Background: We sought to test the hypothesis that transcriptome-level gene signatures are differentially expressed between male and female bipolar patients, prior to lithium treatment, in a patient cohort who later were clinically classified as lithium treatment responders.Methods: Gene expression study data was obtained from the Lithium Treatment-Moderate dose Use Study data accessed from the National Center for Biotechnology Information’s Gene Expression Omnibus via accession number GSE4548. Differential gene expression analysis was conducted using the Linear Models for Microarray and RNA-Seq (limma) package and the Decision Tree and Random Forest machine learning algorithms in R.Results: Using quantitative gene expression values reported from patient blood samples, the RBPMS2 and LILRA5 genes classify male lithium responders with an area under the receiver operator characteristic curve (AUROC) of 0.92 and the ABRACL, FHL3, and NBPF14  genes classify female lithium responders AUROC of 1. A Decision Tree rule for establishing male versus female samples, using gene expression values were found to be: if RPS4Y1 ≥ 9.643, patient is a male and if RPS4Y1 < 9.643, patient is female with a probability=100%.Conclusions: We developed a pre-treatment gender- and gene-expression-based predictive model selective for classifying male lithium responders with a sensitivity of 96% using 2-genes and female lithium responders with sensitivity=92% using 3-genes. Lithium is the most well-established mood-stabilizer in the practice of psychiatry . Contrastingly, a profound area of concern for pharmacogenomic testing reports are that hospitals are not implementing actionable pharmacogenomic alerts in the patient medical records if the patient did not have the pharmacogenomic testing at their hospital laboratory due concerns of being a certified genomics laboratory and concerns of litigation when knowingly prescribing a drug that the patient cannot metabolize and scanned into the medical record.It is important to note that pharmacogenomic reports do not necessarily account for drug-drug-gene interactions – which are often the case – when patients are prescribed three or more medications. In such cases, hospital systems should embed clinical pharmacologist physicians, as is done by leading hospitals globally  that aim to maintain high rates of patient drug safety and hospital quality outcome measures . HoweverIn a 1986, Zetin and colleagues published the results of a study that evaluated four methods for predicting lithium daily dosages, and the final equation resulted in a 147.8mg/day increased dosage-adjustment for male patients  male and female patients who were later clinically classified as lithium treatment responders; (2) male-responders versus male-non-responders; (3) female-responders versus female-non-responders.GSE45484 with the Illumina HumanHT12 V4.0 expression Beadchip GPL10558 platform file to associate gene names and descriptions. The original multisite clinical study recruited patients from Case Western Reserve University, Massachusetts General Hospital, Stanford University, Yale University, and the Universities of: Pittsburgh, Texas Health Science Center at San Antonio, and Pennsylvania  Gene Expression Omnibus (GEO) via accession numberoverlaid the top two-hundred and fifty genes from all results that were reported in subsequent analyses to identify genes with lithium-specific transcriptional differences between genders associated with response to Lithium treatment. In our second analysis, we only selected patients who were classified as lithium treatment-responders, at baseline, and the results from the gene expression differences are reported excluding the sex-specific control genes identified in the first experiment. In our third and fourth analyses, we compared: male-responders vs. male non-responders, and female-responders vs. female non-responders, respectively.To assess for gender-specific differential gene signatures, in our first analysis we grouped patients based on gender alone and not on any other variables . Then, we rationalized that from the results of the gender-specific transcriptome signatures from our first analysis, we will set the top two-hundred and fifty genes as controls in an effort to identify pharmacologic treatment-response transcriptome biomarkers that are not directly linked to the X or Y chromosome. Therefore, weDecision Tree andRandom Forest machine learning algorithms were used for classification following identification of statistically significant DNA microarray genes. This method sets the stage for subsequent analyses aiming to identify gender-specific responder genes with small sample size of three male-responders and six-female responders from the total of sixty patients. Thus, to reiterate, we first utilized the significant results obtained from the gene expression package implemented in thelimma package in R and then applied theDecision Tree andRandom Forest algorithms for classification and determined this to be novel.A graphical depiction of the data analysis methods are shown inRandom Forest algorithm and set the number of Trees to build at 500 with 7 variables at any time for dataset partitioning. Finally, we reported variable importance plots of genes throughout the paper that was used to explain which genes were most important for classifying patients into different reportable subgroups. Final results of theRandom Forest processes for male- and female-responders are located inTo identify if patients were either male or female, we divided the dataset of 120 samples, pre-treatment and post-treatment, from sixty patients into three sub-datasets: (1) training dataset , (2) validation dataset , and the (3) test dataset . However, due to having small lithium treatment responder sample-sizes, when identifying gender-specific responders versus ‘All Other Patients’, we simply used a training dataset  and a test dataset . We then reported the classification performance of the models using the following diagnostic parameters: sensitivity, specificity , and an area under the receiver operator characteristic curve (AUROC). We selected the traditional Decision Tree algorithm to classify male versus female samples using the following parameters: complexity of 0.01, a max depth of 3, minimum bucket of 7, and a minimum split of 20 observations. Further, for classifying male-responders and female-responders, we selected thelimma package (version 3.34.5) and utilizes the Biobase package (version 2.38.0) which both run within the R for Statistical Programming environment   . Due to RPS4Y1, EIF1AY, KDM5D, RPS4Y2; and theXIST gene located on the X-chromosome. Therefore, all subsequent hypothesis-testing were analyzed with the updated male-gender classification for the following NCBI GEO patient samples: GSM1105526 (baseline lithium-non-responder), GSM1105528 (1-month lithium-non-responder), GSM1105546 (baseline lithium-non-responder), and GSM1105548 (1-month lithium-non-responder).RPS4Y1 < 9.643 then the patient is a female with a probability of 100%. Whereas, if theRPS4Y1 ≥ 9.643 then the patient is a male with a probability of 9%. After proceeding with the machine learning analysis of both the ‘training’ and ‘validation’ datasets, the final ‘test’ dataset resulted in the following diagnostic test evaluation parameters: Sensitivity=100% (95% C.I. 66.37%-100.00%), Specificity=100% (95% C.I. 78.20%-100.00%), and an AUROC of 1.a priori FC requirements and were found to beRPS4Y1, EIF1AY, KDM5D, RPS4Y2, andEIF1AY. These five down-regulated male-biased genes were all found on the Y-chromosome. Contrastingly, a total of 10 upregulated female-biased genes were found to be:XIST, S100P, IFIT3, TNFAIP6, IFITM3, IFIT2, CHURC1, ANXA3, ADM, andPROK2. TheRPS4Y1 gene in males  and theXIST gene , found on the X-chromosome, in females resulted in the greatest expression changes between genders. The male-favored genes resulted in a larger expression change than compared to the females.RBPMS2) gene ranked with the greatest FC of -1.351 (unadjusted p=0.00111). Whereas, 9 genes were associated with female lithium responders, with greatest expression change being the major histocompatibility complex class-1-H (HLA-H) at 1.602 . The neuroblastoma breakpoint family member-14 (NBPF14) gene met the Benjamani-Hochberg adjusted p-value criteria and resulted with an expression change of 0.586 (adjusted p=0.0462).RBPMS2 andLILRA5 genes. Likewise, in the test dataset for females: Sensitivity=91.67% (95% C.I. 61.52%-99.79%), Specificity= not calculated due sample size of test dataset, and an AUROC = 1 with theABRACL, FHL3, and theNBPF14 genes. Therefore, we developed a 2-gene predictive model for men and a 3-gene predictive model for women classifying lithium response in bipolar patients from a general population of bipolar patients using transcriptional signatures at baseline, prior to prescribing and treating a patient with lithium.Using the baseline blood sample microarray data, the predictive modeling results for identifying lithium-responders from the complete study population of male and female controls and treatment samples, resulted in a validation/test sample cohort for males of: Sensitivity=95.83% (95% C.I. 78.88%-99.89%), Specificity=not calculated due sample size of test dataset, and an AUROC = 0.92 using theRBPMS2) gene  in male lithium responders and the Ribosomal protein S23 (RPS23) gene  were found to result in the largest expression change differences between subgroups. However, in female responders and female non-responders, the Family with Sequence Similarity 117 Member B (FAM117B) gene  and the Golgin B1 (GOLGB1) gene  were differentially expressed, respectively and shown inThe purpose of this investigation was to define gender-specific transcriptome-level regulators of lithium treatment response prior to the initiation of lithium treatment. We first established the gender-relevant transcriptional control genes across all study-participant blood samples and specifically to male- and female-responders using a differential gene expression threshold of 0.5. We found that in the downloaded data from the Gene Expression Omnibus, some patients were mislabeled as males and females. Therefore in our first quality control analysis that established the methodology for subsequent gender-specific lithium responders, the following Decision Tree rule for accurate classifying of gender: if RPS4Y1 < 9.643, then patient is female with a probability of 100% and if RPS4Y1 ≥ 9.643, then the patient is a male with a lower probability. The differential gene expression threshold of 0.5 was found to be adequate and corroborated with similar studies that used a similar threshold for establishing gene transcription signatures  male-linked gene and the X Inactive Specific Transcript  female-linked gene were the most differentially expressed among genders, which is consistent with previously published studies  achieved the Benjamani-Hochberg adjusted p-value of 0.0462, and has been reported to be associated with cortical neurogenesis  and various data-types  to classify patients in psychiatry, as advocated by the National Institute of Mental Health’s Research Domain Criteria (RDoC), are essential in psychiatry, especially in patients with developmental delay, language difficulty, and conditions of potentially different etiologies than traditionally taught . IdeallyThese findings suggest that when implementing genomic medicine, clinical research teams should move beyond the single-gene approach when screening for treatment response biomarkers. This approach is currently the standard when screening for patient toxicity at standard doses in poor or ultra-rapid metabolizers using drug pharmacokinetics; however, as more transcription factors are discovered that regulate the cytochrome (CYP) P-450 system of genes, multi-gene pharmacokinetic panels are inevitable and may be included in future Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines. Next, medical management of patients with mania and psychosis either with pharmacotherapy and/or behavioral intervention should be tailored to biological gender due to known neuronal circuitry differences in age-matched patients with psychosis  and an AUROC of 1 for female lithium responders (sensitivity=92%). Moreover, by using well-established Bayesian statistical methods, to identify differentially expressed genes and then machine learning, we discovered 2-genes (RBPMS2 and LILRA5) selective for male lithium responders and 3-genes  selective for female lithium responders that will inform physicians and the medical staff of whether the patient will respond to lithium prior to being prescribed the mood stabilizer. Further, due to the small number of patients classified as responders from the clinical trial, our results should be confirmed. Lastly, in an overall context, our results suggest that the methodology used in this analysis may be extended to other therapeutic drug classes and provides insight to the gender-based gene transcriptome differences influencing lithium pharmacodynamics.https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45484Data used in this study are available from The authors realized that the sample size is small and the results will need further confirm. Is there any other similar (lithium treatment response) clinical trial/data available to test the predicting model?After the Step 1 analysis, why not exclude the four samples that their genders had been mis-classified?  Data accuracy is critical for building models, especially when dealing with small sample size.ABRACL,FHL3, andNBPF14, which classified female responders seem not differentially expressed between female responders and female non-responders (Table 5). Please explain why this might be happened.Those three genes, theWhat is the known function of those 5 genes that chosen for classification of male and female responders? The authors might want to discuss the possible molecular mechanism of those gene function related to lithium treatment response in bipolar disorders.Since the authors re-analyzed data from a previously published study, a comparison of the findings from this study to the original one may be necessary. I understood that the original study looked at differences between responder and non-responders, regardless of genders. Is there any gene, of which its expression differentiates responder and non-responder, overlapped with what the authors found in this study? What will be found if apply a machine learning approach to this data set without classifying patients by genders?Using a published data set, the authors built a model based on gender-specific gene expression to predict lithium treatment response in bipolar disorder patients. Here are my questions and concerns:I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Authors have made improvements to the manuscript in terms of clarity of methodology. Hence, now I can comment on their methods. Biostatistical methods are mostly sound.Authors haven’t specified number of responders in their test sets or whether split is class-balanced. But based on their description of 70 – 30 train -test split, sample size is severely limiting for model evaluation, with mere one or two examples of positive class (responders) in test set . As a fellow researcher, I completely respect the motivation and effort behind the efforts here, but we as scientific community should understand that the real danger of generalizing observations based on handful of cases is not so much of being underpowered to detect real effect, but of generating false positives results that add to prevailing burden of irreproducible results.It seems that features  were selected using analyses of both training and testing data partitions. This is called double-dipping and leads to invalid or over-optimistic estimates of model performance. While above two points are deal breakers, I will also mention following points for sake of completion.in fold’ or their choice should be explained.Hyper-parameters should also be selected ‘Baseline performance (chance level accuracy) is rather high due to class imbalance – eg: 25/28 = 89% for male responders. Reporting the confusion matrix will be more useful than sensitivity, AUC etc in such cases.For small samples, consider simple linear models than complex non-linear ones such as random forest to avoid over-fitting. Also, consider leave-one-out or k-fold cross validation instead of single test-train split for better estimate of performance. Hence, in my humble opinion, the manuscript in its current form doesn’t meet the necessary scientific rigor. That is at least without a major revision in machine learning methods, such as learning models to predict treatment response in larger undivided dataset of 60 subjects, appropriate use of feature selection etc. However, machine learning part of study seems have major issues:I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. The study design and methods do not seem to be coherent to a single unifying goal. Whether the goal is to predict the responder using the 'gender' and 'transcriptome' data as features? If so, such a model can be learned using standard machine learning methods. On the other hand, whether the goal is to identify are genes with statistically significant group differences in their expression? If so, this can be achieved by biostatistical inference tests of association.  Please note that task of 'association' and 'prediction' are quite distinct in their formulation and desired objective. Authors have to be really careful about that they trying to test and claim while using both of approaches in conjunction. Study design is a bit unconventional, and hence needs to be motivated and explained better. For example: Performing successive sub-group analyses partitioned on factors like gender have less power, and should be generally restricted to post-hoc tests. Why not simply use standard biostatistical tools such as factorial ANOVA with 'sex' and 'response' as between-subjects factors of interest? I agree with Reviewer #1 that flowchart of analysis pipeline will help the understanding. Steps of sub-group selection and variable/feature selection can be indicated in this flowchart. Care should be taken to avoid the circularity that can arise from selection because statistical inference can be invalid whenever the results statistics are not inherently independent of the selection criteria under the null hypothesis. Authors might be asking too many questions with limited data in hand. Sample size might not enough to study individual effects of multiple factors - such as treatment-type, response and then, the gender. Cell-wise sample sizes resulting from this 8-way split is less than 10 for all but two cells (less than 5 for 3 cells). Suitability of applied statistical tests and generalizability of their claims are questionable here. Authors should also think about 1:2 skew in male:female ratio, which makes this issue worse. Study can greatly benefit from asking specific and limited hypothesized questions.  Also, since multiple objectives are stated, methods and results section can describe each objective separately for sake of better clarity.  Eugene et al. tackles the hard question on the transcriptome predictors of clinical outcome in lithium treatment of Bipolar disorder. This research question is of clinical relevance and importance, however, there might be some issues with how the data was analyzed and presented in the manuscript. Machine learning methods are not described. The methods used for learning of model and its evaluation process needs be specified. Example: How was training and test splits performed? How was feature selection performed? How were the hyper-parameters optimized? Whether the reported performance metrics are for training or testing sets? Whether the discovery dataset used for identifying '250 genes' disjoint from validation set? etc. Without these details, it is hard to comment on validity of a predictive study.   I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Please see our responses in bold-face to the comments from Referee #2. We thank you for reviewing this article.The study design and methods do not seem to be coherent to a single unifying goal. Whether the goal is to predict the responder using the 'gender' and 'transcriptome' data as features? If so, such a model can be learned using standard machine learning methods. On the other hand, whether the goal is to identify are genes with statistically significant group differences in their expression? If so, this can be achieved by biostatistical inference tests of association. Response:First and foremost, I sincerely appreciate you taking time out to review this article and provide constructive feedback. In answering these points, this paper addressed questions that were not addressed in the original clinical study and attempted not to duplicate work previously published. Hence, is why the methods are creative and not employ standard biostatistical inference tests of association. For example, we aimed to test the hypothesis if gender influenced selecting transcriptome signatures associated with lithium efficacy. Therefore, in order to accomplish this task, we conducted a quality control test that identifies known gender-biased transcriptome signatures. This proved to be essential and is why we identified that several patients were misclassified as male/female. Results are shown, documented, and corroborated with other studies identifying the same genes as being gender-specific. Next, standard statistical tests were indeed used, however, we chose to use those adapted for Gene Expression analysis as has been developed using the “limma” package and for machine learning methods, we used the Random Forest algorithm. We then aimed to develop a predictive model to see if this may be a first-step for other medical research teams to validate with further clinical studies and for Clinical Pharmacology laboratories to pursue and develop novel experiments to determine lithium’s effect in cells, tissues, laboratory animals, and later in humans.    Eugene et al. tackles the hard question on the transcriptome predictors of clinical outcome in lithium treatment of Bipolar disorder. This research question is of clinical relevance and importance, however, there might be some issues with how the data was analyzed and presented in the manuscript.  Please note that task of 'association' and 'prediction' are quite distinct in their formulation and desired objective. Authors have to be really careful about that they trying to test and claim while using both of approaches in conjunction. Response:Well said and this is duly noted. We first aimed to identify genes associated with responders and hoped to identify if using those genes would help in creating hypothesis-generating predictors treatment response, only to be later validated by other studies. Thank you for the comment.Study design is a bit unconventional, and hence needs to be motivated and explained better. For example: Performing successive sub-group analyses partitioned on factors like gender have less power, and should be generally restricted to post-hoc tests. Why not simply use standard biostatistical tools such as factorial ANOVA with 'sex' and 'response' as between-subjects factors of interest?Response:While your point with “gender” should be restricted to post-hoc analysis, we are looking to make gender the primary point of our analysis in patients responding to lithium treatment and specifically not repeat the analysis from the original publication in Nature. This was attempted on the original article, however, we clearly identified that there was patient-gender misclassification in the original study. So, we sought another route of analysis to ensure that gender was a primary point of analysis and not side-lined to the post-hoc analyses. However, we do understand that this compromises statistical power and therefore, sought not to analyze the data with ANOVA, because we are addressing minor gene expression level changes that might have real-world clinical insight. This is a hypothesis-generating analysis, however, we do thank you as well for this comment. I agree with Reviewer #1 that flowchart of analysis pipeline will help the understanding. Steps of sub-group selection and variable/feature selection can be indicated in this flowchart. Care should be taken to avoid the circularity that can arise from selection because statistical inference can be invalid whenever the results statistics are not inherently independent of the selection criteria under the null hypothesis. Response:We appreciate the request for having a graphical flowchart depicting the analysis pipeline and have included the figure in the updated version of the manuscript. We are confident that circularity is not an issue, given the specific aims of our analysis. These methods are determined to seeking to identify the influence of linked gender-drug-response to genes at baseline and not after treating with the mood stabilizer. The gender differences in clinical practice are a well-documented reality and we literally sought to identify any signal, on the gene expression level, to address the clinical question rather than entirely use traditional statistical methods which did not necessarily translate to clinical translation. With our results, we are expecting laboratories having strengths in gene knock-down/out and gene over-expression experiments to identify the mechanisms to lithium’s efficacy. This is an old drug and until this day, most textbooks lack knowledge of the drug’s mechanism. These mechanisms may be attributable to biological, biochemical, gene expression, hormonal, and proteomic differences that we are aiming to identify here in this article. Please see the new figure showing the data analysis pipeline used in this paper.Authors might be asking too many questions with limited data in hand. Sample size might not enough to study individual effects of multiple factors - such as treatment-type, response and then, the gender. Cell-wise sample sizes resulting from this 8-way split is less than 10 for all but two cells (less than 5 for 3 cells). Suitability of applied statistical tests and generalizability of their claims are questionable here. Authors should also think about 1:2 skew in male:female ratio, which makes this issue worse. Study can greatly benefit from asking specific and limited hypothesized questions. Response:We do appreciate your robustness in identifying the obvious study limitations due to sample-size, however, we are limited to the feasibility, cost of research, patient population, and all of the work accomplished from the original study team stemming from Case Western Reserve University, Massachusetts General Hospital (Harvard University), Stanford University, Yale University, the University of Pittsburgh, Texas Health Science Center at San Antonio, and the University of Pennsylvania that uploaded this data into the Gene Expression Omnibus (GEO) database maintained by the National Institutes of Health. This was a massive undertaking in a multi-site trial. The sample-size limitations are classically used to not have results generalized, however, we ask you realize that this work not necessarily straight-forward to accomplish with in the real-world of medical care in mental health. Nevertheless, your points are well noted. Response: The 1:2 male:female skew you are referring to is exactly what is seen in clinical medicine. Females tend to respond more so than males and I clearly stated this in the introduction of the paper. We are working with the data that has been uploaded and have not found any other datasets in GEO. However, please understand that our efforts are indeed, as you clearly pointed in the beginning of this review, that this is a difficult clinical question to answer. Rather than saying we do not have enough samples, we aimed to do ‘something’ rather than let the dataset sit in GEO while patients are in need and laboratories have the capability and funding to seek follow-up studies. We appreciate your clear expertise and concern. However, we are working to create hypothesis-generating results to be later confirmed, expanded-upon, and validated for sick patients. Therefore, the generalizability of our claims are clearly limited to the dataset we obtained from clinical study of the aforementioned university hospitals. Of the 60 (sixty) patients treated with Lithium, literally only 9 responded in follow-up my expert medical teams and high quality care. Hence the need to find ‘some signal’ with the data at-hand in the form of expression patterns of lithium treatment responders. Thank you for the statement and again these are well-noted points you stated here.   Also, since multiple objectives are stated, methods and results section can describe each objective separately for sake of better clarity.  Machine learning methods are not described. The methods used for learning of model and its evaluation process needs be specified. Example: How was training and test splits performed? How was feature selection performed? How were the hyper-parameters optimized? Whether the reported performance metrics are for training or testing sets? Whether the discovery dataset used for identifying '250 genes' disjoint from validation set? etc. Without these details, it is hard to comment on validity of a predictive study.Response:Thank you for these questions and comments. We have updated the methods to better explain the approach used in the study. The new graphical analysis pipeline will help in explaining the approach. We also added the final Decision Tree diagrams to identify male- and female-treatment responders. Thank you for the review and we have made considerable updates to this version of the paper to address these concerns and improve this research manuscript.    I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The authors demonstrated that sex-differences gene expression might contribute to lithium treatment response using microarray expression data.A samples size of 60 might be too small to determine the sex effects. Can the sample size n=60 provide adequate power for data interpretation, especially separated men and women for study sex-effect on gene expression?The authors stated that their predictive model for lithium responders with an ROC AUC 0.92 for men, and 1 for women.  If the prediction accuracy is so significant, what are the potential biological mechanisms beyond these genes? More discussion regarding the biology of those genes should be included in the paper. Once again, if the prediction accuracy is so significant, it is needed a replication study using different data sets? In summary, the authors claimed the prediction model with very high accuracy; it should be included either functional validation of those genes or a replication study population.   Major comments:Methods - study design, it might be better to use a flow chart to demonstrate the study design.Methods - study design, please clarify the rationale of filtering out “250” genes.Table 1 shows total study population n=60, but figure 1 legend shows male: n=41, female: n=39? Figure 2:  please elaborate the data presented in Figure 2. The key results for each of the four panels should be summarized in Results.Table 2 and Table 4, the log FC threshold of 0.5 or 0.3 might be too low. The changes in gene expression are very subtle in Table 4.Table 2, are there any gene up-regulated in males? /downregulated in females?Limitations of the study should be addressed in Discussion. Specific comments:I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Major Comment Responses:Response 1: This point is well noted; however, it is important to realize that our gender-effects of gene expression is consistent with other studies noted within the paper and shown below:BMC genomics 15.1 (2014): 33.Jansen, Rick, et al. "Sex differences in the human peripheral blood transcriptome."Frontiers in genetics 7 (2016): 183.Mayne, Benjamin T., et al. "Large scale gene expression meta-analysis reveals tissue-specific, sex-biased gene expression in humans."Further, our gender-specific results met the Benjamini-Hochberg multiple comparisons criteria adjustment due to multiple comparisons.Comment Response 2: We welcome and thank the reviewer’s comments on the biological mechanisms beyond these genes. Clearly, it is well noted and cited in the paper that in clinical practice there is a wide inter-individual variability in the treatment and response to treatments of biplolar disorder. Moreover, these patients were not treated with lithium monotherapy, alone, and therefore further insight into the biological mechanisms were left out due to these patients were treated with an “Optimal Therapy” that includes a variety of other FDA-approved mood stabilizers.In reference to the comment regarding the prediction accuracy, we agree that the study may warrant functional validation in a laboratory; however, it is beyond the scope of our computational psychiatry study and we will leave the functional genomics characterization of the genes to investigators seeking to pursue the findings from our results. Specific Comment Responses: We thank you for your specific comments and have addressed several of the pertinent points in your review. For all differentially expressed results reported throughout tables within the manuscript, we changed the wording from genes up-regulated or down-regulated in males or females to a clearer description statement that of genes-associated with males or females. However, we thought not necessary to include an extra figure, but rather encourage the reader to (1) review the study design section within the methods to better understand the computational approach used in our analysis and (2) read the systematic tabular reporting of the results in the manuscript text as well to understand that study approach. For the caption in Figure 1, we thank you for the comment and have updated the sample sizes for males and female patients. The updated Figure 1 text reads: Males  and Females . The comments regarding: (1) the fold-change of 0.5 and 0.3 being subtle and (2) the study limitations, are already specifically addressed within the original version of the manuscript. Again, it is well established and referenced within the text that small changes in gene expression have already been reported to result in major functional outcomes in human physiology. We will update the variable importance illustration shown in Figure 2 and that will be added to the updated version of the manuscript.


The development of computational methods capable of analyzing -omics data at the individual level is critical for the success of precision medicine. Although unprecedented opportunities now exist to gather data on an individual’s -omics profile , interpreting and extracting meaningful information from single-subject -omics remain underdeveloped, particularly for quantitative non-sequence measurements, including complete transcriptome or proteome expression and metabolite abundance. Conventional bioinformatics approaches have largely been designed for making population-level inferences about ‘average’ disease processes; thus, they may not adequately capture and describe individual variability. Novel approaches intended to exploit a variety of -omics data are required for identifying individualized signals for meaningful interpretation. In this review—intended for biomedical researchers, computational biologists and bioinformaticians—we survey emerging computational and translational informatics methods capable of constructing a single subject's ‘personalome’ for predicting clinical outcomes or therapeutic responses, with an emphasis on methods that provide interpretable readouts. Key points: (i) the single-subject analytics of the transcriptome shows the greatest development to date and, (ii) the methods were all validated in simulations, cross-validations or independent retrospective data sets. This survey uncovers a growing field that offers numerous opportunities for the development of novel validation methods and opens the door for future studies focusing on the interpretation of comprehensive ‘personalomes’ through the integration of multiple -omics, providing valuable insights into individual patient outcomes and treatments. The arrival of precision medicine has led to a more individual-based view of diseases, with characteristics of single subjects being central to the prediction of clinical outcomes and prescription of tailored treatments. This concept is not new; in fact, evidence-based clinical practice guidelines  stratifyCurrent approaches aimed at analyzing disease or other biological processes, therapeutic efficacy and -omic data still leverage well-established cohort-based population analyses such as case-control studies [e.g. gene expression classifiers (GExpCs)], observational trials or controlled intervention trials. These large cohort/group approaches place emphasis on the group average rather than individual participants; though this group average may not represent any actual individual’s personal profile, let alone be meaningful to understanding the profile of a given specific patient. On the other hand, the framework of N-of-1 trials has been applied to repeated measures of a single analyte for over two decades . This apAlthough we now have an unprecedented technical opportunity to gather data relating to an individual’s -omic profile, bioinformatics tools to understand these data comprehensively, and at the individual level, remain underdeveloped. Novel approaches for identifying individualized (single-subject)—and not cohort—signals are required for gathering insights into the biology of diseases and healthy states of individuals. This review focuses on computational methods aimed at analyzing quantitative transcriptomic measurements of an individual and the combination of transcriptome with other -omic data.In this review, we define the personalome as an interpretable personal molecular mechanism profile of an individual derived from one or more scales of -omic data, especially when designed to enable precision medicine. ‘Personal -omics’ means the -omics measures of a single subject. Molecular mechanisms are any molecular functions or biological processes such as a missense mutation in DNA, or a differentially expressed pathway (DEP) at the transcriptome or proteome. To be considered interpretable at the molecular mechanism, the raw -omics profile must have been subjected to analyses performing (i) dimension reduction and (ii) biomolecular interpretation of the mechanisms involved in molecules of life . For exaWe surveyed emerging novel computational biology, biostatistical and translational informatics methods that construct a single subject’s personalome by analyzing transcriptome data to predict outcomes or therapeutic responses without requiring the large cohort needed for conventional approaches.Our review methodology is detailed in the The review is divided according to the type of data inputs in the methods (i.e. transcriptome and integrated -omics). A review of the validations of all methods follows, and finally, we discuss and conclude with the broad challenges, the applications and the opportunities in developing a personalome for precision medicine, i.e. how the single-subject analyses (SSAs) of -omics data can bring novel insights in disease mechanisms specific of a patient and unveil potential patient-specific treatments. A table of content for the review is provided in Transcriptome analysis aims to interpret the quantification of transcribed genetic material, including both coding and noncoding RNA. Different from DNA, which is relatively static, analyses of the transcriptome capture the collective impact of tissue type, sequence variation, regulation, environment, external stimulation (e.g. drug treatments) and interactions between them. High-throughput technologies, such as microarray and RNA sequencing (RNA-Seq), are capable of assessing transcript expression at genome-scale for an individual sample, with RNA-Seq providing unbiased detection, broader dynamic range, increased specificity and sensitivity and easier detection of rare and low-abundance transcripts.The transcriptome provides a snapshot of transcriptional activity under the condition where the RNA was collected, allowing researchers to study the biological impact of certain diseases or effect of treatments . This alt-test ) Although DEG identification often requires a large cohort of samples, a few attempts have been made to identify DEGs from only a pair of transcriptomes. These methods provide an opportunity to identify a set of personalized DEGs of a single subject without requiring costly transcriptome replicates. Among these methods, DESeq  and edgeIn this subsection, we report other methods that create biologically interpretable results from a single subject’s transcriptome bypassing detection of significant differences in gene-level expression to go directly to pathway-level signals (DEPs). Such analyses aim to promote a higher-level interpretation of the underlying gene expression data, providing a holistic view of pathway perturbation, instead of focusing attention on any particular gene. All the approaches belonging to this category incorporate a large body of prior biological knowledge . ThWe identified three methods that require a single sample of an individual and a cohort reference  the number of available normal samples (power), (ii) platform-dependencies and (iii) limited sensitivity to detect pathways that contain only few genes. The large number of normal cohort sample required limits the applicability of these methods in infrequent diseases, or when obtaining appropriate samples and/or defining an appropriate ‘normal’ state is complex. Moreover, the reference cohort may be heterogeneous, and pooling together normal samples means that transcriptome of different individuals is merged, which can obscure stratification and correlation patterns in the normal data.Because of these limitations, other methods have been proposed to circumvent the normal reference requirement using solely a sample from the subject under study. These strategies aim to reduce dimension by injecting domain knowledge while reducing gene-level noise inherent to a single case sample. Two such methods are the FAIME  and ssGSA limitation of these two methods is that they provide a ranking of pathways in terms of their deregulated with respect to other pathways using the gene expression data of the individual (e.g. more or less expressed than an average expression). Therefore, these methods do not identify functionally altered pathways against a reference as in the previous methods because a pathway more or less expressed than average may the normal expected level of expression of that pathway.The output of both ssGSEA and FAIME report DEPs, which allow enhanced functional interpretation of disease-associated biological processes relative to less readily interpretable lengthy lists of DEGs. This approach could be useful when little pathological knowledge is available for the disease or when substantial pathway heterogeneity may underlie the clinical phenotype. In the case of single-subject studies, DEP lists can be used not only to investigate biological mechanisms specific of certain patients but also to suggest potential treatments or combination of treatments based on gene products annotated to the pathology-associated DEP, or other known interactions.In the following, we will focus on single-subject-based methods that analyze paired samples without the requirement of replicates. In this category, we identified the methods known as N-of-1-pathways . These met al. [et al. [et al. [ad hoc normal samples. N-of-1 pathway Wilcoxon, MD and the Clustered-T approaches perform gene set testing, one pathway at a time, in a ‘self-contained’ fashion [The first transcriptome analytic framework for quantifying within-patient differential expression from a pair of samples was introduced by Gardeux et al.  that dev [et al.  extended [et al. . In resp [et al.  develope fashion . This ofet al. developed two procedures, N-of-1-pathways k-Means Enrichment (kMEn) [Seeking gene set test procedures in the paired-sample setting that explore pathway-level expression relative to the rest of the transcriptome (i.e. a ‘competitive’ test), Li t (kMEn)  and Mixtt (kMEn) . The benAll the N-of-1 pathways methods showed their power in the identification of DEPs that result from diverse health disorder , 47–50.Biological processes are highly dynamic, and understanding how diseases evolve over time can reveal factors involved in determining the disease status, progression and compensation. However, -omic technologies are typically gathered at infrequent or even single static points. Comprehensive longitudinal -omics data (i.e. one or more type of omics measured over time) can provide key information for understanding the whole evolution of biological processes and underlying biological mechanisms. Comprehensive longitudinal analyses are typically limited by the substantial associated costs of sample collection and patient follow-up. As a result, with perhaps rare exceptions, long time series experiments have few or no isogenic replicates in single subjects. Traditional analysis of time series gene expression data aims at identifying gene sets that exhibit common or distinct patterns of expression between two or more conditions . The computational complexity to analyze such data is higher, as time course data involves the three dimensions of gene, time and condition. When considering time series data from an individual, several strategies can be applied depending on the experimental setup (i.e. number of time points and condition considered). For example, baseline comparisons can be performed by considering samples gathered during ostensibly healthy physiological states of the patient as the reference population if multiple time points are sampled. Samples gathered during ostensibly healthy physiological states of the patient approaches to extract meaningful knowledge from time series transcriptome data are based on clustering algorithms , hidden et al. [et al. [However, when replicates are not available, few models have been proposed for the identification of DEGs or DEPs from longitudinal data. In et al.  propose  [et al.  developeOne of the major limitations of all transcriptome analyses is their inability to fully capture the dynamics of the represented system because of, for instance, posttranscriptional modifications. To this end, analysis of the product of transcriptome can provide significant insights and source of information.In this section, we will report the state of art of current analyses aimed at analyzing the transcriptome combined with other -omics for SSA. The retrieved studies are summarized in The integration and analysis of different high-throughput molecular assays and data is one of the major topics in precision medicine for understanding patient-specific variations. This approach enables the possibility of obtaining a comprehensive view of the genetic, biochemical, metabolic, proteomic and epigenetic processes underlying a disease that, otherwise, could not be fully investigated by using single -omics approaches. The increased power of multi-omics studies have been already assessed in the understanding of diseases, biomarkers and drug discovery. These methods are based on supervised or unsupervised machine learning techniques and typically aim at classifying patients into cancer subtypes  or are det al. [In our review of the literature, only few computational single-subject algorithms aimed at analyzing transcriptome data combined with other -omics have been proposed in the past years. Chen et al.  pioneereet al. . The artPARADIGM  integratin silico simulation of data or by cross-validation in the same data set. A few methods have validated their results across replicate samples, or have had pathology-associated DEG or DEP results successfully reproduced in independent data set. To our knowledge, only the N-of-1pathways W [in vitro and as a prognostic outcome classifier in a prospective study. In that study, patient-specific DEPs were identified in response to an ex vivo stimulation of their PBMCs with rhinovirus and used to accurately predict risk of asthmatic exacerbation in those same patients over a 2-year follow-up period. This strongly supports the conclusion that the field of single-subject studies of personalomes is an emerging field that is in need of more rigorous validations for translation to clinical practice. Additionally, new validation strategies need to be developed for in vivo and clinical trial validations of personalome imputations.We further classified each publication in this review according to the method(s) used for result validation . The majthways W  SSA methTo better understand the requirement for single-subject studies, we revisit the types of approaches and transformations required for clinicians to interpret the more clinically used method of DNA sequencing. As shown in et al. [ex vivo and trained a classifier predictive of children prone to asthma exacerbations. The dimension of the signal was reduced from the entire transcriptomes of paired samples in 20 subjects (∼106 data points) to the effect size of statistically significant responsive pathways in at least one subject (∼104 data points).This review focuses on comparing and contrasting SSA that incorporates this previously unavailable intermediate step for other molecules of life, such as mRNAs, miRNAs, proteins, methylated DNA regions and metabolites (carbohydrates and lipids). For example, oncologists already use assays for determining expression fold change and protein function of oncogenes and tumor suppressors through the comparison of tumor tissue with external references or unaffected paired tissue. As these curated approaches may not scale to the full omics data for other diseases, we provide emerging evidence that the newly available unbiased SSA enables new types of studies investigating their clinical utility by addressing the gap of biomolecular interpretation of raw omics signal. Among possible studies, we demonstrate that omics clinical prediction classifiers that operate directly at the omics scale may be redesigned for the parsimonious transformed signal of single-subject studies for improved clinical utility. For example, Gardeux et al.  quantifiP-value for each subject, analogous to mechanisms-level features ascribed to a patient. In addition, Zhang et al. [et al. [While many unbiased fully specified GExpCs designed over the entire transcriptome have been published in peer-reviewed journals, few have been FDA approved because of their lack of a mechanistic relationship between the features (gene transcripts) and the disease progression , 80, 81.g et al.  have shog et al. , 82, 83, [et al.  shows thThe development and analysis of personal transcriptome interpretation are essential for precision medicine, as therapeutic decision-making pertains not exclusively to genomic sequences but to Genome x Environment interactions (GxE) as well. For example, isogenic twins may experience different diseases because of their distinct environment exposures, despite sharing identical genomes. Even in the presence of the same diseases, their therapeutic responses may vary as a result of other GxE conditions , 84. TheThis review revealed that ongoing advances in high-throughput technologies, emerging research and clinical questions urge continued investigation and development toward experimentally validated methods for unveiling tailored treatments from patient-specific transcriptomes . In receApproximately half of the bioinformatics methods we surveyed perform a comparison between the single subject’s profile and a reference, most often a cohort of accumulated normal samples or samples of a well-defined disease subtype , 41–43. P-values, considers a limited variance estimate and is validated in independent samples. On the other hand, edgeR and GFOLD are suboptimal as they require user-defined parameters  conditions than in heterogenic ones. We thus recommend clinical or experimental designs that generate a baseline in the same individual, i.e. paired samples , 2 and 5In absence of multiple samples from a single subject with its own isogenic reference, we recommend analyses providing biologically and clinically interpretable results of altered expression against heterogenic references (a population). Among single-sample SSA, we recommend RankComp and individPath for DEG and DEP determination, respectively. RankComp is currently the only method that provides DEGs based on the comparison of a single sample against a reference cohort. While for DEP determination, we suggest individPath because of its rigorous formal model and the small number of transcripts required to detect DEPs.Imputing altered or dysregulated expression of a transcript of a pathway is not feasible for inadequately designed clinical assays or experiments aimed at interpreting a single transcriptome in the absence of any transcriptome reference . To address this, transcript and pathway expression can be compared within a sample using FAIME or ssGSEA. However, the output of higher or lower expression of a mechanism as compared with the sample expression may simply be the normal state of such a mechanism with the interpretation being ambiguous.While transcriptome analyses can provide DEGs and DEPs for single subjects and are the most mature, we anticipate that as the field advances, it will be possible to reveal novel physiological state correlations through the construction and analysis of multi-scale personalomes. The analysis of a single scale (i.e. -omics data) alone cannot reveal the complex picture underlying a disease that may be fully captured only by fusing together multi-omics data  of an individual, i.e. via comprehensive personalome profiles. In fact, the combination of multiple -omics data can lead to the detection of a comprehensive individual variability, essential for providing new insights into disease pathophysiology and mechanisms that may explain the differences in drug responses in the human population. As shown in Analysis of multi-omics dynamic profiles including transcriptome, proteome, methylome and metabolome can additionally provide indicators of real-time phenotypes and physiology in individuals that cannot be obtained through examination of the static genome alone. In doing so, GxE interactions are revealed . -Omics When taking into account the integration of multiple -omics, an important aspect to consider is the variability of data between each -omics, not only with respect to the represented biological process but also with the associated noise levels, identification accuracy, coverage and temporal resolution of data. These differences complicate the integration and joint modeling of multi-omics data. While this is intuitively clear, it remains computationally and experimentally challenging to effectively integrate longitudinal multi-omics data. For one, each biological entity  has different time-dependent modulation and responds to signals on a different specific time scale, even if contributing to the same biological process. Second, biological processes that take place in inaccessible tissues  cannot be feasibly monitored in a longitudinal approach, even if a single sample is possible. Additional challenges are related to the same variables of autocorrelation across repeated measurements, random effects and missing data. Moreover, the design of longitudinal studies of a single subject must account for repeated measures preferably being equally spaced in time, allowing the increase in statistical power of the approach . An obviin vitro, in vivo and clinical trial investigations.Futures studies for SSA of transcriptomes will need to focus on four underreported approaches: (1) variance estimation in isogenic conditions from single-subject measures (without requirement of reference transcriptomes), (2) activity level of pathways  rather than expression direction (overexpressed versus underexpressed), (3) the analysis and integration of comprehensive personal -omics data to infer dysregulated molecules and mechanisms and (4) rigorous validation of DEGs, DEPs, or other results with appropriate As clinical research continues to explore the importance of patient heterogeneity, we encourage more investigators to adopt single-subject study designs and -omics analyses when appropriate to maximize the information made available by high-throughput technologies. Access to these analysis tools may also allow researchers to more thoroughly explore certain rare case studies, outliers and patients-of-special-interest in a way that they could not have done if relying only on traditional large cohort-based statistics. This is particularly true if an isogenic paired-sample study design can be used to answer a meaningful biological or clinical question. The use of personalome integrated with the available external knowledge  can provide new opportunities for developing more robust and comprehensive results that account for all the interacting -omics and temporal behaviors of the biological system of an individual.Finally, personalome researchers should consider a creative application of powerful engineering and mathematical tools that have not yet been applied to study the mechanisms underpinning the personalome of an individual. For example, computational methods used to analyze time series data include generalized linear mixed models, generalized estimating equations, Markov models, nonparametric or semi-parametric models or Bayesian models and dynamic pathway analysis . Howeverbbx149_SuppClick here for additional data file.


Gentiana rigescens is a precious herbal medicine in China because of its liver-protective and choleretic effects. A method for the qualitative identification and quantitative evaluation of G. rigescens from Yunnan Province, China, has been developed employing Fourier transform infrared (FT-IR) spectroscopy and high performance liquid chromatography (HPLC) with the aid of chemometrics such as partial least squares discriminant analysis (PLS-DA) and support vector machines (SVM) regression. Our results indicated that PLS-DA model could efficiently discriminate G. rigescens from different geographical origins. It was found that the samples which could not be determined accurately were in the margin or outside of the 95% confidence ellipses. Moreover, the result implied that geographical origins variation of root samples were more obvious than that of stems and leaves. The quantitative analysis was based on gentiopicroside content which was the main active constituent in G. rigescens. For the prediction of gentiopicroside, the performances of model based on the parameters selected through grid search algorithm (GS) with seven-fold cross validation were better than those based on genetic algorithm (GA) and particle swarm optimization algorithm (PSO). For the SVM-GS model, the result was satisfactory. FT-IR spectroscopy coupled with PLS-DA and SVM-GS can be an alternative strategy for qualitative identification and quantitative evaluation of G. rigescens. Herbal products, a complementary and alternative therapy, are increasingly gaining popularity in daily life and health care all over the world . In the Gentiana rigescens (family Gentianaceae) is a precious and highly appreciated Chinese herbal medicine, which is widely distributed in the southwest of China, especially in Yunnan Province [G. rigescens and it is recorded in Chinese Pharmacopoeia (version 2015) as a quality criterion [Province . As a peProvince . Among triterion . This coriterion .Codonopsis lanceolata samples were different depending on the geographical origin and harvesting time. Therefore, with its wide spectrum of therapeutic properties, it is crucial to provide guidance for the quality control of G. rigescens.To our best knowledge, there are many external factors which can influence the quality of herbal medicines, such as geographical origin, harvest time, processing methods, etc. ,11,12. AG. rigescens. It can provide qualitative information about the molecular structure of the components in G. rigescens with little or no sample pretreatment [G. rigescens from different geographical origins and determine the iridoids in G. rigescens, and the results showed that FT-IR spectroscopy was suitable to provide qualitative and quantitative analyses of G. rigescens [G. rigescens samples from different parts and cultivation years.The conventional analytical methods for qualitative and quantitative analysis usually require operative skills, experience and are labor-intensive in addition to involving organic solvents for sample preparation. In this research, FT-IR spectroscopy, which is fast, clean and cost-effective, was developed to obtain chemical information about reatment ,18. In areatment ,20. For igescens ,22. Simiigescens , developG. rigescens from Dali, Lijiang, Diqing and Yuxi in Yunnan Province. Therefore, a method for the qualitative and quantitative analysis of G. rigescens has been developed employing FT-IR spectroscopy and chemometrics methods such as partial least squares discriminant analysis (PLS-DA) and support vector machines (SVM) regression. The results of gentiopicroside content determined by high performance liquid chromatography (HPLC) have been used as reference data to build our quantitative analysis model.The objective of this study was to provide an efficient, easy-to-operate and non-hazardous alternative to evaluate the quality variation in different parts of G. rigescens.All 179 samples were quantified by the HPLC method. Prior to sample determination, the methodology was validated by measuring the stability, repeatability and recovery based on the previous work in our laboratory . The linG. rigescens from different geographical origins. For the date it can be concluded that samples from Diqing have the greatest gentiopicroside content, followed by those from Lijiang, Dali and Yuxi. Except for the samples from Dali which had the highest gentiopicroside content in leaf, the other three sources showed the highest abundance of gentiopicroside in the roots. It was thus found that not only in the same parts from different geographical origins but also the same part from different geographical origins, the content of gentiopicroside varies greatly. In addition, all samples conformed to the quality standards in the Chinese Pharmacopoeia except the stems from Yuxi (the content of gentiopicroside should be higher than 1.5%).G. rigescens samples show a great dependence on geographical origin, which might be influenced by the conditions of these geographical origins. For example, Yuxi is in the central part of Yunnan Province which is mainly a subtropical area, while the others are in the northwest of Yunnan Province, which belongs to the temperate climate zone area [Paris from different geographic origins [The above results show that one area . This in origins .−1 FT-IR spectra of different parts of G. rigescens from different geographical origins are shown in −1, which is due to the O–H stretch. The bands at 2933 and 2856 cm−1 are CH3 asymmetric stretching and stretching vibration of esters, respectively. The peak around 1937 cm−1 is assigned to the C=O stretching vibration of acid amides [−1 are the main absorption bands of iridoids or glycosides, which correspond to C–O or C–O–C stretching and C–C asymmetric stretching vibrations [G. rigescens by Mi et al. [G. rigescens all contain C–O or C–O–C and C–C bonds.The average 4000–400 cmd amides . In addii et al. , the actIn the PLS-DA and SVM regression models, the samples were divided into two categories: training set and validation set. Two-thirds of the samples were classified as training set and the others were assigned to the validation set by the Kennard-Stone algorithm .G. rigescens samples , stems from different geographical origins (model 2), leafs from different geographical origins (model 3) and three different parts  (model 4). In order to solve the problem of band overlap, baseline drift and noise, spectral preprocessing was applied ,33,34. F samples .G. rigescens roots from Dali, Lijiang, Diqing and Yuxi. In the score plot, samples from different geographical origins can be clustered in a range and distinguished from others. Samples from Yuxi are far away from the other three geographical origins, while the distance of the other three geographical origins are closer. It can be seen that PC1 separates the samples of Yuxi from others, the former is in the central of Yunnan Province and the latter is in the northwest of Yunnan Province, which matches the regularities of gentiopicroside content distribution analyzed by HPLC. PC2 separates the samples of Diqing and Dali from samples of Lijiang.2), root-mean-square error of estimation (RMSEE) and root-mean-square error of cross validation (RMSECV) are listed in 2 is greater than 0.94 and the RMSEE and RMSECV are low, which are less than 0.25. Thereinto, model 1 of samples from Yuxi have the best performance with the highest R2 and lowest RMSEE and RMSECV. As seen in The prediction results of the model parameters determination coefficient  and low RMSEE (0.0877) and RMCECV (0.1689).According to the Galtier criterion, two samples from Diqing (104 and 107) are identified as Lijiang samples erroneously and sample 76 can’t be judged accurately . As seenG. rigescens leaves from Dali, Lijiang, Diqing and Yuxi. The cumulative contribution reached 90.2%, when the first four principal components are employed. In 2, RMSEE and RMSECV of model 3 are shown in 2 (>0.88) and low RMSEE (<0.17) and RMSECV (<0.20). Thereinto, the best performance is for the samples from Yuxi. In The RG. rigescens samples can be separated based on three parts . The first four principal components which represent 87.0% of the explained variance are applied to model 4. It is clear that the roots, stems and leaves can be separated completely. Thia indicates that the metabolic profiles of different parts in G. rigescens are unlike. The stems and leaves samples cluster in two concentrated regions and the distance between them is close, however, the root samples distribute dispersedly whereby the root samples from Yuxi cluster outside the 95% confidence ellipses and the distance between the roots samples from Yuxi and the other root samples are large  and 2Der, all data was normalized in the region between 1 and 2. Then, the parameters c and g in the SVM regression model were selected by a grid search algorithm (GS) with seven-fold cross validation, genetic algorithm (GA) and particle swarm optimization algorithm (PSO). The GS with cross-validation can prevent the problem of overfitting and can be easily parallelized on account of parameters (c and g) . The alg20 and 2−20 to 1, respectively. As can be seen in In this study, the GS algorithm was applied to screen the parameters c and g in the region of 1 to 2t2 (96.39%) and RMSEE (3.1056) for training set and the highest Rv2 (83.57%) and the lowest RMSEP (11.1421) for validation set is obtained by the GS algorithm. Therefore, the GS method gives the best performance for the prediction of gentiopicroside content in G. rigescens.t2, Rt2, RMSEE and RMSEP are achieved and good agreement with the SVM regression model built for predicting gentiopicroside content in G. rigescens is observed.G. rigescens samples were collected from Dali, Lijiang, Diqing and Yuxi in Yunnan Province, China  used as extraction solvent was obtained from Xilong Chemical Company . Chromatography grade acetonitrile and formic acid were purchased from Sigma-Aldrich .Potassium bromide (KBr) was purchased from Tianjin Fengchuan Fine Chemical Research Institute . A gentiopicroside standard was supplied by the National Institute for the Control of Food and Drug control . Analytical grade methanol  after cleaning and dried for 24 h at 60 °C. Then, all samples were powdered in a high-speed blender and passed through an 80 mesh stainless steel sieve, separately. Then, the sieved powders were kept in Ziploc bags and stored at room temperature prior to analysis.The G. rigescens samples were subjected to analysis.A sample (25 mg) of each powder was weighed accurately using an electronic balance  and soaked with 1.5 mL of 80% methanol for 30 min under ultrasonication at room temperature. Before analysis by HPLC, the extracts were filtered through a 0.22 μm membrane filter . All the extracts of Gentiopicroside was determined using an Agilent 1260 Infinity system  equipped with a G1311 diode array detector, a quaternary pump and an on-line degasser. An Agilent Zorbax AB-C18 column  was utilized for the chromatographic separations. The mobile phase consisted of 0.1% aqueous formic acid in water (A) and acetonitrile (B). The gradient elution procedure was as follows: the initial mobile phase composition was set to 5% B for 5 min, then increased stepwise linearly first to 10% B from 5 to 10 min, then to 26% B from 10 to 26 min, and finally decreased to 30% for 30 min. The column temperature was maintained at 30 °C. The flow rate was set at 0.3 mL/min and the injection volume was 10 µL. During the experiment, the detective wavelength was set at 241 nm. Every samples were detected three times, and the averaged spectra were employed for further analysis.−1 with a resolution of 4 cm−1 and a total of 16 co-added scans. Pure KBr spectra were recorded as background spectra for deducting the CO2 and H2O peaks in real-time. Each spectrum was scanned in triplicate under constant temperature (25 °C) and humidity conditions, and the averaged spectra were employed for further analysis.FT-IR spectra acquisition was performed by using a Fourier transform infrared spectrometer  equipped with a deuterated triglycine sulfate detector. Powdered samples (1.2 mg) and 100 mg of KBr were precisely weighed and mixed evenly. Then, the mixed powder was pressed into a tablet by using a table press  for detection. Each FT-IR spectrum was collected in the region of 4000–400 cmBefore analysis, two-thirds of the samples were classified as the training set and the others were assigned to the validation set using the Kennard-Stone algorithm. Qualitative and quantitative models were developing with PLS-DA and SVM regression, respectively. PLS-DA, a supervised analysis method, was successfully applied to the classification of the FT-IR spectra . The basIn this paper, a library for the SVM toolkit LIBSVM 3.21 was applied in data processing which was developed by Chang and optimized by Lin . The per2), root-mean-square error of estimation (RMSEE), root-mean-square error of cross validation (RMSECV) and root-mean-square error of prediction (RMSEP) were considered to evaluate the performance of qualitative and quantitative model.The determination coefficient (R2 (Equation (1)) is the correlation between the measured values and predicted values. Generally, a higher R2 (<1) value means a better performance of both kinds of models [i is the measured value while ŷi is the predicted value. Rf models :(1)R2=∑it is the number of the training set and Nv is the number of validation set. In addition, in the qualitative model, the classification accuracy of the validation set depends on the predicted value (Ypre), and deviation values (Ydev) which are based on the following standards: (1) when Ypre > 0.5 and Ydev < 0.5, the sample of validation set belongs to the class; (2) when Ypre < 0.5 and Ydev < 0.5, the sample of validation set does not belong to the class; (3) when Ydev > 0.5, it means that the sample can’t judge accurately whether it belongs to the class or not [RMSEE, RMSECV and RMSEP were applied to evaluate the precision of model performance (Equations (2)–(4)). The lower RMSEE, RMSECV and RMSEP are, the fitter the models obtained ,53,54. Mhe model :(2)RMSEEs or not ,57.The FT-IR spectra were processed using Omnic . The chromatographic fingerprints were conducted using the Similarity Evaluation System for Chromatographic Fingerprints of traditional Chinese Medicines . The PLS-DA models were created by Simca , while the MSV regression model was created by MATLAB  with the LIBSVM-Faruto toolkit (Version Ultimate 3.1M) . All theG. rigescens. The discrimination of different parts of G. rigescens plants from different geographical origins by using FT-IR spectroscopy in combination with PLS-DA was presented. The feasibility of rapid quantitative analysis of gentiopicroside content in G. rigescens by application of FT-IR spectroscopy in combination with SVM regression was investigated. The results showed that for gentiopicroside determination, the parameter selection method of GS of a SVM regression model provided a good prediction. Overall, FT-IR spectroscopy combined with chemometrics could be a promising method for quality assessment of G. rigescens.In this study, a rapid FT-IR spectroscopic method combined with a chemometrics procedure was developed for the qualitative and quantitative analysis of


FLOating Window Projective Separator (FloWPS), tailored for personalized predictions based on molecular data. This procedure can operate with high throughput genetic datasets like gene expression or mutation profiles. Its application prevents SVM from extrapolation by excluding non-informative features. FloWPS requires training on the data for the individuals with known clinical outcomes to create a clinically relevant classifier. The genetic profiles linked with the outcomes are broken as usual into the training and validation datasets. The unique property of FloWPS is that irrelevant features in validation dataset that don’t have significant number of neighboring hits in the training dataset are removed from further analyses. Next, similarly to the k nearest neighbors (kNN) method, for each point of a validation dataset, FloWPS takes into account only the proximal points of the training dataset. Thus, for every point of a validation dataset, the training dataset is adjusted to form a floating window. FloWPS performance was tested on ten gene expression datasets for 992 cancer patients either responding or not on the different types of chemotherapy. We experimentally confirmed by leave-one-out cross-validation that FloWPS enables to significantly increase quality of a classifier built based on the classical SVM in most of the applications, particularly for polynomial kernels.Here, we propose a heuristic technique of data trimming for SVM termed  This method has been successfully applied for a wide variety of biomedical applications like searching Dicer RNase cleavage sites on pre-miRNA , and then by random selection of samples from the bigger subset. Thus, each resulting dataset contained equal numbers of cases classified as responders and non-responders.To preclude any possible bias that may affect the performance of machine-learning classifiers due to unequal representation of samples in two different classes , numbers of responding and non-responding cases were equalized within each dataset. Equalization was done by taking the full 30 top marker genes, and response statuses  for all selected patients from all datasets are listed on Supplementary Table To engineer a plausible feature space, where the SVM can be applied efficiently, we proposed to select from tens of thousands of individual gene expression features only few of them, which produce a good separation of clinical responders from non-responders. To do so, for every dataset under investigation we selected its particular top 30 genes, whose expression levels taken one by one had the highest ROC AUC values for distinguishing responder and non-responder profiles. We made a number of top informative features equal to 30 because the usual number of samples in considered datasets was not lower than 50 (a direct heuristic number for degree of freedom). These N samples, the leave-one-out procedure has been performed. Each individual sample was removed from the investigation one at a time, so N subdatasets each having N-1 individuals were generated. For each subdataset, the ROC AUC test was performed between responders and non-responders for each gene. The genes were next sorted according to their ROC AUC, and top 30 were selected for each subdataset. The final list of such core informative genes was generated as an intersection between top 30 selected genes for all N subdatasets. For every dataset under investigation, these final core sets are listed in Supplementary Table To produce more robust feature selection, for each dataset having, say, 3 tool termed FloWPS that has a potential to improve the performance of machine learning methods. Since extrapolation is a widely recognized Achilles heel of SVM  as a preprocessing for SVM application.i = 1, N serves as a validation case whose response to the treatment had to be predicted, whereas all remaining samples, j = 1,…(i-1),(i+1),…,N, collectively acts as a training dataset, and this procedure is repeated for all the samples. For machine leaning without data trimming, in a predefined feature space F =  every sample i, given for the test, is assigned by a classifier, constructed to (N-1) samples used for training.Since the number of samples in most of the datasets used here was relatively low, we tested our classifier using the leave-one-out cross-validation method, which introduces lesser errors than the standard five-bin cross-validation scheme generally applied for bigger datasets. According to the leave-one-out approach, for each sample F for all N testing samples, we propose using an individual space Fi, which contains individually adapted training data (of N-1 samples) for the testing sample i. It can be implemented using the following heuristics  we extract a subset Fi (m), where m is a parameter. A feature jf is kept in Fi(m) if on its axis there are at least m projections from training samples, which are larger than jf (i), and, at the same time, at least m, which are smaller than jf (i). The procedure for extraction of a subset Fi(m) is illustrated in Figure F = . A violet point stands for the validation sample in the feature space. Turquoise dots represent scattering of the training points. For example, the m-condition for the feature f2 is satisfied when m = 0,1,2,3,4,5 , whereas for the feature f1 it is satisfied only for m = 0 .(1) From the whole predefined feature space Fi (m) we keep for training only k closest samples (from given (N-1) samples); k is also a parameter .(2) In i = 1, N, and m and k parameter values, the predicted classification values are obtained . Considering known response status for each sample i, it is possible to calculate AUC values for a whole set of samples as a function over whole range of the parameters m and k  pairs.Hence, for every individual k Figure . Since tm,k)-settings for a final classifier , where max(AUC) is the maximal value of AUC, taken over the set of all possible  pairs, and the parameter p equals to a user-defined confidence threshold. To illustrate performance of this approach, we took two alternative values of p = 0.95 or 0.90, and then considered all the  pair positions on the AUC topogram. We next screened for the positions where AUC exceeded the threshold θ, and the total combination of these positions was taken as the prediction-accountable set S  for a certain validation case should be calculated by averaging the SVM predictions, P, over the whole set of positions belonging to the prediction-accountable set S, according to the formula: FP = Smean).We propose an algorithm of achieving the optimal  topogram . During our calculations, the FloWPS classifier was first fitted for the training dataset without a sample  to be classified. For these all (N-1) samples AUCi was calculated as a function of data trimming parameters m and k  topogram , which is shown as a black horizontal line on Figure p and scheme of gene selection, FloWPS classifier demonstrated the ROC AUC exceeding the corresponding value for the classical SVM , Figure p = 0.90. The advantage of FloWPS over SVM is more essential in the conditions vulnerable to SVM overtraining, e.g., for linear kernel with high values of the cost/penalty parameter (C = 100 or 1000) or for polynomial kernel, where SVM may be easily overfitted. Fortunately, FloWPS precludes such overfitting, thus raising AUC and decreasing FDR. The same pattern was also seen for the Sn, Sp, ACC and MCC values  and different values for cost/penalty parameters m and k for FloWPS. Namely, a maximum for AUC as a function of PC number is found and then used as the optimal number of PCs for an SVM-based prediction.Note that FloWPS is not the only possible data reduction/feature selection method, which may be used for preprocessing to improve the classifier’s quality. To try a simple alternative to FloWPS, which is, however, not specific to individual samples, we did calculations based on PCA mode rather than original features. The number of PCs taken for building the SVM model, may act as a parameter, which is optimized in a manner similar to optimization of p = 0.90  is many hundreds times larger than the numbers of patients. So, the first problem in such applications was to extract an appropriate number of features, in which space one could achieve a classifier-predictor with a high level of quality. There are many authors focused to resolve the preprocessing problem . Some feglobal (like SVM) and local (like kNN)   methods ike kNN) , means tr Figure . The latPS Table , 5. The PS Table .We hypothesize that FloWPS and data trimming may be also helpful for improving other learning methods based on multi-omics data, including nowadays-flourishing deep learning approaches .preprocessCore  topograms, like Figure mathplotlib Python library  and TARGET, and had been previously anonymized by the teams, who had worked with them.NB designed the overall research, suggested the principles of data trimming and prediction-accountable set, and wrote most parts of the manuscript. VT performed most part of calculations. MS suggested datasets with clinical responders and non-responders and performed feature selection. AM wrote the initial version of computational code. AS adapted this code for parallel calculations. AG tested and debugged the computational code. IM and AB essentially improved the manuscript upon the draft version has been prepared. AB preformed the overall scientific supervision of the project.VT, MS, AS, AG, AB, and NB were employed by OmicsWay Corporation, Walnut, CA, United States. AM was employed by Yandex N.V. Corporation, Moscow, Russia. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.