As DNA sequencing technologies are improving and getting cheaper, genomic data can be utilized for diagnosis of many diseases such as cancer. Human raw genome data is huge in size for computational systems. Therefore, there is a need for a compact and accurate representation of the valuable information in DNA. The occurrence of complex genetic disorders often results from multiple gene mutations. The effect of each mutation is not equal for the development of a disease. Inspired from the field of information retrieval, we propose using the term frequency (tf) and BM25 term weighting measures with the inverse document frequency (idf) and relevance frequency (rf) measures to weight genes based on their mutations. The underlying assumption is that the more mutations a gene has in patients with a certain disease and the less mutations it has in other patients, the more discriminative that gene is.We evaluated the proposed representations on the task of cancer type classification. We applied various machine learning techniques using the tf-idf and tf-rf schemes and their BM25 versions. Our results show that the BM25-tf-rf representation leads to improved classification accuracy and f-score values compared to the other representations. The highest accuracy (76.44%) and f-score (76.95%) are achieved with the BM25-tf-rf based data representation.As a result of our experiments, the BM25-tf-rf scheme and the proposed neural network model is shown to be the best performing classification system for our case study of cancer type classification. This system is further utilized for causal gene analysis. Examples from the most effective genes that are used for decision making are found to be in the literature as target or causal genes. Complex diseases with genetic components arise from different combinations of the mutations in DNA. With the help of the decreasing cost of sequencing technologies, large scale sequencing datasets are being curated. Machine learning methods can be helpful in analyzing the huge genome data. However, a suitable representation technique for the sequencing data is still a problem to be solved. In this paper, we propose using statistical methods from the field of information retrieval for the representation of mutation information in DNA. The proposed representation methods are evaluated for the task of cancer type classification.As stated in the 2017 report of National Center for Health Statistics [Cancer classification has been primarily based on the morphological appearance of the tumor. Medical images such as magnetic resonance images (MRI) –5, X-rayAnother commonly used data type for cancer classification is gene expression data. While a number of studies utilizing gene expression data have addressed the classification of cancer types –14, thisThe biotechnology improvements and the automation of sequencing systems have increased the speed and lowered the cost of human DNA sequencing, which enabled the usage of this data type for disease diagnosis. The variants or mutations in the DNA of an individual can be identified by comparing the DNA sequence of the individual to the DNA sequence of a reference genome maintained by The Genome Reference Consortium  and storIn this paper, we propose adapting and using term weighting techniques  from the information retrieval field for weighting genes based on mutation information. As far as we know, these techniques have not been used on variant data before. The proposed gene weighting techniques are evaluated for the task of cancer type classification. Our results demonstrate that the best performing information retrieval based model (BM25-tf-rf) outperforms the C-score based approach. When the best performing classification model is analyzed, the most effective genes in the classification of certain cancer types are found to have been also proposed as causal or target genes in the previously published studies. These literature findings support the effectiveness of our representation models.Term weighting methods from the field of information retrieval have been proposed for the representation of mutation information within genomic data.A comparison of these data representation schemes for the task of cancer type classification has been performed using a wide range of machine learning methods.The best performing representation and classification model are utilized for causal gene analysis.Our work brings the following contributions: In this section, we describe the proposed data representation models as well as their utilization with machine learning algorithms for cancer type classification. An overview of the developed system is shown in Fig. The Cancer Facts and Figures 2017 annual report  states tIn the binary model, a gene is represented with 1 if there is a mutation in that gene, and it is represented with 0 otherwise. Hence, the resulting data set has the gene labels as features and binary values for each feature. The binary value for each gene feature is extracted from the annotated VCF file. If the gene label exists in the annotated file, the value is 1, and it is 0 otherwise. If there are more than 1 mutation for a gene, the feature value for that gene is still set to 1.This representation model is applied by using two different gene lists. The first model is constructed with the known causal genes for the selected cancer types. The causal genes are obtained from OMIM  by usingThe CADD framework , 23 is aComplex diseases are in general developed from the combination of various mutations in the genes. Each mutation may influence the evolution of the disease at different levels. In order to express these differences, we proposed to utilize the tf-idf (term frequency-inverse document frequency) weighting method. Tf-idf is a term weight calculation technique used commonly in the information retrieval and text mining research areas. In , tf-idf In our context, the tf-idf value measures how important a gene mutation is to a sample in a collection of samples. Mutations in genes that are found in most samples have low tf-idf values, whereas genes with rare mutations are granted higher weights. With this strategy, we aim to increase the impact of the existence of rare mutations and suppress the effects of common mutations in the classification task, since common mutations may not be a sign of a disease.g and sample s is the multiplication of tf, that is term frequency, and idf, that is inverse document frequency, values. The tf value for a gene g and sample s is taken as the count of mutations of gene g in sample s. The higher the number of mutations for a gene in a sample, the more tf weight is assigned to this gene. The df value, that is document frequency, for a gene g is taken as the count of samples in the collection that contain mutations of gene g. For a sample collection of size N, idf of gene g is calculated as shown in Eq. g, the less discriminating power this gene will have as a feature in cancer type classification. So it is assigned a lower idf score. In this model, instead of binary values, the calculated tf-idf weights of the genes are used as feature values. The main equation of tf-idf is presented in Eq. A mutation can be rare in the collection, however, it may be effective for samples with particular cancer types. In order to account for the class information, tf-rf (term frequency-relevance frequency) based data representation is adapted. Similarly to tf-idf, tf-rf is also used in information retrieval and text mining. Unlike tf-idf, tf-rf is a supervised statistical measure proposed in . It is ug and sample s is the multiplication of tf, that is term frequency, and rf, that is relevance frequency, values. The tf value is computed in the same way as in tf-idf. The rf value of gene g and class c is calculated as in Eq. a is the number of samples in class c which contain mutation in gene g, and b is the number of samples in other classes which contain mutation in gene g. In our context, the tf-rf value measures how important a gene mutation is to a sample by using the information of its class label. If the particular gene mutation is encountered more in one class compared to the other classes, the corresponding rf and tf-rf values are higher than for the other classes. As shown in Eq. g and sample s is the multiplication of BM25-tf, that is BM25 definition of term frequency, and idf, that is inverse document frequency, values. BM25-tf value for a gene g and sample s is calculated as in Eq. Ls and Lave are the length of sample s and the average sample length for the whole collection, respectively. We model the samples with the same features. Therefore, in our representation model, Ls is equal to Lave. When we use this equality, Eq. k is used as a smoothing parameter for tf. The idf definition is the same as in tf-idf. BM25, often called Okapi, is a ranking function used by search engines to rank matching documents according to their relevance to a given search query . For ourg and sample s is the multiplication of BM25-tf, that is BM25 definition of term frequency, and rf, that is relevance frequency, values. The BM25-tf value is computed in the same way as in BM25-tf-idf. The rf definition is the same as in tf-rf. For BM25-tf-rf, the term frequency definition in BM25 is used instead of the classic term frequency in tf-rf. As shown in Eq. tf and BM25-tf values for different values of k are shown when the number of gene mutations changes in the range from 1 to 10. The figure demonstrates that, the tf values, which are represented by empty circles, keep increasing as the number of mutations increases. Even a point mutation may be significant for the occurrence of a certain disease. Therefore, a gene with n mutations is not necessarily n times more important than a gene with 1 mutation for disease detection. As shown in Fig. k in BM25-tf dampens the effect of high tf values.The effect of the smoothing parameter k is illustrated in Fig. A wide range of machine learning algorithms are applied to investigate the effects of the proposed mutation based DNA representation models in the task of disease classification. Naive Bayes (NB), K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Logistic Regression (LR), One-Layer-Perceptron (Perceptron) and Feed-Forward Multilayer Neural Network (NN) are run on the prepared datasets.For the Feed Forward NN, the model is composed of two or more fully connected layers. Except the last layer, the number of nodes is halved at each layer. If the first layer has N units, then the second layer has N/2 units. With this strategy, each layer represents the information from the previous layer with less units. After each fully connected layer, a dropout is applied. As there are 10 classes, the last layer has 10 nodes with softmax activation function. Categorical cross-entropy is employed as the loss function. The number of epochs is 50 and the batch size is 50.All experiments are implemented with Python. For the traditional machine learning algorithms, the scikit-learn library  is used.The input datasets are first divided into 80% training and 20% test sets. Parameter tuning is accomplished using 10-fold cross-validation on the training set. Testing is also accomplished in 10-folds. In each fold, the model with the best parameters is trained with one of the training sets from the initial cross-validation experiment, which was performed over the 80% of the data, and testing is performed over the test set . By this strategy, the instances in the independent test set are not included in any step of either parameter tuning or training, and the effects of minor changes of the training data are also indicated. The reported results are the micro-averaged scores and standard deviations on the independent test set. Accuracy, f-score, precision, recall, false positive rate (FPR) and area under the receiver operating curve (roc-auc) are used as the performance measures.For BM25-tf, a range of k values between 0.6 and 2 are used in the parameter tuning phase. The BM25-tf-rf representation model and the Feed Forward NN are used in the parameter tuning setup. The classification results for different values of k are presented in Table A parameter tuning phase is applied for each machine learning algorithm and data representation model. The best parameter set is used in the test phase. The default values are used for the parameters that are not tuned. NB and LR are applied with the default parameters. The range or list of values used in parameter tuning for the other machine learning algorithms are presented in Table The success of the mutation based data representation schemes also depends on the selected gene list. The initial experiments are applied on the binary representation to observe the classification performances over the causal and full gene sets. LR, SVM with linear kernel and Perceptron are selected as pilot algorithms. As shown in Table Machine learning algorithms are applied in order to explore the effects of the proposed data representations. Table The accuracy scores of NB, KNN and SVM with polynomial kernel are below 57% and the f-score results are below 59%. The remaining 5 algorithms obtain accuracy and f-score levels above 60% (except SVM-rbf with tf-idf). We will focus on these better performing algorithms. The BM25-tf-rf representation scheme leads to the best accuracy and f-score results for all of the 5 algorithms. In addition, the BM25-tf-rf data representation results in nearly 2 to 4 percent accuracy and f-score improvement and 0.01 to 0.05 roc-auc improvement compared to the second best representation for all of the 5 successful algorithms. When we consider the FPR results, BM25-tf-rf leads to lowest values in all of the 5 successful algorithms.p-value < 2.5e−10, and with the tf-rf and BM25-tf-rf representations, the paired t-test produces a p-value < 1.1e−05. These results show that the additional statistical information hidden in the BM25-tf-rf representation provides significant gain compared to the other representation models. When we compare the Feed-Forward NN f-score values for the C-score and BM25-tf-rf models, the paired t-test produces a p-value < 3.2e−06. This significant difference states that, although the BM25-tf-rf scheme doesn’t utilize the various properties of mutations that are expressed in C-score, it is more successful for the differentiation of cancer types with its class-based statistical approach for the mutations. We can conclude that the BM25-tf-rf scheme is a suitable representation tool for the mutation information in VCF files for the cancer type classification task.For the Feed-Forward NN f-score results with binary and BM25-tf-rf representations, the paired t-test produces a The most accurate algorithm (76.44% accuracy and 76.95% f-score) is the Feed-Forward NN with the BM25-tf-rf representation scheme, despite the extra network cost. The precision and recall results for the NN on BM25-tf-rf representation are similar with the accuracy value. The roc-auc result is also the highest compared to the other results in Table p-value < 2.35e−05. For the Perceptron and NN f-score results with BM25-tf-rf, the paired t-test produces a p-value < 1.7e−06. Thus, the multilayer feed forward neural network model is found to be significantly more accurate than the single layer perceptron and LR with BM25-tf-rf.For the LR and NN f-score results with the BM25-tf-rf representation scheme, the paired t-test produces a BM25-tf-rf based representation leads to improved performance results compared to the other representations with almost all machine learning algorithms in our experiments. In addition, the multilayer feed forward neural network model achieves better cancer type classification performance compared to the other machine learning algorithms in our experiments for all data representations except tf-idf. Therefore, we used the results for the NN algorithm with the BM25-tf-rf representation model for further discussions on class-level performance.Table % accuracy and 55.52% f-score. This decrease can be dependent on the fact that only 15% of all mutations are exonic. When only intronic mutations are used, the classification performance decreases to 74.39% accuracy and 75.54% f-score. This relatively lower decrease in the performance can be explained by the vast majority of mutations being intronic. The paired t-test produces a p-value < 4.8e−02 for the f-score results of the Feed-Forward NN with BM25-tf-rf representation with only intronic and all mutations. The utilization of all exonic and intronic mutations for input representation leads to statistically significant improvement in f-score performance. Similar to recent studies stating that malignancy-driving mutations can also occur outside the coding region [In the previous sections all exonic and intronic mutations in the dataset have been utilized to compare the data representation and classification models. The results have shown that the BM25-tf-rf is the best performing representation model and the multilayer feed forward neural network is the best performing classification model in our experiments. By using these best models, a new experiment setup is created to explore the effect of the location of the mutations in the classification result. The exonic and intronic mutations are used separately. The experiment results are presented in Table g region , 38, ourThe main goal of genomic studies for diseases is to propose target or causal genes. BM25-tf-rf is found to be superior compared to binary, tf-idf and C-score for the representation of DNA mutations for the task of cancer classification. We further analyze our best model (BM25-tf-rf and Feed Forward NN) for the most effective genes in decision making.In Figs. For the specific heat maps in Figs. In Fig. The effect of BM25-tf-rf weighting is illustrated through an example for the HAPLN3 and BRF1 genes of two sample patients. Sample-79 is a breast cancer patient and Sample-164 is a brain cancer patient. HAPLN3 and BRF1 have been found relevant to breast cancer in the literature and they are both found to be among the most effective genes for our classification system. But there is no scientific evidence about the relationship of these genes and brain cancer. The BM25-tf-rf value for HAPLN3 in Sample-79 is 1.016021 whereas it is 0.794813 in Sample-164. There is a nearly 28 percent increase in the weight of this gene for the breast cancer sample. For BRF1, the BM25-tf-rf value in Sample-79 is 1.323967 whereas it is 1.085498 in Sample-164. There is a nearly 22 percent increase in the weight of this gene for the breast cancer sample. When we consider the Feed Forward NN classification model, the weights for these gene features is the same for both samples. Therefore, the distinction of predicted cancer type results arise from the difference in BM25-tf-rf weights. This difference effects the decision and helps to distinguish between cancer types.In Fig. Since there is literature evidence for the disease-relatedness of a subset of the most effective genes in classification using NN model with BM25-tf-rf, the other most effective genes that are not studied yet might also have causal roles in cancer development. According to these evidences, NN trained with the BM25-tf-rf representation of the mutations in the VCF files, can also be used for the purpose of finding new candidate genes for cancer types.Complex genomic diseases are caused by changes in DNA that alter cell behavior. The impact level of each mutation may be different for various diseases. In order to model this diversity, being inspired from the document representation techniques in the information retrieval domain, we proposed different mutation based statistical genomic data representation schemes.We utilized VCF files, which contain mutation information in the DNA, for the classification of cancer types as a case study. Cancer, in general, results from a combination of several genomic alterations, which can be addressed in variant calls data. We evaluated the performance of the proposed data representation schemes with a wide range of machine learning algorithms. Our experiment results showed that BM25-tf-rf based representation is more successful at modeling VCF data compared to the binary, tf-idf and C-score based representation schemes. Each cancer type may develop as a result of different gene mutations. The supervised weighting approach of tf-rf successfully reflects this class-mutation relationship. The normalization effect of BM25-tf further improves the classification performance of tf-rf. We investigated the most effective mutated genes in our proposed system for breast and lung cancers. A subset of the resulting genes have also been suggested as causal or target genes in previously published studies, which demonstrates that the proposed approach can also be used to recommend candidate genes.The introduced data representation models are evaluated for the task of cancer type classification, which is an important problem in bioinformatics, since the appropriate treatment is determined according to the primary site. However, they can also be utilized for other genetic diseases, which we plan to investigate in our future studies.


Assay for Transposase-Accessible Chromatin (ATAC)-cap-seq is a high-throughput sequencing method that combines ATAC-seq with targeted nucleic acid enrichment of precipitated DNA fragments. There are increased analytical difficulties arising from working with a set of regions of interest that may be small in number and biologically dependent. Common statistical pipelines for RNA sequencing might be assumed to apply but can give misleading results on ATAC-cap-seq data. A tool is needed to allow a nonspecialist user to quickly and easily summarize data and apply sensible and effective normalization and analysis.t, Bayes factor, and a wrapped version of the standard exact test in the edgeR package. We compared the precision, recall, and F-score of each detection method on resampled datasets at varying replicate, significance threshold, and genes changed and found that the Bayes factor method had the greatest overall detection power, though edgeR was slightly stronger in simulations with lower numbers of genes changed.We developed atacR to allow a user to easily analyze their ATAC enrichment experiment. It provides comprehensive summary functions and diagnostic plots for studying enriched tag abundance. Application of between-sample normalization is made straightforward. Functions for normalizing based on user-defined control regions, whole library size, and regions selected from the least variable regions in a dataset are provided. Three methods for detecting differential abundance of tags from enriched methods are provided, including bootstrap Our package allows a nonspecialist user to easily and effectively apply methods appropriate to the analysis of ATAC-cap-seq in a reproducible manner. The package is implemented in pure R and is fully interoperable with common workflows in Bioconductor. Assay for Transposase-Accessible Chromatin (ATAC)-cap-seq can be conceptualized as a combination of two pre-existing, widely used methods; the high-throughput sequencing of DNA from targeted enrichment capture performed on DNA fragments obtained from prior ATAC . ATAC-seA typical ATAC-cap-seq may be done by beginning with an ATAC-seq library as described previously . Next, sAnalysis of sequence reads from ATAC-seq begins with mapping and alignment to a genome followed by peak detection to identify read-enriched regions. A wide range of tools have been developed to perform peak finding, notably, MACS , HOMER , and SICThe enrichment capture step can produce a dataset with characteristics for which workflows designed for many thousands of windows may not give the best results. In particular, the number of regions represented in the target set may be small (tens rather than some thousands). Also, the selected regions in an enrichment capture experiment are likely to be related biologically and can conceivably covary as a small number or even a single unit. The count of each feature is also dependent on the magnitude of its abundance; the capture step results in overrepresentation of highly abundant features in the captured mixture. These unique features of ATAC-cap-seq data mean that normalization and differential count estimation must be applied carefully.The tools and methods for solving this problem exist, but they have not been used together frequently in bioinformatics analysis, which has tended toward whole-genome, nonenriched sample analysis. Consequently, a nonspecialist user may find it difficult to bring useful methods together. Hence, a workflow that is based around these methods would prove useful to those beginning ATAC-cap-seq analysis from a nonspecialist background.t tests and a Bayes factor t test are provided. The package is implemented in pure R, its base objects are standard Bioconductor and, as such, is designed to be fully interoperable with common workflows in the Bioconductor framework.A key aim of our atacR package is to allow the user to easily assess the success of their ATAC enrichment experiment and determine what further preparative work is required. It achieves this with comprehensive summaries and functions for diagnostic plots. Application between sample normalization is made straightforward. Functions to apply preselected control gene normalization, library size normalization, or normalization based on the least varying regions in the sample are implemented. Differential count estimation functions for the application of edgeR exact test, bootstrap The atacR workflow is based around three major steps: data loading and inspection, identification of best targets to use for normalization, and detection of differential count estimates. The package provides functions that make each step of the workflow straightforward and helps to make these potentially complex analyses more reproducible and the components re-useable in different contexts. Tutorial vignettes are provided that can be loaded directly from the R console.The atacR package relies on Bioconductor SummarizedExperiment  containeThe atacr object describes sample metadata, bait locations, and the counts in target and nontarget windows. Generic summary and plot methods are available that quickly present diagnostic information from which the success of the experiment with respect to read alignment to on–off targets can swiftly be ascertained. Functions operating on this object each have a "by" parameter that allows the user to specify on–off target subsets to analyze. As the atacr object is essentially an R list, new data containing the counts after application of any processing step can be added to a custom slot and analyzed using atacr functions in the same syntax.Data in the atacr object can be assessed for sample bias using specialized plot functions on a per sample and treatment basis. Plots can be generated using functions for whole sample count histograms, chromosome coverage density, Per sample plot of log ratio versus average intensity (MA) plots, heat maps comparing sample counts, density plots of genome regions' designated on–off target, and density plots of variability in regions nominated as normalization controls. See Fig.atacR provides a small set of useful normalization methods applicable to small sets of target windows or those in which the large proportion show the same change in differential accessibility. A straightforward library size normalization is provided. For most ATAC purposes, this will be underpowered because the small number of windows or high proportions of changing windows will cause skew between samples. This method is useful when the experiment has reasonably high counts (>20 mean) and it is certain few windows (<10%) will display differential counts. The atacR package also implements a dynamic method based on estimating the goodness of fit (GoF) measure described in . This met test and Bayes factor method are also provided. These can be run in a single factor manner on pairs of samples or on all samples simultaneously with a common reference sample specified by the user.The atacR package implements three methods of detecting differential abundance; the standard and effective edgeR method is wrapped for ease of use. A bootstrap F-score of each method on simulated ATAC-cap-RNA-seq data at varying replicates, significance thresholds, and genes changed. To create a simulated dataset, we examined counts from three independent RNA-capseq datasets of 52 target-enriched regions. These showed a double peak in the count distribution, though the residual to the mean count was roughly normally distributed . We used the count set as a sample from which to randomly select base counts; from these, a preselected number was multiplied in all replicates of the treatment by a preselected factor to represent differential expression. Experimental noise was also simulated for each count. At each combination of parameters , whereas edgeR outperforms at lower levels (approximately 10%).We compared the precision, recall, and tted Fig.. The edgt test, and Bayes factor t test were performed on each run using atacR and counted as a true positive (TP), false positive (FP), or false negative. TP was defined as the number of windows set with differential counts that were correctly called by the detection method. FP was defined as the number of windows that were called but were not set with differential counts. FN was defined as the number of windows that were set as differential but were not called differential. From this precision, recall and F were calculated as follows: To run simulations, 52 fake genome windows were defined in a control and treatment experiment. The counts for each window were selected from a dataset of 156 counts from a pilot wild-type Arabidopsis RNA-cap-seq experiment. These counts are stored in the atacR package as a data object "athal_wt_counts" for re-use. At each run of the simulation, the replicates per treatment, number of counts changed, fold ratio by which the counts changed, and the significance level at which detection was carried out were varied. For each combination of parameters described in TableThe simulated data experiments were carried out in RStudio. The entire experiment code is provided in the Supplemental Materials. These are executable RMD files that can be rerun to reproduce our experiment exactly in the R programming language.The version of atacR used was 0.4.13. The base counts that were modified in simulations are available in the atacR package in the object “atacr::athal_wt_counts.”Simulations and analyses were run on an Apple Macintosh computer with R and OS specifications as described in Table 2Project name: atacRhttps://github.com/TeamMacLean/atacrProject home page: Operating system(s): Platform independentProgramming language: RLicense: GNU GPL 3The library is provided as an R package that can be installed from Github using devtools::install_from_github(’TeamMacLean/atacr’).GIGA-D-18-00020_Original_Submission.pdfClick here for additional data file.GIGA-D-18-00020_Revision_1.pdfClick here for additional data file.GIGA-D-18-00020_Revision_2.pdfClick here for additional data file.GIGA-D-18-00020_Revision_3.pdfClick here for additional data file.Response_to_Reviewer_Comments_Original_Submission.pdfClick here for additional data file.Response_to_Reviewer_Comments_Revision_1.pdfClick here for additional data file.Response_to_Reviewer_Comments_Revision_2.pdfClick here for additional data file.Reviewer_1_Report_ -- Noboru Jo Sakabe1/29/2018 ReviewedClick here for additional data file.Reviewer_1_Report_(Revision_1) -- Noboru Jo Sakabe5/23/2018 ReviewedClick here for additional data file.Reviewer_2_Report_ -- James Lewis3/15/2018 ReviewedClick here for additional data file.Additional FilesClick here for additional data file.


Application of microarrays in omics technologies enables quantification of many biomolecules simultaneously. It is widely applied to observe the positive or negative effect on biomolecule activity in perturbed versus the steady state by quantitative comparison. Community resources, such as Bioconductor and CRAN, host tools based on R language that have become standard for high-throughput analytics. However, application of these tools is technically challenging for generic users and require specific computational skills. There is a need for intuitive and easy-to-use platform to process omics data, visualize, and interpret results.We propose an integrated software solution, eUTOPIA, that implements a set of essential processing steps as a guided workflow presented to the user as an R Shiny application.eUTOPIA allows researchers to perform preprocessing and analysis of microarray data via a simple and intuitive graphical interface while using state of the art methods.The online version of this article (10.1186/s13029-019-0071-7) contains supplementary material, which is available to authorized users. Omics data have become an integral part of biological studies as researchers leverage these techniques to obtain a broader perspective of complex biological phenomena. Scientific community resources such as CRAN and Bioconductor  contain eUTOPIA is developed in R programming language with a graphical interface layer designed by using R Shiny  web deveThere are seven main steps in the workflow: ‘DATA INPUT’  against the background signal by minfi R package [A quality control report can be generated from microarray raw data by affyQCReport R package , yaqcaff package . The expscale, quantile, and loess perform normalization on log2-scaled intensities and ratios, and the vsn method uses a variance stabilizing transformation, which performs better for weakly expressed features. Normalization of Illumina methylation arrays is performed by using the methods from minfi R package with options of background subtraction, control feature measure, dye bias, and quantile normalization.Normalization of the expression and methylation signals distribution across the samples is performed respectively with methods from the limma  and minf, dye, RNA quality, experiment date, etc.) or by hidden sources of variation not explained by the known variables. Known biological  and technical  variables are provided by the user in the phenotype information, while unknown sources of variations can be identified by using the sva function from sva R package [prince function from swamp R package [confounding function from the swamp R package. This information is used to identify batch variables as known technical or surrogate variables which are associated with strong sources of variation and are not correlated with biological variables of interest. These identified batch variables can be justifiably corrected to remove technical noise from the data. Finally, the correction is performed with ComBat [sva function described above). The batch correction process implemented in eUTOPIA applies the ComBat function iteratively to remove one batch covariate at a time while the rest are modeled as covariates of interest. The ComBat function can process only one batch covariate at a time, and this process of blocking other batches ensures clear separation of variation adjustment effects for each batch without any interference from the adjustment of other batches.In microarray data analysis, a fundamental step is to attenuate the effects associated with technical variables (batch effects) while retaining the variation associated with biological variables. Batch effects can arise for multiple reasons, most commonly when the experiments are conducted in multiple batches, and the data is pooled together for processing. These batches can contribute to the variability of the features and could introduce a systematic error in their assessment, ultimately leading to incorrect results in the worst scenario . Batch e package . First,  package , and theh ComBat  functionlmFit function from the limma R package fits gene-wise linear models to the microarray data. The user defines the design for the model by providing the biological variable of interest and covariates . The contrasts of interest are then specified to obtain contrast specific coefficients from the original coefficients of the linear model. The eBayes function is applied to assess differential expression by using the fitted model with the contrast coefficients. Final reporting of the differentially expressed genes is performed by using the toptable function where adjusted p-value for the multiple comparisons can be obtained by specifying methods “Holm”, “Hochberg”, “Hommel”, “Bonferroni”, “Benjamini & Hochberg”, “Benjamini and Yekutieli” or “False Detection Rate”. Differential analysis results for comparisons defined by the user are reported in tabular format and with other meaningful visualizations. The dynamic plots help to perform a preliminary interpretation of the analysis results. The distribution of the differential features by fold-change magnitude and significance can be observed for a chosen contrast by means of the volcano plots. The expression profile of user-specified top significant features from one or more contrasts can be inspected from the heatmap. Comparison of differential features from different contrasts by set intersections is represented as Venn diagrams or UpSet plots. And the distribution of signal for one or more gene(s) of interest in sample annotations  can be inspected by means of box plots. A user manual with sample data analysis and plot descriptions is provided in Additional file Linear models allow to model the covariate dependencies between samples. The differential analysis is performed with linear model implementation in the R package limma. The The functional capabilities of eUTOPIA are showcased here by processing a publicly available dataset GSE92900  obtainedThe quality control of the raw data is performed with the arrayQualityMetrics R package. The quality report represents the distribution of intensities in arrays, the comparison between arrays Fig. , individIn Fig. Normalization is performed by using the quantile method for between-array normalization. The normalized data can be observed by the representation of expression values as box plot, density plot, and mean difference plot (MDplot) Fig. .Fig. 3NoThe difference in the distribution of expression values in different arrays can be observed in the box plot before normalization Fig. a, suggesThe difference in the distribution of expression values from individual channels of different arrays can be observed from the smoothed curves in the density plot Fig. c before The larger scale of log-2 ratios  observed in the MDplot before normalization Fig. e shows tThe known batches in the data are identified by inferring the variation associated with the sample annotations from the prince plot and by observing the significance of interrelatedness between the sample annotations from the confounding plot. The ‘group’ annotation variable is considered as the variable of interest. The prince plot Fig. a displayp-value: 0.004) with only the second principal component, while the identified batch variable ‘array’ was significantly associated with all three principal components  and ‘n.mice’ was significantly associated with the first principal component . In the prince plot generated from the data after batch correction  with the second principal component. While the surrogate variables ‘svaD.1’ was significantly associated with the first principal component , ‘svaD.2’ was significantly associated with the second and third principal components , and ‘svaD.3’ was significantly associated with the third principal component . In the prince plot generated from the data after batch correction , ‘rCNT’, ‘SES’, and ‘tCNT’ to the control ‘Ctrl’ samples. The differential expression analysis results are filtered by logFC cutoff threshold 1 and P. Value cutoff threshold 0.05. Differential expression results are explored by the medium of plots Fig.  to classeUTOPIA allows users to reliably process microarray data and generate visual interpretations of the results seamlessly via a simple yet intuitive interface. It is focused on the preprocessing of microarray data, thus providing an agile and robust alternative to more comprehensive tools. Both commercially available and free software for microarray data preprocessing either provide limited methodological options or force the user to design an appropriate pipeline from the tools provided. This can be a daunting task for researchers with limited experience in omics data analysis. eUTOPIA is designed to balance both reliability and flexibility.The case study showcases eUTOPIA’s features that enable the user to perform array data preprocessing and analysis. eUTOPIA’s guided workflow helps to easily preprocess the data, identify sources of unwanted variation, remove them and evaluate the sanity of corrected data. The graphical user interface allowed the ease of defining a linear model to test the data and the wide choice of provided dynamic plots help to classify and characterize conditions by sets of differential features and expression patterns.We compared eUTOPIA against a set of microarray analysis tools that are free for academic use and have a graphical interface, namely AGA , shinyMeProject name: eUTOPIAProject home page:https://github.com/Greco-Lab/eUTOPIAOperating system(s): Platform independentProgramming language: R ShinyLicense: GNU GPL 3.Any restrictions to use by non-academics: noneAdditional file 1:eUTOPIA user manual with sample analysis. (DOCX 15056 kb)Additional file 2:Phenotype table. This phenotype table contains information about the microarray , sample identification and grouping , quality estimation of extracted RNA , technical information (n.mice), and information associated with microarray experiments . (XLSX 14 kb)Additional file 3:Carbon nanomaterial intrinsic properties. This table contains the description and intrinsic properties of the carbon nanomaterials used in the case study. General description of the nanomaterial and nanomaterial shape are provided along with the average values of the intrinsic properties’ length, diameter, and surface area. Aspect ratio is the average length divided by the average diameter. (XLSX 10 kb)Additional file 4:Comparison table. Comparison between eUTOPIA and other open source tools with the same scope. (XLSX 11 kb)


The original GEP can be reproduced from the CIC with an accuracy comparable to technical replicates of the same experiment. Although we use an unsupervised approach to train the autoencoder, we show different values of the CIC are connected to different biological aspects of the cell, such as different pathways or biological processes. This network can use CIC to reproduce the GEP of the cell types it has never seen during the training. It also can resist some noise in the measurement of the GEP. Furthermore, we introduce classifier autoencoder, an architecture that can accurately identify cell type based on the GEP or the CIC.Understanding cell identity is an important task in many biomedical areas. Expression patterns of specific marker genes have been used to characterize some limited cell types, but exclusive markers are not available for many cell types. A second approach is to use machine learning to discriminate cell types based on the whole gene expression profiles (GEPs). The accuracies of simple classification algorithms such as linear discriminators or support vector machines are limited due to the complexity of biological systems. We used deep neural networks to analyze 1040 GEPs from 16 different human tissues and cell types. After comparing different architectures, we identified a specific structure of deep autoencoders that can encode a GEP into a vector of 30 numeric values, which we call the  After treating the stem cells with a differentiation protocol, an important question is whether the produced cells have obtained true identity of the desired cell type2.Accurate identification of cell types has received significant attention due to several applications in research and clinics. For instance, one major goal of regenerative medicine is to differentiate pluripotent stem cells, such as embryonic stem cells (ESCs) or induced pluripotent stem cells (iPSCs), to some specific tissue required for a patient, such as neurons or cardiomyocytesβ-cell identity; pluripotency markers such as Nanog which are used to identify pluripotent stem cells; and membrane markers that label several types of cancer cells. The application of marker genes, however, is limited to specific cell types. For many cell types, no markers are identified so far that can accurately characterize them. Furthermore, the expression of many marker genes is not exclusive to one particular cell type. For instance, Oct4 that is used in many studies as a pluripotency marker3 is also expressed in non-pluripotent cell types such as adult stem cells4. One important possible solution is to recognize the cell identity using the whole-genome gene expression profile (GEP), rather than a small subset of marker genes6.There are some specific marker genes identified whose expression patterns can be used to characterize different cell types. Some examples are the expression of Insulin, which confirms the 7. This method and empirical Bayes analysis8 were used to classify 88 normal and prostate cancer samples into different sub-types9. Linear algebraic and statistical methods such as non-negative matrix factorization, Kullback-Leibler divergence and non-negative least squares were employed in an unsupervised method for cell type identification. We applied it to 77 samples for discriminating three cell types10. Although shown to be successful for a few cell types, whether these methods can be extended to analyze tens of different cell types needs further investigation.Despite its importance, there have been limited efforts to systematically recognize cell identity through the GEP. Classical differential expression analysis and clustering methods are used to discriminate a limited number of cells. An extension of the nearest centroid classification method was suggested for classification of cancer types11. It is an online tool that can analyze Microarray gene expression profiles of mouse or human and assign them to each of 16 human or 20 mouse tissues or cell types. CellNet requires a minimum 60 samples of each tissue to derive the global and tissue-specific gene regulatory networks. The subnetworks of the union of these networks are then identified using a community detection algorithm called Infomap12, from which the tissue-specific subnetworks are identified using Gene Set Enrichment Analysis (GSEA)13. Expression levels of the genes belonging to the tissue-specific subnetworks are then used to train Random Forest binary classifiers for each tissue. As a limitation, it is a cumbersome task to hand-pick an equal number of samples from each cell types that have the same Microarray platform and undergo perturbations, in order to derive the gene regulatory networks. Furthermore, deriving a highly accurate gene regulatory network by only using GEPs is challenging.Although there are many models developed so far to classify a few different cell types, there are few generic models to classify an arbitrarily large number of cell types with good accuracy, one of which is CellNetA major obstacle to classical machine learning methods such as Support Vector Machine (SVM) or Random Forest is the presence of hundreds, or even thousands of genes, which are differentially expressed among different cell types, without causal connection to the cell identity. Their expression change can be even more significant than the master regulators of the cell identity due to a number of reasons, such as differences among batches, culture conditions, strains, or treatment protocols of different cell types. As a result, a simple classifier might highlight a group of genes as discriminators of the cell identity for one training dataset, which fail to correctly predict the identities of another dataset.15, speech16, face17, image and text18 recognition, robotics19, and web search20. It has also been used in several bioinformatics applications including predicting protein binding sites in DNA and RNA21, DNA replication initiation and termination zones22, protein secondary structure23 and folding24, residue-residue and protein-protein interaction25, non-coding DNA function prediction26 and inferring expression of target from landmark genes27.Recently, deep learning has made breakthroughs in different scientific areas such as games28.Deep autoencoders are a group of deep neural networks that were originally developed to learn dimensionality reduction. They are usually given some data as the input, and they are expected to generate the same data as the output. The data needs to pass an internal layer that has significantly fewer neurons than the dimensionality of the data, hence it learns to encode and decode the data with the minimum loss. They have received considerable attention due to several reasons. They are usually unsupervised and can be trained using unlabelled data. They can learn internal patterns of the data, such as correlations among different variables, in order to reduce its dimensionality. Furthermore, there are several extensions of deep autoencoders that can perform additional tasks such as denoising the dataDue to the internal patterns of the GEPs such as coexpression of large clusters of genes we hypothesized the dimensionality of GEPs can be significantly reduced using deep autoencoders. By doing so, we can transform the GEP into a smaller number of features that can reproduce the whole GEP. We also hypothesized that these features might reflect different aspects of the cell biology. Furthermore, we speculated the cellular identity can be revealed by these features. Here we employ and extend deep autoencoders to address these hypotheses.11. There was an equal number of 65 samples per cell type or tissue with at least 10 different biological perturbations to ensure the diversity of the samples from each tissue. Presence of different datasets and perturbations was important to prevent overfitting.We obtained normalized expression profiles of 20184 genes in 1040 samples of 16 different human tissues and cell types from different datasets of NCBI GEO, which were collected and preprocessed in CellNetFigure Our first task was to determine a particular architecture of autoencoders that can accurately reproduce GEPs. For this purpose, we tested 10 different architectures with distinct layer configurations and activation functions. After selecting the outperforming architecture, we tested different sizes of the CIC layer and selected a particular architecture with 30 neurons in the CIC layer, as shown in Fig. n = 780) as the training dataset. Then we measured its performance on the remaining 25% of the GEPs (n = 260), as the test dataset. Each test sample was encoded to a CIC and then decoded to a reproduced gene expression profile (REP). The distance between each REP and the original GEP was measured by Mean Square Error (MSE). We observed a stable decreasing MSE trend during the training.We asked whether a 20184-dimensional GEP vector can be accurately compressed in a 30-dimensional CIC, without losing the data. To address this question, we trained the selected network architecture Fig.  by 75% oTo ensure the robustness of the results and lack of overfitting, we also performed a 10-fold cross-validation with balanced sampling. As shown in Fig. While the average MSEs showed a small average distance between each pair of GEP-REP, we questioned whether this holds for each individual pair. To answer that, for each GEP in the test data, we counted the number of other GEPs that were closer than the paired REP. There was an average number of about 16 samples per tissue in the test data, many of which were from biological or technical replicates. Hence if a REP was too different from its GEP, it was likely that many other test samples were closer to the GEP. Figure We also performed the reverse experiment: for each test-case REP, we sorted all test-case GEPs according to their similarity (measured by either higher correlation or lower MSE) and identified the ranking of the original GEP that matched the REP. For 100% of the test-cases, the correct GEP was ranked the first. It means for each REP, its original GEP is more similar than any other test sample.in-vitro isogenic cell lines that are cultured in equal culture conditions.To further illustrate this, three examples are provided in Fig. n = 260 test-cases the closest other GEP based on Spearman correlation coefficient . Also, the MSE values are significantly lower between REP-GEP pairs rather than GEP-GEP .To further scrutinize this, we identified for each of ent Fig.  and MSE ent Fig. . Then weTaken together, these experiments show the expression profiles are reproduced from the CICs with an accuracy comparable to profiling a technical replicate of the same sample.Next, we compared the performance of our method against other widely-used dimensionality reduction algorithms. For that purpose, we transformed GEPs to 30-dimensional spaces using ordinary PCA, Non-negative Matrix Factorization (NMF), Polynomial-kernel PCA, Cosine-kernel PCA and Radial Basis Function (RBF)-kernel PCA. For each method, we used the inverse transformation to reproduce 20184-dimensional REP vectors and measured the MSE between each pair of GEP-REP. As shown in Fig. The next question was whether the autoencoder that is trained to generate the CICs based on some specific training cell types can also generate accurate cell identity for the other cell types, i.e. it can generalize to unseen cell types without retraining.To answer this question we reserved all 65 B-cell samples for test and used 975 samples of all other 15 tissue types to train. After training an autoencoder on this dataset, we generated cell identity codes (CICs) for the test cases, and subsequently reproduced the expression profiles of B-cells using CICs. In all 65 samples, the reproduced gene expression profiles were closer to the original gene expression profiles of each B-cell sample than to any of the other 975 gene expression profiles of the training dataset. Both correlation and MSE results were significantly better for REPs than for the samples of other tissues.This observation was surprising since there were other groups of blood cell types such as T-cells or macrophages available in the dataset. If the autoencoder had not generalized, it would have produced cell identity codes for B-cells that were similar to the cell identity codes of T-cells or macrophages. As a result, the reproduced gene expression profiles of those cell identity codes would be closer to the GEP of T-cells or macrophages available in the training dataset, rather than those of the original B-cell sample.2 fold-change >1 and Benjamini-Hochberg adjusted p-value ≤ 0.05) in B-cell REPs. This analysis identified 380 of such unique genes, that were used for a pathway analysis using Enrichr29. B-cell receptor signaling pathway of Homo sapiens was ranked the highest according to a Z-score criterion, with an adjusted p-value of 0.026.We also questioned whether the REPs have a B-cell biological identity. For that purpose, we performed a differential expression analysis between B-cell reproduced gene expression profiles and T-cell original gene expression profiles. We selected the genes that were significantly enriched  and Support Vector Machines (SVM) to determine the tissue or type of the cells, from the 16 available tissue/cell types, using cell identity codes. Both RF and SVM were trained on the same 75% training samples that were used for training the autoencoder.30. Both classification algorithms were separately trained on the PCA transformation of the same training set. Then, all trained classifiers were applied to the test samples.To compare the accuracy of the results, we also used Principal Component Analysis (PCA) to reduce the dimension of the original gene expression profiles from 20184 to 30 dimensions, equal to the dimension of the cell identity codes. Conjunction of PCA with classification algorithms is widely used in many different scientific areas, particularly to prevent overfitting by reducing the number of parameters to be learned from the data after PCA-based dimensionality reductionOut of 260 test cases, 25 and 27 samples were misclassified on the PCA transformation of the GEPs by Random Forest and SVM, respectively. Using cell identity codes, the number of misclassifications by Random Forest was slightly reduced to 16. SVM produced the same number of errors  by both SVM and Random Forest methods. The 60% error improvement in classifier autoencoder, in comparison with PCA, was an evidence for the capabilities of cell identity codes in determining the cell identity . As an example, if the first CIC component represents the cell division process, then it should have the maximum effect on the reproduced expression values of the genes that are involved in cell division.To answer this question, we sought to determine which subset of genes are most influenced by changing the Ci(x) be the i-th component of CIC for a given GEP x. We call σi as the mean and standard deviation of Ci(x) for all available samples x, respectively. We used the trained classifier autoencoder to compute the values σi for all values i(1 ≤ i ≥ 30). Then we produced a REP by feeding the i, we only changed the i-th component of σi to Let 31. The gene set enrichment p-values were adjusted using the Bonferroni method. For each gene set, all biological processes, cellular compartments, and pathways that were significantly enriched were analyzed; see Fig. p-values of pathways. To have fewer nodes for better visualization, we used a slightly more stringent cutoff of 0.01 in cellular compartments and biological processes analyses.To analyze the pathway and Gene Ontology (GO) of these 30 gene sets, we used ToppCluster, an online tool for enrichment analysis of multiple gene setsFor instance, the CIC component 11 , lateral plasma membrane (CIC component 25), cytosolic part (CIC component 7), ribosome (CIC component 21), and the nucleolus (CIC component 29). There are a few cases that two or more CIC components are linked to the same compartment .Vast collections of biological processes and pathways are also represented by different CIC components. Likewise, most of them are linked to one CIC component. This shows important aspects of cellular biology are learned by the classifier autoencoders in an unsupervised approach (i.e. without providing any training data about GO or pathways). Our enrichment analysis with ToppCluster showed significant connections between CIC components and additional areas of cell biology such as molecular functions, protein domains, microRNAs, human phenotypes, disorders, and drugs. See Supplementary Figs p-value adjustment and 0.01 as the cutoff was to reduce the number of nodes for visualization. Using Benjamini-Hochberg false discovery rate p-value adjustment and a threshold of 0.05, which are usually used for GO and pathway analysis, significantly increases the number of pathways, processes and functions that are influenced by CIC components . Additional networks and the list of 30 gene sets that are influenced the most by CIC components are provided as supplementary information.The stringent choice of Bonferroni We also questioned how resistant can our model be against noise. This noise can come from the measurement methods such as Microarray or RNA-seq, stochasticity of the gene expression process in the cells, environmental changes or other factors.For that purpose, we first normalized the expression levels of each gene in all samples between 0 and 1, to control the noise level among all the genes. We performed 5-fold cross-validation. In each round, 80% of randomly selected samples were used as the training set and the remaining 20% as the test set. We ensured each sample was used in the test dataset exactly once. Before each round of cross-validation, all weights and bias parameters of the network were restarted to random values to ensure the training dataset of the previous round is unseen in the new round. The noise was generated from a Gaussian distribution The resulting classification accuracies for the test dataset were between 98.6% to 100% in five rounds of cross-validation, with an average accuracy of 99.4% and a standard error of 0.002. These results confirmed the CIC codes can be used to accurately classify the cell types even in presence of some noise.11. The cell types included Embryonic Stem Cell (ESC), ovary, skin, neuron, hematopoietic stem and progenitor cells (HSPC), macrophage, B-cell, T-cell, endothelial fibroblast, skeletal muscle, heart, kidney, lung, liver, and colon. All of the data were selected from the NCBI Gene Expression Omnibus (GEO)32 and shared a common microarray platform (Affymetrix HG133 plus 2). There were at least 10 different conditions per cell type/tissue, that was essential for generalization of the learning process and to ensure that our network is not learning only a particular cell type of specific laboratory.The preprocessed microarray expression profiles consisting of 20184 unique genes in 1040 biological samples from 16 human tissues or cell types (65 samples per tissue) were obtained from the CellNet package2 transformed and quantile normalized before our analysis. Differential expression analysis was performed using the linear regression and Bayesian analysis of the R/Bioconductor package limma33.The raw expression profiles were logThere are many dense clusters in gene regulatory networks and the members of each cluster are usually co-expressed, which made us hypothesize that the information of gene expression profiles (GEPs) can be compressed in a significantly lower dimension, such that the whole GEP can be reproduced from the lower dimension data. To test this hypothesis, we decided to employ autoencoders for dimensionality reduction of the GEPs.34 or dimensionality reduction and have been shown to outperform other dimensionality reduction algorithms such as PCA for particular classes of data including images35. Furthermore, autoencoders can extract useful features from the data and significantly reduce the computational cost of the downstream analysis by replacing the whole data with a vector of smaller dimension.Autoencoders are a class of deep neural networks that have been applied to important applications such as denoising36 and human gene expression data37. The deep architectures of autoencoders contain multiple layers between input and output. This type of autoencoder has been used to analyze yeast transcriptome profiles38.A simple form of an autoencoder consists of one hidden layer that is fully connected to both input and output layers. The training data is fed to both input and output layers, and the aim is to reduce the dimensionality of the data in the hidden layer. This form of autoencoder has been used to analyze bacteriaThe first methodological challenge was to design a particular architecture of the deep autoencoders that could accurately reconstruct the gene expression profiles after training. To address this challenge, we created 10 different architectures of autoencoders, each consisting of an encoder and a decoder part that was attached serially Fig. . They coFor this experiment, we selected 1000 genes with the highest variance of expression among all 1040 samples to speed up the training time. The encoder part of each neural network contained 1000 input neurons and 30 code neurons. Symmetrically, the decoder part started with 30 code neurons and ended with 1000 output neurons.The encoder parts included 3 to 5 layers of linear and non-linear neurons Fig. . A subseAs shown in Fig. To ensure the stability of the results, we performed 10-fold cross-validation. Error-bars show the standard errors of the MSE values. Standard errors were generally very small that showed measured MSEs are stable and the models are not overfitted to a particular portion of the training data.The second methodological challenge was to determine the best size of code to reduce the dimensionality of the GEPs without loss of data. For this purpose, we created a set of 10 autoencoders similar to the optimal architecture in the previous experiment, but different sizes of the code layer. In this experiment, we used the complete expression profiles of 20184 genes. All autoencoders included a 20184:2000 fully connected linear layer, a LogSigmoid layer, and then the 2000 neurons were fully connected linearly to a code layer of size 10 to 100 neurons. Again, 75% of data was randomly selected for training, and the remaining 25% was used as the test data.The performance of the networks on the test samples are shown in Fig. One further observation was the lower MSE levels in this experiment, in comparison with the previous experiment on 1000 genes with the highest expression variances. In the presence of many genes with subtle changes in expression among different samples, a lower error rate was reasonable here.Again, we performed 10-fold cross-validation without stratification. The standard errors, depicted as the error bars, are quite small that means the robustness of our results.We trained an autoencoder with the optimal architecture that we determined previously to compress the gene expression profiles of 20184 genes in 780 training samples. In each iteration, a randomly selected training sample was fed into the neural network, and all of the weights were updated by an SGD algorithm. The learning rate was reduced during iterations of the same epoch, but restarted to the original value by each new epoch.We used the sequential network architecture in all of our scenarios. Training was performed using the stochastic gradient descent algorithm with mean squared error (MSE) criterion. The learning rate of 0.01 did not work very well for many cases; hence, we used the learning rates between 0.001 and 0.003. There were a number of epochs in each training procedure. Each epoch consisted of several iterations, and training data were fed into the network in each iteration. The network parameters were saved after each epoch and used for the next epoch, but the decayed learning rate restarted after each epoch. For most cases, we used 200 epochs and 100 iterations per epoch.c (e.g. cell-type), the cross-entropy loss can be calculated on the output of a soft-max function as follows:To have non-linear layers, we used several typical transfer functions mentioned above. The formulas for the functions are as follows:During the work, we had to ensure the results were robust and the training was not overfitted towards a particular portion of the data. For this purpose, we performed additional experiments using 10-fold cross-validation. In our normal experiments, we randomly selected 75% of the samples as the training dataset and the remaining 25% as the test dataset. In 10-fold cross-validations, however, we randomly partitioned all of the samples among 10 groups of equal size. In each round of cross-validation, one group was taken as the test dataset, and the other 9 groups were used as training dataset. Each round of training was started from the scratch, i.e. the network parameters such as weights and biases were restarted to the random initial values. By this way, we ensured the test group is unseen after training the network with the other 9 groups. The test results of all of 10 rounds, such as MSE or correlation values, were merged together by calculating the mean value and standard error.31. Nominal p-values were adjusted using Bonferroni or Benjamini-Hochberg methods, with 0.01 or 0.05 as p-values. While all of the different settings are considered as statistically significant, we increased stringency for some cases to keep the number of nodes suitable for visualization. A complete list of enriched terms can be determined using Supplementary Table We used ToppCluster multi gene-list enrichment analysis online application to determine GO terms, pathways, diseases, drugs, domains, and microRNAs associated with the 30 gene lists associated with the cell identity code components39.Both pre- and post-processing of the data and visualization of results were achieved by custom scripts in the R statistical language. We used several R/Bioconductor packages including ggplot2, parallel, data.table, and plyr. Networks were visualized using GephiWe used the script language Lua with the package Torch7 to implement deep neural networks. To increase efficiency, we used Graphical Processing Unit (GPU) through the library CUDA for some of the training procedures. In each training procedure, the whole data was read from tabular text files and all samples were permuted using a fixed random seed. A random subset of 75% of all samples was used to train the networks, and the remaining 25% to test. The data was transformed into Torch Tensor for CPU, and Cuda Tensor for GPU training/testing.We used several Torch7 packages, including nn, torch, cutorch, cunn and cudnn. Several neuron types were used in our analyses including the linear fully connected layers, rectified linear units (ReLUs), sigmoid, logarithmic sigmoid, hyperbolic tangent, soft-max, soft-plus, and shrink.We also developed the same architectures in TensorFlow, and did not observe a change in the results due to platform change.In order to ensure that there is no overfitting in our learning process we experimented with adding L1 and L2 regularization as well as dropout. However, as far as the results show, no meaningful change was observed between the models trained with and without regularization.We used a Linux server running Fedora 24 version 4.7.5–200. It contained 4 AMD Opteron(tm) 6386 SE processors, with 64 total cores running at 2.8 GHz and 512 GBytes of main memory. Training of neural networks was performed using the Cuda driver on an NVIDIA Tesla K20c GPU running at 706 MHz with 5120 MB memory.The present work can be extended in several ways. Batch normalization can be employed to limit the variation of values in different genes and datasets. Weight normalization can help to control the variations of weights in each layer. Moreover, more efficacious activation functions such as ReLu, PeakyReLu, Swish, etc. can be used to avoid some issues such as vanishing moments and saturation which can dramatically decrease the network’s performance. Finally, hyperparameter tuning algorithms such as grid or random search can be used to evaluate different networks and chose the best one among all.Supplementary figuresCell Identity Codes: Understanding Cell Identity from Gene Expression Profiles using Deep Neural NetworksSupplementary Dataset


TP53 mutation. This approach is also applied to TCGA Pan-Cancer, which identify repeatedly predictable signatures across tumor types including immune features in lung squamous and basal-like breast cancers. These Elastic Net DNA predictors could also be called from DNA-based gene panels, thus facilitating their use as biomarkers to guide therapeutic decision making.The ability to characterize and predict tumor phenotypes is crucial to precision medicine. In this study, we present an integrative computational approach using a genome-wide association analysis and an Elastic Net prediction method to analyze the relationship between DNA copy number alterations and an archive of gene expression signatures. Across breast cancers, we are able to quantitatively predict many gene signatures levels within individual tumors with high accuracy based upon DNA copy number features alone, including proliferation status and Estrogen-signaling pathway activity. We can also predict many other key phenotypes, including intrinsic molecular subtypes, estrogen receptor status, and  Effective precision medicine strategies rely on the ability to predict tumour behaviour based on molecular characteristics. Here, the authors build models to predict multiple distinct gene expression patterns using DNA copy number alterations In particular, many gene expression signatures have been developed to define specific cancer phenotypes varying from proliferation rates to features of the tumor microenvironment7. These mRNA expression features, along with protein expression, somatic mutations, and clinical features provide a comprehensive molecular portrait of tumors. Integrating multi-platform genomic data together to elucidate the relationship between genotype and phenotype is critical to understanding genetic causes underlying tumor behaviour8. Building predictive models for key tumor driving phenotypes would be valuable to stratify patients for personalized treatments, especially in the clinical setting where gene expression profiling is usually not available and DNA information is routinely collected thanks to available somatic mutation gene panel tests.Tumorigenesis is often driven by multiple types of aberrations in DNA leading to diseases of enormous complexity and heterogeneity. The ability to dissect this heterogeneity is crucial to understanding cancer mechanisms, and for identifying patient subgroups for personalized treatments. One limitation to capture this heterogeneity lies in the characterization of disease phenotypes. With the effort of many consortiums including The Cancer Genome Atlas (TCGA), large-scale multi-platform genomic data are now available, providing an opportunity to study cancer phenotypes on a molecular level and through multiple technology types8, as well as an Elastic Net predictive modeling strategy9, to build models of complex tumor phenotypes using somatic DNA copy number alterations (CNAs). Our results identify associations between many gene expression signatures and CNAs, and between protein expression features and CNAs. Generally, we present an approach that could be applied to many other tumor types for which multi-platform genomic data are available, to evaluate the relationship between CNAs and complex phenotypes, and where predictive models of therapeutic importance could be developed using what are now common place DNA-based clinical tools.In this study, we use an archive of experimentally and computationally derived gene expression signatures, alongside other well-known clinical and molecular features, as a framework to identify genetic drivers and build predictive models for solid epithelial cancer phenotypes. We use an integrative genomics approach including a genome-wide association analysis12. We applied a panel of 543 published gene expression signatures measuring diverse tumor phenotypes including active signaling pathways, the aforementioned known prognostic clinical models, tumor microenvironment features , and features of DNA amplicons and deletions13, onto 1038 breast cancers from the TCGA breast cancer project4 . Potential DNA CNA drivers of a signature should have positive correlations and increased copy number gains in samples with high signature scores, whereas potential repressors would have negative correlations and increased frequencies of copy number losses. Through this approach, we analyzed association landscapes for each signature, noting many expression signatures had no such associations.We first aimed to investigate the possible associations between DNA copy number alterations (CNAs) and multiple gene expression signatures. We initially focused on breast tumors, where multiple gene expression signatures are already in common clinical use. 8 Fig. . For eac8. All 52 signatures of Gatza et al. were included here, and in particular we focused on the RB-LOH signature15, noting that the current analysis used data on a much larger cohort of TCGA breast tumors (n = 1038 vs. n = 476); in addition, another systematic difference between the two studies is that Gatza et al. used gene expression microarrays while we used mRNA-seq. Despite these methodological differences, there was a high concordance between our association landscape for RB-LOH signature and that published by Gatza et al. .We looked at the reproducibility of the association landscapes by comparing our results to those from Gatza et al. for the same signaturesal. Fig. ; both lan = 3500) tumor analysis of Hoadley et al.17, namely a “basal signaling” signature and an “estrogen signaling” signature, both showed many informative associations. For the basal signaling signature, we identified previously known associations including loss/deletion of genes involved in DNA repair such as RAD17, RAD50, PALB2 and BRCA1 3. Models were trained to classify samples into those with high signature scores (top third) versus low signature scores (bottom two-thirds). The area under receiving operating characteristics curve (AUC) values were used to evaluate model performance . Notably, the three signatures that we highlighted for the association landscapes, namely RB-LOH signature, basal signaling signature, and estrogen signaling signature, were all highly predictable (AUC > 0.85) as shown by corresponding receiving operating characteristics (ROC) curves  indicating that these RNA-based phenotypes can be well explained by DNA-based information  amplification and RB1 deletion were only present in the Luminal B prediction model 2. Many studies have addressed the relationship between protein levels and mRNA abundance and concluded that mRNA transcript levels predict protein levels about 50% of the time29. A few studies have also investigated the influence of DNA copy number on protein expression and find some proteins with significant correlations, typically those that are the target of amplification or deletion31. However, these studies assessed correlations on individual genes. Here, we used Elastic Net model building to take into account the whole genome to predict protein expression. Using the aforementioned definition of “highly predictable”, we were able to accurately predict 16 out of the 216 protein expression levels present in the RPPA arrays including a phosphoprotein HER2-pY1248 (test set AUC > 0.75)  data measuring 216 proteins and phospho-proteins coming from TCGA breast cancer samples (75) Fig. . Among tERBB2 and 17q were selected with the largest coefficients, by both the model for HER2 RPPA protein expression, and by the model guided by HER2 clinical IHC status  and ER clinical IHC status  when making use of DNA copy number information only. The feature landscapes of these two models were complex with many positive and negative predictors. Notably, luminal features 16p gain and 16q loss were included in the model, consistent with the fact that ER positivity is prevalent in luminal tumors  excluding HLA and IGH genes. Only a few mutations passed the test set AUC threshold of 0.75, namely TP53, CDH1, MAP3K1 . Prediction accuracies differed by subtype in many cases . We also evaluated if DNA CNA-based Elastic Net model can successfully classify the two lung cancer histologies. Results showed that we can classify LUAD vs. LUSC with very high accuracy (AUC = 0.98 and 0.97 for training and test set), consistent with previous finding that LUAD and LUSC have distinct somatic drivers44  and lung squamous cell carcinoma (LUSC)4 identified successful models with high accuracy (AUC > 0.75) for multiple tumor types. Not surprisingly, there were more gene signatures that were highly predictable in tumor types that have more CNA events , and gene signatures of cellular proliferation status; in fact, we were able to predict many of these signatures with very high accuracy (AUC > 0.9) on a true test set that even used different gene expression and DNA copy number technologies . Taken together each gene signature’s association landscape and Elastic Net feature landscape provides CNA regions for further investigation for potential genetic drivers. In addition, further application of our Elastic Net modeling strategy to a variety of other molecular phenotypes including molecular intrinsic subtypes, protein expression levels and somatic mutation status (including tumor mutation burden) revealed the ability to accurately predict many key phenotypes in breast cancer. These models may be clinically useful and could provide an orthogonal approach for calling key features like ER, PR, and HER2 status in breast cancer, especially in equivocal cases, given the growing use of DNA exomes and somatic mutation gene panels in the cancer clinic.The ability to predict key tumor phenotypes, like mutation status or biomarker levels or complex expression phenotypes, is critical to understanding the biological complexity of solid epithelial cancers. Nowadays for breast cancer, protein expression analysis is required for ER, PR, and HER2, and small panel  gene expression tests are common. For lung cancer, DNA-based gene panel testing is included within the standard of care, and expression analyses for proteins  are growing in prominence, in large part due to immunotherapy. Many solid epithelial cancers, particularly breast and lung, are thought to be at least partially DNA copy number driven because a large number of copy number events occur, and many are known genetic driversFor the analyses presented here, we chose to dichotomize the expression signatures into the highest tertile versus the bottom two tertiles; however, we also evaluated Elastic Net models where the expression signatures were treated as continuous variables, and these were also successful for those models that showed high AUCs when tested as dichotomous variables .Collectively our results demonstrate the ability to build CNA-based predictors for multiple key cancer phenotypes for breast and non-small cell lung cancer patients. While most research focuses on finding genetic drivers of tumorigenesis, our work carries important implications that critical complex tumor phenotypes can be predicted using DNA information, which could be potentially used in the clinic.4. Illumina HT-29 v3 expression data for the METABRIC project (n = 1992 samples) were acquired from the European Genome-phenome Archive at the European Bioinformatics Institute3. For TCGA breast cancer and lung cancer gene expression data, gene-level RNA-Seq reads were upper-quartile normalized and log2 transformed, filtered to genes that were expressed in over 70% of samples, median centered and sample-wise standardized within each data set. For METABRIC microarray gene expression data, acquired data were filtered to genes that were expressed in over 70% of samples and were median centered for each gene and standardized for each sample. Gene expression data for all other tumor types were downloaded from GDC PanCanAtlas publication site (https://gdc.cancer.gov/about-data/publications/pancanatlas). For each tumor type, gene expression data were filtered to genes that were expressed in over 70% of samples, median centered and sample-wise standardized within each tumor type.Illumina HiSeq 2000 RNA sequencing data for human breast cancer and lung cancer (both Lung Adenocarcinoma and Lung Squamous Cell Carcinoma) were acquired from The Broad Institute TCGA GDAC Firehose36. For METABRIC, median-centered and standardized gene expression were used to calculate correlation to each subtype centroid11  genome build, gene-level copy number score were derived through the extreme method as used in GISTIC2 (ref. 51): Genes that fell completely within a CBS-identified copy number segment were assigned corresponding segment value. Genes that overlapped with multiple segments were assigned the greatest amplification or the least deletion value among the overlapped segments. Genes with no overlapping segments were excluded from further analyses. GISTIC2 gene-level copy number data for all other tumor types were downloaded from GDC PanCanAtlas publication site with no further processing (https://gdc.cancer.gov/about-data/publications/pancanatlas).GISTIC2 gene-level copy number data for human breast cancer and lung cancer were acquired from The Broad Institute TCGA GDAC Firehose with no further processing . For the METABRIC project, copy number segmentation data using circular binary segmentation (CBS) algorithm were acquired from the European Genome-phenome ArchiveNormalized protein expression data for human breast cancer were acquired from The Broad Institute TCGA GDAC Firehose with no further processing.36. MAF file was first filtered to only include the following variant classifications: Frame_Shift_Del, Frame_Shift_Ins, In_Frame_Del, In_Frame_Ins, Missense_Mutation, Nonsense_Mutation, Nonstop_Mutation, RNA,Splice_Site, Translation_Start_Site. A binary gene by sample matrix of 1 indicating any mutation and 0 indicating no mutation was then constructed based on the filtered MAF. Mutation load for each sample was then determined by the total number of mutated genes in that sample.Mutation Annotation Format (MAF) data from 2015 TCGA Lobular Breast Cancer dataset were used52 and were partially summarized by Tanioka et al.13. The complete list of genes in each signature and their references is shown in Supplementary Data 53 was used to first merge gene expression matrix with corresponding training set and then Pearson/Spearman correlation/Euclidean distance was computed for each sample in the merged data. For several algorithm-based signatures, corresponding R code is provided to calculate each signature (See code availability). All 543 signatures were applied to TCGA breast cancer and lung cancer data as well as METABRIC data. 504 median-expression based signatures were applied to Pan Cancer data.A panel of 543 previously published gene expression signatures were used to fully characterize cancer phenotypes. These 543 signatures were obtained from multiple publications or GSEA8 on TCGA breast cancer cohort with matched gene expression and copy number data excluding all Normal-like samples (n = 1038). For each signature, a spearman rank correlation, both positive and negative, was used to compare gene-level copy number score with signature score. A one-sided Fisher’s exact test was used to compare either frequency of CNA gain or loss in samples with high signature score (top quartile) and those with low signature score (bottom three quartiles). For each analysis, Benjamini–Hochberg multiple testing correction was used to adjust p-values for each signature across all genes. Significant threshold was set to 0.01 to identify genes that were significant in both analyses.To identify associations between CNAs and gene expression signatures, two independent statistical tests were usedp-value in the model. Fisher’s exact test, Benjamini–Hochberg multiple testing correction and significant threshold of 0.01 were done the same way as described above.To identify subtype-adjusted associations between CNAs and each gene expression signature, a linear model was used instead of the spearman rank correlation to take into account molecular subtype as confounding variables: signature ~ CNA +  + (1|HER2) + (1|LumA) + (1|LumB). Positive/negative correlation was determined by the coefficient of CNA and 9. Generally, gene-level CNA scores were first collapsed to segment-level CNA scores. The complete list of genes in each segment is shown in Supplementary Data 222. Each segment score was calculated as the mean CNA score across genes within the segment. For each cancer phenotype, total sample was split into 70% training set and 30% testing set (R package sampling) stratified by clinical variables: overall survival, gender, race, ER status, PR status and HER2 status, histological subtype, pathologic stages and molecular subtype when available. Models were built on training set only. Tuning grid were determined with alphas over a range from 0.1 to 1 by 0.1 and a sequence of 100 lambdas. The minimum and maximum of lambda values were determined by fitting generalized linear models with each alpha value on training set (R package glmnet). 200 rounds of Monte-Carlo cross validation with default training percentage of 0.75 (R package caret) were used to select the tuning parameters. The optimal parameter combination was determined to have the best classification accuracy. Model with the optimal parameters was then applied to test set and other validation sets if available. Receiving operating characteristics (ROC) curves were constructed and area under ROC curve (AUC) values were used to evaluate model performances (R package ROCR). We consider phenotypes with AUC values above 0.75 as highly predictable.We used Elastic Net modeling approach, which is a regularized regression method that linearly combines the L1 and L2 penalties of the Ridge Regression and Least Absolute Shrinkage and Selection Operator (LASSO), to build DNA CNA-based predictors of cancer phenotypesn = 1038) as in association tests described above. For somatic mutations, all IGH and HLA genes were removed and only genes that have mutation frequency greater than 5% and/or significantly mutated genes identified in 2015 TCGA Lobular Breast Cancer analysis36 were included.For predicting gene expression signatures, protein expression, and mutation load that had continuous scores, models were built to classify samples with high scores (top third) versus low scores (bottom two-thirds). For molecular subtype, clinical receptor status, cancer histology, and mutations that had binary outcomes, models were built to classify each outcome. For breast cancer gene expression signatures, Normal-like samples were excluded  and LUSC (n = 512), which were split into training and testing sets balanced for clinical variables: overall survival, gender, pathological stages and histology (LUAD or LUSC). Models were built on training set and applied to testing set. Models built on TCGA breast cancer training set were also applied to the whole lung data set. Models were also built within LUAD and LUSC separately.For gene signature and histology prediction using the non-small cell lung cancer data, the whole TCGA lung data set was used that combined both LUAD  as well as predicted probability by Elastic Net prediction model (top third vs. bottom two-thirds). Kaplan–Meier curves were drawn  and log rank Further information on research design is available in the Description of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary InformationReporting Summary