We provided evidence that this subtype relates to tumor microenvironment. Collectively, these results might contribute to more precise personalized therapeutic strategies for each subtype of ESCC patients in the near future.ESCC  is a heterogeneous cancer with diverse prognosis. Here, to explore the biological diversity of ESCC, we employed gene expression profiles from 360 ESCC tumors from East Asians to establish a comprehensive molecular classification and characterization of ESCC. Using the specific 185-gene signature generated by unsupervised consensus clustering of gene expression data, we defined four subtypes associated with distinct clinical metrics: tumors with high metastasis associated with EMT  and active MAP4K4/JNK signaling pathway; tumors with high chromosomal instability with up regulated MYC targes; well differentiated tumors with less aggressive and moderated tumors. The clinical relevance of these subtypes was stated by significant differences in prognosis. Importantly, 24% of all ESCCs ( Esophageal cancer is caused by the malignancy of cells found in the esophagus. ESCC is the sixth most lethal cancer detected worldwide and approximately 70% of the ESCC occurs in China . BesidesNFE2L2 mutations and SOX2 amplification and East Asians showed the higher rates of mutations of NOTCH1, ALDH2, ADH1B and CDK6. All ESCC patients from USA and Canada had mutations in SMARCA4. However, the respective molecular classification of each population and difference in gene expression has not been elucidated.Several studies in recent years have applied microarray or RNA-seq technology to explore gene expression profiles in ESCC –6 by focRecently, three subtypes were identified based on the mRNA expression data from 59 ESCC individuals in Malawi, of which classification could be distinguished by their expression of cell cycle and neutral transcripts . BesidesHere, we explored 360 ESCC tumors from East Asians to establish a robust molecular classification based on unsupervised consensus clustering of mRNA expression profiles. Subsequently, the association with every subtype based on the clinical data, pathological data, chromosomal alterations and tumor microenvironment were assessed. The tumor microenvironment has been demonstrated to be associated with various tumor gene signatures and useful for prognosis across many cancers , 11.n = 30) and GSE45670 (n = 28). These datasets were corrected for technical batch effects and merged into a dataset of 58 cases using DWD method before clustering. The analysis defined four clusters with most robust classification , ESCC2 , ESCC3 , ESCC4   Figure  with preIn this study, we have applied the 185 gene signature classifiers into four independent gene expression datasets for validation of the subtypes. All the 185 genes were projected onto each data set. Following which the R package PAMR was used to calculate the posterior probability of each sample associated with four subtypes. A sample is categorized into one subtype with the maximal posterior probability that at least greater than 0.5. The classifier was validated in GSE23400, GSE47404 and GSE53624 datasets and found that all four subtypes were assigned with comparable proportions of samples . Moreoven = 7] vs. 17.3% [n = 8]; P = 7.909 × 10−3, Fisher exact test, Figure n = 5] vs. 25% [n = 6]; P = 1.556 × 10−2, Fisher exact test, Figure n = 10) of ESCC1 samples were well differentiated with borderline significance  of the ESCC2 samples were poorly differentiated  of ESCC1 samples were well differentiated  of ESCC2 samples were poorly differentiated  and copy number (CN) gain or loss Figure . Samplesn = 58) is not significant due to insufficient survival information. Nevertheless, we found significant differences in overall survival of the four subtypes in validation set GSE53624  pathway [MAP4K4 may also help in treating ESCC2 tumors. Likewise, actin is an important protein in mammalian cells, which can promote cells to move, polarise, divide and maintain organization. Actin-binding and actin filament proteins can reorganize the actin cytoskeleton, and drive cancer cell migration and invasion [ZEB1 and VIM  was applied to determine gene sets which were more abundant in specific subtypes. During the investigation, our focus was on ESCC2 and ESCC4 subtypes associated with clinical signatures. The ESCC2 subtype was significantly enriched in gene sets namely, GCM_MAP4K4, ACTIN_BINDING and ACTIN_FILAMENT Figure . Of thesarcinoma . The inf pathway . Furtherinvasion . Componeinvasion  as gene M Figure , while 21 Figure . These rMYC targets V1 and V2  tumors for ESCC4 subtype. Based on the clinical features and signaling pathway proposed above, the four subtypes were identified as: ESCC1, “well-differentiated”; ESCC2, “metastasis-associated”; ESCC3, “moderated”; ESCC4, “CIN+”.The ESCC4 subtype showed more abundant expression of genes involved in hallmark 2 Figure . MYC haset genes , 21. MYCn = 58) and GSE53624 (n = 119) that are in non-log space respectively, as input to evaluate 22 distinct immune cell types based on 547 signature genes. It was observed that all the ESCCs were commonly found to contain plasma cells and macrophages. . In particular observations revealed that, ESCC1 tumors were mostly well differentiated compare to ESCC2 tumors in the validation sets GSE47404 and GSE53624 and have better survival than other subtypes. Notably, the ESCC3 subtype is much less well characterized and needs further investigation. Only two of 59 tumors were classified in ESCC3 (GSE33426), which might due to tumor microenvironmental contaminations. Nevertheless, GSE47404 has similar percentage of ESCC3, suggesting that there might be other factors leading to the imbalanced percentage of ESCC3 in GSE33426 instead of microenvironmental contaminations. Relatively small sample sizes were used in our analysis (less than 100) and inter-patient tumor heterogeneity is large, which is more likely to be the cause.In summary, we have built a molecular classifier for ESCC based on analysis of gene expression profiles, and identified four distinct subtypes  that are associated with different clinical and molecular characteristics. These four distinct subtypes were validated in four primary data sets, even in microdissected tumors. The ESCC2 tumors were mostly metastatic that are associated with poor differentiation, EMT and poor prognosis. This subtype has been previously reported on colon cancer , our anaIn this analyses showed that ESCC2 samples have a significantly higher stoma and immune content. This is consistent with previous study in colorectal cancer  and ovarOur analysis was limited by lack of ESCC microarray data, clinical tracking information and molecular characteristics. Further study with large ESCC cohorts is needed to confirm the significance and robustness of the classifier. On the hindsight, it would motivate investigations into associations between clinicopathological signatures and these subtypes . Recognin = 30) [n = 28) [n = 53) [n = 59) [n = 71) [n = 119) [In this study, six independent ESCC microarray datasets from GEO Datasets comprising a total of 360 unique samples with stage I–III primary ESCC from East Asians were used. The discovery dataset contains 58 cases with stag II to III ESCCs from two datasets, GSE38129 ([n = 28) , whereas[n = 59)  and GSE4n = 119) . Detail Initially, the CEL files from GEO datasets were downloaded and the two datasets (GSE38129 and GSE45670) were normalized using fRMA  independConsensus clustering  was implInitially, fRMA was used to process GSE23400 and GSE33426 data set. In case of GSE47404 and GSE53624 dataset (Agilent Microarry), quantile normalized microarray data was directly downloaded from GEO Datasets. For each preprocessed data set obtained, expression profiles from probe sets were collapsed to unique genes using collapseRows. The signature genes that were not included in the validation data sets were replaced by the most correlating gene which was expressed in validation sets. Finally, the PAM classifier was used for each preprocessed data sets for classification of gene expression data.P values were estimated by 1,000 permutations. Also, un-filtered GSE38129 data set was used for analysis.GSEA  was perfThe proportion of stromal and infiltrating immune cells were measured with ESTIMATE , a gene x. Analyses were done with 100 permutations with default parameters and results were filtered by a maximum p-value of 0.05.The data subsets GSE38129, GSE45670 and GSE53624 were evaluated by applying CIBERSORT  with the


GPS. This signature was developed by training on microarray expression data and validated using three independent integrated microarray data sets, with samples of stage I non‐small‐cell lung cancer after complete surgical resection. In this study, we first validated the cross‐platform robustness of 9‐GPS by demonstrating that 9‐GPS could significantly stratify the overall survival of 213 stage I lung adenocarcinoma (LUAD) patients detected with RNA‐sequencing platform in The Cancer Genome Atlas . Applying 9‐GPS to all the 423 stage I‐IV LUAD samples in TCGA, the predicted high‐risk samples were significantly enriched with clinically diagnosed metastatic samples . We further modified the voting rule of 9‐GPS and found that the modified 9‐GPS had a better performance in predicting metastasis states . With the aid of the modified 9‐GPS for reclassifying the metastasis states of patients with LUAD, the reclassified metastatic samples presented clearer transcriptional and genomic characteristics compared to the reclassified nonmetastatic samples. Finally, regulator network analysis identified TP53 and IRF1 with frequent genomic aberrations in the reclassified metastatic samples, indicating their key roles in driving tumor metastasis. In conclusion, 9‐GPS is a robust signature for identifying early‐stage LUAD patients with potential occult metastasis. This occult metastasis prediction was associated with clear transcriptional and genomic characteristics as well as the clinical diagnoses.Our laboratory previously reported an individual‐level signature consisting of nine gene pairs, named 9‐ CIconfidence intervalsC‐indexconcordance indexCNAcopy number aberrationsDE genesdifferentially expressed genesFDRfalse discovery rateGEOGene Expression OmnibusGOGene OntologyHRhazard ratiosLUADlung adenocarcinomasNSCLCnon‐small‐cell lung cancerOSoverall survivalREOrelative expression orderingRFSrecurrence‐free survivalTCGAThe Cancer Genome Atlas1et al., et al., et al., et al., et al., et al., et al., et al., et al., et al., et al., et al., et al., et al., et al., et al., et al., Among patients with non‐small‐cell lung cancer (NSCLC), which accounts for approximately 85% of all lung cancer cases, nearly 50% are lung adenocarcinomas (LUAD)  of nine gene pairs (denoted as 9‐GPS) with the majority rule, which is highly robust in data measured by different microarray platforms , and validated that 7/9‐GPS performed better in terms of sensitivity of metastasis detection, overall survival (OS), and 5‐year recurrence rate in both the TCGA RNA‐sequencing data and another two independent test data sets measured by two different microarray platforms. Then, we focused on providing evidences that 7/9‐GPS can aid in the identification of genomic and transcriptional characteristics of primary tumor tissues of patients with metastases by reclassifying the metastasis states of all patients with LUAD in TCGA. The high‐risk stage I patients predicted by 7/9‐GPS, compared with the low‐risk stage I patients, were also characterized by these genome lesions, greatly increasing the confidence of 7/9‐GPS for identifying occult metastasis of stage I patients in the clinical application . For the 277 samples of stage I patients with recorded OS data, 64 samples with records of receiving adjuvant chemotherapy, radiotherapy, and/or target treatments were excluded from survival analysis. Of the remaining 213 samples of stage I patients , which were processed with the GISTIC algorithm , the normalized count values processed by RSEM method were extracted and log 2‐transformed as the gene expression measurements. For gene mutation data of the 423 stage I–IV samples derived from the Illumina Genome Analyzer DNA Sequencing GAIIx platform, only the nonsynonymous mutations were included, and a discrete mutation profile including 17 821 genes was generated. Copy number aberrations (CNAs) of the 423 stage I–IV samples were downloaded from TCGA Firehose (http://www.ncbi.nlm.nih.gov/geo/). Two tests were performed using the six data sets produced by two microarray platforms, including 301 stage I LUAD samples in test 1  generated by Affymetrix Plus 2.0 and 28 samples in test 2 (GSE29016) generated by Illumina HT‐12 V3.0. The two test data sets both had recorded OS data, while test 1 also had recorded recurrence data. The microarray data sets produced by Affymetrix U133A were not analyzed as only six gene pairs were measured in this platform. Additionally, GSE50081 data also include 33 primary tumor samples of LUAD patients with lymph node metastases and 94 primary tumor samples of LUAD patients without metastases. The raw mRNA expression data set was preprocessed using the Robust Multi‐array Average algorithm  votes for high (or low) risk, where Ea and Eb represent the expression levels of Ga and Gb, respectively. In this study, we also evaluated the performance of 9‐GPS based on a strict voting criterion for low‐risk identification.The prognostic 9‐GPS signature consisting of nine gene pairs . The recurrence‐free survival (RFS) was defined as the time from surgery to recurrence or the final documented date (censored). Here, the 5‐year recurrence rate of patients was also used for survival analysis, which should be a better end point for prognosis study of early‐stage lung cancer due to high comorbidity during the usually long survival time. Survival curve was estimated using the Kaplan–Meier method and compared using the log‐rank test  genes between two groups of samples. We used the GO function algorithm  .The 33.1et al., P = 0.0318, HR = 1.88, 95% CI: 1.05–3.37, C‐index = 0.55, Fig. P = 0.0244, HR = 2.03, 95% CI: 1.09–3.75, Fig. 9‐GPS for prognostic prediction of early‐stage NSCLC patients after complete surgical resection, as described in Fig. 3.2P = 0.0015, Fig. et al., To study the association of the risk classifications of samples predicted by 9‐GPS with clinically diagnosed metastasis states, 9‐GPS was applied to all the gene expression of 423 stage I–IV primary tumor samples in TCGA. Based on the majority voting rule, 175 and 248 patients were stratified into high‐risk and low‐risk groups, respectively. In the clinically diagnosed metastatic group, the proportion of samples identified as high risk was 51.59%, which was significantly higher than the corresponding proportion (35.34%) in the clinically diagnosed nonmetastatic group : 84.85% of the 33 primary tumor samples of patients with metastases were identified as high‐risk samples by 7/9‐GPS, which was significantly higher than the corresponding frequency of 68.09% in the 94 primary tumor samples of patients without metastases . When compared the two DE gene lists . The clearer transcriptional differences between the two reclassified groups indicated that the reclassification of metastasis states of LUAD patients with the aid of 7/9‐GPS could capture more DE genes by reducing the influence of the samples with occult metastases. Functional enrichment analysis showed that the 5042 DE genes (denoted as metastasis‐related DE genes) identified after sample reclassification were significantly enriched in several pathways associated with tumor metastasis . Additionally, we found that the median of the mutation count per sample for the reclassified metastatic samples was 80, which was significantly more than the corresponding median count (44) for the reclassified nonmetastatic samples      with significantly different amplification frequencies between the 157 primary tumor samples of patients with metastases and 266 primary tumor samples of patients without metastases in TCGA . In contrast, we were able to find 21 genes with significantly different mutation frequencies and 27 genomic regions with significantly different CNA frequencies between the redefined metastatic and nonmetastatic groups . All the 48 genomic lesions are displayed in Table Notably, we found that 45 of the 48 genomic lesions characterizing the difference between the reclassified metastatic samples and nonmetastatic samples also had significantly different mutation or CNA frequencies between the stage I high‐risk and low‐risk samples identified by 7/9‐GPS  could be directly regulated by some genomic lesions  could be regulated by some genomic lesions indirectly  and 332 downstream metastasis‐related DE genes that were directly linked to the ‘driver’ genes. As shown in Fig. TP53  for 139 stage I LUAD samples stratified by 9‐GPS based on the majority voting rule in TCGA.Fig. S2. The survival analyses of the high‐risk samples identified by 7/9‐GPS but not by 9‐GPS and the risk samples concordantly by 7/9‐GPS and 9‐GPS in TCGA.Fig. S3. Prognostic performance of 9‐GPS based on the majority voting rule in two test data sets.Fig. S4. The boxplot of proliferation scores in the high‐risk and low‐risk samples identified by 7/9‐GPS, respectively.Fig. S5. The genomic characteristics between the high‐ and low‐risk groups predicted by 7/9‐GPS in stage I LUAD patients.Fig. S6. Prognostic performance of quantitative gene expression signatures in 213 stage I lung adenocarcinoma samples in TCGA.Table S1. The clinical information of stage I LUAD samples in TCGA.Table S2. The 423 stage I‐IV LUAD samples detected with multiple omic‐data in TCGA.Table S3. The functional pathways enriched with metastasis‐related DE genes.Table S4. The genomic characteristics between the reclassified metastatic and nonmetastatic groups with aid of 7/9‐GPS.Table S5. The genomic characteristics between the stage I high‐risk and low‐risk samples identified by 7/9‐GPS.Table S6. The functional pathways enriched with differentially expressed genes regulated by each ‘driver’ for reclassified metastatic samples.Click here for additional data file.


DNA methylation is an important tissue-specific epigenetic event that influences transcriptional regulation of gene expression. Differentially methylated CpG sites may act as mediators between genetic variation and gene expression, and this relationship can be exploited while mapping multi-tissue expression quantitative trait loci (eQTL). Current multi-tissue eQTL mapping techniques are limited to only exploiting gene expression patterns across multiple tissues either in a joint tissue or tissue-by-tissue frameworks. We present a new statistical approach that enables us to model the effect of germ-line variation on tissue-specific gene expression in the presence of effects due to DNA methylation.Our method efficiently models genetic and epigenetic variation to identify genomic regions of interest containing combinations of mRNA transcripts, CpG sites, and SNPs by jointly testing for genotypic effect and higher order interaction effects between genotype, methylation and tissues. We demonstrate using Monte Carlo simulations that our approach, in the presence of both genetic and DNA methylation effects, gives an improved performance  to detect eQTLs over the current eQTL mapping approaches. When applied to an array-based dataset from 150 neuropathologically normal adult human brains, our method identifies eQTLs that were undetected using standard tissue-by-tissue or joint tissue eQTL mapping techniques. As an example, our method identifies eQTLs by leveraging methylated CpG sites in a LIM homeobox member gene (LHX9), which may have a role in the neural development.Our score test-based approach does not need parameter estimation under the alternative hypothesis. As a result, our model parameters are estimated only once for each mRNA - CpG pair. Our model specifically studies the effects of non-coding regions of DNA  on mapping eQTLs. However, we can easily model micro-RNAs instead of CpG sites to study the effects of post-transcriptional events in mapping eQTL. Our model’s flexible framework also allows us to investigate other genomic events such as alternative gene splicing by extending our model to include gene isoform-specific data.The online version of this article (doi:10.1186/s12859-017-1856-9) contains supplementary material, which is available to authorized users. It has been long established that regulatory regions in higher eukaryotes activate gene transcription in a tissue-specific manner , 2. ThesVal158Met (rs4680) that is associated with differential COMT expression across regions of the brain during the course of the illness  with mean j at a single locus by using the following vectorized form of the linear mixed model – For a positive integer yij is a t×1 vector of gene expression data, αt is the tissue-specific intercept  represents the number of copies of the minor allele. The random effect ai∼N1, where β (additive genetic effect), ϕ (G×M effect), the proportion of variation explained by the G×T effect G×M×T effect lme4 [p value obtained using lm function in R by fitting the following linear regression model.We use 1000 data replicates to evaluate the type I error and for power calculations. Simulations were performed by varying the following parameters- age lme4 , 30 in tcis-SNP pair, TBT-eQTL analysis was performed using the following linear regression model – Y is either gene expression data and G represents genotypes encoded as the number of copies of minor allele. The test statistic is the minimum p value over the total number of tissues from linear regressions performed separately in each tissue for each mRNA - SNP pair. Statistical significance was determined at a nominal p value of 0.05 for all power simulations .For each mRNA - GSE15745; dbGAP Study Accession: phs000249.v1.p1).Fresh frozen tissue samples of the cerebellum (CRBLM), frontal cortex (FCTX), caudal pons (PONS) and temporal cortex (TCTX) were obtained from 150 neuropathologically normal samples . GenotypGene expression on four brain regions are publicly available as rank-invariant  normalizEach gene expression probe was then adjusted for known variation contributed by batch effects and biological covariates such as tissue bank, gender, hybridization batch and numeric covariates such as post-mortem interval (PMI) and age as well as unknown variation using surrogate variable analysis (SVA) model . \documcis-eQTL detected significantly improved when multiple PCs were removed from the expression data [It has previously been shown that the number of ion data .Methylation data, obtained as a “series matrix file” consisted of Beta-values, which represent the ratio of methylated probe intensity and the overall intensity (sum of methylated and unmethylated probe intensities) . We follphs000249.v1.p1) following requisite author permissions. The genotype data was recoded into a SNP matrix of values 0, 1 and 2 representing minor allele counts. Samples with African and Asian ancestry were removed from the analysis in order to keep the data relatively homogeneous with patients of European-Caucasian ancestry. These SNPs were filtered on the missing-ness of the individual data and the SNP data , followed by MAF (included SNPs with MAF ≥ 0.05)and Hardy-Weinberg equilibrium  in the same order using PLINK [The genotype data was obtained from dbGAP database  JAGUAR, 9) A potential strategy to combine two models to maximize eQTL discovery, and 10) Reproducibility. (PDF 635 kb)Supplementary material. Supplementary material expanding on 1) Our model, 2) Individual components of our joint score test statistic, 3) Description of various null hypotheses, 4) Null and power simulations of our joint score test statistic, 5) Gibbs et al. dataset preprocessing, 6) Design of our data analysis, 7) KEGG pathway analysis on the results from Gibbs


Since tumor is seriously harmful to human health, effective diagnosis measures are in urgent need for tumor therapy. Early detection of tumor is particularly important for better treatment of patients. A notable issue is how to effectively discriminate tumor samples from normal ones. Many classification methods, such as Support Vector Machines (SVMs), have been proposed for tumor classification. Recently, deep learning has achieved satisfactory performance in the classification task of many areas. However, the application of deep learning is rare in tumor classification due to insufficient training samples of gene expression data. In this paper, a Sample Expansion method is proposed to address the problem. Inspired by the idea of Denoising Autoencoder (DAE), a large number of samples are obtained by randomly cleaning partially corrupted input many times. The expanded samples can not only maintain the merits of corrupted data in DAE but also deal with the problem of insufficient training samples of gene expression data to a certain extent. Since Stacked Autoencoder (SAE) and Convolutional Neural Network (CNN) models show excellent performance in classification task, the applicability of SAE and 1-dimensional CNN (1DCNN) on gene expression data is analyzed. Finally, two deep learning models, Sample Expansion-Based SAE (SESAE) and Sample Expansion-Based 1DCNN (SE1DCNN), are designed to carry out tumor gene expression data classification by using the expanded samples. Experimental studies indicate that SESAE and SE1DCNN are very effective in tumor classification. Tumors, which seriously endanger human health, are part of the major malignant diseases in the world. Early detection of the tumor is under a very important meaning for the better treatment of patients. The emergence and development of DNA microarray has promoted the research of tumor at the molecular level -3. By miAmong all classification methods, deep learning models show very good performance and draw more and more attention. Deep learning models have many advantages over conventional methods. On the one hand, deep learning models intrinsically learn a high level representation of the data so that avoiding laborious work . On the Among a variety of deep learning models, Stacked Autoencoder (SAE)  is a widConvolutional Neural Network (CNN)  plays a CNN is the most widely used method in the field of image processing. Generally, a 2-dimensional image sample is taken as the input of CNN to implement convolution operation. However, each sample is a 1-dimensional array in tumor gene expression data which makes traditional CNN models not applicable for tumor classification. Fortunately, 1-dimensional CNN (1DCNN), a special CNN model, is proposed, and it requires the input is a 1-dimensional vector. 1DCNN has been used to analyze 1-dimensional sample in many areas. For example, Hu et al. have successfully utilized 1DCNN to process the spectral channels . But theA large number of labeled data are usually required for training the deep learning models, including SAE and 1DCNN. However, the number of labeled samples of tumor gene expression data is quite small. For instance, there are 60 labeled samples in colon data  and onlyThe main contributions of our work are summarized as follows. Firstly, for the first time, 1DCNN, an excellent deep learning model, is successfully applied to the tumor classification task. Secondly, a novel sample expansion method is proposed to deal with the problem of insufficient labeled samples when using deep learning models to implement tumor classification.The remainder of the paper is structured as follows. In Section This section shows the experimental results. In this paper, microarray data was used to perform our experiment. We performed our method on three publicly available gene expression datasets, i.e., breast cancer , leukemiWe tested the proposed SESAE and SE1DCNN on three tumor datasets: breast cancer , leukemia was tested. For each a, we provided the parameters of SESAE and SE1DCNN on different tumor datasets. For SESAE and SE1DCNN, the choices of parameters might not be the best but effective for tumor classification.For each dataset, Inf-FS method was adopted as the dimensionality reduction algorithm to select genes. For fair comparison, 500 genes were selected by Inf-FS for each method. We performed 10-fold cross-validation and results were presented in terms of the average classification accuracy. In this subsection, the number of corrupted genes a = 1,2,3,4,5were tested. We tested the number of nodes of hidden layers in SESAE. Simultaneously, we also tested the number and size of convolution filters and the size of filters in max pooling. For each dataset, we took 20% samples of each class to expand the training samples and the rest 80% samples as testing samples. The parameters and classification accuracies of SESAE and SE1DCNN with different number of corrupted genes on breast cancer were summarized in Tables a = 1,2,3,4,5, the number of training samples is 2505, 1255, 835, 630, 505, respectively. From Table a = 1. From Table a = 1 and a = 2, SE1DCNN can reach the best performance 95.33%.Here, a = 1,2,3,4,5, the number of training samples is 6513, 3263, 2171, 1638, 1313, respectively. From Table a = 1. From Table a = 1.The parameters and classification accuracies of SESAE and SE1DCNN with different number of corrupted genes on leukemia dataset were summarized in Tables a = 1,2,3,4,5, the number of training samples is 6513, 3263, 2171, 1638, 1313, respectively. From Table a = 1. From Table a = 2.The parameters and classification accuracies of SESAE and SE1DCNN with different number of corrupted genes on colon cancer were summarized in Tables a were provided in Table a = 1, SESAE and SE1DCNN have the best results 99.88% and 99.94%, respectively. For leukemia, in the case of a = 1, SESAE and SE1DCNN have the best results 99.78% and 99.84%, respectively. For colon dataset, in the case of a = 1, SESAE and SE1DCNN have the highest accuracies 99.96% and 99.98%, respectively. The results indicate that the corrupted samples can be correctly classified and the meaningful features are successfully captured by the corrupted samples.Furthermore, we expanded all the samples, and tested whether the corrupted samples can be correctly classified. The classification accuracies of SESAE and SE1DCNN on three datasets with different To demonstrate the effectiveness of SESAE and SE1DCNN for tumor classification, traditional 1DCNN , traditiOn all the three datasets, SE1DCNN has better performance than all the other methods. On colon and breast cancer datasets, except for SE1DCNN, SEASE outperforms the other methods. On leukemia dataset, except for SE1DCNN and 1DCNN, SESAE have the best performance among all the 5 methods. This indicates that our SE method is very effective in classifying tumor data. Without SE method, 1DCNN outperforms traditional SAE, SAE in , SAE witx as an input firstly. Secondly, x is mapped to y via an encoder. Thirdly, the Autoencoder attempts to reconstruct x by decoding, y and generates the reconstruction vector z. Finally, the Autoencoder calculates the reconstruction error between x and z. In Figure x is stochastically corrupted to y via an encoder. Thirdly, DAE reconstructs x by decoding y, and generates the reconstruction vector z. Finally, DAE calculates the reconstruction error between x and z with a loss function.In the classification problem, it is particularly critical to obtain a good feature representation. The traditional Autoencoder (AE) can learn a useful representation by encoder. However, we cannot obtain robust features by using Autoencoder. A very different strategy is proposed by Vincent et al. to get a high-level representation: cleaning partially corrupted input, or in short denoising . There aX, corrupted samples X, Corrupted samples are much more likely to be outside and farther from the manifold than the uncorrupted ones. Thus stochastic operator X, on or near the manifold. Note that when The process of denoising, that is, mapping a corrupted sample back to an uncorrupted one, can be given an intuitive geometric interpretation under the so-called manifold assumption, which states that natural high dimensional data concentrates close to a non-linear low-dimensional manifold. Based on uncorrupted samples unded in . As a kiunded in  show thaunded in .m genes and n samples. For each sample in X, SE method randomly chooses floor is a function that is rounded down, and it guarantee the number of expanded samples is an integer. And each processed sample is saved for future operations. In this approach, n samples. Finally, n raw samples are merged into one matrix to be taken as training data.Thanks to the denoising idea, in this paper, a novel Sample Expansion method is proposed to address the problem of insufficient training samples for tumor gene expression data. Denote a to 2. In Figure X, X, X are processed in the same manner. Finally, Y that can be taken as the training data. By utilizing SE, a large number of training samples can be obtained. The expanded samples maintain the merits of the corrupted data and address the problem of insufficient training samples of gene expression data to a certain extent.We give the visualization of SE method in Figure We attempt to interpret the rationality of SE from a biological perspective. Generally, the differential expression of multiple genes may be expected to result in various diseases. Moreover, the realization of a biological process usually requires interaction of multiple genes. Unfortunately, we cannot accurately determine which combinations of genes are the decisive ones we want. After being processed by SE, the non-corrupted genes in each expanded sample are taken as a combination. The corrupted samples can generate a variety of gene combinations. Some of these gene combinations may indicate distinct biological processes or different gene co-expression . From thAn Autoencoder usually has three layers: one input layer, one hidden layer, and one reconstruction layer  denotes the activation function. Here, the sigmoid function is taken as the activation function.During training, the input y is mapped to a reconstruction layer by a decoder. The reconstructed value is denoted as z. This step can be written asWz denotes the hidden-to-output weights, bz denotes the bias of output units.Then, Wy =Wz =W This can help to halve model parameters. Therefore, three groups of parameters, W, by and bz, need to be learned.In this paper, we hold the following constraint: x and zcost  denotes the reconstruction error. The weight updating rule can be defined asη denotes the learning rate.The goal of Autoencoder is to minimize the reconstruction error between y. If an Autoencoder perfectly recovers the input from y, y can maintain enough information of the input. In addition, the learned nonlinear transformation in y can be regarded as a good feature extraction process. Therefore, stacking the encoders can minimize the loss of information in data. In the meantime, the abstractive and invariant information can be preserved in the deeper features. All these characteristics promote us to choose Autoencoder to extract deep features for tumor gene expression data.After the model training, the learned feature lies in the hidden layer, which can be used for classification. It can also be used as the input of a higher layer to learn a deeper feature in deep learning models. The power of Autoencoder lies in the form of reconstruction-oriented training. During reconstruction, Autoencoder only uses the information in The Stacked Autoencoder (SAE) can be constructed by stacking the input and hidden layers of Autoencoder together. The SESAE is designed by applying the SE method to SAE for tumor classification. The SESAE architecture is given in Figure CNN is a classical deep learning model that exploits spatially local correlation by enforcing a local connectivity pattern between neurons of adjacent layers. CNN architecture consists of various combinations of convolutional layers, max pooling layers and fully-connected layers. Neurons in the same convolutional layer are sparsely connected to the neurons in the next layer and share the same weight. Weight sharing can reduce the number of trainable parameters and make CNN an effective model. The output of a convolutional layer is usually taken as the input of a max pooling layer. Max pooling layers divide the input into multiple non-overlapping windows and output the maximum value for each window. Max pooling can reduce the computation complexity for upper convolutional layers and provide translation invariance of features from the location. In the classification task, a fully-connected layer is used to integrate all the feature maps of the last pooling layer.The training process of CNN contains two key steps: forward propagation and back propagation. The former step computes the actual classification results with current parameters while the later step updates the trainable parameters to narrow the gap between the actual classification results and the desired classification results.xi as the output of the i-th layer and the input of the next layer. Define xi to beWi is a weight matrix of the i -th layer and bi is an additive bias vector of the i -th layer. In Eq. f (ui) is the activation function of the i -th layer. In this paper, the Rectified Linear Unit (RELU) is taken as the activation function. For a classification problem with C classes and N training samples, the squared-error loss function 30], RoffInf-FS is an excellent feature selection method by ranking the importance individuates candidate features. The most appealing characteristic of Inf-FS is that the importance of a feature is assessed by considering all the possible subsets of all the features. In addition, the relevance and redundancy of each feature are influenced by all the other ones. Numerous experiments demonstrate that Inf-FS outperforms many classical feature selection methods, such as SVM-RFE , Fisher In this paper, the framework of tumor gene expression data classification via Sample Expansion-based deep learning is given in Figure Firstly, Inf-FS is used as the dimensionality reduction strategy to select genes.mean(X) is the mean of the dimensionality reduced data matrix X by row, std(X) is the standard deviation of X by row, Secondly, the dimensionality reduced data is normalized. We use the following normalizationThirdly, SE method is used to obtain a large number of training samples. The gene expression data is divided into two parts: training data and testing data. We use SE method to increase the number of labeled training data by using the separated training data.Finally, two deep learning models, SAE and 1DCNN, are adopted to classify tumor data. The expanded and raw training data are merged into a matrix that is used as the new training data to train SAE and 1DCNN. We test SESAE and SE1DCNN by using the testing data.In this paper, two sample expansion-based deep learning models, Sample Expansion-Based Stacked Autoencoder (SESAE) and Sample Expansion-Based 1D Convolutional Neural Network (SE1DCNN), are designed to classify tumor gene expression data. Firstly, since feature selection is more believable than feature extraction, an excellent feature selection method Inf-FS is used to reduce the dimensionality of gene expression data. Secondly, inspired by the denoising idea in DAE, a Sample Expansion method is proposed. The expanded samples not only have the benefits of corrupted data in DAE but also solve the problem of insufficient labeled training samples of gene expression data to a certain extent when using deep learning models. We also give an interpretation of SE method from a biological perspective. Finally, since SAE and CNN can provide excellent classification effect in many fields, the applicability of SAE and 1DCNN on gene expression data is discussed. A 4-layer SAE and a 7-layer 1DCNN are designed to classify the tumor gene expression data by using the expanded samples and raw samples.a. This is a guide to the choice of parameters. Moreover, we expanded all samples to determine the effectiveness of the corrupted samples. The high classification accuracies of SESAE and SE1DCNN on three datasets demonstrate that the corrupted samples are very useful. Traditional 1DCNN, traditional SAE, SAE in [We tested the proposed SESAE and SE1DCNN on three tumor datasets: breast cancer, leukemia and colon cancer. We first provided the parameters of SESAE and SE1DCNN on different tumor datasets with different , SAE in , SAE wit, SAE in , and SofThe limitation of this paper is mainly the explanation of SE method from a biological perspective is insufficient. In this paper, we give a short interpretation of SE method from a biological perspective. We believe that the non-corrupted genes in expanded samples can be taken as a gene combination and the corrupted samples can generate a variety of gene combinations. These gene combinations may indicate distinct biological processes or different gene co-expression. From this perspective, the class of samples can be represented more effectively by large number of gene combinations, thereby improving the performance of tumor classification. The pathogenesis of the tumor needs to be studied in future to discover the useful combinations of genes. By analyzing these combinations of genes, our SE method may give a more persuasive interpretation. In future, we will focus on the biological meaning of different combinations of genes to propose a more reasonable sample expansion strategy for tumor classification when using deep learning.


While Illumina microarrays can be used successfully for detecting small gene expression changes due to their high degree of technical replicability, there is little information on how different normalization and differential expression analysis strategies affect outcomes. To evaluate this, we assessed concordance across gene lists generated by applying different combinations of normalization strategy and analytical approach to two Illumina datasets with modest expression changes. In addition to using traditional statistical approaches, we also tested an approach based on combinatorial optimization. We found that the choice of both normalization strategy and analytical approach considerably affected outcomes, in some cases leading to substantial differences in gene lists and subsequent pathway analysis results. Our findings suggest that important biological phenomena may be overlooked when there is a routine practice of using only one approach to investigate all microarray datasets. Analytical artefacts of this kind are likely to be especially relevant for datasets involving small fold changes, where inherent technical variation—if not adequately minimized by effective normalization—may overshadow true biological variation. This report provides some basic guidelines for optimizing outcomes when working with Illumina datasets involving small expression changes. Microarray studies have been particularly successful for identifying genes with large expression changes in conditions such as cancer. The challenge is to extend microarray technology into robust identification of smaller gene expression changes. This requires array platforms with a high degree of sensitivity and specificity and data analysis tools that generate accurate results. While increasing experimental group sizes can improve the detectability of subtle changes, one major challenge in microarray analysis is the detection of small, but “real”, expression changes in small datasets.The Illumina microarray platform has become one of the main platforms for “transcriptomic” studies. Each Illumina BeadChip array comprises randomly positioned silica beads, each containing hundreds of thousands of copies of a specific 50-nucleotide probe sequence. On average, each probe is replicated on at least 15 beads randomly distributed across each array. The large number of replicate beads minimizes artefacts that may arise due to intra-array location and other factors and provides a high degree of internal technical replication, facilitating generation of reliable raw data ,3,4,5,6.lumi [limma [The technology has performed well in comparative studies of different platforms by the Microarray Quality Control (MAQC) consortium ,8 and otlumi , limma [i [limma  and othei [limma , which ui [limma . HoweverIn addition, as Bioconductor packages require knowledge of the R programming language, they are currently used primarily by researchers with stronger computing backgrounds and by more specialized research groups doing large quantities of array analysis. These approaches are less commonly used by researchers doing occasional array studies or performing downstream analyses of array data provided under contract by large facilities or by researchers with restricted computing expertise, as is the case for many graduates from biological disciplines. Most novice Illumina microarray users instead rely on established “black box” procedures developed by Illumina and other companies. Therefore, while the Illumina platform appears well-suited to working with datasets involving small expression changes, as described above, the effects of different computational approaches need to be investigated more closely. In this study, we have examined how different normalization and differential expression analysis tools may influence analyses of small, low fold-change datasets on this platform. Following initial scanning of BeadChips by Illumina’s BeadScan software, there are three phases of processing of scanned BeadChip data (bead level data): (1) Local background subtraction and averaging of probe replicates generating bead summary data; (2) Transformation and normalization; (3) Analysis of differential expression. The different data processing steps and associated issues are briefly reviewed below.beadarray package available through R/Bioconductor allows the user to work with raw bead level data [Initial data pre-processing in the proprietary Illumina GenomeStudio (formerly BeadStudio) software provides users with bead summary data in the form of a single signal intensity value for each probe. This value is calculated by subtracting the local background from the signal intensity for each bead, then taking the mean of all beads containing a given probe. While the vel data , these dvel data . For thei.e., maximizing true positives and minimizing false negatives).Raw bead summary intensity values are usually normalized by one or more transforming functions. Reasons for normalizing can include forcing a normal data distribution or increasing comparability between probes, samples, chips, machines or platforms. Even small technical variations  can sometimes cause considerable differences in signal intensities. The overarching aim of normalization is to reduce differences due to technical variation , while conserving true biological effects . Using GenomeStudio, this correction involves subtracting the mean signal of negative control probes in a particular array from each bead summary value in that array. While recommended by Illumina, several groups suggest this particular method is flawed [Prior to normalization, it is generally recommended that a correction step be performed to adjust for between-array differences in non-specific signal intensities , microarray data are generally normalized by one of several different approaches. Here, we have investigated all four optional normalization strategies in the GenomeStudio software: ary data ,20; Rankp-value approach allows estimation of false positive error probability, which can be considerable when conducting large numbers of comparisons. Yet, conversely, the methods currently used to adjust for multiple comparisons [Following normalization, different analytical approaches are used to identify genes with altered expression between experimental conditions. The challenge for any analytical approach lies in reducing false positives (Type I or α errors), while avoiding false negatives (Type II or β errors). The use of a statistical parisons  are ofteparisons ,23. As oMax Cover -FS) [Max Cover -FS considers primarily the distribution of the test and control replicates relative to one another and the ability of each probe to discriminate between replicates from different classes . It is not based upon fold-change cut-offs or the statistical significance of comparisons of mean expression measures.In addition to exploring the effects of different normalization strategies, we have also assessed how outcomes are affected by applying each of three different analytical approaches to the normalized data. Two of the three approaches tested used statistical significance as the inclusion criteria: GenomeStudio differential expression and GeneSpring differential expression. GenomeStudio was assessed because it is the Illumina proprietary software. GeneSpring is a widely-used, commercially available application with a number of features not present in GenomeStudio, including additional statistical capabilities. The third analytical approach assessed was a Max Cover -k Feature Set approach -FS) ,25,26,27We analysed two comparable Illumina datasets with relatively small expression changes. These were from (i) heart and (ii) brain samples of biological replicates of mice fed a short-term high iron diet and control mice fed a normal diet. Short-term high iron diets cause only modest changes in heart gene expression , and ourAll animal work was approved by the Animal Ethics Committee of the University of Western Australia. Male mice of the AKR strain were fed either normal chow or a high-iron diet . The high-iron regimen used results in significantly higher iron indices and iron loading in the liver . At 10 wTotal RNA was isolated using TRI Reagent (Ambion), purified and concentrated using the RNeasy MinElute Kit (Qiagen) and amplified with the Illumina TotalPrep RNA Amplification Kit (Ambion). Gene expression was assessed in biological replicates using Illumina Sentrix MouseRef-8 (v1.1) BeadChip arrays. BeadChips were scanned using Illumina BeadArray reader and BeadScan software. For each tissue, all sample preparation and microarray experimentation was done simultaneously using arrays from the same batch, in order to avoid any potential batch effects. Following quality control assessment of microarray data, one control heart RNA sample was flagged as an outlier and removed from further analysis.Average, Cubic Spline, Quantile and Rank Invariant) available in GenomeStudio v2010.3 (Illumina). Non-normalized data were also examined. The algorithms and parameter settings used to assess differential gene expression were:(1)p value < 0.05 in comparisons of the control and test classes were considered to be detecting differential expression. A more detailed description is given in the GenomeStudio Gene Expression Module User Guide [GenomeStudio v2010.3—The Illumina Custom algorithm in the GenomeStudio software assesses three components of variation . Probes returning a er Guide .(2)t test (p < 0.05).GeneSpring GX 11.0 Software—The usual default settings of the GeneSpring program apply further transformation and normalization steps; however, this can introduce substantial artefacts when applied to data already normalized by other approaches. For these reasons, these additional normalization steps were not applied. Differential expression was determined by an unpaired (3)Max Cover -FS is a multivariate method that selects a set of probes that, as a collective, can discriminate well between the experimental test and control groups [Max Cover -k Feature Set Approach—l groups . This all groups  is used l groups . Secondll groups . This isl groups ,26,27. TBead summary data were normalized separately for each dataset , using each of the four normalization procedures  was applied to background-subtracted data normalized using the strategies described above and assessed for differential gene expression using GenomeStudio.No Normalization) and performing differential expression analysis in GenomeStudio.In addition to investigating the effects of these normalization strategies and analytical approaches on background-subtracted data, we also investigated the effect of omitting background correction before normalizing data using the four available options  followed by replicate summarization by fitting a linear model and differential expression analysis using moderated t-statistics with empirical Bayes’ shrinkage of the sample variances [lumi, this involved background correction using bgAdjust, variance stabilizing transformation and robust spline normalization, followed by replicate summarization and differential expression analysis using the limma functions described above [To compare these various approaches to those available through the Bioconductor project, bead summary data were exported from GenomeStudio and analysed with the Bioconductor packages limma  and lumima [lumi , using pariances . For lumed above .p-value, which is the probability of seeing a certain signal level without probe-target hybridization [p-value > 0.01  in both the control group and the high iron group were eliminated from further analysis. As illustrated in To avoid distortion of the results by noise, we removed probes returning signals that were highly likely to be due to non-specific background signal rather than specific probe-target hybridization. The specificity of individual probe signals was estimated using the detection dization . All proDifferent combinations of normalization and analysis approaches were applied as detailed in the Results section. The degree of agreement of the resulting probe sets, henceforth termed “concordance”, was calculated as either a number or a percentage. In the first instance, the concordance of two probe sets generated by different normalization strategies or analytical approaches was defined as the number of overlapping probes between the two sets. In the second instance, the concordance was defined as the percentage of overlapping probes calculated against the total number of probes in each particular probe set. Comparable measures, notably number of overlapping genes (NOG) and percentage of overlapping genes (POG), have been used previously to assess outcome concordance ,34.In this study, we will be considering concordance in three separate contexts: (1) the concordance between the probe sets generated by the different normalization strategies; (2) the concordance between the probe sets generated by the various types of differential expression analysis approaches; and (3) the concordance between the pathways enriched within each probe set.A schematic summarizing the different steps in normalization, differential expression analysis and subsequent filtering is given in The Database for Annotation, Visualization and Integrated Discovery (DAVID ) was useAverage, Cubic Spline, Quantile, Rank Invariant) or the No Normalization option to background-corrected data, followed by each of the three analytical approaches -FS). These probe sets were then filtered to remove probes that returned a detection p-value above 0.01 in both conditions in order to eliminate probes at background levels.For each of the two datasets , a total of 15 probe sets was generated. As summarized in Both datasets showed generally small expression changes (<2-fold), with only 2.1% and 0.4% of changes being over 2-fold in the heart and brain datasets, respectively. Irrespective of the normalization strategy used, probe sets generated from the brain arrays contained a smaller number of probes than those from the heart arrays , consistMax Cover -FS), giving a total of 30 comparisons.In order to determine the influence of normalization on probe set concordance  gave comparable levels of concordance; however, the Average method produced smaller probe sets with a generally lower mean concordance in the brain dataset , we compared the concordance of the probe sets identified by each of the three different analytical approaches. . For each normalization strategy, a probe was classed as having “full concordance” if it was identified by all three analytical approaches, “partial concordance” if identified by two of the three approaches or “no concordance” if identified by only one approach. For each particular normalization strategy -FS gave the largest probe sets and, therefore, the greatest number and proportion of discordant probes -FS approach returned larger probe sets for the brain dataset. The probe sets generated by lumi and limma were highly concordant with one another (>90% for both heart and brain datasets). For the heart dataset, the lumi and limma approaches both identified more than 90% of the probes found by all analytical combinations involving Cubic Spline, Quantile and Rank Invariant, with GenomeStudio analyses showing the greatest percentage concordance, though possibly due to the smaller size of GenomeStudio probe sets -FS approach; however, this may simply reflect the large size of probe sets generated using this method, as described above.To determine how the results obtained using these approaches compare with those obtained using more flexible, yet computationally-demanding, tools available through the Bioconductor project, data processing and analysis of the heart and brain datasets was undertaken using two Bioconductor tools designed for analysis of Illumina microarrays: obe sets . ConcordAverage, Cubic Spline, Quantile, Rank Invariant) with each of the three analytical approaches -FS). For each normalization strategy, we determined the number of concordant pathways across the different approaches, where “concordant” denotes pathways common to two or more approaches -FS generated probe sets with the most discordant probes -FS approach . In contrast, approaches that yielded relatively few discordant probes, such as Quantile or Rank Invariant in combination with GeneSpring, failed to identify this potentially important pathway as significantly enriched. It was observed that approaches that generally show high probe set concordance can still fail to identify pathways of probable importance. One example was the “insulin signalling pathway”. Diabetes is one of the classical triad of symptoms seen at advanced stages of the human iron overload disorder hemochromatosis and iron overload arising due to various causes has been associated with insulin perturbations and type 2 diabetes ,39. FurtAverage normalization with Max Cover -FS, which had a relatively large number of discordant probes, identified the pathway “acute myeloid leukaemia” . This pathway was not detected by other approaches, yet is a potential true positive of probable clinical mechanistic relevance, since there is evidence for a relationship between acute myeloid leukaemia and gene mutations associated with hemochromatosis [Max Cover -FS approach, therefore, was not only successful in identifying most of the concordant probes identified by the other analytical approaches, but also identified additional discordant probes of probable relevance.Conversely, approaches that generally show high probe set discordance may sometimes identify pathways of potential importance not picked up by other approaches. For example, analysis of gene lists from the heart dataset generated using omatosis . The Max-No Normalization strategy may be poorly suited to discovery-driven research.The -Background correction in GenomeStudio generally led to a reduction in the size of probe sets, but did not affect percentage concordance.-Cubic Spline, Quantile and Rank Invariant generally gave comparable outcomes for a particular analytical approach, although performance sometimes varied between the datasets.  Of the four Illumina GenomeStudio normalization strategies, -Max Cover -FS) often generated quite different probe sets that were enriched for different pathways, even when using the same normalization strategy.Different analytical approaches  performed well in most analyses. Except in the case of Cubic Spline and Quantile normalization, the high degree of concordance observed when using these methods is unlikely to be an artefact arising from similarities in the normalization procedures, as the various strategies use fundamentally different mathematical approaches. The findings suggest that some form of normalization should be applied, since the The variability in probe sets generated by different normalization strategies makes it difficult to recommend one that will invariably perform best for any analytical approach and any dataset. For optimal performance for discovery-driven research, we would suggest comparing all four normalization strategies for each new investigation.lumi and limma. However, it typically generated substantially smaller probe sets than the other approaches and so may miss a considerable number of important genes in some datasets. GeneSpring generally identified a higher total proportion of fully and partially concordant probes than other approaches. Max Cover -FS also generally identified high numbers of fully and partially concordant probes and in addition found further probes not identified by other approaches. While some of these additional probes may be false positives, some appear to represent real changes that help identify additional pathways of biological relevance.Similarly, it was shown that the same normalization strategy can give very different outcomes when used with different analytical approaches. The most accessible analysis software for Illumina users, the proprietary Illumina GenomeStudio, does well in that most of the probes it identified were concordant with the other methods investigated, including the Bioconductor tools Max Cover -FS has a very different mathematical basis from the analytical approaches based on statistical significance . While this may decrease the numbers of fully concordant probes in comparisons of these approaches, those probes that are jointly identified by such very different methods are more likely to represent robust findings. Therefore, in addition to recommending that more than one normalization strategy be used, the use of more than one analytical approach, preferably not restricted solely to statistical testing, is also recommended. The findings also suggest that important pathways and processes may be overlooked if only one approach is used to analyse differential gene expression, further highlighting the need for using combinations of approaches. As there were often considerable differences between the findings for the two datasets, it is not possible to recommend a single combination of normalization strategy and analytical approach that will be optimal in all circumstances, particularly since the two datasets examined here were relatively similar (different tissues from the same model) and differences may be even greater for other datasets. Due to individual variability, there may be no “correct” approach—statistical methods may do better in some sample sets, in particular those with low variability, but may miss useful findings in others. The optimum combination of methods will also vary depending on whether the main aim is to minimize false positives, as required for class prediction aimed at biomarker discovery, or to maximize true positives and minimize false negatives, as required in class comparison or class discovery studies.Max Cover -FS appears to yield important findings of biological relevance; yet, as a non-statistical approach, it is not amenable to multiple testing correction. It would be unfortunate if this valuable complementary method were to be discarded solely on these grounds.The use of multiple approaches to identify robust changes differs from more conventional microarray analysis pipelines that utilize multiple testing corrections to avoid false positive findings; however, in this case, we believe it is appropriate. This point is particularly relevant since the GenomeStudio software does not allow the removal of low signal probes (representing non-expressed genes) prior to differential expression analysis, thereby increasing the burden of multiple testing. In addition, a priori, they generally result in relatively large fold differences between samples. As our study specifically focused on datasets with small fold changes, it was not feasible to adopt a similar approach in our evaluation. Reference RNA that contains many transcripts of known concentration would be ideal for testing the ability of different approaches to identify true positives and true negatives. However, as far as we could determine, reference RNA of this type is not commercially available. Instead, experiments seeking to evaluate reproducibility across platforms or across processing and analysis approaches have relied on either titrations of two distinct RNA reference samples   or “spiket al., unpublished data), these specific changes exceeded the 1.4-fold threshold. Similarly, the small magnitude of most of the fold changes under investigation made it infeasible to test many results by quantitative reverse transcription PCR (qRT-PCR), which is often employed as a method for validating microarray findings. Other groups have reported that fold changes of less than 1.4 by microarray generally show poor correlation with qRT-PCR . While wTherefore, one important limitation of the study is that the accuracy of different outcomes could not be directly assessed and using concordance to estimate accuracy may not always give a true picture. While outside the scope of the present study, future research could compare microarray results obtained using different analytical approaches with other sensitive multiplex or transcriptome-wide technologies, such as other array platforms, RNA-seq, NanoString or Fluidigm. However, it is important to note that human and other technical errors will affect quantitative differential expression analysis by any method, and any comparison requires that the analysis methods for the comparison technology have been shown to be accurate for low fold changes. As far as we are aware, this has not yet been achieved. For example, RNA-seq is biased towards high expression transcripts, so the accuracy of differential expression determinations will vary depending on the expression levels of the transcript. Identifying probes as differentially expressed by two or three different methods and detecting enrichment of molecular pathways of strong biological relevance provides some assurance in the accuracy of the findings, as noted above. Also, the strong performance of particular approaches with respect to identifying concordant probes for two different datasets suggests a high degree of reliability in generating robust probe sets. lumi [limma [Some of the issues addressed in this study may be circumvented by using larger replicate numbers or more sophisticated analytical algorithms. However, even when using high end software packages, consideration should still be given as to how different computational approaches affect study outcomes for different datasets . Furtherlumi  or limmai [limma  or otherMost of the issues raised here are not unique to the Illumina platform. On other platforms, normalization and analysis methods can affect precision, sensitivity and other factors, and a method that is optimal in one context may be problematic in others ,44. The In conclusion, this study has identified a range of potential pitfalls in analysing low expression fold-change datasets and highlights the need for future studies using reference datasets of known positives. While these issues are particularly relevant for datasets where expression changes are expected to be modest, many similar considerations are likely to apply for datasets where most gene expression changes are large, since these will usually still also contain some genes of biological interest with small expression changes. Important effects may be overlooked if there is a habitual routine of using only one approach to investigate all array datasets in a laboratory or commercial testing service. The findings presented here provide guidelines to help researchers optimize outcomes when working with datasets involving small expression changes. Notably, it is proposed that microarray data should be routinely subjected to alternative normalization and analysis procedures and comparisons made between these to obtain more robust gene lists and pathway identifications.


In addition, standard RNA sequencing adds variability due to variable transcript length and amplification. Targeted probe-sequencing technologies such as TempO-Seq, with transcriptomic representation that can vary from hundreds of genes to the entire transcriptome, may reduce some components of variation. Analyses of high-throughput toxicogenomics data require renewed attention to read-calling algorithms and simplified dose–response modeling for datasets with relatively few samples. Using data from induced pluripotent stem cell-derived cardiomyocytes treated with chemicals at varying concentrations, we describe here and make available a pipeline for handling expression data generated by TempO-Seq to align reads, clean and normalize raw count data, identify differentially expressed genes, and calculate transcriptomic concentration–response points of departure. The methods are extensible to other forms of concentration–response gene-expression data, and we discuss the utility of the methods for assessing variation in susceptibility and the diseased cellular state.Cell-based assays are an attractive option to measure gene expression response to exposure, but the cost of whole-transcriptome RNA sequencing has been a barrier to the use of gene expression profiling for  Additional challenges are associated with animal to human extrapolation, as well as concerns over the ethics and expense of animal testing. These challenges were described in the National Toxicology Program’s (NTP) 2004 Vision and Roadmap for the 21st Century, and the National Research Council’s (NRC) report on Toxicity Testing in the 21st Century  database has compiled toxicological endpoints and gene expression data from rats (in vivo and in vitro primary hepatocytes) and humans (in vitro primary hepatocytes) on 170 hepato- and renal-toxicants at multiple doses and time points  is a database of over 1 million gene expression signatures, or perturbations, generated using a targeted hybridized bead-base flow sorter  from eite points .The advent of next-generation sequencing (NGS) technology has allowed for dramatic advances in the characterization of genomic, epigenomic, and gene expression endpoints. In contrast to reverse transcriptase PCR and microarrays, NGS results in reduction or elimination of numerous sources of variation . AnticipTM (Templated Oligo assay with Sequencing readout), which was originally adapted from RASL-seq -cardiomyocytes were treated with three different doses of four chemicals to assess their effects on gene expression and concentration–response point of departure (POD) (GEO accession number GSE105050).To date, much of the effort to characterize toxicity transcriptomic endpoints has focused on individual concentration–responses from drugs and a small number of environmental chemicals. Targeted sequencing allows for more economical interrogation of the transcriptome and opens the door for high-quantity, high-throughput assessment of drug/chemical concentration–response. We report here a pipeline for utilizing TempO-Seq , a targeted RNA sequencing technology, to assess gene-transcript concentration–response relationships to chemical exposure. Much of the pipeline is extensible to any transcriptional profiling of chemical response, where sample sizes are likely to be modest. For proof of principle, we illustrate using 2,982 selected genes that include the “S1500+”iCell cardiomyocytes (cat. no.: CMC-100-010-001), and cardiomyocyte plating and maintenance media were purchased from Cellular Dynamics International . Reference chemicals isoproterenol (CAS: 7683-59-2) and propranolol (CAS: 525-66-6) were purchased as part of EarlyTox Cardiotoxicity Screening kits (cat. no.: R8211) from Molecular Devices LLC . Nifedipine (CAS: 21829-25-4) and dofetilide (CAS: 115256-11-6) were purchased from APEXBio . Dimethyl sulfoxide  was purchased from Santa Cruz Biotechnology . Penicillin/streptomycin solution (cat. no.: 10378016) and 0.4% Trypan Blue solution (cat. no.: 15250061) were obtained from Life Technologies .5 cells/ml. Twenty-five microliters of cell suspension was then transferred per well to a 384-well microplate, yielding a final cell density of 5,000/well. Tissue-culture treated microplates  were gelatinized for 2 h at 37°C with 25 μl 0.1% gelatin in water before cardiomyocytes were plated. After disposal of the gelatin solution and addition of the cell suspension, microplates were kept at room temperature for 30 min. Cells were then incubated at 37°C and 5% CO2 for 48 h. The plating medium was then exchanged with 40 μl of maintenance medium containing 1:500 penicillin/streptomycin solution per well. Maintenance medium was replaced every 48–72 h until day 13 post-plating. The maintenance medium was then exchanged with 50 μl fresh medium per well and incubated overnight. Cells were treated the next morning (day 14 post-plating) with 12.5 μl 5× chemical solutions in 0.5% DMSO (v/v) in media (vehicle) in addition to untreated or vehicle-treated negative control wells, and incubated at 37°C and 5% CO2. Following 24 h of incubation, the cell medium was discarded, and cardiomyocytes were lysed with 10 μl 1× lysis buffer provided in the TempO-Seq assay kit . Lysate-containing microplates were agitated at 300 rpm using a benchtop microplate shaker and stored at -80°C until further use.iCell cardiomyocytes were plated and maintained according to the manufacturer’s recommendations  and in accordance with previously published protocols with minor adjustments , 2016. ITM  (GSE105050).Differential gene expression patterns and concentration–response relationships were analyzed using TempO-Seq States) , a targe States) . The seq States) . In brieTemposeqcount installs all dependencies in a Python virtual environment. It is released as an open-source software under the GNU General Public License and available from https://github.com/demis001/temposeqcount. Complete installation instructions are provided. Note that a unix operating system is required for this portion only.2(fold change) (l2fc) and p-values from DESeq2 for dofetilide and nifedipine were analyzed through the use of IPA (Ingenuity® Systems2). A core analysis was run using: User Dataset as reference, cutoff of p < 0.05, and all other values as default. The cardiac-related pathways  for Tox Functions under Diseases and Functions were chosen for Supplementary Figure The loghttps://github.com/jshousephd/HT-CBA.Sample dataset, hash file, figures, and R Scripts for generating all figures and processes are available from The transcriptomic analysis methods discussed here focus on assessment of differentially expressed genes (DEGs) and concentration–response assessment using counts from TempO-Seq experiments. However, the methods and processes described herein are extensible to most types of count data. The sample dataset used and provided for illustration consists of 2 sets of 12 vehicle controls and 4 chemical treatments at three concentration levels . Two inotropic agents, the β-adrenergic receptor agonist and antagonist isoproterenol and propranolol, are known negative controls for cardiac QT prolongation. Nifedipine is a calcium channel blocker used to treat hypertension and dofetilide is an antiarrhythmic.Figure 1, the analysis pipeline can be broken into four major steps: (1) generation of the count matrix from sequenced reads (coded in python), (2) quality control and count normalization, (3) identification and visualization of DEGs, and (4) concentration–response modeling and POD assessment. POD assessment combines output from tcpl , each file with reads resulting from an experimental condition. In traditional RNA-seq, individual fastq files are each aligned to a reference sequence individually using a short read aligner such as STAR, BWA, or bowtie  and thentemposeqcount to facilitate this process using a Ruffus framework in Python  consisting of genei rows and treatmentj columns for downstream analysis.Accordingly, we developed an application called n Python  which is aligner  and STAR aligner  applicatFigure 1; steps II, III, and IV) consists of R scripts that are publicly available3. The inputs to the rest of the pipeline consist of the count matrix (K) generated in step I, and an experimental layout file (hereafter referred to as hash file) from the experimenter. Although we are using this process for counts from the TempO-Seq assay, the pipeline here can be applied to other types of high-throughput sequencing data. Multiple attributes can be included in the hash file for each treatment, but column names of the count matrix must have a corresponding column entry in the hash file. Gene features are filtered for >1 count per row across the experimental count matrix, which resulted in 100 genes removed, leaving 2,882 for subsequent analyses . Prior to normalization, sample count totals are evaluated graphically  and samples (columns) failing to exceed a user defined minimum count threshold  are removed from subsequent analyses. The design of concentration–response experiments for multiple compounds often uses shared controls. For the data examined herein, there were 24 vehicle controls. In the pipeline, controls are examined by principal component analysis . In addition, we examine the average correlations of each control sample with the remaining samples , an approach termed the “D statistic” and similar to that used by the GTEx Consortium . Pairwise correlations and scatterplots were also examined for controls prior to normalization  was then normalized experiment-wide at the treatment level with DESeq2, which models read counts using a negative binomial distribution and normalizes based on a model that uses dispersion estimates for each gene across all treatments (The remainder of the pipeline (nsortium  to filteeatments .2 fold change (l2fc) values, p-values, and adjusted p-values computed for each gene and combined into a single dataset for each chemical. For the sample data, as seen in the summary plot of the number of DEGs per treatment , nifedipine was the most transcriptionally active treatment with identified differential gene expression for nearly a third of interrogated transcripts . Dofetilide affected 425 transcripts, while isoproterenol and propranolol had little effect on transcription, with 32 and 29 identified DEGs, respectively .Differentially expressed genes were determined using DESeq2 prior to concentration response modeling. DEGs were first identified for the maximum concentration of each treatment, with logFigure 4 with the sample dataset. Using all l2fc values for each chemical, the first three principal components were plotted . In the sample data, the two drugs with effects on the heart rhythm (isoproterenol and propranolol) clustered together while the others (nifedipine and dofetilide) were quite distinct. A heatmap of l2fc values further illustrates the similarities between the isoproterenol and propranolol, while highlighting how both nifedipine and dofetilide are each different from these QT-prolongation controls and from each other . The overall magnitude of transcription effects from each chemical is shown in the boxplot of absolute l2fc values .A typical TempO-Seq experiment may have 50–75 unique concentration-chemical combinations making it important to examine how they group together regarding differential gene expression. There are several ways to do this, and we have illustrated some of them in 10 scale that is one average dose distance below the lowest treatment concentration . Our dataset contained 24 vehicle controls and a single treatment at each of the three concentrations. Thus, since one control was removed in QC, the dosing vector for this experiment consists of 23 values of -2, followed by -1, 0, and 1, while the response vector consists of normalized counts data at each control/treatment. To allow for zero counts, normalized counts were log2(counts + 0.5) transformed and mean-centered to vehicle controls. An example plot of the data is shown in Figure 5B.A critical step in human health assessment for a chemical compound is the determination of the POD from a dose/concentration–response relationship. In the sample data, iPSC cardiomyocytes were exposed to either vehicle or three increasing drug concentrations . Vehicle controls were assigned a dose value on the logFigures 5A,B). The q-values , are calculated for: (1) the moment-corrected correlation (MCC) trend test , (2) MCC for treatment doses only , and (3) Wilcoxon’s statistic for a difference between vehicle control values vs. treatments . MCC is a trend procedure that is intended to be powerful while retaining robustness to a wide variety of distributional forms of the data test . If the overall trend  in the concentration–response is not significant, the experimental maximum dose is assigned as the POD. For those genes where the overall trend is significant, control counts are compared to treatment counts using a Wilcoxon Rank Sum test. If these two groups are different from each other , the gene/treatment is selected for concentration–response modeling. If controls are not different from treatments but did pass the overall trend test for significance, we then assess whether a trend exists in treatments only . If the treatment-only trend is significant, it is also selected for POD modeling. For those remaining genes that are not significant for the treatment only trend test, a report is generated for the remaining items for manual experimenter follow-up.To facilitate decision-making accompanying our concentration–response modeling, we created a process tree that utilizes statistical flags . For those gene/treatments identified for concentration–response modeling, “dose” vectors and response counts are first fit with tcpl functions . We also assess a four-parameter hill function fit using the R-DRM package , although other POD approaches or benchmark dose . Although nifedipine caused more than twice the number of differentially expressed genes as dofetilide , dofetilide actually had a smaller mean POD . Propranolol is not shown in Figure 6D as it had no genes exhibiting concentration–response relationships.As seen in unctions  with a c package  where thark dose  could beTempO-seq is a new technology with few publications to date and no standard pipeline for analysis. A recent study by As sequencing has become more affordable, the number of experiments with sequencing (expression) data has grown exponentially, and multiple computational tools are available for different steps in the analysis. Similarly, many dose–response modeling tools are available , includiin vitro studies.Gene expression data have been an important contributor to the mechanistic studies in toxicology and other biomolecular fields . ToxicogThe methods outlined in this manuscript provide a framework for highly automated assessment of transcriptomic concentration–response POD estimates. Although we have used targeted sequencing data, these methods are extensible to any kind of concentration–response count data, including whole-transcriptome RNA sequencing. Probe-based targeted RNA-seq technology does have many advantages. These procedures are extremely fast and can be run on desktop computers instead of computing clusters. The biases associated with the purification and library creation in RNA-seq are not applicable with this technology. However, it is important to note that this targeted RNA-seq technology does not capture underlying genetic variation in a region of interest. The probes are highly specific to the 50-mer being interrogated. The methods described here have been made freely available, to provide tools to characterize the transcriptomic dose response and concentration response effects of drugs and chemicals in novel targeted-probe high-throughput formats.The gene expression response signatures identified by our pipeline can be used for hazard assessment, drug repurposing, and disease characterization . We noteIn summary, with the advent of cheaper sequencing technology and targeted sequencing technology, it has become feasible and necessary to utilize the value of gene expression data in high-throughput experiments, for dose response characterization, for perturbation signature identification, and for biological read-across assessment. The methods herein provide a reproducible, largely automated framework to utilize such sequencing data to identify treatment-induced DEGs and concentration response estimates.JH: experimental design, methodology development, programming, writing and editing, and submitting. FG: experimental design, all wet-lab work, and writing and editing. DJ: experimental design, methodology development, programming, and writing and editing. Y-HZ: methodology development. IR: study conception, experimental design, support, and writing and editing. FW: study conception, experimental design, support, writing and editing.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.