncRNAs supported by split reads in IDC vs. DCIS with Pearson correlation coefficient (PCC) ≥ 0.9.Click here for additional data file.Table S11. List of gene expression assaysClick here for additional data file.


RNA sequencing technologies have allowed researchers to gain a better understanding of how the transcriptome affects disease. However, sequencing technologies often unintentionally introduce experimental error into RNA sequencing data. To counteract this, normalization methods are standardly applied with the intent of reducing the non-biologically derived variability inherent in transcriptomic measurements. However, the comparative efficacy of the various normalization techniques has not been tested in a standardized manner. Here we propose tests that evaluate numerous normalization techniques and applied them to a large-scale standard data set. These tests comprise a protocol that allows researchers to measure the amount of non-biological variability which is present in any data set after normalization has been performed, a crucial step to assessing the biological validity of data following normalization.In this study we present two tests to assess the validity of normalization methods applied to a large-scale data set collected for systematic evaluation purposes. We tested various RNASeq normalization procedures and concluded that transcripts per million (TPM) was the best performing normalization method based on its preservation of biological signal as compared to the other methods tested.Normalization is of vital importance to accurately interpret the results of genomic and transcriptomic experiments. More work, however, needs to be performed to optimize normalization methods for RNASeq data. The present effort helps pave the way for more systematic evaluations of normalization methods across different platforms. With our proposed schema researchers can evaluate their own or future normalization methods to further improve the field of RNASeq normalization. Several RNA sequencing (RNASeq) technologies provide transcriptomic expression data, but the data may vary based on a variety of often-uncontrollable experimental conditions . ConsequPrevious studies have compared normalization methods to determine which method best preserves biological reality while reducing experimental noise . Most ofThe most common way to evaluate normalization techniques is to compare the results of raw and normalized data to quantitative real-time PCR (qPCR), which is considered the gold standard in terms of true expression values . With reIn this article, we demonstrate the utility of combining large, standardized data sets and comprehensive, standardized tests to evaluate the efficacy of different RNASeq normalization methods at different scales of analysis. We combined these tests into a single protocol to facilitate future research.We performed two experiments to assess the validity of common normalization methods (see methods section) on a data set generated by the Sequencing Quality Control (SEQC) consortium. Experiment 1 was designed to quantitatively assess the relative contributions of biology and technology as sources of variability. Experiment 2 was designed to test the internal linear logic of each normalization method by analyzing individual genes from the same sequencing facility.The three forms of variability that we identified are 1) site dependent batch affect, (2) biological differences, and (3) residual or unexplained. The decomposition into sources of variation as a proportion of total signal is presented in Table  site depBased on these results, TPM is the best performing normalization method because it increases the proportion of variation attributable to biology compared to the raw data (90% of genes had a significant association between site and gene expression and 49% of genes had a significant association between sample and gene expression). It is the only normalization method tested that meets this basic criterion increasing biological variability from 41% (raw) to 43% (TPM). TPM does increase site dependent error (Raw: 41% to TPM: 45%) but also reduces the residual variability (Raw: 17% to TPM:12%). This observation is important since residual variability is the worst form of the three types of error to have because it is created solely by uncontrollable experimental conditions. This is unlike biological variability, which is desired, or site dependent variability, which is traceable and therefore often correctable. Residual variability comes from non-desirable experimental problems that are not easy to identify or correct. TPM, Quantile, and Log2 are the only tested methods that reduced residual variability. However, based on our results, Log2 transformation reduces the biological signal to practically nothing making it one of the worst methods possible for normalizing data when the goal is downstream biological analysis. Quantile, though it reduces residual variability, also slightly reduces biological variability .If the majority of variability after normalization cannot be attributed to biology, then the majority of what the researcher is measuring is not grounded in underlying biological truth. Instead, they are measuring experimental bias either from batch effect or other non-biological sources of error. This is highly problematic if biological results are to be concluded from experiments where the majority of the measurable variability is not caused by biology. Consequently, a normalization method should increase the proportion of biological variability so that researchers are measuring biological truth and not experimental error when addressing their biological hypotheses.The second experiment was designed to test whether normalization preserved linearity by analyzing four individual genes chosen for their common study and/or use in medical sciences. TP53 (tumor suppressor), GAPDH (house-keeping gene), CD59 (hemophilia related), and POLR2A (RNA synthesis) were all used from the same site source . We performed individual gene experiments by comparing two independent gene sample preps and their mixture models. This created a linear relationship between samples A and B, with mixture samples C (=75% A and 25%B) and D (=25% A and 75% B) lying on the linear fit . As an example, comparing TMM and raw data it is readily observable that individual clusters representing different library preps are still evident after TMM normalization. This is a problem since normalization should remove such non-biologically founded artifacts in RNASeq experiments.not using GAPDH either as a control gene for wet lab experiments or in any form of normalization procedures since its differential expression is likely to skew any normalization or control procedure.The gene GAPDH was not consistent between samples A and B. This indicates that GAPDH, often used as a control gene, is not an ideal gene for use as an internal control since its expression level varies widely between different tissue types as indicated in Fig. Finally, of the biologically oriented normalization methods, transcripts per million (TPM) was amongst the highest performers. TPM did not introduce new or unwanted structure to the data. Further, it did reduce the noise generated from the library preps in all four of the gene cases. This is also true of the total read count normalization, a similar simple biologically oriented normalization technique. This leads to the observation that the most effective normalization method applied to RNASeq data should be the least complex method and should be biologically rather than purely mathematically or statistically driven.. We presented both a standardized data set designed to perform systematic evaluations along with two tests to determine the underlying validity of different normalization criteria. One of these criteria was the analysis of variance of site and sample type. Related to this we address another fundamental point that site dependent variability is quantifiable and therefore could be addressed during analysis especially when using joint distributions in linear models . These considerations should be taken into account when evaluating the effectiveness of normalization methods since theoretically a perfect model could completely eliminate the site dependent error downstream. In our case TPM had higher In this paper we proposed a methodological protocol for the systematic evaluation of normalization methods for RNASeq analysesWhile some methods tested better than others, the aim of this research was to offer a protocol for normalization evaluation; that is, other normalization methods not utilized in this article should be tested with this large standardized set of data and evaluated in this standard manner. To that end, we are publishing all of our code and data from this project in Supplemental files so that other researchers will be able to evaluate their own normalization methods for any RNASeq data. One limitation of our study was that it only applied to normalization methods for RNASeq data, which are platform specific. However, we have proposed a set of mathematical methods and tests that should translate to other platforms.Normalization is of vital importance to accurately interpret the results of genomic and transcriptomic experiments since normalization controls for experimental error while preserving biological truth. In this study we presented two tests to assess the validity of various normalization methods applied to a large-scale data set collected for systematic evaluation purposes. We tested different RNASeq normalization procedures and concluded that TPM was the most effective normalization method. More work, however, needs to be performed to optimize normalization methods for RNASeq data. The present effort helps pave the way for more systematic evaluations of normalization methods across different platforms. With our proposed protocol researchers can evaluate their own or other normalization methods that were not tested in this article to further improve the field of RNASeq normalization.The Sequencing Quality Control (SEQC) consortium is a large, diverse, and trusted collaboration that includes many participating academic, government, and industry partners. The organization is well established and has been evaluating transcriptomic technologies since 2006, when it was called the MAQC  consortium . The conThe large, standardized data sets provided by SEQC facilitate the development of a systematic approach to evaluate the technologies used in RNA-Seq. Along with the aforementioned pipeline methods the data sets are well-suited for evaluating normalization methods because they were specifically designed for reproducible comparative analyses. Unlike past studies to evaluate normalization methods, ours is unique in the quality, consistency, completeness, and scope of the data, enabling us to perform a broad range of tests. The importance of using standardized methods, well-documented RNA, and extensive replicates cannot be overstated. Mixing samples in fixed ratios allows for an internal validation of the normalization techniques using linearity. Matching qPCR was conducted on all of the samples, facilitating evaluation against external “ground truth”. Cross-site read count files are also available, allowing for the evaluation of normalization across multiple sites.The specific data used in our analysis was 10 data matrices consisting of raw RNASeq counts per transcript from the SEQC study, 10 random data matrices (uniformly randomized without replacement of the 10 SEQC data matrices), and one data matrix consisting of a random Poisson distribution of counts. Since all of the values contained within the 10 randomized files are the same, the overall distribution of the values is the same as the nonrandom files except that all of the patterns in the data have been randomized. In this way, the over-dispersion problem can be ignored for the 10 randomized datasets. Of the 14 original site files that SEQC generated, four had to be excluded from the analysis. Three of the files from the ROCHE 454 platform were excluded due to the lack of technical replicates (1 replicate per site per sample). Without technical replicates, the normalization methods, especially TMM and DESeq, will not function properly. Additionally, the New York Genomics (NYG) file had to be excluded from our analysis due to inconsistencies in annotation. Unlike the other Illumina and Life Sciences read count files, the NYG site used gene symbols instead of RefSeq IDs and included fewer genes. The following normalization methods were employed in Experiment 1 in addition to the raw count data:Raw counts ;TMM - the trimmed mean of means, as implemented in edgeR ;The size factors, as implemented in DESeq ;RPKM, reads per kilobase transcript per million mapped reads ;TPM, transcripts per million mapped reads ;Quantile normalization ;Log2 transformationThese normalization methods were selected based on several criteria. They are most often used in other normalization method comparison studies and are the most commonly used in practice. They provide a variety of approaches to addressing the question of normalization. Two of the methods (Quantile and Log2) are purely mathematical non-biological approaches to normalization whereas TPM and RPKM are biological transformations based on transcript size. Specifically, Log2 may not be strictly defined as normalization but is included since it is so commonly used in research. The other two -- TMM and DESeq -- use a combination of both biologically based and mathematically based approaches to normalization. Although both methods are not designed to directly normalize data they are both commonly used methods for normalization and differential expression analysis, and thus were included in the study. Raw counts are used both as a control and because some researchers do not perform any normalization on the data prior to analysis. All of the normalization methods are implemented in R by our group with the exception of TMM (EdgeR package) and size factors (DESeq package).This statistical test measures the amount of variability and identifies the source of that variability. There are three main sources of variability in genomic data: (1) site dependent or batch effect variability (2) biological variability, and (3) residual or other variability. Any normalization method should decrease the amount of variability attributable to site dependence and residual variability and thus increase the proportion of variability attributed to biology. To test this we performed a two-way ANOVA across our feature set where one variable was the different sites and the other was the four different samples, A, B, C and D. This allowed us to isolate the source of variability and its proportional weight of total variability.p-values for each gene across all samples. We took the median of both MSE p-value for inclusion in the figures and tables. Biological error is determined to be changes based on the biological differences of the four sample types. Residual error is any form of error that is not attributable either biology or site. Looking across the entire genome in increasing gene size we could access how each normalization method was able to deal with these three different forms of error. It is important to note that a good normalization technique should decrease site dependent and residual error while increasing the proportion of total signal that is attributable to biology. This is because biological signal is the only form of signal that we wish to preserve after normalization, so site and residual errors should, theoretically, decrease as a proportion of the total amount of error after normalization.After having performed the two-way ANOVA three different forms of error were identified; 1) site dependent error, 2) biologically dependent error and 3) residual error. Site dependent error is error from between different sites and institutions. Specifically these measurements were recorded as MSE (variance) and Raw counts ;TMM - the trimmed mean of means, as implemented in edgeR ;The size factors, as implemented in DESeq ;RPKM, reads per kilobase transcript per million mapped reads ;TPM, transcripts per million mapped reads ;Quantile normalization ;We performed these individual gene experiments using only the Australian Genome Research Facility data to keep the site source consistent for this experiment. We used the same normalization methods as for use in experiment 1, with the exception of a log2 transform. This is because a log2 transform will not change any linear relationships in the data, and this experiment is designed to see how normalization affects internal linearity.The four genes we selected were TP53, GAPDH, CD59, and POLR2A. TP53 was chosen since its relationship to cancer is well established. GAPDH was selected because it is commonly relied upon as a constitutively expressed housekeeping gene used as a control in many experiments. CD59 and POLR2A were chosen due to their high and consistent median expression across all four samples (A-D). For this reason the raw data does not indicate that there is any significant expression difference between A and B for either of these genes, making them good test cases.