Alternative splicing is a biological process during gene expression that allows a single gene to code for multiple proteins. However, splicing patterns can be altered in some conditions or diseases. Here, we present BANDITS, a R/Bioconductor package to perform differential splicing, at both gene and transcript level, based on RNA-seq data. BANDITS uses a Bayesian hierarchical structure to explicitly model the variability between samples and treats the transcript allocation of reads as latent variables. We perform an extensive benchmark across both simulated and experimental RNA-seq datasets, where BANDITS has extremely favourable performance with respect to the competitors considered. Alternative splicing plays a fundamental role in the biodiversity of proteins as it allows a single gene to generate several transcripts and, hence, to code for multiple proteins . Howeverplug-in approach by inputing transcript estimated counts (TECs) and treating them as observed counts, thus neglecting the uncertainty in the estimates. In an attempt to mitigate this issue, rats [G test based on the multinomial distribution, which assumes all biological replicates to share the same relative transcript abundance.A significant challenge of DTU, and in general of DS, is that transcript-level counts (i.e. the number of RNA-seq reads originating from each isoform), which are of primary interest, are not observed because most reads map to multiple transcripts . Quantification tools  such as ue, rats  inputs TdiffSplice function) [Instead of considering TECs, some methods, such as DEXSeq  and limmunction) , performunction) . An alteunction) , 18 propunction) , 19, whiIn order to overcome the limitations of current methods for DTU, we present BANDITS , a R/Bioconductor package to perform DTU between two or more groups of samples, based on RNA-seq data. BANDITS uses a Bayesian hierarchical model, with a Dirichlet-multinomial structure, to explicitly model the sample-to-sample variability between biological replicates, which allows each sample to have distinct transcript relative abundances due to biological variability. BANDITS inputs the equivalence classes and respective read counts, and treats the transcript allocations of reads as latent variables, i.e. as parameters that are sampled, jointly with the model parameters, via Markov chain Monte Carlo (MCMC) techniques. In this way, our method models the uncertainty arising from reads mapping to multiple transcripts . ECCs can be obtained by aligning reads either to a reference transcriptome, with pseudo-aligners Salmon  and kallDespite the abundance of DS methods available in the literature, BANDITS introduces some unique features and, in both simulation and experimental data analyses, shows very favourable performance with respect to all the competitors we considered. Additional file K transcripts and N samples  from a given group. We define the latent vector of transcript-level counts for the ith subject as kth transcript in the ith sample, with Xi), where kth transcript, for with where δ+. If precision estimates are not computed, all δk parameters follow a vaguely informative prior distribution (see the “Methods” section).In order to exploit the information from other genes, we take advantage of DRIMSeq  to inferSince most reads map to multiple transcripts, transcript-level counts are typically not observed directly. BANDITS inputs, for every gene, the equivalence classes of transcripts and respective counts, while the transcript-level counts are treated as latent variables and are sampled together with the model parameters (see the “Methods” section). In ECs with transcripts from more than 1 gene, the gene allocation of reads is also treated as a latent variable and sampled within the MCMC scheme .After discarding an initial ity test . To avoilk being the effective length of the kth transcript, for kth isoform, while the denominator term is a scaling factor to ensure that We introduce a conceptual distinction between the probability that reads map to a transcript, which depends on the transcript length, and the probability that a gene expresses a transcript. While the former parameter is typically used to test for DTU, we argue that the latter should be employed instead, because it reflects the number of transcripts expressed by a gene, independently of their length. We use the mean relative abundance of transcripts, mentclass2pt{minimA and B, with average transcript relative expression, for the kth transcript, After inferring the model parameters, we test for DTU by comparing ω and D denotes the input data (i.e. the ECCs); and μ and covariance Σ. In order to test for DTU at the gene level, BANDITS performs a multivariate Wald test [ω, to test the set of hypotheses  control is obtained by adjusting p values via the Benjamini-Hochberg correction [Our method also can unravel the specific transcripts that are DU by testing, for the rrection .BANDITS also outputs conservative gene- and transcript-level scores, as well as a measure of the strength of DTU (see the “Methods” section). Furthermore, our method also allows to test for DTU between 3 or more conditions . We will refer to the former and latter datasets as “3 vs. 3” and “6 vs. 6”, respectively. Further details about the simulations are reported in the “Methods” section, while a description of differential analyses and software versions can be found in Additional file By editing Soneson et al.’s  pipelineAs a third scenario, we considered the 6 vs. 6 simulation and filtered transcripts, before the differential analyses, based on Salmon estimated counts: we kept transcripts with least 10 counts  and an average relative abundance of at least 0.01.diffSplice function), rats, and SUPPA2. We also consider the conservative gene- and transcript-level scores from BANDITS, BANDITS_inv, and BANDITS_maxGene (see the “Methods” section), as well as the ones from BayesDRIMSeq and cjBitSeq that we call BayesDRIMSeq_inv and cjBitSeq_inv. For cjBitSeq transcript-level test, we used the probability that a transcript is not differentially used; note that this does not guarantee FDR control. Genes and transcripts with less than 20 and 10 estimated counts , respectively, are excluded from figures and tables.We benchmarked BANDITS against several competitors: BayesDRIMSeq, cjBitSeq, DEXSeq, DEXSeq on ECCs (DEXSeq_ECCs), DEXSeq on TECs (DEXSeq_TECs), DRIMSeq, limma . TPR vs. FDR curves are visible in Additional file We also applied the previous DTU models to two RNA-seq experimental datasets. First, we studied the human data from Best et al. , 34, conFigure p value below 0.01, which we define as our “validated GO set”. We then inferred enriched GO terms based on the significant genes from each method, i.e. with FDR below 0.01 and 0.05, and selected the most significant 338 GO terms. The fractions of terms in the “validated GO set” captured by BANDITS and BANDITS_inv are amongst the highest and vary between 0.32 and 0.35, while for the other methods, this value ranges between 0.14 and 0.35  analysis based on the 82 validated genes and obtained 338 out 22,911) enriched GO terms with a 1 enricheMoreover, to add biological perspective, in Additional file We further considered a second human experimental dataset . Here, wp values, while Additional file Figure We performed a computational comparison of all the methods considered in the 6 vs. 6 simulation study, with and without transcript pre-filtering. Analyses were run on 12 cores, when parallelization was allowed, on our Opteron 6100 server.dexseq_count.py function which translates the genomic alignments of reads into exon bin counts. On the opposite side, DEXSeq_TECs and DRIMSeq, which use transcript estimated counts, are the fastest methods to run. Overall, BANDITS is significantly faster than cjBitSeq, DEXSeq, and limma, but slower than DEXSeq_ECCs and than tools using TECs; nonetheless, BANDITS has a 3 time speed-up when pre-filtering transcripts, bringing it close to DEXSeq_ECCs. Considering this significant computational gain, and the improved performance obtained when pre-filtering transcripts .Figure Note that, except cjBitSeq, DEXSeq, and limma, the cost of alignment (via STAR) and quantification  is much higher than the cost of the differential analyses, making the overall cost of the full pipelines of these methods similar.Additional file To investigate how method performance is influenced by gene abundance, we stratified the results of the 6 vs. 6 simulation study, and of both experimental data analyses according to gene expression, by grouping genes into lowly (first tertile), medium (second tertile), and highly expressed (third tertile).In the simulation study  allocations of reads as latent variables; model parameters and latent variables are sampled via MCMC techniques. We designed benchmarks, based on three simulation studies and two experimental data analyses, where we compared BANDITS against the most popular methods for differential splicing. Results highlight BANDITS strong performance and provide a comprehensive guide for users interested in choosing a tool to investigate DS.A limitation in common to all methods considered is to rely on an annotated transcriptome , which may lead to inaccurate inference in case of misannotated transcripts and genes ; this phFinally, we note that BANDITS, although developed with a focus on RNA-seq data, can also be applied to long-read sequencing data. Soneson et al.  found thWe presented BANDITS, a novel Bayesian method to investigate differential splicing from RNA-seq data. At present, our tool is the only method that jointly models the variability between biological replicates, by allowing for sample-specific proportions, and the mapping uncertainty of reads, by sampling their transcript (and gene) allocations. BANDITS is also the first DS tool to correct for the transcript effective lengths, allowing it to recover the actual probability of expressing a transcript. Our method tests, both, genes and transcripts for DS and allows comparisons between more than two groups. We also introduce a measure of the DTU strength, which can be used as an alternative way to rank genes.In all simulation and experimental datasets analysed, BANDITS has extremely favourable performance and exhibits good FDR and excellent FPR control. Furthermore, despite requiring full MCMC inference, it is computationally competitive, particularly after applying reasonable expression level filters.Finally, BANDITS is released as a R/Bioconductor package, which makes it easy to update, distribute, and integrate within existing data analysis pipelines.δ1,...,δK are positive, we sample them and formulate their prior in the logarithmic scale, a common choice to improve mixing of positive parameters.Since the Dirichlet parameters prior_precision function), we specify a vaguely informative prior distribution for the logarithm of the Dirichlet parameters: If gene-wise precision parameters are not computed (via log(δ+) as: K−1 Dirichlet parameters a priori are distributed as follows: δ+ is equally distributed across the K transcripts. In order to obtain the prior distribution for log(δK), we apply the change of variable via the Jacobian transformation [Instead, if gene-wise precision parameters are available, we compute the mean and variance of their logarithm, ormation .J equivalence classes available for a given gene as Cj indicates the list of transcripts present in the jth equivalence class. Note that ECs not supported by any read are not included in C. The number of reads compatible with Cj in the ith sample is denoted by Cj. We introduce the vector jth EC that were generated from the kth transcript in the ith sample, with k∉Cj.We define the set of Clearly, a is true, and 0 otherwise. Intuitively, πT(i) to ensure that reads are only allocated to the transcripts in Cj.where kth transcript by adding counts across ECs: Once EC reads have been allocated to the respective transcripts, we can compute the corresponding counts for the If an equivalence class has transcripts from more than one gene, the probability vector mentclass2pt{minimburn-in (minimum 2000), as well as the number of iterations the MCMC is run for after the initial burn-in .BANDITS users can specify an initial number of iterations to discard as burn-in, BANDITS performs Heidelberger and Welch (HW) stationarity test [log(P(δ|π))∝log(P(π|δ))+log(P(δ)), by adding the log-posterior densities from all groups and performing a global convergence diagnostic test. A wider burn-in is removed, if estimated via HW test; moreover, if HW stationarity test is rejected at the 0.01 significance threshold, the full MCMC output is discarded and the algorithm is run again (up to three times).To ensure the posterior chains have reached convergence, after discarding the pre-specified ity test  on the mp value below 0.1, BANDITS runs a second MCMC chain and, after removing the burn-in, recomputes the outputs based on the aggregation of the two chains.Furthermore, when a gene-level test has a K equations are linearly dependent, we only need to test K−1 parameters; hence, we rewrite the system of hypothesis as:For every gene, we test the system of hypothesis : since ta degrees of freedom, and bT and b−1 indicate the transpose and inverse of b, respectively. In order to choose the transcript to remove from the test, k′, we considered several options: randomly drawing one of the K transcripts, the transcript with the smallest expression, the isoform with the smallest difference between conditions, and averaging the p values obtained from all K possible choices of k′. After benchmarking all four approaches, we choose the last one, because in our simulation studies it provided the highest sensitivity and best FDR control (data not shown).where y normal , with meald test  based onkth transcript: ωk, inferred from the posterior chains.Similarly, we test for differential usage in individual isoforms, by considering the system of hypothesis for the Additional file p value, defined as p values; in this way, a transcript can only be selected if the corresponding gene is also significant.We propose two conservative scores for gene- and transcript-level testing. The former is inspired by work from Papastamoulis and Rattray , where tNote that in the Best et al. experimental data analysis, 41% of the validated genes are inferred to have distinct dominant isoforms between conditions, while this value decreases to 17% when considering non-validated genes; this fact seems to empirically justify our intuition of moderating Papastamoulis and Rattray’s inversion criterion.DTU_measure, to measure the intensity of the differential usage change between conditions, similarly to fold changes in differential expression analyses. Given a gene with K transcripts and estimated mean relative transcript abundance A, and B, DTU_measure is defined as the summation of the absolute difference between the two most expressed transcripts: A and a different transcript is always chosen in group B.For two group comparisons, we also propose a score, called The 3 vs. 3 simulated data is taken from Soneson et al. ; when siIn both cases, reads were simulated via RSEM  using thrsem-calculate-expression module) was used on the human sample SRR493366 (http://www.ebi.ac.uk/ena/data/view/SRR493366) to estimate transcripts per million (TPM), which were then scaled to have 40 million reads for each sample and, via a mean-dispersion relationship derived from real data [First, RSEM .rsem-simulate-reads module, 101 bp paired-ended fastq files for each sample.Finally, the simulated transcript counts were used as input to RSEM to simulate, via We performed a computational benchmark of BANDITS, based on the 6 vs. 6 simulation, to investigate how computational times scale with respect to the sample size. We selected 2 and 3 samples per group and ran BANDITS on a 2 vs. 2 and 3 vs. 3 group comparison. In all cases, 12 cores from our Opteron 6100-based server were used, and the same transcripts were pre-filtered, based on the transcripts selected from the 6 vs. 6 analysis.The computational cost scales less than linearly as the sample size increases: BANDITS took 43.5 min when using 2 samples per group, 50.5 with 3, and 58.8 with 6.Additional file 1 Methodological details, additional tables and figures.Additional file 2 Review history.


A decade ago, the advancements in the microbiome data sequencing techniques initiated the development of research of the microbiome and its relationship with the host organism. The development of sophisticated bioinformatics and data science tools for the analysis of large amounts of data followed. Since then, the analyzed gut microbiome data, where microbiome is defined as a network of microorganisms inhabiting the human intestinal system, has been associated with several conditions such as irritable bowel syndrome - IBS, colorectal cancer, diabetes, obesity, and metabolic syndrome, and lately in the study of Parkinson’s and Alzheimer’s diseases as well. This paper aims to provide an understanding of differences between microbial data of individuals who have been diagnosed with multiple sclerosis and those who were not by exploiting data science techniques on publicly available data.This study examines the relationship between multiple sclerosis (MS), an autoimmune central nervous system disease, and gut microbial community composition, using the samples acquired by 16s rRNA sequencing technique. We have used three different sets of MS samples sequenced during three independent studies , ,  and this approach strengthens our results. Analyzed sequences were from healthy control and MS groups of sequences. The extracted set of statistically significant bacteria from the  dataset samples and their statistically significant predictive functions were used to develop a Random Forest classifier. In total, 8 models based on two criteria: bacteria abundance (at six taxonomic levels) and predictive functions (at two levels), were constructed and evaluated. These include using taxa abundances at different taxonomy levels as well as predictive function analysis at different hierarchical levels of KEGG pathways.%) and the third hierarchical level of KEGG pathways (70.95%). The second dataset’s 18 MS samples  and 18 self-reported healthy samples from the  dataset were used to validate the developed classification model. The significance of this step is to show that the model is not overtrained for a specific dataset but can also be used on other independent datasets. Again, the highest classification model accuracy for both validating datasets combined was obtained at the genus level of taxonomy (70.98%) and third hierarchical level of KEGG pathways (67.24%). The accuracy of the independent set remained very relevant.The highest accuracy of the classification model was obtained at the genus level of taxonomy . As science is increasingly evolving into a multidisciplinary field, the intersection of several scientific subjects is considered to be the area of potential scientific contribution. The research of the microbiome and the relationship of microbiome with the host organism is one of the scientific fields that demonstrates a swift growth in development . This waMicrobiota is defined as a network of harmonious and pathogenic microorganisms that are present on and inside the humans. Besides the gut microbiota, the nasal, oral, skin and vaginal microbiota have been examined extensively . The intThe association with the human host of majority of microbiota bacteria is either commensal or beneficial, thus considered to be non-pathogenic . BacteroThe makeup of the gut microbiota can be studied using the fecal samples that are collected in a non-invasive manner. Since the methods for collecting samples from other segments of the gastrointestinal system are characterized as invasive, this might suggest that the study of the gut is limited . NeverthThe 16s rRNA gene has been used as the key identifier for the classification of microorganisms that reside in the human gut since the mid-1980s . This geThe gut microbiota and the human host have a symbiotic relationship. Dysbiosis may occur when commensal bacteria are outnumbered or replaced by pathogenic once. Various intersample variance studies of gut microbiome report potential for utilization of these results for sample classification. The links have been discovered between an imbalance in gut microbiota and various diseases such as colorectal cancer, inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS), diabetes, obesity, metabolic syndrome, malnutrition, and rheumatoid arthritis –13. In tThis study aims to explore the differences between gut microbiome samples (obtained using 16s rRNA sequencing technique) of individuals with and without MS and use those differences to develop a computation model that would likely distinguish those two groups of samples.The paper aims to devise a computational model that will discriminate healthy and MS condition samples. We use data science and machine learning techniques to develop this model. This process starts with intersample analysis and identifications of characteristics of healthy versus MS samples which are based on taxonomical and functional analysis. The discovered variance is used to develop a classification model (based on machine learning techniques) that can be used to identify, with a high probability, if a new sample is with or without MS condition. This approach can be applied to the disorders (other than MS) by building a similar model. This would involve a new intersample analysis and development of a new classification model.development of the classification model based on predictive functions,validation of the model by testing its classification power on independent sets of gut microbiome data (data even coming from different cultures).The work in this paper is continuation of the work published in . BesidesWe identified several studies that explored gut microbiome and MS, and therefore potentially have data that could be used in our analysis –25. All In this study we used three datasets. The modelling dataset  was usedFor validation purposes, a new dataset was constructed. The samples of self-reported healthy individuals were taken from the United States population constructed in the American Gut project  and repoThe 16s rRNA sequencing method was used to obtain data sequences analyzed in this study. The first set of samples was obtained by . In the tt_ind_solve_power method from statsmodels Python library. The inputs to this function were: effect size (the difference between the two means divided by the standard deviation), alpha value (significance level set to 0.05) and expected power of 80% [Even though the total number of female and male samples in the dataset is known, the individual samples did not have gender labels. This is very unfortunate as this variable has a great potential in the analysis of MS samples. In the data cleansing phase, the samples of individuals already treated for MS were removed, thus we kept only samples of individuals that received no MS treatment. The number of healthy samples to be used was calculated so the statistical power of the t-test is maximized. For this purpose, the r of 80% . Once thWhen the data cleaning phase was complete, the final counts were 28 for MS samples and 35 for control.For verification purposes, we considered two additional datasets. The idea here is to test whether the computational model is dataset specific or it is also successful in classification on additional independent datasets. The 18 samples of individuals with MS disease were obtained from the dataset introduced in , and 18 GreenGenes database at 99% identity (version 13_5). Each identified OTU found in the database, with taxonomy resolution starting from the phylum to the species level, has been assigned a name. Using the database names instead of the OTU identifiers, enables easier data understanding, as the phylogeny of the specific bacteria is identified. The resulting table, containing the sample’s count of each specific bacteria, is outputted from the pipeline. The absolute abundance of bacteria was normalized and used for the intersample variance analysis.The sequences were identified and quantified using the pipeline developed with QIIME2 tools . The pipBesides bacterial identification, another available information about the gut microbiome is a predictive functional analysis of the present bacteria. In this paper, we explored the usage of this information to identify the intersample variance as well.To predict the functional composition of a metagenome using 16s marker gene and reference genomes database, the PICRUSt (phylogenetic investigation of communities by reconstruction of unobserved states) computational tool has been used. PICRUSt uses a reconstruction algorithm to predict the gene families present and combines them to compose the whole metagenome. This ‘predictive metagenomic’ approach provides useful insights into the functional links between the phylogeny members .train_test_split method, the data sets were split into training and testing sets. In our implementation, 80% of data is allocated for training, and 20% is allocated to be used for model testing. The reported scores are averages after a 5-fold cross-validation was performed. We generated 100 decision trees. With the increase in the quantity of decision trees, the classifier’s accuracy was increasing as well, at the expenditure of the computational time. The accuracy of the model was calculated by the comparison between the model’s predicted target data and the actual target data. The identical process was iterated on data from six taxonomic levels and two hierarchical levels of predictive functions separately.Training of the classifier and the development of the classification model was accomplished using the Random Forest (RF) classification algorithm. This decision is based on research reported in . This suThe feature importance scores were computed for all the study variables and used for classification model verification. Furthermore, the highest importance features were extracted and applied as the new data for the repeated training the classifier. The accuracy was compared to the initial iteration to ratify that this data forms a robust initial basis for the decision model development. Figure As previously stated, we developed a classification model for each of the six taxonomical levels. The pipeline described in “In this case, each of these taxa is a potential feature that can be utilized in the classification model for assigning samples to the appropriate groups. The standard procedure that is applied in machine learning is feature selection and its aim is to remove irrelevant and redundant features. The model with the fewer features is more accurate if the most important features are selected. For feature selection we followed approach similar to one described in . In thist-test was applied and establish the taxa that had a statistically significant difference between the groups. We used p<0.05 as the cut-off value for the statistical significance. The identified taxa were the initial candidates for the features. The initial training of the classifier used all the candidate features.To initially select the classifier’s features, the independent % at the genus level of taxonomy which is a significant result.The RF classifier also produces the score for each of the used feature. The feature importance scores obtained from the initial training of the RF classifier were used to further restrict features for the final training of the classifier. The final list of features (taxa), for model training and testing at each taxonomical level, is presented in Table The tables from the PICRUSt analysis containing the KEGG pathways were used as the input for the statistical analysis. Two computational models were developed using the third and second hierarchical levels of KEGG pathways. The feature selection again was done in two steps that resulted in improved accuracy of the model. The first step of the analysis was to identify pathways that were statistically significant between the two groups. For this task, the t-test has been used since the data was normalized in the previous steps.p-value is p<0.04, were considered to be statistically significant for the classes and extracted to be used in the model training phase. At the third hierarchical level of functions, 91 functions were extracted as statistically significant. The second hierarchical level has also been tested using the t-test. There was a total of 41 functions related to both, healthy control and MS samples. All the functions whose p-value is p<0.05 were extracted, and the number of those was 20. The list of predictive functions was further restricted by only using the ones with the highest importance as identified by the initial run of the RF algorithm. The final list of the used predictive functions is listed in Table There were a total of 328 functions related to both, healthy control and MS samples at the third hierarchical level, meaning the lowest level gene function identifiers. All the functions, whose The functions identified as significant were used for the development of additional two predictive models . The training and testing data was split using the 5-fold cross validation technique. Multiple runs of the algorithms were run and each time The developed model was tested using two independent datasets. The first dataset  providedThe combined accuracy of prediction using the abundance of bacteria on different taxonomy levels is summarized in Table Even though our two validation datasets come from two different studies, the classification accuracy and its trends are very similar. This data demonstrates that classifier performance is robust to the batch effect.The purpose of this study was to study gut microbiota bacterial diversity of MS patients and develop a computational model to distinguish MS patients from the healthy patients by examining their gut microbiome sample 16s amplicon sequences. We developed eight such classifiers and validated their accuracy on an independent dataset.To develop classifiers, we needed to first identify the important features - those are the taxa (or predictive metabolic functions) that had a significant difference between the MS and control group. The comparison between our results and previous MS studies is summarized in Tables The classifiers based on the abundance of bacteria are more accurate than ones based on predictive functions. We need to be careful about drawing conclusions here as these results do not suggest that there exist less variance in the metabolic potential than in microbiome structure. The tool that we used (PICRUSt), uses microbiome structure to predict the metabolic potential and has limited accuracy. To fully understand the potential of the metabolic influence, we would need to use metabolomics.The accuracy rises as we look at the higher resolution of taxonomy. As we used samples obtained using the 16s rRNA sequencing technique, we expected the abundance to be accurate up to the genus level as this the limitation of this technique described in the literature . This isThe 16s rRNA sequencing technique distinguishes MS patients from healthy ones with over 70% accuracy using a test considered to be non-invasive. This value represents a significant value for medical decision making. Though this is a valuable contribution, further improvements can be made so that accuracy would be raise.Overall, this study considered 99 samples, which is a somewhat small number for data science. The mitigating factors are: only two groups were studied, classification was binary, and the sizes of the groups were approximately the same. More samples need to be considered to construct even more applicable and accurate results. The usage of the independent datasets for the validation of the model(s) strengthens our results greatly. With an independent dataset, we were able to achieve similar classification power and that confirms the relevance of the classification model amidst the sample size used. The availability of more samples of individuals with MS condition, as well as improvements in sequencing resolution, would possibly allow the creation of an even more accurate classification model(s).The human gut contains several sections that host microbial communities but exhibit different environments. The only non-invasive method accessible for collecting gut samples is via stool. Determining the bacterial origin and contributions of different sections of the gastrointestinal system can be difficult. Besides, there are many parameters that can influence intrasample variance such as different age, sex, ethnicity, genetic backgrounds, different diet habits, and life in different environments . In futuThe change of sample sequencing technique can introduce further improvements. The candidate technique is shotgun metagenomics sequencing. As this technique reports accuracy at the finer resolution of taxonomy than 16s, we would be able to continue accuracy improvement by using the models developed on a taxonomic level beyond the genus . This woThe findings presented here may be used to construct models that would distinguish medical conditions other than MS (i.e. Parkinson’s and Alzheimer’s disease).obtain a significantly sized set of samples for both condition and control groups,perform bioinformatics algorithms to extract desired information from samples (either bacteria abundance or predictive metabolic function),perform statistical analysis of obtained data to get a set of distinguishing features to provide as an input for the training of a classifier,train the classifier and use the results to conclude the list of features,use classifier to predict that an unknown sample is either with the condition or not.To apply the methods presented in this paper in other clinical domains, we would need to perform the following steps:This paper describes the process of developing a computational model that discriminates microbiome samples of healthy individuals and ones diagnosed with MS. In addition to performing intersample analysis based on taxa abundances, the intersample analysis of predictive functions in the human organism was also performed. The initial development of this work was started in . In thisSeveral previous studies report similar findings on significant taxa for MS –23. The While we demonstrated that a classifier can distinguish MS and healthy samples, we did not include samples of other cohorts in our study  so we have not evaluated how those cohorts would be classified in our model. For this reason, further studies in this direction are needed. In addition to increasing the accuracy, additional cohorts need to be studied. The results of this approach should be taken with caution and augmented with other diagnostics, especially for individuals who do not clearly belong to the two studied groups.We were limited by the dataset(s) that are currently available. We were not able to examine important variables that we have evidence that either affect MS condition or microbiome structure. Those variables include but are not limited to: age, sex, diet, BMI, geography, household microenvironment... In order to analyze these variables, we need datasets with the appropriate metadata and significant size for multivariable analysis. The availability of such datasets in the future could allow creation of even more precise computational models.


Drug response differs substantially in cancer patients due to inter- and intra-tumor heterogeneity. Particularly, transcriptome context, especially tumor microenvironment, has been shown playing a significant role in shaping the actual treatment outcome. In this study, we develop a deep variational autoencoder (VAE) model to compress thousands of genes into latent vectors in a low-dimensional space. We then demonstrate that these encoded vectors could accurately impute drug response, outperform standard signature-gene based approaches, and appropriately control the overfitting problem. We apply rigorous quality assessment and validation, including assessing the impact of cell line lineage, cross-validation, cross-panel evaluation, and application in independent clinical data sets, to warrant the accuracy of the imputed drug response in both cell lines and cancer samples. Specifically, the expression-regulated component (EReX) of the observed drug response achieves high correlation across panels. Using the well-trained models, we impute drug response of The Cancer Genome Atlas data and investigate the features and signatures associated with the imputed drug response, including cell line origins, somatic mutations and tumor mutation burdens, tumor microenvironment, and confounding factors. In summary, our deep learning method and the results are useful for the study of signatures and markers of drug response. Drug response in cancer patients vary dramatically due to inter- and intra-tumor heterogeneity and transcriptome context plays a significant role in shaping the actual treatment outcome. Here, the authors develop a deep variational autoencoder model to compress gene signatures into latent vectors and accurately impute drug response. Although such data were much valuable, it was based on preclinical features and suffered from limitations on small sample size and high heterogeneity. On the other hand, The Cancer Genome Atlas (TCGA) project4 has released comprehensive multi-omics datasets in 33 cancer types/subtypes. Unfortunately, the pharmacogenomics information is generally unavailable in these cancer datasets, leading to a strong gap between the cancer genomic studies and therapeutic responses at the pan-cancer level. To fill in this gap, several studies have attempted to infer drug response for TCGA data by developing various prediction models, including statistical models that link gene expression to drug response5 and lncRNA-drug prediction models using Elastic Net regression6.A systematic investigation of the association between cancer genomics data and their therapeutic possibilities can greatly aid the translation of theoretical tumor biology. Several large-scale pharmacogenomics projects have been conducted at the cell line level with many conditions, substantially advancing our knowledge of drug response. These projects include the Cancer Cell Line Encyclopedia (CCLE)9, e.g., BRAF V600E mutation11, or a few signature genes12. However, mutational status alone is insufficient to determine the oncogenic state of a given cancer sample or recommend the appropriate therapeutic choice. This issue becomes worse for those drugs with general cytotoxicity or having multiple molecular targets. Second, cancer heterogeneity is a well-known factor contributing to the great variability in drug response in cancer patients. For instance, in lung cancer with KRAS mutations, the epithelial–mesenchymal transition (EMT) program has been shown to underlie the variability in response to KRAS inhibitors13. The transcriptional context may play deterministic roles in shaping the response to inhibitors and should be included in the prediction model. Third, the measured drug response data from cell lines are typically noisy and sometimes biased for certain drugs. Notably, cancer cell lines have their own lineage origins, but many existing methods train their models without distinguishing specific types of cell lines. For example, for the drugs whose targets are predominantly mutated in solid tumors, training a prediction model using both hematopoietic cell lines and cell lines with solid tumor origins may reduce the power, when compared to the models trained using only the latter type of cell lines. There are many factors that may influence the accuracy of prediction models for drug response. It is important to use the input from a number of informative molecules with potential contributing roles rather than a few targeted genes.In silico prediction  of drug response currently remains challenging due to several factors. First, the selection of targeted therapies on specific oncogenes in clinical practice has been limited to only a few specific mutationsIn this work, we developed a deep-learning approach for drug response prediction based on deep regenerative models. By applying the variational autoencoder (VAE) framework, we generated representative models for >1000 cell lines using their baseline expression profile and trained prediction models for drug response based on the latent representation of the baseline expression profiles. Rigorous quality assessment and validation were implemented, including cross-validation, multiple replications, and cross-panel evaluation. Because the models were built on a baseline gene expression profile, they are widely applicable. We thus applied the models to large cohorts such as the TCGA pan-cancer samples with gene expression profiles and conducted association tests to infer various drug–molecule associations, including somatic mutations, copy number variants (CNVs), gene expression, and drug-pathway associations. The results revealed the landscape of molecular features in association with drug response in 33 cancer types that were beyond the information obtained by using the typical cell line models.50 data from CCLE and GDSC  and we did it for each measured compound. Conceptually, the observed (measured) drug response can be decomposed into the expression-regulated component , the component by other explanatory elements , and the component due to uncertainty   Fig. . The comn = 6163). A parameter sweep was conducted to determine several key parameters that would affect the resultant models, including the latent size, learning rate, batch effect, and the number of epochs. We used the loss between the original data and reconstructed data to evaluate the fitted model. As shown in Supplementary Fig. 15 to represent the data and observed major clusters organized by cell lineages. As shown in Fig. The original cell lines from the CCLE and GDSC projects represented a wide spectrum of cell line lineages, such as the epithelial, mesenchymal, and hematopoietic background. In our VAE model, we included 1100 cell lines from 19 tissues of origin, with each tissue being required to have at least 20 cell lines. We also filtered for genes that were most variably expressed across cell lines to build the VAE models , the one with the highest average holdout R2 was selected for each drug in the following analyses.The VAE model itself was fitted to achieve a compressed representation of the input data but it was not tailored toward a better prediction of drug response. Unlike the traditional principal component analysis (PCA), the compressed representation from VAE is not unique. With the same input matrix, there could be numerous matrices to faithfully represent the input data, each of which might be slightly different from others and individually serve better to one or some drugs than the other drugs. Hence, we generated 100 VAE models to build a pool for the following drug response imputation. For each drug, we applied an Elastic Net (EN) strategy using the latent vectors from each VAE model to train the VAEN prediction models. We used a fixed alpha of 0.5 and conducted fivefold cross-validation to select lambda. We then evaluated the selected latent vectors  using tenfold cross-validation by a standard multivariate linear regression. The average coefficient of determination R2 to evaluate the model stability. As shown in Supplementary Table We tested several strategies to preprocess the original gene expression data before inputting them into the VAE models. Specifically, we tested 1) z-score normalization of each feature across samples and shrinking each feature to the  range (hereafter shortened as Z01); 2) z-score normalization of all genes for each sample (ZS); and (3) rank-based inverse normal transformation (Rank). We also tested using the sigmoid activation function or the ReLU activation function in VAE, resulting in six types of VAE models. We applied tSNE to visualize the compressed latent matrices. As shown in Supplementary Fig.  z-score  z-score 50 and GDSC IC50 (PCC ranged between 0.10 in erlotinib and 0.58 in PF2341066) was not as strong as that between CCLE ActArea and GDSC LN_IC50 (PCC ranged between 0.17 in PD-0332991 and 0.75 in nilotinib). Therefore, we used CCLE ActArea to represent the drug response. In the comparison of the predicted drug response among the cell lines, we found a positive correlation between the observed and predicted drug response  in ≥20 cancer types  followed by the same Elastic Net-based regression models (named PCA + EN). As shown in Supplementary Fig. To compare with standard methods, we also used the raw expression of the 6163 genes, instead of VAE compression, to train prediction models following the same Elastic Net strategy (hereafter named the gene + EN model). Similar strategies have been reported previouslyR2 when they were trained by using solid cell lines only, while PLX4720 received both increased self PCC and average holdout R2  and the other using all but hematopoietic cell lines . Among the 24 CCLE drugs, we found four drugs  showed improved average holdout n = 1100) and TCGA samples . We predicted the drug response for each sample for each of the 24 CCLE compounds and each of the 251 GDSC compounds. Here, we used CCLE to demonstrate the model performance. There were three drug-response profile sets for each compound: the observed response in a limited number of cell lines  = 9.81 × 10−9, P(set 2) = 6.88 × 10−10, P(set 3) = 1.08 × 10−14), PD-0325901 (P(set 1) = 2.41 × 10−7, P(set 2) = 2.61 × 10−8, P(set 3) = 2.07 × 10−16), PLX4720 (P(set 1) = 8.55 × 10−6, P(set 2) = 1.36 × 10−8, P(set 3) = 1.08 × 10−11), and RAF265 (P(set 1) = 6.00 × 10−5, P(set 2) = 3.82 × 10−6, P(set 3) = 1.21 × 10−4) , the middle half (Q25-75), and the higher quarter of samples (Q75) ordered by increasing EGFR expression). We found an increased EGFR expression led to a reduced response to BRAF inhibitors in BRAF mutant samples but not in BRAF wild-type samples, consistent with previous studies that EGFR activation enhances resistance to BRAF inhibitors18. Such a conditional association was confirmed in all three sets of drug responses to all four compounds with which BRAF showed association . In GDSC, we used -LN(IC50) as the measurement of drug response, and thus, the predicted values had the same trend as ActArea, i.e., a high predicted value indicates a high sensitivity. We then conducted an enrichment analysis of the samples from each cancer type in the sensitive or the insensitive groups (Fisher’s exact test). As shown in Fig. 50) of TCGA samples using the GDSC model), there was a high proportion of samples with sensitivity to PLX4720 in SKCM  and UVM . All these enrichment patterns were replicated by both CCLE and GDSC models. Further investigation of cell lineage and cancer types versus drug response also revealed patterns that were consistent with previous reports19  as well as using the GDSC model (P = 2.52 × 10−9). In CCLE, two MET inhibitors were measured (PF2341066 and PHA665752), both of which showed significant association with increasing MET expression . The predicted response to each of these compounds was also significantly associated with MET expression or MET amplification . This is likely due to the complex tumor microenvironment and confounding factors such as co-occurrence of mutations, CNVs, and other omics-level complications.Further validation using the drugs with well-annotated targets confirmed the high quality of the predicted drug response. These included lapatinib, MET inhibitors , BRAF inhibitors (PLX4720 and RAF265), and several MEK inhibitors. As shown in Fig. ion Fig. . For MEK20. We conducted survival analyses of these samples by stratifying the samples using the predicted response. As shown in Fig. P = 0.0023) than that with low predicted response. BRCA samples treated with paclitaxel showed marginal significance  = 2.30, Fig. ETV4, ETV5, DUSP6, and SPRY2 were all among the 50 most positively associated genes with AZD6244 sensitivity. EMT genes were previously reported as predictors for erlotinib response21. In our results, we found a significant association between EMT genes with the predicted erlotinib response . For AZD0530 , we found a previously reported gene set for SRC signaling22, which consisted of eight genes, was significantly associated with the drug in CCLE  and marginally significant by the GDSC model . It is worth noting that, although the drug response profiles were initially imputed using gene expression data at the low-dimensional space , there were several steps during the data encoding and decoding processes, including nonlinear transformations. Thus, there were no data circulation problems.To further validate the imputed drug response, we collected previously reported gene signatures for drugs. To examine the association between the gene expression of these signature genes and our imputed drug response, we fitted linear regression models. For each gene in each cancer type, we split samples into three groups according to the gene expression: samples with gene expression in the lower quarter of the expression range (Q25), samples in the middle half of the expression range (Q25–Q75), and samples in the higher quarter of the expression range (Q75). We fitted linear regression models by taking the group variable as a quantitative type such that the model evaluated the trend of the drug response changing across the three groups of samples (similar to a trend test instead of ANOVA). GSE33072) was part of the Biomarker-integrated Approaches of Targeted Therapy for Lung Cancer Elimination (BATTLE) trial21. We used the subset of samples treated with erlotinib and predicted their response to the drug using our VAEN models. As shown in Fig. P = 0.0047) and the CCLE S-model (P = 0.0088). Using GDSC models, the high-response group showed marginally significant improvement in GDSC A-model (P = 0.058) and GDSC S-model (P = 0.056). As a comparison, the PCA + EN models failed to distinguish the samples. The gene + EN model trained using GDSC showed nominal significance but the model using CCLE failed to show the difference  included 68 non-small cell lung cancer (NSCLC) cell lines plus one normal lung cell line, all of which were profiled using the Illumina HumanWG-6 v2.0 expression beadchip21. Note that this dataset was generated by the microarray platform. However, because our model used rank-based normalization, we could conveniently transform the data and made predictions using the trained VAEN models. These cell lines have been studied to develop a 74-gene EMT signature. We repeated the original study and separated these cell lines into an epithelial-like subgroup (n = 44) and a mesenchymal-like subgroup (n = 25). As shown in Fig. P = 6.88 × 10−11, CCLE S-model: P = 3.61 × 10−8, GDSC A-model: P = 6.59 × 10−5, and GDSC S-model: P = 4.39 × 10−4) showed a significant difference between the two subgroups and the epithelial-like group had favored response, which was consistent with literature reports. The third dataset (GSE65185) included 24 melanoma patients carrying BRAF V600 who were treated with vemurafenib23. We used the predicted response to PLX4720 to distinguish the samples by the median value. As shown in Fig. P = 0.031 using CCLE S-model and P = 0.0049 using GDSC A-model, Fig. R2 and was selected for the prediction, whereas in GDSC, the A-model showed better performance and was selected. In contrast, both the PCA + EN model and the gene + EN model failed to distinguish samples with different survival statuses  in all three datasets  in CCLE and the predicted drug response in TCGA samples. Figure Drugs with cytotoxicity were found associated with a higher tumor mutational burden (TMB). In CCLE, the cytotoxic compounds (irinotecan and topotecan) had similar drug response profiles and were often clustered together. We examined the TMB, measured as a log-transformed number of mutations per sample, in TCGA samples. For each drug in each cancer type, we defined the top quantile (25%) most responsive samples as responders to the drug and the remaining samples as non-responders. By comparing TMB in responders with non-responders in each cancer type , we found that in multiple cancer types, responders to cytotoxic compounds (irinotecan and topotecan) tended to have an increased TMB Fig. . This trP < 0.01 and difference of TMB > 0.2) were statistically enriched with DNA replication (P = 8.88 × 10−6) and cell cycle (P = 0.019)  were statistically enriched with EGFR signaling (P = 8.84 × 10−3) and PI3K/mTOR signaling (P = 0.037)  Fig. . On the 37) Fig. .28, LGG to three subtypes 29, and THCA to two subtypes (Braf-like and Ras-like)30 following the original marker publications of TCGA. A total of ~3000 candidate genes from 33 cancer types  were investigated for the gene–drug association (required to have ≥10 mutations in at least one cancer type). Notably, this analysis was conducted using deleterious nonsynonymous SNVs. Benign nonsynonymous SNVs and small insertions and deletions (indels) were excluded. To control the false discovery rate, we conducted multiple test corrections for each drug in each cancer. As a result, we observed 2343 sensitive gene–drug associations and 288 insensitive associations for the 24 CCLE compounds (Benjamini–Hochberg (BH) adjusted P, or PBH, <0.05)  and nominally significant in UCEC (raw P = 0.016). FLT3 was significantly associated with sorafenib in LAML (PBH = 2.03 × 10−5), a multikinase inhibitor, including FLT3. Association test of somatic mutations with GDSC compounds was also conducted  and also to reveal novel drug–gene associations. For somatic mutations, we examined their potential association with each drug in each cancer type using deleterious single-nucleotide variants (SNVs). Moreover, considering that some targeted drugs are quite specific on particular mutations in kinase domains, we conducted the association test for mutation clusters according to their locations in the protein sequence. We split three cancer types into subtypes to avoid strong associations due to subtype stratification: BRCA to four subtypes 05) Fig. . Among t2L2 Fig. . We alsoPBH < 0.2, after excluding associations involved in long genes, defined as those with >200 k bases), involving 116 clusters from 32 unique genes in 24 cancer types . Other clusters were also observed in EGFR, FGFR3, IDH1 (R132), PIK3CA, SF3B1, SPOP, and VHL . The samples with mutations outside of the investigated cluster were not included in this test, so the wild-type samples were free of mutations in the same gene. In total, we identified 723 cluster–cancer–drug associations   Fig. . HoweverEGFR was located, was mainly associated with kinase inhibitors in LGG, such as 17-AAG (P = 1.10 × 10−8) and RAF265 (P = 9.76 × 10−13). Chromosome 8, where the proto-oncogene MYC was located, was mainly amplified in BRCA and was associated with multiple drugs (P(17-AAG) = 1.76 ×10−9, P(AZD0530) = 4.62 × 10−11, P(irinotecan) = 3.26 × 10−13, P(TAE684) = 1.80 × 10−16, P(RAF265) = 3.67 × 10−21). Notably, the EGFR inhibitor, lapatinib, was significantly associated with ERBB2 amplification , which further confirmed the accuracy of our VAEN models. As for CNV loss, deletion of CDKN2A/CDKN2B on chromosome 9 was associated with multiple drugs in multiple cancer types. On chromosome 16, a region with the gene WWOX deletion in ESCA was significantly associated with multiple drugs (P(AZD6244) = 5.89 × 10−18, P(paclitaxel) = 3.31 × 10−17, P(PD-0325901) = 4.85 × 10−18, and P(TAE684) = 1.43 × 10−15). Notably, this region represents one of the most common chromosomal fragile sites and deletion of WWOX frequently occurs in multiple cancer types31. Hence, the driver event that leads to the strong association with drug response remains unclear.The gene–drug association due to copy number changes occurred in several chromosomes, especially chromosomes 3, 7, 8, 10, 12, and 17 Fig. . Amplifi32. Interestingly, we found that several of these TME cells were associated with drug response. First, cancer-associated fibroblast (CAF) genes and extracellular matrix (ECM) genes were negatively associated with lapatinib sensitivity, i.e., increased CAF/ECM gene expression was associated with insensitivity to lapatinib. Although lapatinib is a HER2 inhibitor and mainly used in the BRCA HER2 + subtype, this negative correlation trend was observed in many cancer types , P < 0.05 and NES < 0, GSEA). It stays in line with previous observations that response to the HER2 inhibitor is dependent on the microenvironment of tumor samples33, where stroma genes34 and CAF genes played roles in resistance to both chemo- and targeted therapies35. In our work, we found that many stroma genes were among the most negatively associated genes with lapatinib sensitivity. The two EMT-related genes, ZEB1 (t = −16.71) and SNAI2 (t = −13.94), were both negatively associated with the response to lapatinib.Drug response in cancer patients is much more complicated than in cell lines due to the complex tumor microenvironment, tissue composition , and co-occurrence and interaction among different levels of alterations. Tumor tissues are surrounded and infiltrated with cells that are collectively known as tumor microenvironment (TME). TME consists of extracellular matrix (ECM), fibroblast, neuroendocrine cells, adipose cells, and immune and inflammatory cells and is involved in nearly every step of tumorigenesis36 (referred as TIS genes). As shown in Fig. 17. Notably, these results for studying tumor microenvironment are hypothetical without validation and future work is needed to warrant the validity of the results.Second, we explored 18 genes that were previously reported with the predictive power of T-cell inflammationBRAF mutations showed no association with BRAF inhibitor PLX4720 in SKCM  or THCA . As reported previously17, in cell lines, response to BRAF inhibitors was found influenced by EGFR expression . For example, in SKCM, when the activity of “downregulation of ERBB2 ERBB3 signaling pathway” was included in the model, the interaction item was statistically significant (P = 1.79 × 10−5) and only in the Q25 subgroup, where the pathway showed low activity, that group with mutant BRAF was sensitive to BRAF inhibitors (P = 4.92 × 10−3) . In the Q25 subgroup where the pathway EGFR downregulation had low activity, the samples with mutant BRAF were significantly sensitive to BRAF inhibitors (P = 0.048). These results revealed that drug response in cancer samples is much more complex than in cell line models and many confounding factors could impact the response.In addition to the tumor microenvironment, tumor heterogeneity is also a factor to influences drug response. In examining the mutation–drug association, we noticed in some cases, the results were inconsistent with a priori knowledge of drug targets. For example, −3) Fig. . In THCAIn this study, we have built a variational autoencoder model to regenerate gene expression profiles in > 1000 cell lines. The latent vectors, the internal principal variables, were used to build prediction models for a total of 261 compounds from two pharmacogenomics projects (24 CCLE and 251 GDSC compounds). The imputed drug response profiles in 33 cancer types, augmented with comprehensive multi-omics data from TCGA, provided a useful resource to further explore genomic signatures that are associated with drug response.Our methods have many advantages and the results have made it possible to explore the drug response of cancer samples on a much fine scale. First, although the previous research has made great efforts to reveal genomic signatures associated with drug sensitivity and resistance, these results were mainly based on cell line models. An accurate prediction of drug response profile in cancer samples would enable the recapitalization of known signatures and novel signatures, whereas the latter ones have been often missed due to cell line models or sample size limitation. Second, our analysis of the classification of compounds based on their response profiles revealed unique groups and signatures. Although it has been previously noticed that targeting compounds designed for particular genes or mutations and compounds with general cytotoxicity belong to two generally different groups of drugs, our examination of TCGA data identified the association between drugs and TMB, which was infeasible from cell line models. Third, we explored many aspects to identify genomic signatures in a pan-cancer fashion, including somatic mutations, somatic CNVs, and gene expression. The positive associations between AZD6244 and the previously reported 18-gene signature proved the robustness and accuracy of our results. The negative associations between CAF genes were insightful for breast cancer therapeutic selection. In addition, we found several drugs were positively associated with increased tumor inflammation scores. This is promising, indicating that cancer samples with higher TIS  are more sensitive to those drugs, especially those BRAF inhibitors.R2 had the least overfitting, as evaluated by using the 14 shared drugs in TCGA samples that were predicted by CCLE models and by GDSC models (data now shown). Notably, we trained the model using the data within one panel only , and then evaluated the performance  using the data in the same panel. Thus, a model trained on one panel is independent of that trained on the other panel, even for the 14 drugs that were profiled by both the CCLE and GDSC panels. Second, we explored different normalization methods and activation functions to identify the model with the best performance yet with high generalizability. With rank-based normalization of gene expression data, our method can be applied to a wide variety of datasets, including microarray-based data, as shown in Fig. Our method also has several technical advantages. First, we trained 100 autoencoders to represent the original cell lines. This process provided a pool of latent vectors  for drug response. Notably, overfitting has been a widely recognized challenge in many machine learning and deep-learning applications. To overcome the potential overfitting problem, we used the out-sample PCC to select prediction models. We actually attempted to use the canonical measurements such as accuracy and F1 score. We found that using the out-sample R2). Two example drugs are 17-AAG and Paclitaxel. However, with limited data for validation, the PCA-based model for paclitaxel did not perform well to separate the pCR group from the non-pCR group. Therefore, future validation would be required to assess these prediction models. Furthermore, for some drugs, although we observed high prediction accuracy in the cell line models, their response in cancer samples was heterogeneous. We could explain the association between BRAF mutations and BRAF inhibitor PLX4720. However, for other drugs, such as erlotinib, we only observed a marginally significant association between EGFR and erlotinib, and we were not able to figure out the potential confounding factors. This could be due to mutations in other genes or other forms of mutations , which could complicate the response to erlotinib. Therefore, investigation of drug response in cancer samples is much more complicated and requires consideration of many contexture factors and covariates.There were several limitations of our work. For some drugs, we were unable to improve the prediction accuracy no matter how the models were fit, such as LBW242 . We downloaded RPKM values from RNA-sequencing (RNA-seq) data. Each cell line has its original cell lineage. The pool of tested cell lines could be matched to a wide range of cancer types, including both solid cancers and hematopoietic and lymphoid tissues. We excluded the lineages that had less than 20 samples. Our working dataset included 1100 cell lines from 19 cell lineages, which were used to build the VAE models. We selected the most variably expressed genes to construct the VAE models. Note that the same gene expression data in those cell lines were used for drug response prediction for CCLE and GDSC drugs.The Cancer Cell Line Encyclopedia (CCLE)1. We downloaded data from the CCLE  data portal, including mutations and drug response measured by the activity area (named “ActArea”). The GDSC project assessed drug response for 251 compounds using the same pool of cell lines. We downloaded the fitted dose-response file from the GDSC website . In this study, we used the log-transformed IC50 (LN_IC50) in the Elastic Net models, although AUC can also be used for the same purpose.The CCLE project assessed the pharmacological profiles of 24 anticancer compounds in 504 cell lines39. We retrieved the TCGA samples that had somatic mutations, CNVs, and mRNA expression data. The cancer type abbreviations are listed in Fig. All data were downloaded from the UCSC Cancer Genome Browser XenaAll RNA-seq data were transformed using the log2(RPKM + 1) format. Transformation based on rank-reversed percentile was used to preprocess the RNA-seq and microarray data.40 with a TensorFlow backend (version 1.0.1)41, was used to implement the VAE. The encoder is a process to encode the input vector with a mean vector and a standard deviation vector, respectively, followed by a nonlinear transformation, e.g., the rectified linear units (ReLU) or the Sigmoid activation (see main text). We defined the loss function as the mean squared error plus the KL loss. As for VAEN, a regression model was trained for each drug following a Elastic Net42 strategy with 5-fold cross-validation to select lambda. We used the average R2 in the holdout samples to select the model. CCLE models were trained using CCLE drug response and CCLE genomics data. GDSC models were trained using GDSC drug response and CCLE genomics data.We implemented a three-layer VAE model, with the input layer, encoder, and latent layer, decoder, and output layer Fig. . The pytDrug–gene associations based on somatic mutations were assessed using Wilcoxon rank-sum test by comparing the predicted drug response in the mutant samples versus the wild-type samples for each gene. Drug–gene associations based on gene expression were assessed using the Pearson correlation coefficient.Further information on research design is available in the Supplementary InformationReporting Summary


The relationship between the m6A modification pattern and immune cell infiltration in GBM remains unknown. Utilizing expression data of GBM patients, we thoroughly explored the potential m6A modification pattern and m6A-related signatures based on 21 regulators. Thereafter, the m6A methylation modification-based prognostic assessment pipeline (MPAP) was constructed to quantitatively assess GBM patients’ clinical prognosis combining the Robustness and LASSO regression. Single-sample gene-set enrichment analysis (ssGSEA) was used to estimate the specific immune cell infiltration level. We identified two diverse clusters with diverse m6A modification characteristics. Based on differentially expressed genes (DEGs) within two clusters, m6A-related signatures were identified to establish the MPAP, which can be used to quantitatively forecast the prognosis of GBM patients. In addition, the relationship between 21 m6A regulators and specific immune cell infiltration was demonstrated in our study and the m6A regulator ELAVL1 was determined to play an important role in the anticancer response to PD-L1 therapy. Our findings indicated the relationship between m6A methylation modification patterns and tumor microenvironment immune cell infiltration, through which we could comprehensively understand resistance to multiple therapies in GBM, as well as accomplish precise risk stratification according to m6A-related signatures.Glioblastoma (GBM) is a group of intracranial neoplasms with intra-tumoral heterogeneity. RNA N6-methyladenosine (m Glioblastoma (GBM) is the most common lethal neoplasm of the central nervous system, accounting for approximately half of primary brain tumors and almost 60% of all types of gliomas . Even afRepresented by immune checkpoint inhibitors (ICIs), Chimeric Antigen Receptor T-Cell immunotherapy (CAR-T), cancer vaccines, and oncolytic viruses, immunotherapy produces sustained killing of cancer cells by activating the patients’ own immune system. Since these immunotherapies reportedly produce durable effects on several cancers, these methods have also been applied to primary intracranial malignancies, including newly diagnosed and recurrent glioblastoma , 8. The 6A) RNA methylation is the most dominant form of epigenetic regulation, occupying approximately 0.3% of total adenosine residues  to calculate GBM patients’ m6A modification score (MMS). According to the MMS, we can further predict the clinical outcomes of GBM patients. Using the MPAP, we can determine m6A modification patterns in disease tissue by using only conventional bulk transcriptome data, which provides novel perspectives of GBM in an efficient and inexpensive way.Traditional bulk sequencing provides genetic information at the resolution of individual samples, thus there is a limitation whereby it cannot identify specific cells in the given tissue. Hence, single-cell RNA sequencing (scRNA-seq) emerged as a practical tool to thoroughly distinguish each cell cluster, including immune cells in normal and tumor tissue . Due to et al, n = 21; Ducray et al, n = 48; Gravendeel et al, n = 163; Kamoun et al, n = 19; Murat et al, n = 84; Rembrandt et al, n = 209);  and corresponding clinical data were obtained for downstream analysis, which was sequenced using Affymetrix expression arrays . Before acquiring the expression data, the data had undergone robust multi-array average normalization, followed by quantile normalization using R package “affy.” The median of genes with multiple probe sets was selected as the final expression value. To eliminate the batch effect produced not by biological differences but by technical biases, we adopted the “Combat” function in the sva package based on the classical Bayesian algorithm. In addition, somatic datasets for glioblastoma and low-grade glioma were obtained from TCGA and Copy Number Variation (CNV) data was downloaded from the UCSC Xena website (https://xena.ucsc.edu/).From Gliovis (gliovis.bioinfo.cnio.es), a published data visualization web tool for brain tumor expression profile data uploaded on Gene-Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) , six gli6A regulators, we extracted expression profiles of 21 m6A regulators from integrated GBM microarray datasets. Eight methylases , two demethylases , and 11 RNA binding proteins  were included for unsupervised clustering analysis. Thereafter, we utilized the ConsensusClusterPlus package to run an unsupervised consensus clustering one thousand times to divide GBM patients into stable subgroups based on different m6A modification patterns  of 21 regulators of GBM expression data to explore different m6A modification patterns between tumor and normal tissue, as well as each GBM cluster based on consensus clustering.To further explore the regulation mode of mpatterns . The R pTo estimate immune cell infiltration level, we applied single-sample gene-set enrichment analysis (ssGSEA) using traditional microarray expression data . To idenhttps://www.gsea-msigdb.org/gsea/msigdb) for GSVA inputting.29 Next, we performed GSVA using R package “GSVA” on each subgroup to compare relative enrichment level of immune-related KEGG pathways   regression, an efficient regression approach for high-dimensional data with large correlated covariates (6A methylation-based immune cell infiltration assessment pipeline (MPAP). Combining Robustness and LASSO regression, we established a MPAP based on 13 genes and its correlation coefficients. Simultaneously, our MPAP was also validated in another GBM cohort. Then, in univariate and multivariate analysis, m6A modification scores (MMS) obtained from the MPAP were proven to be independent prognostic factors in both training and verification sets  approach to establish survival curves and log-rank tests to calculate p-values between each group. To find the optimum cut-off value of each dataset, we adopted R package survminer, which examined the efficiency of all potential cut-off points. Applying receiver operating characteristic (ROC) curves, we estimated the specificity and sensitivity of the predictive model, which was implemented using R package pROC. Correlation coefficients among 21 m6A regulators were calculated using the Spearman correlation analysis and transformed by -log10. In the training set and validation set, we used multivariate analysis and calculated the hazard ratio (HR) to compare the predictive efficacy between the clinical information and our predictive model.R software (version 3.6.0) was used for all statistical analysis and 6A RNA methylation regulators was determined, including eight methyltransferases (writer), two demethylases (eraser), and 11 RNA binding proteins (reader). To illustrate the process by which we constructed the MPAP and what datasets were applied in our study, a schematic workflow was developed dividing the overall work into four steps broadly  than GBM (495), except HNRNPC, FMR1, and WTAP, which demonstrated that the somatic mutation frequency of GBM patients among m6A regulators tended to be higher than that of LGG patients. This data implies that higher somatic mutation frequency of m6A regulators may contribute to the malignant degree of gliomas. The somatic mutation frequency of 21 m6A regulators was depicted in GBM samples. Totally, in 495 GBM samples, 41 obtained alterations accounting for 10.43%. IGF2BP1 displayed the highest mutation rate, while WTAP and HNRNPC did not display any mutation  alteration analysis, we observed widespread CNV alteration on 21 regulators, among which amplification and deletion vary. CBLL1, HNRNPA2B1, ELAVL1, and YTHDF1 displayed the prevalent CNV amplification, while ZC3H13, HNRNPC, METTL3, and WTAP displayed the opposite  are depicted in the heatmap , which confirms the robustness of our model in a different GBM cohort  for the publication of any potentially identifiable images or data included in this article.YP and KX wrote the manuscript and drew the figures. YL and YZL collected the data. QL wrote the manuscript and supervised the entire project. All authors contributed to the article and approved the submitted version.This work was supported by the National Natural Science Foundation of China (grant number 81802974)The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


To understand the physiological changes that occur in response to spaceflight, mice are transported to the International Space Station (ISS) and housed for variable periods of time before euthanasia on-orbit or return to Earth. Sample collection under such difficult conditions introduces confounding factors that need to be identified and addressed. We found large changes in the transcriptome of mouse tissues dissected and preserved on-orbit compared with tissues from mice euthanized on-orbit, preserved, and dissected after return to Earth. Changes due to preservation method eclipsed those between flight and ground samples, making it difficult to identify spaceflight-specific changes. Follow-on experiments to interrogate the roles of euthanasia methods, tissue and carcass preservation protocols, and library preparation methods suggested that differences due to preservation protocols are exacerbated when coupled with polyA selection. This has important implications for the interpretation of existing datasets and the design of future experiments.  •Experimentation is necessary to understand how organisms respond to space•Specialized protocols are used for preserving biological samples on the ISS•RNAseq datasets are impacted by preservation protocols used on the ISS•Impacts can be alleviated with improved carcass preservation protocols Transcriptomics; Space Sciences Spaceflight places multiple stresses upon the human body including altered gravity fields and exposure to cosmic radiation, which lead to health risks for spacefaring humans . DecadesDespite success of the rodent model, sample collection under such difficult conditions introduces confounding factors that need to be identified and addressed. These are related to hardware limitations, small sample size, and severe restraints on astronaut crew availability. Successful experiments must work within these constraints to produce meaningful insights. In response, the first Rodent Research (RR) mission established new capabilities for conducting reliable long-duration experiments using rodents with on-orbit sample collection. Animals can either be euthanized onboard the ISS or returned to Earth alive. Both approaches introduce confounding factors. The former is experimentally challenging but preserves the sample during exposure to microgravity, whereas the latter exposes the animal to re-entry stresses, and sampling occurs only after a variable lag between landing and euthanasia—essentially sampling re-adaptation to Earth conditions in addition to the response to spaceflight. Inconsistent handling of samples necessitates a clear understanding of how dissection and preservation protocols affect downstream data generation.We previously showed, using transcriptomic, proteomic, and immunohistochemical data from the Rodent Research-1 (RR-1), Rodent Research-3 (RR-3), and Space Transportation System (STS)-135 missions, that lipotoxic pathways are activated in rodent liver in two different strains of mice that were flown for as long as 42 days in space . BecauseHere we now compare RNA-sequencing (RNAseq) datasets generated from livers preserved using these distinct protocols . We findTo assess gene expression differences in liver samples from the RR-1 NASA Validation mission , RNA wasSince livers from only 2 FLT and 2 GC animals in the RR-1 NASA Validation mission were preserved via the immediate method, RNA from livers prepared via the immediate method from two additional studies, the RR-1 CASIS mission  Cadena  and a grTo further investigate the observed differences in preservation method, RR-1 NASA FLT and GC liver samples derived from the carcass preservation method were grouped together (C) and FLT and GC liver samples derived from the immediate preservation method were grouped together (I). DGE was evaluated in carcass versus immediate samples. Many more genes were differentially expressed in carcass versus immediate samples  than in either FLT-C versus GC-C (619 DEGs) or in FLT-I versus GC-I (67 DEGs) samples, further supporting preservation method as the primary driver of variance in RR-1 NASA liver samples A. Gene sin vitro and/or in cells: Dcp2 (Nudt20), Nudt3, Nudt16, Nudt2, Nudt12, Nudt15, Nudt17, Nudt19. In addition, Dxo acts on partially capped mRNAs  slow freezing in dry ice (DI) to mimic the most common method of carcass preservation used in RR missions to date, (2) snap freezing by submersion in liquid nitrogen (LN2), and (3) segmenting the carcass into thirds and preserving in RNAlater, mimicking the preservation method used in the Rodent Research-7 mission. After preservation, all carcasses were stored at −80°C until further processing.Although it is a common practice to dissect mice immediately after euthanasia, due to limitations in crew time for spaceflight experiments, immediate dissection is not always possible. Thus, most tissues are preserved Carcasses from mice in groups 2–4 were partially thawed, and quadriceps and livers were dissected, then snap frozen, and stored at −80°C until RNA extraction to mimic the protocol most commonly implemented when carcasses return from spaceflight missions, including RR-1. A summary of all liver and quadriceps tissues evaluated in the present ground-based tissue preservation study are summarized in Global gene expression and transcript integrity were evaluated in liver samples from groups 1–4 to identify differences in DGE resulting from the carcass and immediate preservation protocols. PCA showed overlap among immediate samples despite differences in tissue preservation methods A. Similalater or LN2 exhibit more overlap with immediate preserved liver samples than those from carcasses preserved in dry ice  injection of ketamine/xylazine and the most common euthanasia method used in standard laboratories is CO tissues  and S2.  tissues A–S7D. PC tissues A or quad tissues B samples tissues C and qua tissues D samplesHerein, we show that protocols used to preserve mouse carcasses on-orbit have large effects on gene expression patterns as measured by RNAseq. Indeed, changes in gene expression due to preservation condition overwhelmed those due to spaceflight. Gene set enrichment analysis showed that many GO terms enriched due to carcass preservation were involved in RNA processing. This correlated with reduced transcript integrity (relatively poor coverage of the 5′ end of transcripts) in samples from carcasses preserved on-orbit when these were sequenced with a polyA enrichment RNAseq protocol.Although RNAseq following polyA selection can more efficiently quantify gene expression , ribodepvia the exosome complex or the 5′ end following removal of the 5′-methylguanosine cap. Deadenylation of cytoplasmic mRNA is the rate-limiting step in mRNA degradation and is catalyzed by one of two complexes: the CCR4-NOT complex, which consists of 10 subunits , and the PAN2-PAN3 deadenylation complex consisting of two subunits   . We obsein vitro and in cells, whereas an additional five—Nudt2, Nudt12, Nudt15, Nudt17, Nudt19—have decapping activity in vitro. In addition, the Dxo family of proteins acts on partially capped mRNAs (Three proteins—Dcp2 (Nudt20), Nudt3, Nudt16—have decapping activity both ed mRNAs . AlthougWe observed a marked difference in the 5′ to 3′ coverage bias between liver and quadriceps samples. Although liver samples were sensitive to carcass preservation via slow- or snap-freeze, quadriceps samples were not. There are a number of possible explanations for this. First, it could be due to the surface exposure of the quadriceps, which would lead to more rapid quenching of biochemical processes. Second, inherent differences in the transcript pool, mRNA half-lives, and enzymatic complement of liver and quadriceps could offer a biological answer. Although we cannot distinguish between these mechanisms, our observations are consistent with previous results showing that postmortem changes to mRNA is tissue dependent .The poor transcript integrity in slow-frozen carcasses sequenced with a polyA enrichment protocol was not evident in pre-sequencing QC analyses. Indeed, all samples had RIN values >7, and there was no correlation between the gene expression differences and RIN. This distinguishes our results from previous studies showing a strong correlation between RIN values and loss of 5′ coverage . TherefoAlternatively, if effective pre-sequencing QC metrics cannot be developed, a number of analytical approaches could be utilized. In one category are methods that calculate additional metrics such as mRIN  and TIN de facto gold standard. The first is rapid freeze of carcasses in liquid nitrogen and subsequent storage at −80°C, followed by partial thaw, dissection, and tissue preservation in liquid nitrogen. Although this led to some loss of 5′ transcript coverage, it had the fewest DEGs  and no enriched GO terms  when compared with immediate dissection. Alternatively, segmentation of carcasses and immersion in RNAlater and subsequent storage at −80°C, followed by partial thaw, dissection, and tissue preservation in liquid nitrogen resulted in better maintenance of 5′ transcript coverage but an increased number of DEGs , although no GO terms were enriched . As euthanasia protocols can change serum biomarkers .This study did not generate unique materials. When applicable, excess RNA is available for request from the NASA Institutional Scientific Collection (www.genelab.nasa.gov) (https://doi.org/10.17632/5hzrgfgxct.1. Scripts used for processing RNAseq data are available at https://github.com/nasa/GeneLab_Data_Processing.All sequencing data are available at NASA GeneLab (asa.gov) . polyA-easa.gov) . polyA-easa.gov) . Data frasa.gov) . Ribodepasa.gov) . Liver dasa.gov) . Quadricasa.gov) . Raw datAll methods can be found in the accompanying