SampleLASSO), built on-the-fly for each new target sample to be imputed, outperform models based on fixed gene relationships. Extensive evaluation involving three machine learning algorithms , two gene subsets (GPL96–570 and LINCS), and multiple imputation tasks (within and across microarray/RNA-seq datasets) establishes that SampleLASSO is the most accurate model. Additionally, we demonstrate the biological interpretability of this method by showing that, for imputing a target sample from a certain tissue, SampleLASSO automatically leverages training samples from the same tissue. Thus, SampleLASSO is a simple, yet powerful and flexible approach for harmonizing large-scale gene-expression data.While there are >2 million publicly-available human microarray gene-expression profiles, these profiles were measured using a variety of platforms that each cover a pre-defined, limited set of genes. Therefore, key to reanalyzing and integrating this massive data collection are methods that can computationally reconstitute the complete transcriptome in partially-measured microarray samples by imputing the expression of unmeasured genes. Current state-of-the-art imputation methods are tailored to samples from a specific platform and rely on gene-gene relationships regardless of the biological context of the target sample. We show that sparse regression models that capture sample-sample relationships (termed  High-throughput gene expression technologies—especially microarray  and RNA-Affymetrix Human Genome U133 Plus 2.0 Array (GEO ID: GPL570) accounts for only 22% of the >2 million samples. The next most popular Affymetrix Human Genome U133A Array (GEO ID: GPL96) accounts for another 11% of the samples, but only covers <12k genes. Therefore, it is a significant challenge to gain insights about the full complement of genes in the human genome across the diversity of biological samples and unique experimental conditions in existing microarray data.The purpose of these publicly-available data is to enable other researchers to use published datasets to reproduce original findings, reuse datasets in new ways to answer new questions , or combIn addition to these researcher-submitted microarray datasets, concerted effort has also been put into defining a reduced set of genes that can be measured and then be used to accurately recover the expression of all the other genes ,13. The partially-measured microarray sample to impute the expression of all unmeasured genes in that sample to reconstitute a complete transcriptome  microarray platform based on samples from the GPL570 (whole-genome) platform  always shows competitive performance with the best performing methods, and in particular is always the best performing method when using data from one expression platform to impute data in another expression platform. We also demonstrate the biological interpretability of this method by showing that, for imputing a given sample from a certain tissue, the SampleLASSO model automatically up-weights training samples from the same tissue type.In this study, we demonstrate that using a sparse-regression method that leverages information from the most similar samples provides more accurate predictions than other methods while also providing a highly interpretable underlying model. Current state-of-the-art imputation methods train machine learning models that capture the relationship of the predetermined set of measured genes to each predefined unmeasured gene (or set of unmeasured genes). Then, during imputation in an expression sample with the same measured/unmeasured genes, these methods use the pretrained models to impute the expression of the unmeasured genes. We propose a variant of this approach that we call s Figure . All thes Figure . ExtensiAffymetrix Human Genome U133 Plus 2.0 Array from NCBI GEO  to then impute the expression of an additional 5 277 genes that are only present in the newer genome-scale platform Affymetrix Human Genome U133 Plus 2.0 Array (i.e. GPL570)   . This ged genes) ,18. ThisAffymetrix Human Genome U133 Plus 2.0 Array and ARCHS4 data we processed. We used a file that contains the number of PubMed articles a gene was mentioned in . To mitigate data leakage, we ensured that entire datasets were assigned to splits, thus keeping all expression samples from the same experiment (dataset) together in the same split. The data was also temporally split, with the oldest expression samples being placed in the training and validation sets, and the newest samples going into the test set. To speed up hyperparameter tuning, which consisted of training >500 000 individual models, we further subsetted the validation set by taking 10% of the expression samples from each experiment in the full validation set  , least absolute shrinkage and selection operator (LASSO), a fully-connected feedforward deep neural network (DNN), and a conditional generative adversarial network (GAN).In this study, we evaluated imputation methods using four distinct machine-learning algorithms: KNN is a machine learning algorithm that predicts the target variable for every new example based on the target variables of the k most similar examples in the training data. To impute a given target variable, we used a weighted average of the measured target variable from the k most similar examples based on Euclidean distance, with the weight equal to the inverse of the distance. KNN imputation is a widely-used imputation method for gene expression and provides a strong baseline , GeneLASSO, GeneDNN and GeneGAN. For intuitive, pictorial schematics of the methods, see We used these four algorithms—KNN, LASSO, DNN, GAN—to leverage two distinct types of signals—gene–gene similarities (across samples) and sample–sample similarities (across genes)—for imputing the expression of unmeasured genes in a new partially-measured sample, resulting in six methods referred to in this study as SampleKNN and GeneKNN are the most straightforward and popular implementation of KNN for gene expression imputation. For a new partially-measured expression sample to be imputed, SampleKNN works by first finding the k most similar samples in the training set based on the expression of all measured genes, and then imputing the expression of each unmeasured gene with the weighted average of that gene's expression in the most similar training samples. Thus, the major biological signal used is the similarity between samples (across genes). Conversely, GeneKNN works on a gene-by-gene basis. For each gene that is missing (unmeasured) in a new sample, the method first finds the k measured genes most similar in their expression pattern across all the samples in the training set, and then imputes the expression of the unmeasured gene with the weighted average of the expression of those k genes in that new sample. Thus, the major biological signal used is the similarity between genes (across samples).GeneLASSO is the traditional, widely adopted means of implementing LASSO for gene expression imputation. Here, using the fully-measured training set, a separate sparse regression model is trained for each unmeasured gene, to predict its expression based on a linear combination of all the measured genes. Then, given a new partially-measured sample, the expression of every unmeasured gene is imputed using that gene's pre-trained model, with the predicted expression being equal to the sum of the expression of the measured genes in the new sample weighted by the model coefficients. Akin to GeneKNN, the main source of biological signal for GeneLASSO is gene–gene expression similarities. As an alternative to GeneLASSO, which requires information about which genes are unmeasured in a new sample and a pre-trained model for each of those genes, in this study, we propose a simple alternative called SampleLASSO. Given a new partially-measured sample, SampleLASSO builds a single model on-the-fly that predicts that sample's expression profile based on a sparse linear combination of all the samples in the training set only using the subset of genes measured in the new sample. Here, for every sample to be imputed, the coefficients of the trained model in essence finds the relationship of that sample to all samples in the training set. Then, all the unmeasured genes are imputed using this trained sample-specific model. The main source of biological signal in SampleLASSO, thus, comes from sample similarities. We note a method similar to SampleLASSO has been reported before  (S_array) . HoweverGeneDNN and GeneGAN use deep learning to predict the expression of (a fixed set of) unmeasured genes using a single model that captures complex, nonlinear relationships between the unmeasured genes and the measured genes using the training set. Thus, the main biological signal in GeneDNN and GeneGAN is also from gene-gene relationships.k: the number of most similar training examples to consider in KNN and GeneDNN and GeneGAN, we used all the ∼13k samples in the validation set for hyperparameter tuning as: (i) this most closely mimics the setup in the original papers and (ii) deep learning models additionally use the validation set to determine which epoch (i.e. how many passes through the data the model goes through) yields the best model. For the analysis using the SEEK data, the best hyperparameter across all the tasks and gene subsets using the Affymetrix Human Genome U133 Plus 2.0 Array and ARCHS4 data was used for all ten platforms. For more information on hyperparameter tuning, see Section 1.4 of the Supplemental Material.For both KNN and LASSO, there is only one hyperparameter that needs to be tuned; ed works ,19, and ed works , and theRMSE is the root mean square error, ith gene in the jth sample.A commonly used metric for evaluating gene expression imputation methods is Normalized Root Mean Square Error (NRMSE). The NRMSE for a gene  in Section 2.2 of the Supplemental Material.SampleLASSO models by examining if the We evaluated the interpretability of s that we built a SampleLASSO model for, we calculated a z-score, SampleLASSO model for the jth sample, and Specifically, for a given target sample SampleLASSO model was trained for each manually labeled test sample and used in the z-score analysis above  . HoweverEquation .SampleLASSO, GeneLASSO, SampleKNN, GeneKNN, GeneDNN and GeneGAN. These methods are evaluated in two settings with different sets of unmeasured genes , k-Nearest Neighbors (KNN), a fully-connected feedforward neural network (DNN), and a conditional generative adversarial network (GAN) Figure . Combinis Figure : (i) thes Figure . Lastly,Affymetrix Human Genome U133 Plus 2.0 Array)  re-analysis of novel genes in experimental settings captured in the vast number of microarray datasets and (ii) joint analysis and integration of RNA-seq and microarray data based on a common set of genes. We evaluated the performance of using ARCHS4 RNA-seq data to impute Affymetrix Human Genome U133 Plus 2.0 Array microarray data using the GPL96–570 and LINCS gene subsets .Although microarray platforms like the ARCHS4 database)   Figure . We noteAffymetrix Human Genome U133 Plus 2.0 Array data. For each of the 10 platforms, the set of measured genes was determined by taking the genes common to a given platform and the Affymetrix Human Genome U133 Plus 2.0 Array data, excluding any gene that was in the set of 90 unmeasured genes. It can be seen that SampleLASSO is the best performing method across all ten platforms, whereas both deep learning models perform quite poorly. For more information on the analysis using the SEEK data, see Section 2.5 of the Supplemental Material.For this analysis, we choose a set of 90 genes common to all platforms, and containing highly-, moderately-, and scarcely-studied genes, as the set of unmeasured genes. For all ten platforms, we used the same 78 319 training samples from the SampleKNN offers immediate interpretability via the biological/experimental contexts of the k-nearest training samples picked by the method, however it is not very accurate in imputing unmeasured genes. Since we devised SampleLASSO with this desirable property in mind, we tested if this new method also offers biological interpretability in addition to providing very accurate imputation. Since gene expression samples have clear signals pertaining to their tissue-of-origin  as well as all the code to re-generate the results and figures on GitHub (https://github.com/krishnanlab/Expresto). In addition, we also provide a user-friendly function that performs imputation using SampleLASSO given a file of expression data. The function will return the imputed expression values as well as a list of the most utilized samples from the training set , whereas if we only included genes common to all the platforms, that expression set would only contain the expression of 6017 genes.In this study, we propose a simple, new method termed SampleLASSO is a sparse regression model that trains a machine learning model on-the-fly for every expression profile that needs to have expression values imputed. We demonstrate that SampleLASSO is a highly accurate method for imputing unmeasured genes based on an extensive evaluation on three different imputation tasks (within and across technologies), two imputation settings that differ in the number of measured genes by an order of magnitude, and across multiple gene expression platforms. SampleLASSO’s strength comes from its ability to effectively leverage information from samples from the same biological context.In addition to helping estimate the performance of imputation methods, our analyses in different imputation settings highlight various data standardization/normalization scenarios. When using microarray data to impute microarray data, the training data and validation/testing data (including both measured and unmeasured genes) are quantile normalized to the same distribution. When using RNA-seq data to impute RNA-seq data, samples only undergo within-sample normalization (using TPM) without any between-sample normalization. When using RNA-seq data to impute microarray, the training set data is not jointly normalized but the validation/testing data are. The fact that microarray data has a much lower imputation error than the RNA-seq also points to RNA-seq profiles having very different data distributions due to not being (quantile) normalized across samples, coming from many different sequencing platforms, and having a broader dynamic range than microarray data. While we have performed an analysis showing that jointly normalizing RNA-seq and microarray data improves imputation , future SampleKNN performs poorly because, for a given microarray sample to impute, it finds the closest RNA-seq samples, which come from a different data distribution. On the other hand, GeneKNN has relatively good performance because it works completely within the training data (RNA-seq) to find the genes nearest to a particular gene and then uses these gene relationships for imputation within the microarray data. Even though GeneLASSO similarly captures gene relationships only using the training data (RNA-seq), the mapping in the form of model coefficients does not transfer to microarray samples as easily as nearest neighbors. Similar issues, in addition to potential overfitting to the training set, thwart the performance of GeneDNN , and even a neural network with just one hidden layer of 9000 units would be over double the size of a GeneDNN model with three hidden layers of 9000 units each in the LINCS gene subset setting.Nevertheless, deep-learning-based imputation techniques have a number of practical drawbacks. Foremost among them is the very large number of hyperparameters, in addition to other aspects of model optimization such as regularization and dropout, that need to be tuned to build an accurate model. This drawback is further amplified by the fact that these optimal parameters are likely to change depending on the platform or technology in question. Deep learning models are also hard to scale in terms of model size, with the number of weights growing nonlinearly with increasing layer number and layer size. Additionally, the current state of the standard hardware is such that only ∼32GB of a model can fit into the memory of a GPU, and anything more requires the utilization of multiple GPUs. For example, this is why methods such as GeneDNN and >40 h for GeneGAN per platform using expensive GPU hardware. In contrast, SampleLASSO took just 4 h per platform in SEEK using only one processor on one computational node. As each SampleLASSO model is independent of the others, this is an ‘embarrassingly parallel’ task and can be greatly sped up by running on many CPUs at once.The analysis using the SEEK data—which has not been carried out in any other study on imputation and which reflects the real world task of imputing data from many expression platforms—highlights a few more limitations of the deep learning methods. First, the time it takes to get imputed expression values from deep learning methods is determined by the size of the training set, and once the model is trained, predicting test set values is relatively fast. Therefore, there was no difference in runtime for the deep learning models whether we imputed 10 000 samples or just 90, and this runtime ended up being >20 h for SampleLASSO and GeneLASSO train a model for every sample or gene, respectively. This is an important real world consideration as a researcher only may wish to add a limited number of ‘genes of interest’ to a given set of microarray samples, and SampleLASSO allows for a much quicker exploratory analysis of combining datasets across platforms.Secondly, in the SEEK data analysis, the deep learning methods perform noticeably poorly for almost all platforms. This is likely due to the fact that, since we are only imputing 90 genes, deep learning methods no longer benefit from the multi-task learning that happens when many thousands of genes are all predicted at once. In contrast, The performance of the deep learning methods on the SEEK data could be improved if the model hyperparameters were optimized for each platform. This is not realistic in practice as there are numerous ways of combining data from expression platforms, and the computational time and the technical expertise required to optimize a deep learning model in a given imputation setting are not available to most researchers. We also note that the deep learning methods perform best when the training and testing data are similar  to aid in reproducing all our findings. Using a convenient function in our code, researchers can also use SampleLASSO to readily impute unmeasured genes in their samples of interest in any of the following practical settings: (i) complete the expression profile of publicly-available microarray samples from any platform to make them comparable to the human whole-genome microarray, (ii) predict the expression of genes absent in standard microarrays (e.g. most non-protein coding genes) using RNA-seq to impute microarray samples and (iii) fill in and effectively use genome-scale chemical and genetic perturbation expression data from LINCS based on the measured landmark genes.In conclusion, we propose https://doi.org/10.5281/zenodo.3971092) and all code is available on Github (https://github.com/krishnanlab/Expresto).The data from this study is available on Zenodo (gkaa881_Supplemental_FileClick here for additional data file.


With the rapid development of single-cell genomics, technologies for parallel sequencing of the transcriptome and genome in each single cell is being explored in several labs and is becoming available. This brings us the opportunity to uncover association between genotypes and gene expression phenotypes at single-cell level by eQTL analysis on single-cell data. New method is needed for such tasks due to special characteristics of single-cell sequencing data.We developed an R package SCeQTL that uses zero-inflated negative binomial regression to do eQTL analysis on single-cell data. It can distinguish two type of gene-expression differences among different genotype groups. It can also be used for finding gene expression variations associated with other grouping factors like cell lineages or cell types.https://github.com/XuegongLab/SCeQTL/.The SCeQTL method is capable for eQTL analysis on single-cell data as well as detecting associations of gene expression with other grouping factors. The R package of the method is available at  Expression quantitative trait locus or eQTL analysis is an important approach for studying the association between variations in the genotype and gene expression, which may help to reveal the underlying regulation relationship. Technologies that can sequence in parallel both the genomes and transcriptomes of single cells are being developed recently , 8. ThesMethods for identifying eQTLs have been well studied for microarray data and bulk RNA-seq data. Typical methods of eQTL mapping include linear regression and ANOVA, where the expression level is taken as the dependent variable and the genotype at a single-nucleotide variation (SNV) site is the explaining factor , 16. MosThe phenomenon of excess of zero values is common in single-cell RNA-seq (scRNA-seq) data , 9. TherWe model scRNA-seq data as the outcome of two processes. One is that transcripts are captured in the sequencing and the corresponding gene gets non-negative expression values. The other is that transcripts are missed or the gene is not expressed in the cell, which will result in zero values in the data. The second process causes scRNA-seq data to have excess zero values. We find the negative binomial (NB) distribution can fit the non-zero parts of scRNA-seq data well in a way similar to bulk RNA-seq data Fig. a, but thg and genotype s, there is probability p that the transcription is off and we have the observation of a zero value in a cell, and probability 1 − p that the gene is expressed with values being described as following a negative binomial distribution. We call the probability p as zero ratio for simplicity. We write these asTherefore, we use a zero-inflated negative binomial (ZINB) regression to model the scRNA-seq data as we have done in . For genμ and θ are the mean and shape parameter of the negative binomial distribution. We call a SNV to be an eQTL of a gene if the zero ratio p and/or the mean of negative binomial distribution μ is significantly correlated with the genotype of the SNV in a way thats is the genotype (or other distinguishing factor to group the cells), parameters α1, α2, β1, β2 and the shape parameter θ are to be estimated from the data. Using maximum likelihood method to estimate the parameters, we get the log-likelihoods of the full model that includes the genotype as the explaining factor (β1 ≠ 0 or β2 ≠ 0) and of the reduced model that does not include the genotype (β1 = 0 or β2 = 0).where η is a natural parameter, T(x) is a sufficient statistic, and A(η) is used to guarantee the integral of pdf to be 1. Link function describes how the expectation of corresponding variable is related to the linear combination of independent variables:g is the link function. For NB distribution, the link function isThe generalized linear model contains two parts: distribution hypothesis and link function. In distribution hypothesis, the probability density function (pdf) of over-dispersed exponential families isk degree of freedom. The k is the difference between parameter numbers of the full model and the reduced model. We use the deviance as the test statistic to test for whether β1 = 0 or β2 = 0. By these two hypothesis tests, we can identify whether the gene expression have association with the genotype (or other factor used to group cells) and what kind of association it is. We call this method as SCeQTL and developed a software package in R to implement it (https://github.com/XuegongLab/SCeQTL/).According to the generalized linear model theory , the devSingle-cell parallel sequencing technologies of both the genome and transcriptome are currently only available in very few labs, and the current genomic coverage of such technologies may not be sufficient for genotyping analysis yet. So it is still hard to find public datasets including both single-cell genomic sequencing data with sufficient depth and single-cell RNA-sequencing data of the same single cells. Therefore, we first did a series of simulation experiments to study the performance of SCeQTL. Both genotype and phenotype data were simulated simultaneously under different effect size. We applied SCeQTL and the widely used Matrix eQTL  on the ss = 0, 1, 2), we generated three SNVs (denoted as SNV 1–3) with different genotype frequencies, and conducted four experiments (denoted as Experiment 1–4) for each SNV. These experiments aimed to mimic four scenarios: transcription status set at same or different level while expression values change with genotypes, and expression values set at same or different level while transcription status change with genotypes. Under each scenario, we experimented on changes across different effect sizes. Ten significant gene-SNV pairs were randomly generated for each SNV, and were taken as the ground truth in performance analysis. Gene expression metrics were generated by ZINB model of different parameters. We define three types of genes that are associated with genotypes in the simulation model: genes whose expression values differ among genotypes (Ges), genes’ transcription status differs among genotypes (Gts), and both transcription status and expression values differ among genotypes (Gs). Table We simulated genotype and gene expression data of 1500 cells of three SNVs and 20 genes in each simulation experiment. Considering the probable effect of different frequencies of three genotypes  of ROC curves to demonstrate performance of the two methods. Larger AUC value means higher accuracy. Figure β2 increasing; and in Fig. β1 increasing. Both cases resulted in similar mean in three genotypes which Matrix eQTL cannot distinguish. But once the influence of zero ratio or NB mean became dominant, the power of Matrix eQTL would recover, as is shown in the right part of curves in Fig. In more complicated situations when both transcription status and expression values are different Fig. b, d, AUCβ2 = 0.05. SCeQTL can detect the slight difference in NB mean, but Matrix eQTL cannot. Figure β2 = 0.15 and Fig. β1 = 0.5, respectively. Again, SCeQTL found the differences very significant, while Matrix eQTL found them insignificant. They could support our analyses above.Figure Currently public datasets with both genotype and transcriptome sequenced in the same single cells are still rare, and those only few available datasets still have very limited coverage in SNVs. We therefore used a real RNA-seq dataset without cell-level genotype but with multiple groups of cell attributes to further study the performance of SCeQTL. The data we used is a scRNA-seq dataset of human preimplantation embryo cells of different embryo days . We spliWe first checked whether the non-zero expression data were fitted well with our model using the ‘checkdist’ function in our package. We randomly picked some genes and drew Q-Q plot to compare gene expression distribution with several distributions. Figure p-values of the two methods under the null hypothesis of no eQTL. Figure p-value distributions under null hypothesis, which were obtained by randomly generating and permuting the “genotype” (the embryonic days in this experiment) and use two methods to calculate the p-values. The p-value distribution of SCeQTL is close to uniform distribution between 0 and 1, while the p-value distribution of Matrix eQTL has clear deviation from uniform.We applied both SCeQTL and Matrix eQTL  on thesep-values obtained by SCeQTL and Matrix eQTL are 9.37 × 10−9 and 0.002, respectively. The test has been done for all the 23,981 genes in this dataset. This eQTL is very significant for SCeQTL but will not significant for Matrix eQTL after multiple-test correction. One reason is that the negative binomial distribution fit the single-cell data better than normal distribution. On the other hand, the zero values in scRNA-seq data caused the means of the three groups to be almost equal, so that Matrix eQTL could not detect the difference. Figure p-values with SCeQTL and Matrix eQTL are 1.09 × 10−45 and 0.004, respectively. Matrix eQTL can’t detect differences of this type either.On the eQTL results in the experiment of true embryonic day with gene expression, we found that results of the two methods largely overlapped, but there were noticeable cases on which SCeQTL worked better. Figure p-value less than 10−40, 70 genes with p-value less than 10−30 and 200 genes with p-value less than 10−20. In these genes that are significantly associated with cell lineages, we found some have been reported as lineage specific genes in the literature. For example, for EPI-specific genes PRDM14, GDF3, TDGF1, NODAL, SOX2 and NANOG, their p-values are 5.7 × 10−45 、 4.7 × 10−20, 6.5 × 10−16, 4.0 × 10−21, 5.1 × 10−37 and 4.8 × 10−25; for TE-specific genes GATA2, GATA3 and DAB2, their p-values are 4.1 × 10−20, 6.7 × 10−26 and 3.3 × 10−21; and for PE-specific genes HNF1B, PDGFRA and GATA4, their p-values are 5.7 × 10−32, 1.7 × 10−23 and 2.0 × 10−35, respectively. All these lineage-specific genes ranked at top 200 in our result. It is worth noting that quite a lot of these genes have shown obvious transcription status differences in our SCeQTL analysis, which may imply that gene transcription in single cells are undergone on-off regulation in many scenarios. To double check the reliability of the SCeQTL discoveries, we manually checked some of the results and found most significant genes truly have differences among the compared groups. Figure P-values of SCeQTL in the examples are 4.1 × 10−50, 4.8 × 10−33 and 1.5 × 10−23 respectively. These experiments showed the potential for discovering associations in single cells that cannot be identified using existing eQTL methods. The biological implication of those associations can be important and deserve further investigations.We also experimented on this dataset using cell lineage as the factor to distinguish three cell groups, and found SCeQTL could successfully find results that can be confirmed with biological knowledge on embryonic cell lineages. By dividing the samples by cell lineages EPI (epiblast), TE (trophectoderm) and PE (primitive endoderm), we applied SCeQTL to find genes that vary among different cell lineages. Among all 23,981 genes, SCeQTL found about 20 genes with A limitation of the proposed SCeQTL method is that the computation cost is relatively high if applied for eQTL analysis at whole-genome scale. It can take a few minutes to analyze a few hundred gene-SNV pairs on a single computing node. This is mainly due to the iterative procedures in estimating the parameters. However, for most single-cell studies, the cells are from the same tissue sample or closely related samples. We can expect that the number of SNVs among the cells that need to be studied for eQTL analysis is not too large to make the computing cost of SCeQTL a severe issue in practical applications. This will also not be an issue when we use SCeQTL to analyze the association of gene expression with other factors as we did in the application examples.The analysis of the associations of genotypic variations with gene expression as well as alternative splicing  is a funWe proposed a new method for eQTL analysis on single-cell genomic and transcriptomic parallel sequencing data and developed a software package SCeQTL to implement the method. Experiments showed that the method can reveal associations that cannot be identified with existing eQTL methods developed for bulk data. It can also be applied on tasks of finding the association of gene expression with other grouping factors that distinguish cells into different types. It provides an effective tool for exploring gene regulation relationships at single-cell level.gij is the expression level of gene i in sample j. The denominator is obtained by calculating the geometric mean across non-zero samples. As discussed in [Multiple steps of data pre-processing are necessary before using SCeQTL. Firstly, we remove the effect of the library size. We use the normalization method in DEseq  for thismentclass2pt{minimNext, single-cell RNA-seq data are noisy and we need to remove genes and variants which are not suitable for the analysis. Genes with read counts less than a certain threshold  are treated as not expressed and are therefore removed. We only consider genes whose variances are greater than a certain threshold . For genomic variants, only variants with at least two genotypic groups in the dataset and each genotype has at least 5 samples (cells) are further considered.When we enter the iteration of analyzing every gene-variant pairs, pairs that don’t have enough non-zero values  in one genotype are reported. The estimation of distribution parameters can be far away from true values in this situation. And we find that in real data, there are samples whose expression level is much higher than the others. If we include these samples into consideration, the mean of negative binomial distribution will be overestimated. So we treat these samples as outlier and use robust z score as defined below to remove them , where MAD stands for the median absolute deviation:The package ‘pscl’  is used n, the models becomeγ1 and γ2 to be estimated. The hypothesis test process is the same as non-covariates one. As a special consideration in single-cell studies, potential correlations among single cells from the same individual or from the same cell type can be modeled in this covariate vector to make sure that the associations detected with SCeQTL are not due to those factors.It is common that some hidden covariates may exist in the sampled population, such as age, gender, or other clinical variables. It is important to remove the effect of them from the eQTL study, as otherwise a high percentage of results will be false discoveries. SCeQTL allows user to define a covariate vector x as possible confounding factors to be considered in the analysis. With covariate vector x ∈ Rhttp://github.com/jdstorey/qvalue). Since several publications come up with other methods for multiple test correction in eQTL mapping [p-value or false discovery rates and set the threshold according to other correction methods.We provide two ways to control the false discovery, Benjamini-Hochberg (BH) method  and the  mapping , 13, use


Preprocessing of transcriptomics data plays a pivotal role in the development of toxicogenomics-driven tools for chemical toxicity assessment. The generation and exploitation of large volumes of molecular profiles, following an appropriate experimental design, allows the employment of toxicogenomics (TGx) approaches for a thorough characterisation of the mechanism of action (MOA) of different compounds. To date, a plethora of data preprocessing methodologies have been suggested. However, in most cases, building the optimal analytical workflow is not straightforward. A careful selection of the right tools must be carried out, since it will affect the downstream analyses and modelling approaches. Transcriptomics data preprocessing spans across multiple steps such as quality check, filtering, normalization, batch effect detection and correction. Currently, there is a lack of standard guidelines for data preprocessing in the TGx field. Defining the optimal tools and procedures to be employed in the transcriptomics data preprocessing will lead to the generation of homogeneous and unbiased data, allowing the development of more reliable, robust and accurate predictive models. In this review, we outline methods for the preprocessing of three main transcriptomic technologies including microarray, bulk RNA-Sequencing (RNA-Seq), and single cell RNA-Sequencing (scRNA-Seq). Moreover, we discuss the most common methods for the identification of differentially expressed genes and to perform a functional enrichment analysis. This review is the second part of a three-article series on Transcriptomics in Toxicogenomics. The development of omic sciences gave unprecedented insights into physiological and pathological mechanisms at a molecular level, arguably in almost all the areas of life sciences, including toxicology . The tecThe production of large data sets from transcriptomics experiments encouraged the birth of high-throughput studies, led by big consortia, aimed at generating public repositories that contain humongous amounts of gene expression data, which have been made available to the scientific community ,6,7,8,9.However, to carry out a rigorous analysis and obtain reliable results, a correct analytical procedure should be employed. Despite the well defined pipelines for generic transcriptome data analysis have been already designed , well esIn this work, we outline current standards, available resources and good practices for the bioinformatics analysis of transcriptomics data in toxicogenomics, generated from both microarray and RNA-Seq technologies. We discuss the new opportunities and challenges provided by single-cell RNA-seq and the analytical differences with the bulk RNA-Seq. Finally, we cover the most used algorithms for differential expression analysis and to perform a robust functional annotation, in order to elucidate the toxicity-related cellular processes.The hypothesis underlying a microarray analysis is that relative gene expression levels are represented as fluorescence intensities. Indeed, microarray experiments allow investigating relationships between biological samples based on expression patterns. Thus, biologically relevant patterns are identified by investigating each gene expression ratio between different conditions . HoweverThe current standard of a microarray data preprocessing pipeline is shown in The gene expression patterns measured from microarray experiments can be significantly affected by different sources of systematic and random errors that may occur at different levels of the experiment . For exaAnother invaluable form of quality assurance that is specific for toxicological applications, as well as assessing the effects of engineered nanomaterials (ENMs) on RNA levels, is the use of spike-in probes, in order to account for variation between the arrays. Typically, spike-in kits consist of a mixture of multiple positive control transcripts at known concentrations which, for instance, anneal to complementary probes on the microarray. Therefore, the design of the assay is able to control for any abnormalities in the labelling and hybridisation procedure including the potential interference by ENMs, which may occur on fluorescent dyes in use .Microarray data commonly show a large number of probes in the background intensity range. Usually, these probes’ intensities do not show a marked variability across arrays. Hence, they combine a low variance with low intensity. Thus, in many cases, they might be detected as differentially expressed, although they are barely above the “detection” limit and are not very informative in general. For these reasons, such probes are filtered out prior to further preprocessing steps.Normalization plays an important role in the microarray preprocessing since it allows to adjust the individual hybridization intensities in order to perform meaningful biological comparisons . In thisGene expression data coming from microarray experiments can be affected by non-biological variables. The variability in the gene expression values due to these types of variables is known as batch effect. Batch effects can arise for multiple reasons, such as ambient conditions during the sample preparation and handling, amplification, labelling, hybridization protocol, different sites/laboratories in which the experiments are performed, different chip or platform types and different scanners . These bA fundamental step in the analysis is to attenuate the effects associated with batch variables while retaining the variation associated with biological variables. As previously discussed, to be able to properly correct the batch effects, the experimental design, including sample randomization and proper metadata annotation, needs to be carefully considered. Batch estimation and correction can be performed by using the surrogate variable analysis (SVA) R library . Known bThese identified batch variables can be corrected to remove technical noise from the data. The R ComBat function , from thhttp://brainarray.mbni.med.umich.edu) from which the custom CDF file can be downloaded and used in combination with Bioconductor libraries to annotate Affymetrix microarray data.Accurate mapping of the microarray probes to genomic elements, such as genes or regulatory regions, is essential to generate reliable biological findings. However, the manufacturers of microarray platforms typically provide incomplete and/or outdated probe annotations, which often rely on older reference genome and transcriptome versions that differ substantially from up-to-date sequence databases . To dealA huge collection of computational tools is available, both on CRAN and Bioconductor , to procThe state of the art of a typical RNA-Seq preprocessing pipeline is shown in Similarly to microarray experiments, deep sequencing procedures may suffer from certain biases, which should be detected and corrected through an accurate quality check prior to subsequent analyses. From an experimental point of view, a pre-analitical check of the quality of the extracted RNA is necessary. There is currently no consensus to establish whether a sample is unusable based on the levels of RNA degradation. Thus, while standardized RNA quality metrics such as the Degradometer  or the Rhttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Such software is nowadays employed in the analysis of data obtained by Illumina platforms, but it was previously used also to analyse data produced from Roche 454 and Solid platforms. FastQC allows multi-sample analysis and provides a user-friendly and easy-to-use graphical interface. Beyond a general overview of the analysed sets of reads, reporting information like the number of reads in the considered file, their length and percentage of GC dinucleotides, the software shows the per-base quality of the entire set of reads. The quality is measured through the Phred score, expressed by the following formula: p indicates the error base-calling probability [http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) and the FASTX toolkit (http://hannonlab.cshl.edu/fastx_toolkit/). Such software can be used to both remove residual adapters used in the library construction and to trim the low-quality bases. Furthermore, FastQC provides information about the presence of contamination of the samples , presence of non-detected bases along with the reads, and overrepresented sequences.Therefore, the first step for an accurate analysis of sequencing-based transcriptomics data is the quality check of the raw reads. This step is necessary in order to highlight biases and/or library contamination possibly occurred during the library preparation or sequencing procedure. One of the most widespread software to perform a quality check of the raw reads is FastQC (bability . In genehttps://ccb.jhu.edu/software/hisat2/index.shtml). This novel approach may be definitely considered a current gold standard in the processing of the Next Generation Sequencing (NGS) reads in current and upcoming toxicogenomics experiments.The subsequent step is the alignment of the RNA-Seq reads onto a reference genome, in order to assign each of the sequenced reads to a specific location on the genome/transcriptome. This procedure is extremely challenging from a computational point of view. First, a number of reads that spans between a few millions to hundred millions undergoes alignment on a reference genome, whose dimension, in human, is around 3 billions base pairs. Moreover, since transcripts in eukaryotic genomes contain not contiguous regions , alignment tools for RNA-Seq reads should handle spliced alignment with very large gaps. Therefore, Trapnell and colleagues  developehttp://www.ensembl.org) and GENCODE (https://www.gencodegenes.org) provide more comprehensive and updated annotations. Ensembl includes automatically annotated entries, while GENCODE derives from merging the Ensembl automatically annotated entries and the manually curated entries reported by the HAVANA team of the Wellcome Trust Sanger Institute from the Vega database [Once the read mapping is accomplished, it is crucial to assign the mapped reads to certain genomic features . Since this task is aimed at the quantification of gene/transcript wise expression, the choice of the annotation is important. Nowadays, plenty of different annotations, produced by different consortia, are available. Such annotations are rapidly evolving as novel transcripts and splice isoforms are discovered and validated . As a codatabase .Since the transcriptome analysis in a toxicogenomics setting is aimed at the study of gene expression deregulation upon the exposure of a certain compound on a biological system, a correct quantification of gene expression is crucial. In this step, the aligned raw reads are summarized into a count matrix which can be used for differential expression analysis . The couIn order to carry out a reliable downstream analysis, as well as differential expression and functional annotation of transcriptome data, the samples need to undergo normalization and filtering steps. The former is needed since the transcript quantification step is directly dependent on the length of the transcripts and the library size, in order to make comparable (1) the expression levels among the transcripts of the same sample, (2) the sequenced samples between each other. The latter is aimed at removing the low read counts since they correspond to the irrelevant biological features. Regarding the normalization of RNA-Seq data, the classical and most widespread method is the Reads per Kilobase of exon model per Million mapped reads (RPKM) . This meAs for hybridization based experiments, also high-throughput sequencing experiments may suffer from non-biological sources of variation. As already mentioned for the microarrays, Principal Component Analysis (PCA) can be a precious instrument in order to identify the features affected by batch surrogates. Principal components are able to capture both biological and technical variability and, when estimated after the biological variables have been taken into account, it is able to quantify the effects of artefacts in the data . The abowww.scRNA-tools.org) [scRNA-seq technologies became more widespread and provided unprecedented opportunities for exploring gene expression profiles at single cell resolution, which greatly revolutionizes transcriptomic studies. Since the first publication about scRNA-seq methodology in 2009 , a numbeols.org) . Nonetheols.org) , inDropsols.org) , SEQC [7ols.org)  and zUMIols.org)  were proSince both bulk RNA-seq and scRNA-seq generally sequence transcripts into reads to generate the raw data in .fastq format, and the scRNA-seq data are often structurally identical to bulk RNA-seq data, the principles and methods used for data preprocessing of bulk RNA-seq, perhaps slightly modified, can be employed in most of the steps of scRNA-seq data preprocessing. For this reason, we will focus on analytical procedures which are specific for scRNA-Seq.In scRNA-seq, one of the key challenges is to identify and remove low-quality cells that are damaged, dead or mixed with multiple cells. Typically, cell-level QC metrics are used to remove problematic cells. After cell QC, normalization is carried out for accurate comparisons of a gene’s expression across samples. Normalization methods developed for bulk RNA-seq are often used for scRNA-seq data. However, their suitability with respect to scRNA-seq data is still unexplored. For normalization of scRNA-seq data, several methods such as SCnorm  and ScraAmong steps in the data preprocessing pipeline for scRNA-seq, data correction aims to remove technical, biological and batch effects. The technical and biological effects can be regressed out by a simple or variant of regression model. Several methods to mitigate the batch effects in scRNA-seq data, such as Mutual Nearest Neighbor (MNN) and K-nearest neighbor Batch Effect Test (KBET), were proposed. From a recent comparison of classical batch effect correction methods , Combat To exclude the low-quality data, which makes the downstream analysis difficult and may lead to misinterpretation, a series of QC analyses is required to ensure that the data quality is sufficient for downstream analysis. Three covariates are used for the QC: the number of counts per barcode (count depth), the number of genes per barcode, and the fraction of counts from mitochondrial genes per barcode ,76. The Human scRNA-seq data are high dimensional since they measure expression levels of thousands of genes  in a large number of cells. Feature selection is a key step in many single-cell RNA-seq analyses to keep only genes that are informative of the variability in the single-cell RNA-seq data. Highly Variable Genes (HVGs) method , which rA common data analysis pipeline for scRNA-seq data includes the identification of distinct cell types to unravel the cellular heterogeneity of samples. The identification should be carried out after QC and normalization. This task is typically solved by unsupervised clustering methods and manual annotation based on canonical gene markers  markers in the immune cells). The clustering algorithms specialized for the scRNA-seq data are generally declined from some type of general k-mean, graph-based, density-based, or hierarchical clustering. The cells are grouped into clusters based on the similarity of their gene expression profiles. After clustering, the gene markers are used to characterize and annotate the clusters with a meaningful biological label. Since the manual annotation is a time-consuming process and not reproducible across different experiments within and across research groups, a growing number of classification approaches based on machine learning algorithms and artificial neural networks are being adapted to automatically label cells. Recently, twenty-two automatic cell type identification methods were evaluated using 27 publicly available scRNA-seq data sets of different sizes, technologies, species, and levels of complexity . In the High-throughput transcriptomics technologies represent diverse technical solutions to increase the throughput and reduce the cost of gene expression profiling, and frequently this includes measuring less than the full genomic complement of genes. For descriptions of individual technologies, please refer to the part I in this series of reviews. Preprocessing protocols can also be highly customized and manufacturer specific, e.g., the L1000 technology . In thisp-values is applied to avoid errors given by the multiple testing procedure. The classical approach to control for multiple testing is by familywise error rate (FWER), which focuses on avoiding the Type I errors  in a very strict way. Examples of methodologies that fall into this category are the Bonferroni method and its variant Holm’s method [p-values is made. However, the aforementioned procedures have been proven to be overconservative for many applications of genomics. Nowadays, the most popular method for multiple testing adjustment is the one developed by Benjamini and Hochberg [p-value that explains how significant is that difference. The user may set up a threshold on the p-value and fold-change to identify the final set of differentially expressed genes.A gene is considered to be differentially expressed if the difference between its expression measured in two experimental conditions is statistically significant. For microarray experiments, differential expression analysis can be performed in R by using the limma package . Limma us method , as wells method , and Hocs method  respectiHochberg , which cHochberg ,94. FinaThe limma package works well also with RNA-Seq experiments, with few adjustments for this data type. Indeed, limma transforms the read counts matrix in log2-counts-per-million (logCPM) and the mean variance relationship can be modelled with two different approaches: precision weights and an empirical Bayes prior trend ,96. For The functional annotation of genes is an essential step to further interpret the results of transcriptomics experiments. This analysis is usually performed in order to characterize the biological role of a certain gene of interest  in the cell, as well as its activity in one or more molecular processes and its localization in the cell. The main functional annotation databases are the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways , ReactomP-values represent the difference between the observed and the expected overlaps between the differentially expressed genes in the experiment and the functional gene set, as well as the numbers of genes involved. Multiple testing correction is performed to control for spurious results. However, as the Fisher’s exact test assumes that differential expression of one gene does not depend on the others (no inter-gene correlations), the test can give nonspecific results with long gene lists. Variants of the classical Fisher’s exact test implemented in e.g., the topGO-package can mitigate nonspecific results from large gene sets at the top of the GO-hierarchy by taking advantage of the dependencies in the directed-acyclic graph structure of the database [Gene overlap or over-representation analysis with the Fisher’s exact test is the most commonly used statistical method. database .p-value limits, which may be one of the biggest obstacles towards implementation of standardized bioinformatics pipelines in toxicogenomic approaches [p-value is p-values of zero are especially detrimental). Parametric methods can also be used and may have higher sensitivity, as long as they consider inter-gene correlations, such as the limma CAMERA method [Gene Set Enrichment Analysis (GSEA), which is also referred to as functional class scoring, is a rank-based threshold-free method that does not rely on differentially expressed genes to perform pathway analyses but uses all available gene expression information ,105,106.proaches . Large nproaches . To avoiproaches ,104. LarA method . OverallProgrammatic larger-scale analyses can be performed with the methods in the limma R package. R and Cytoscape-based workflows can also combine visualizations with pathway analyses in a very powerful manner ,110. FisSince more and more toxicogenomics studies involve the comparison of the effect of different materials at the same time, we recently proposed a graphical tool implemented in R, called FunMappOne , that enThe newly flourished TGx field is lacking rational guidelines for the preprocessing of large scale data deriving from transcriptomics experiments. Addressing this drawback is crucial in order to carry out reliable risk assessment and toxicity prediction. A proper setting of the data preprocessing assures a robust outcome from the downstream analysis, and, in turn, allows to correctly answer the biological questions posed by the study. For this reason, it is important to highlight that the outcome of the analysis, such as the degree of toxicity of a chemical, often depends on thresholds or parameters of algorithms set in all the analytical steps. Thus, particular attention must be paid to these aspects to perform a thoughtful final decision.In this review, which is the second part of a three papers series, we outlined the best practices in the preprocessing of transcriptomics data derived from DNA microarray, bulk RNA-Seq and scRNA-Seq technologies, and we make them available to the TGx community. Since the batch effect evaluation step is not often considered in the routine analytical practice, we pointed out the most widespread methods and procedures to evaluate and, eventually, correct the data from technical variability. Moreover, we covered the golden standard methods to perform basic steps of the downstream analysis, including differential expression analysis and gene functional annotation. In conclusion, this review article represents a reference survey of “good practices” for transcriptomics data analysis in TGx.


The lack of well-structured metadata annotations complicates the reusability and interpretation of the growing amount of publicly available RNA expression data. The machine learning-based prediction of metadata (data augmentation) can considerably improve the quality of expression data annotation. In this study, we systematically benchmark deep learning (DL) and random forest (RF)-based metadata augmentation of tissue, age, and sex using small RNA (sRNA) expression profiles. We use 4243 annotated sRNA-Seq samples from the sRNA expression atlas database to train and test the augmentation performance. In general, the DL machine learner outperforms the RF method in almost all tested cases. The average cross-validated prediction accuracy of the DL algorithm for tissues is 96.5%, for sex is 77%, and for age is 77.2%. The average tissue prediction accuracy for a completely new data set is 83.1% (DL) and 80.8% (RF). To understand which sRNAs influence DL predictions, we employ backpropagation-based feature importance scores using the DeepLIFT method, which enable us to obtain information on biological relevance of sRNAs. This leads to missing and/or inaccurate annotations and requires revisions and manual corrections by an expert  are crucial for the reuse of data. A detailed description of the biological conditions in which data have been obtained is required to extract new information from the obtained data. The data should be findable, accessible, interoperable, and reusable, which ultimately facilitates knowledge discovery  expression profiles by formulating this prediction as a classification problem. sRNAs are short (<200 nt), usually noncoding RNA molecules with many crucial biological functions Storz, . The basTo distinguish between biological conditions, different ML methods were applied. In Guo et al.  and Hadlhttp://sea.ims.bio) , a database containing well-structured manually curated ontology-based annotations of publicly available sRNA-Seq data. All data from the SEA were analyzed with the same workflow . We used 4243 annotated human sRNA-Seq samples from the SEA.For our analysis, we used data from the sRNA expression atlas  experiments as well as on “one data set out” experiments. We have demonstrated how backpropagation can provide a biological interpretation of relevant features for the DL classification.We augmented tissue, sex, and age based on human sRNA-seq expression profiles. We used sRNA-Seq data from the SEA  that contains 4243 samples and annotations in 350 data sets. The relatively large number of high-quality samples allowed us to use DL for data augmentation. Each sample contained annotations, sRNA expression counts of ∼35,000 sRNAs, and expression information of potentially viral and bacterial transcripts (∼5600 contaminants), according to the output of the OASIS 2 sRNA analysis application  in the SEA  accordink intervals, k = 2, 3, 4. Age is a continuous variable and its exact prediction is a regression problem that might be highly inaccurate when solely based on sRNA expression information. To use the same methods as those used for the prediction of other annotation fields, we grouped ages into Data scaling and filtering are described in detail in Fiosina et al. . In brienum_of_classes) and transformed to categorical variables consisting of 0's and 1's. We used the ReLU function to activate the input and the hidden layers, and the softmax function for the output layer, for a multiclass classification. As a loss function, either a binary or categorical cross-entropy was used. For the NN training, Adam optimizer was used. The NN was trained for 50 epochs with a batch size of 30.We used a fully connected neuronal network (NN) architecture. It comprised one input layer with the number of inputs equal to the number of variables after the initial filtering. The NN contained three hidden layers with 1000, 250, and 250 neurons and dropout rates of 0.5, 0.4, and 0.4, respectively (achieved by a grid search). The number of neurons in the output layer was equal to the number of predicted classes. The output data (the annotation classes) were encoded with integers  for the classification and feature importance calculation. We used the top-1000 features rated by their Gini index for the second stage classification with an increased number of trees (500 instead of 100). The mtry parameter was equal to the square root of the number of features. Given that the performance of RF method can be strongly affected by class imbalance, we downsampled large classes to the size of the smaller classes.We implemented two types of CV to investigate the accuracy of the data augmentation. First, we used the average accuracy of fivefold CV. In this scenario, the training cells and test samples were randomly selected a priori, so that in most cases, samples from each data set (experiment) could be included in the training and test sets.Then, we performed “one-data set-out” classifications, where specific data sets were removed from the training set and incorporated into the test set after ensuring that the respective tissues still remained in the training data set.Throughout this article, we refer to the fivefold CV as “cross validation” and the validation for unseen data sets as “one data set out.”i, each input neuron j, and each output neuron k, a score iCj, k,  is calculated, which represents an importance of an input j for an output k in the i-th input sample.To biologically trace the decisions of the DL model to the input features, we used DeepLIFT scores. DeepLIFT , to change the classification results. For each sample D2 and set the expression of sRNAs j 0. We stop the process when the classification changes from k to ′k  or to any other class k″≠k . The corresponding average number of steps was applied to a matrix, which demonstrated “stability” of class (or “class similarity”). As a reference input, we used a vector of 0's.We ordered the differences All the scripts for DL classification are developed in R based on the “keras” library. The RF models are also implemented in R, using the “randomForest” library. For quality metrics, we used R “caret” package. We used the Python 3.5 “sklearn.manifold” t-SNE library to build the t-SNE plots. DeepLIFT was implemented using the “deeplift” Python library version 0.6.9.0.We aimed to predict the tissue class, that is, grouped tissue (2). Although grouping leads to a smaller number of classes, it increases the samples per class. This should reduce the problems due to class imbalances and the overfitting of very small training classes. The prediction was based on sRNA expression profiles only.To compare the performance of the DL and the RF models for data sets with a different degree of imbalance, we excluded classes for which <9 or 15 samples were available. As detailed in the Section 2, the aggregation of samples revealed six tissues with more than one data set per tissue  presents an accuracy of 76.4% for binary output and 63.2% for four intervals. The RF models performed slightly worse on average with a maximum accuracy 75.8% for binary output and four intervals and 57.5% for four intervals.DL-based models are called “black boxes” because it is often unclear how the models arrive at their decisions. However, particularly in biological and medical settings, it is important to understand what enables algorithms to classify a sample, as the feature may be related to a cause as well as to a possible treatment. We investigated the explainability of automatic metadata augmentation with DL models, using backpropagation with the DeepLIFT method.To visualize the backpropagation results, we used heatmaps that represented the scores for each individual class –10.The figures demonstrate that the visual representations of DeepLIFT scores may explain the factors important for a particular output of the NN. This shows that deep neural networks can indeed offer explainable and interpretable results.D1j, k for each tissue k as outlined in Section 2.3.4. Using these scores, we selected the top N = 300 sRNAs j and calculated the average expression for each class , automatic metadata augmentation can be a good option to annotate the missing metadata using sRNA expression levels. The DL-based classification accuracy of tissue and sex predictions reaches 98% and 77%, respectively, the classification of age groups (or the regression of age) seems to demonstrate an inferior performance. In general, metadata augmentation, as undertaken in this study, is dependent on the occurrence of the tissue of interest, or a similar tissue, in the training data set of the classifier. Another general problem is that of class imbalances and very rare classes. In this study, we have used an ontology-based grouping of rare classes to higher ontological nodes to increase the number of samples for a given class. In future work, we plan to use a hierarchical classification, from general to specific tissue classes, to investigate the classification performance across the ontological hierarchy. We have also demonstrated that, in general, the inclusion of contamination profiles in classification models improves the accuracy for sex and age.sRNA expression profiles seem to be suitable for the augmentation of tissue information. A CV-based tissue group classification achieves an accuracy >98%. In the “one data set out” scenario, with a specific data set with a specific bias missing from the training data, samples from the unseen data set are classified with an accuracy of ∼80%.For sex classification, the DL model achieved an accuracy of ∼77%, which may not be sufficient for accurate sex classification. This relatively low accuracy indicates that there may be no sex-specific expressed sRNAs for the X- or Y-chromosomes. Similarly, we obtained an accuracy of 77% for predicting whether a person is younger or older than 65 years. For a split into four intervals, accuracy decreased to 64%, indicating that the sRNA transcriptome does not consistently change with age.Lastly, we demonstrated that DL models can be explained both for individual samples and on average. For this purpose, the DeepLIFT scores demonstrated very promising results.


Given the complexity and diversity of the cancer genomics profiles, it is challenging to identify distinct clusters from different cancer types. Numerous analyses have been conducted for this propose. Still, the methods they used always do not directly support the high-dimensional omics data across the whole genome (Such as ATAC-seq profiles). In this study, based on the deep adversarial learning, we present an end-to-end approach ClusterATAC to leverage high-dimensional features and explore the classification results. On the ATAC-seq dataset and RNA-seq dataset, ClusterATAC has achieved excellent performance. Since ATAC-seq data plays a crucial role in the study of the effects of non-coding regions on the molecular classification of cancers, we explore the clustering solution obtained by ClusterATAC on the pan-cancer ATAC dataset. In this solution, more than 70% of the clustering are single-tumor-type-dominant, and the vast majority of the remaining clusters are associated with similar tumor types. We explore the representative non-coding loci and their linked genes of each cluster and verify some results by the literature search. These results suggest that a large number of non-coding loci affect the development and progression of cancer through its linked genes, which can potentially advance cancer diagnosis and therapy. A few methods have been developed to leverage omics data  to solve cancer classification. However, these omics data always focus on the coding regions (2% of the human genome). Cancer classification methods based on the high-dimensional raw data across the whole genome are rare. Our approach addressed crucial and fundamental limitations in existing approaches. ClusterATAC used adversarial learning to handle the limited but high-dimensional omics data. Feature selection techniques are used to analyze the essential non-coding loci and their regulatory genes for each cluster. The outcome can lead to a deeper understanding of the regulatory mechanisms that lead to cancer development and progression. We successfully obtained 22 cancer subgroups form the ATAC-seq profiles of 401 TCGA samples. We observed that most subgroups follow the 'Cell-of-Origin' pattern, consistent with the recent study. There were significant differences between the 22 clusters. More than 70% of the subgroups were homogeneous for a single cancer type. We identified the representative loci and the corresponding regulatory genes of each cluster. We found that these loci and genes are always tumor-specific and responsible for the occurrence and development of the related tumor. Cancer is a heterogeneous complex disease that poses a severe threat to human health and can occur in most organs of the human body . The chaWith the rapid development of next-generation genome sequencing technology, large cancer research projects, such as The Cancer Genome Atlas (TCGA)  and the The ATAC-seq technology is introduced into cancer profiles analysis since it can mark the open chromatin sites and predict transcription factor (TF) binding sites across the whole genome . HoweverRecent advances in the artificial intelligence (AI) field allows deep learning applied to a large number of research fields. With the development of novel modeling techniques, more and more types of neural networks have been proposed, such as multilayer perceptron, convolutional neural network, auto-encoder, recurrent neural network, and generative adversarial network. Deep learning has not only gained enormous success in image recognition, object detection, speech recognition, natural language understanding but also gradually began to be applied creatively in molecular biology-related studies  . EncoderTo illustrate that ClusterATAC can achieve stable and reasonable results in the clustering of ultra-high dimension data, we constructed two tumor datasets. The first dataset comprised 401 TCGA samples with complete ATAC-seq data and clinical profiles . The ATAC-seq profiles were obtained from the previous analysis . The secWe applied ClusterATAC on the ATAC-seq data to discover the distinct patterns of samples across 23 cancer types. During the clustering process, we used the normalized ATAC-seq peak scores as the feature to represent the samples. Since each sample's feature dimension is very high , to avoid overfitting, we developed the Encoder-GAN to handle the ATAC-seq and obtained the latent variables corresponding to the individual. We introduced GAN since it can enhance the representation ability of the deep learning approach. After the GAN training, the model successfully extracted the features from the original data as the input of the clustering process . Based oWe denoted the 22 clusters as C1-C22 and arranged samples according to the cluster labels. We evaluated the similarities of samples by calculating the correlation of the features generated by Encoder-GAN and performed the heatmap to achieve the visualization of the clusters . We obseNext, we explored the rationality of ClusterATAC's results on the RNA-seq dataset. ClusterATAC achieved five clusters (labeled C1~C5) on the BRCA tumors. We use the heatmap to visualize the similarity matrix of the low-dimensional features of ClusterATAC . There aWe used the constructed ATAC-seq data set and RNA-seq data to benchmark clustering methods. On the ATAC-seq data set, according to the previous analysis result , we set On the ATAC-seq data set, each method found the clustering with a significantly different survival . ClusterTo estimate the performance of different algorithms more accurately, on the two benchmark datasets, we also used permutation tests  to calcuOn the ATAC-seq data set, two independent large-scale clustering analyses have been done, and the clustering schemes are found to be highly consistent ,15. HoweTo measure the similarity of distinct clustering methods, we used the variation of information (VI) analysis . Of all To illustrate that ClusterATAC identified diverse patterns of the TCGA pan-cancers, we analyzed the clustering results of ClusterATAC (C1~C22) and DensityPeakCluster (D1~D18). We found that there are two distinct differences between the results of ClusterATAC and DensityPeakCluster. First, for brain cancer and kidney cancer, the clustering scheme of ClusterATAC is more detailed than the clustering scheme of DensityPeakCluster. The density method classifies all brain cancer patients into one category (D5), while ClusterATAC can more accurately distinguish between GBM (C4) and LGG (C16) in brain cancer. Although both diseases belong to brain cancer, the five-year survival rate of LGG is 59.9%, while GBM is already very serious, and the average survival time of patients is no more than 15 months . Our cluBesides, we analyzed the clustering results of ClusterATAC with the 28-iClusters solution . AlthougThe clustering results of ClusterATAC suggested that different types of cancer have different biomarkers in the non-coding regions, and some biomarkers of the mixed clusters are associated with multiple types of tumors. Based on the clustering solution of ClusterATAC, we explored the biomarkers corresponding to different clusters across the whole genome, obtained their corresponding genes, and analyzed their regulatory mechanisms. For each of the 22 clusters, we marked the target cluster samples with positive labels, and the other individuals are labeled as negative labels. We used the random forest as the feature selection algorithm to identify its corresponding biomarkers. The random forest method input is the ATAC peaks of the samples, and the output is the 0 or 1 labels. The Gini importance of the random forest is used for the selection of candidate biomarkers. After the model fitting, we reported the five most critical non-coding regions and their associated genes for each cluster .Based on these findings, we conducted a literature search to obtain reliable cluster-specific biomarkers . FirstlyNext, we explored the heterogeneous clusters and their corresponding critical loci. For squamous histology cancers, three clusters were corresponding to them: C7, C13, C22. Among them, the representative region of C7 corresponds to the gene ANXA8. Studies have shown that ANXA8 is a molecular marker associated with lymph node metastasis in oral squamous cell carcinoma . The repCurrently, deep learning methods are gradually being used for the analysis of cancer genomic data. In this work, we proposed ClusterATAC, a deep-learning-based clustering model for the cancer classification. The central component of ClusterATAC is the Encoder-GAN, which can learn the nonlinear representation of the complex raw data and transfer them to the coded low dimensional features. With these extracted features, GMM is another component responsible for the unsupervised clustering. Moreover, we used the Davies-Bouldin index to determine the appropriate number of clusters from a reasonable range of values. ClusterATAC successfully obtained 22 clusters form the ATAC-seq profiles of 401 TCGA samples. We observed that most of the clusters follow the ‘Cell-of-Origin’ pattern, which is consistent with the recent study. There were significant survival differences between the 22 clusters. More than 70% of the clusters were homogeneous for a single cancer type. On the ATAC-seq dataset and RNA-seq dataset, ClusterATAC has achieved excellent performance. We used the random forest to select the representative loci and the corresponding regulatory genes on each cluster of the Pan-cancer data set. These loci and genes are always tumor-specific and responsible for the occurrence and development of the related tumor. These findings indicated that the ClusterATAC clustering results have the potential opportunity to develop cancer therapeutics.The 22-cluster solution of ClusterATAC reveals the critical role of regulatory elements in non-coding regions for cancer classification. The input of the model is the ATAC peak score of the non-coding loci so that each cluster can finally link to representative non-coding loci and regulatory genes. Since most of the clusters link to a specific tumor type, we can indicate that a significant number of genomic loci and regulatory genes are tumor-specific. For example, C5 refers to thyroid cancer, whose representative locus is 8q24.2, and the regulatory gene is TG. They are closely related to thyroid cancer since the corresponding protein of the TG gene is produced by the thyroid gland. For another example, the representative region of C16 is 1q22, and its regulatory gene BCAN is essential in promoting the progression of glioma.An essential component of ClusterATAC is Encoder-GAN, a model based on the generative adversarial network. Recently, GAN architecture has become the most popular generation model. To the best of our knowledge, ClusterATAC is the first to introduce GAN for the modeling of ATAC-seq data. The GAN architecture has many extensions and improvements, depending on the scenario of applications. Most GAN-based models focus on sampling from random distributions and generating high-quality samples. These models combine the discriminator with the generator and improve the quality of the generated data. In contrast, Encoder-GAN introduces adversarial learning of the discriminator and the encoder and performs a nonlinear representation of raw data accurately. At the same time, we downgrade the decoder network structure and minimize the reconstruction error to maximize the description of the encoder in the autoencoder architecture. These innovative network designs enhance the representation ability of the approach.ClusterATAC has limitations. Currently, it only supports the clustering of single omics data. Since most methods of molecular classification are based on the multi-omics data integration, to obtain more accurate clustering results, our future work is to collect somatic mutation, gene expression, DNA methylation, protein expression, and other omics data, and introduce GAN into integrative clustering analysis to expect more meaningful results.The ClusterATAC framework took the genome-wide high-dimensional omics data as the input and predicted the cluster labels of each sample across the 23 TCGA cancer types. The first component of the ClusterATAC framework is Encoder-GAN, which reduces features to low-dimensional space for running clustering algorithms. In deep learning, autoencoder is frequently used for nonlinear dimensionality reduction in an unsupervised manner. Autoencoder is composed of multiple coding layers (encoder) and decoding layers (decoder). The encoder is corresponding to the learning of efficient nonlinear data representation. Similar to the PCA, the encoder provides a low-dimensional representation of sophisticated features. The decoder maps the low dimensional space to the original data space. The combination of Encoder and Decoder completes the reconstruction of the data. We proposed Encoder-GAN as a Deep architecture based on the WAE framework . It accuEncoder-GAN was developed based on autoencoder architecture. It made two improvements. Firstly, since the goal is to learn the accurate representation of the raw data and then perform robust cluster analysis, but not to reconstruct the raw data, the decoder of Encoder-GAN responsible for data generation is simplified to the linear regression model. By downgrading the decoder structure and minimizing the reconstruction error, the representation capability of the encoder achieved maximum enhancement. Secondly, we introduced a discriminator to the encoder of the network (different from the design of most GANs which enhance the ability of the decoder). In the related research of GAN, the min-max game is usually between discriminator and decoder to strengthen the decoder's ability to generate samples from the random distribution. However, since our model does not need the data generation, but aim to improve the low-dimensional representation with the encoder, inspired by the idea of the WAE framework, we lead a min-max game between discriminator and encoder. The role of the discriminator in Encoder-GAN is to distinguish whether the latent distribution matches the prior. Encoder-GAN emphasizes that the latent variable's distribution should close to the prior, thereby improving the accuracy of the coding network.x and transfers them to the latent representation z. At the same time, the decoder uses the input of z and transfer it to the reconstructed signal x':The encoder takes the input signal of Q(z|x) is the density function of the encoder, and G(x'|z) is the decoder's density function. We used the squared loss LREC to minimize the Euclidean distance between x and x' and enhance the representation performance of the encoder:Where z and assumed that it follows to the prior distribution P(z). Let D be the discriminator function, z' be the positive points that sampled from the P(z), and z be the negative points from the output of encoder Q(z|x). The discriminator is trained with encoder together to distinguish z and z'. We used the min-max game-based adversarial training to update the parameters of encoder Q and discriminator D simultaneously:Next, we structured the latent variable P(z) and the encoder outputs from the posterior distribution Q(z|x). The encoder Q is learned to make the encoded output as close as possible to the prior P(z). With the adversarial learning, the performance of the encoder and the discriminator was improved. We used the loss functions of the two networks to facilitate the solution using the gradient algorithm and combined them as the loss function of the GAN. This training process aims to minimize adversarial loss:The discriminator D is learned to distinguish the samples from the prior distribution LD is the loss of the discriminator, LG is the loss of the decoder, and LGAN is the loss of the GAN. Next, the GAN process is combined with the reconstruction process:Where 1 is the weight of LGAN, and λ2 is the weight of LREC. For each iteration of the training. The model parameters of the discriminator are updated based on LD by descending:Where λLGAN and LREC to update the model parameters of Q and G by descending:Since the encoder network participates in the two modules: reconstruction and prior regularization, we combined The network structure of Q, G, and D are shown in H with a mixture of finite Gaussian distributions. For the patient with index n, the feature input for the model is denoted as hn. Let M be the number of mixture components, and pi be the density function of the ith Gaussian distribution. The density function of the model takes the form:We used the latent factors extracted from the Encoder-GAN as the input for the clustering component of our framework. The clustering procedure is based on the Gaussian Mixture Model (GMM). The model can be thought of as a generalized form of the k-means clustering. Relative to the hard decision of the K-means, it supports to generate the probability that each patient belongs to a different subgroup. Let μ and Σ are the mean and covariance of the Gaussian distributions, π =  is the weight of the Gaussian component of the model. In the training process of the model, the parameters Where ing data . After tWe collected ATAC-seq data and RNA-seq data from the TCGA project. The ATAC-seq data set includes 23 types of cancer of the TCGA Pan-Cancer Atlas  The change of the discriminative loss and generative loss of GAN in each cluster during the training process. B) The change of the Davis-Bouldin index during the training process.(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(A) Kaplan Meier survival plot showing that clusters significantly have different survival patterns. (B) t-SNE visualization on the extracted 200 features from the model.(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S5 Fig(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.S2 Text(DOCX)Click here for additional data file.S3 Text(DOCX)Click here for additional data file.