Temporal associations of plasma levels of the secreted phospholipase A2 family and mortality in severe COVID-19.

Previous research suggests that group IIA-secreted phospholipase A2 (sPLA2-IIA) plays a role in and predicts lethal COVID-19 disease. The current study reanalyzed a longitudinal proteomic data set to determine the temporal relationship between levels of several members of a family of sPLA2 isoforms and the severity of COVID-19 in 214 ICU patients. The levels of six secreted PLA2 isoforms, sPLA2-IIA, sPLA2-V, sPLA2-X, sPLA2-IB, sPLA2-IIC, and sPLA2-XVI, increased over the first 7 ICU days in those who succumbed to the disease but attenuated over the same time period in survivors. In contrast, a reversed pattern in sPLA2-IID and sPLA2-XIIB levels over 7 days suggests a protective role of these two isoforms. Furthermore, decision tree models demonstrated that sPLA2-IIA outperformed top-ranked cytokines and chemokines as a predictor of patient outcome. Taken together, proteomic analysis revealed temporal sPLA2 patterns that reflect the critical roles of sPLA2 isoforms in severe COVID-19 disease.

The karyometric signature is altered in fallopian tubes with serous tubal intraepithelial carcinoma.

OBJECTIVE: Recent evidence suggests that the fimbriated end of the fallopian tube harbors the precursor cells for many high-grade ovarian cancers, opening the door for development of better screening methods that directly assess the fallopian tube in women at risk for malignancy. Previously we have shown that the karyometric signature is abnormal in the fallopian tube epithelium in women at hereditary risk of ovarian cancer. In this study, we sought to determine whether the karyometric signature in serous tubal intraepithelial carcinoma (STIC) is significantly different from normal, and whether an abnormal karyometric signature can be detected in histologically normal tubal epithelial cells adjacent to STIC lesions. METHODS: The karyometric signature was measured in epithelial cells from the proximal and fimbriated portion of the fallopian tube in fallopian tube specimens removed from women at: 1) average risk for ovarian cancer undergoing surgery for benign gynecologic indications (n = 37), 2) hereditary risk of ovarian cancer (germline BRCA alterations) undergoing risk-reducing surgery (n = 44), and 3) diagnosed with fimbrial STICs (n = 17). RESULTS: The karyometric signature in tubes with fimbrial STICs differed from that of tubes with benign histology. The degree of karyometric alteration increased with increasing proximity to fimbrial STICs, ranging from moderate in the proximal portion of the tube, to greatest in both normal appearing fimbrial cells near STICs as well as in fimbrial STIC lesions. CONCLUSION: These data demonstrate an abnormal karyometric signature in STICs that may extend beyond the STIC, potentially providing an opportunity for early detection of fallopian tube neoplasia.

Heterogeneous Domain Adaptation With Adversarial Neural Representation Learning: Experiments on E-Commerce and Cybersecurity.

Learning predictive models in new domains with scarce training data is a growing challenge in modern supervised learning scenarios. This incentivizes developing domain adaptation methods that leverage the knowledge in known domains (source) and adapt to new domains (target) with a different probability distribution. This becomes more challenging when the source and target domains are in heterogeneous feature spaces, known as heterogeneous domain adaptation (HDA). While most HDA methods utilize mathematical optimization to map source and target data to a common space, they suffer from low transferability. Neural representations have proven to be more transferable; however, they are mainly designed for homogeneous environments. Drawing on the theory of domain adaptation, we propose a novel framework, Heterogeneous Adversarial Neural Domain Adaptation (HANDA), to effectively maximize the transferability in heterogeneous environments. HANDA conducts feature and distribution alignment in a unified neural network architecture and achieves domain invariance through adversarial kernel learning. Three experiments were conducted to evaluate the performance against the state-of-the-art HDA methods on major image and text e-commerce benchmarks. HANDA shows statistically significant improvement in predictive performance. The practical utility of HANDA was shown in real-world dark web online markets. HANDA is an important step towards successful domain adaptation in e-commerce applications.

Identification of an allele-specific transcription factor binding interaction that regulates PLA2G2A gene expression.

The secreted phospholipase A 2 (sPLA 2 ) isoform, sPLA 2 -IIA, has been implicated in a variety of diseases and conditions, including bacteremia, cardiovascular disease, COVID-19, sepsis, adult respiratory distress syndrome, and certain cancers. Given its significant role in these conditions, understanding the regulatory mechanisms impacting its levels is crucial. Genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs), including rs11573156, that are associated with circulating levels of sPLA 2 -IIA. Through Genotype-Tissue Expression (GTEx), 234 expression quantitative trait loci (eQTLs) were identified for the gene that encodes for sPLA 2 -IIA, PLA2G2A . SNP2TFBS ( https://ccg.epfl.ch/snp2tfbs/ ) was utilized to ascertain the binding affinities between transcription factors (TFs) to both the reference and alternative alleles of identified SNPs. Subsequently, ChIP-seq peaks highlighted the TF combinations that specifically bind to the SNP, rs11573156. SP1 emerged as a significant TF/SNP pair in liver cells, with rs11573156/SP1 interaction being most prominent in liver, prostate, ovary, and adipose tissues. Further analysis revealed that the upregulation of PLA2G2A transcript levels through the rs11573156 variant was affected by tissue SP1 protein levels. By leveraging an ordinary differential equation, structured upon Michaelis-Menten enzyme kinetics assumptions, we modeled the PLA2G2A transcription's dependence on SP1 protein levels, incorporating the SNP's influence. Collectively, these data strongly suggest that the binding affinity differences of SP1 for the different rs11573156 alleles can influence PLA2G2A expression. This, in turn, can modulate sPLA2-IIA levels, impacting a wide range of human diseases.

Adapting Nanopore Sequencing Basecalling Models for Modification Detection via Incremental Learning and Anomaly Detection.

We leverage machine learning approaches to adapt nanopore sequencing basecallers for nucleotide modification detection. We first apply the incremental learning technique to improve the basecalling of modification-rich sequences, which are usually of high biological interests. With sequence backbones resolved, we further run anomaly detection on individual nucleotides to determine their modification status. By this means, our pipeline promises the single-molecule, single-nucleotide and sequence context-free detection of modifications. We benchmark the pipeline using control oligos, further apply it in the basecalling of densely-modified yeast tRNAs and E.coli genomic DNAs, the cross-species detection of N6-methyladenosine (m6A) in mammalian mRNAs, and the simultaneous detection of N1-methyladenosine (m1A) and m6A in human mRNAs. Our IL-AD workflow is available at: https://github.com/wangziyuan66/IL-AD.

Temporal Associations of Plasma Levels of the Secreted Phospholipase A 2 Family and Mortality in Severe COVID-19.

Previous research suggests that group IIA secreted phospholipase A 2 (sPLA 2 -IIA) plays a role in and predicts severe COVID-19 disease. The current study reanalyzed a longitudinal proteomic data set to determine the temporal (days 0, 3 and 7) relationship between the levels of several members of a family of sPLA 2 isoforms and the severity of COVID-19 in 214 ICU patients. The levels of six secreted PLA 2 isoforms, sPLA 2 -IIA, sPLA 2 -V, sPLA 2 -X, sPLA 2 -IB, sPLA 2 -IIC, and sPLA 2 -XVI, increased over the first 7 ICU days in those who succumbed to the disease. sPLA 2 -IIA outperformed top ranked cytokines and chemokines as predictors of patient outcome. A decision tree corroborated these results with day 0 to day 3 kinetic changes of sPLA 2 -IIA that separated the death and severe categories from the mild category and increases from day 3 to day 7 significantly enriched the lethal category. In contrast, there was a time-dependent decrease in sPLA 2 -IID and sPLA 2 -XIIB in patients with severe or lethal disease, and these two isoforms were at higher levels in mild patients. Taken together, proteomic analysis revealed temporal sPLA 2 patterns that reflect the critical roles of sPLA 2 isoforms in severe COVID-19 disease.

Group IIA secreted phospholipase A2 is associated with the pathobiology leading to COVID-19 mortality.

There is an urgent need to identify the cellular and molecular mechanisms responsible for severe COVID-19 that results in death. We initially performed both untargeted and targeted lipidomics as well as focused biochemical analyses of 127 plasma samples and found elevated metabolites associated with secreted phospholipase A2 (sPLA2) activity and mitochondrial dysfunction in patients with severe COVID-19. Deceased COVID-19 patients had higher levels of circulating, catalytically active sPLA2 group IIA (sPLA2-IIA), with a median value that was 9.6-fold higher than that for patients with mild disease and 5.0-fold higher than the median value for survivors of severe COVID-19. Elevated sPLA2-IIA levels paralleled several indices of COVID-19 disease severity (e.g., kidney dysfunction, hypoxia, multiple organ dysfunction). A decision tree generated by machine learning identified sPLA2-IIA levels as a central node in the stratification of patients who died from COVID-19. Random forest analysis and least absolute shrinkage and selection operator-based (LASSO-based) regression analysis additionally identified sPLA2-IIA and blood urea nitrogen (BUN) as the key variables among 80 clinical indices in predicting COVID-19 mortality. The combined PLA-BUN index performed significantly better than did either one alone. An independent cohort (n = 154) confirmed higher plasma sPLA2-IIA levels in deceased patients compared with levels in plasma from patients with severe or mild COVID-19, with the PLA-BUN index-based decision tree satisfactorily stratifying patients with mild, severe, or fatal COVID-19. With clinically tested inhibitors available, this study identifies sPLA2-IIA as a therapeutic target to reduce COVID-19 mortality.

Group IIA Secreted Phospholipase A 2 Plays a Central Role in the Pathobiology of COVID-19.

There is an urgent need to identify cellular and molecular mechanisms responsible for severe COVID-19 disease accompanied by multiple organ failure and high mortality rates. Here, we performed untargeted/targeted lipidomics and focused biochemistry on 127 patient plasma samples, and showed high levels of circulating, enzymatically active secreted phospholipase A 2 Group IIA (sPLA 2 -IIA) in severe and fatal COVID-19 disease compared with uninfected patients or mild illness. Machine learning demonstrated that sPLA 2 -IIA effectively stratifies severe from fatal COVID-19 disease. We further introduce a PLA-BUN index that combines sPLA 2 -IIA and blood urea nitrogen (BUN) threshold levels as a critical risk factor for mitochondrial dysfunction, sustained inflammatory injury and lethal COVID-19. With the availability of clinically tested inhibitors of sPLA 2 -IIA, our study opens the door to a precision intervention using indices discovered here to reduce COVID-19 mortality.

Correction to: binomialRF: interpretable combinatoric efficiency of random forests to identify biomarker interactions.

An amendment to this paper has been published and can be accessed via the original article.

binomialRF: interpretable combinatoric efficiency of random forests to identify biomarker interactions.

BACKGROUND: In this era of data science-driven bioinformatics, machine learning research has focused on feature selection as users want more interpretation and post-hoc analyses for biomarker detection. However, when there are more features (i.e., transcripts) than samples (i.e., mice or human samples) in a study, it poses major statistical challenges in biomarker detection tasks as traditional statistical techniques are underpowered in high dimension. Second and third order interactions of these features pose a substantial combinatoric dimensional challenge. In computational biology, random forest (RF) classifiers are widely used due to their flexibility, powerful performance, their ability to rank features, and their robustness to the "P > > N" high-dimensional limitation that many matrix regression algorithms face. We propose binomialRF, a feature selection technique in RFs that provides an alternative interpretation for features using a correlated binomial distribution and scales efficiently to analyze multiway interactions. RESULTS: In both simulations and validation studies using datasets from the TCGA and UCI repositories, binomialRF showed computational gains (up to 5 to 300 times faster) while maintaining competitive variable precision and recall in identifying biomarkers' main effects and interactions. In two clinical studies, the binomialRF algorithm prioritizes previously-published relevant pathological molecular mechanisms (features) with high classification precision and recall using features alone, as well as with their statistical interactions alone. CONCLUSION: binomialRF extends upon previous methods for identifying interpretable features in RFs and brings them together under a correlated binomial distribution to create an efficient hypothesis testing algorithm that identifies biomarkers' main effects and interactions. Preliminary results in simulations demonstrate computational gains while retaining competitive model selection and classification accuracies. Future work will extend this framework to incorporate ontologies that provide pathway-level feature selection from gene expression input data.

On fusion methods for knowledge discovery from multi-omics datasets.

Recent years have witnessed the tendency of measuring a biological sample on multiple omics scales for a comprehensive understanding of how biological activities on varying levels are perturbed by genetic variants, environments, and their interactions. This new trend raises substantial challenges to data integration and fusion, of which the latter is a specific type of integration that applies a uniform method in a scalable manner, to solve biological problems which the multi-omics measurements target. Fusion-based analysis has advanced rapidly in the past decade, thanks to application drivers and theoretical breakthroughs in mathematics, statistics, and computer science. We will briefly address these methods from methodological and mathematical perspectives and categorize them into three types of approaches: data fusion (a narrowed definition as compared to the general data fusion concept), model fusion, and mixed fusion. We will demonstrate at least one typical example in each specific category to exemplify the characteristics, principles, and applications of the methods in general, as well as discuss the gaps and potential issues for future studies.

Personalized beyond Precision: Designing Unbiased Gold Standards to Improve Single-Subject Studies of Personal Genome Dynamics from Gene Products.

Background: Developing patient-centric baseline standards that enable the detection of clinically significant outlier gene products on a genome-scale remains an unaddressed challenge required for advancing personalized medicine beyond the small pools of subjects implied by "precision medicine". This manuscript proposes a novel approach for reference standard development to evaluate the accuracy of single-subject analyses of transcriptomes and offers extensions into proteomes and metabolomes. In evaluation frameworks for which the distributional assumptions of statistical testing imperfectly model genome dynamics of gene products, artefacts and biases are confounded with authentic signals. Model confirmation biases escalate when studies use the same analytical methods in the discovery sets and reference standards. In such studies, replicated biases are confounded with measures of accuracy. We hypothesized that developing method-agnostic reference standards would reduce such replication biases. We propose to evaluate discovery methods with a reference standard derived from a consensus of analytical methods distinct from the discovery one to minimize statistical artefact biases. Our methods involve thresholding effect-size and expression-level filtering of results to improve consensus between analytical methods. We developed and released an R package "referenceNof1" to facilitate the construction of robust reference standards. Results: Since RNA-Seq data analysis methods often rely on binomial and negative binomial assumptions to non-parametric analyses, the differences create statistical noise and make the reference standards method dependent. In our experimental design, the accuracy of 30 distinct combinations of fold changes (FC) and expression counts (hereinafter "expression") were determined for five types of RNA analyses in two different datasets. This design was applied to two distinct datasets: Breast cancer cell lines and a yeast study with isogenic biological replicates in two experimental conditions. Furthermore, the reference standard (RS) comprised all RNA analytical methods with the exception of the method testing accuracy. To mitigate biases towards a specific analytical method, the pairwise Jaccard Concordance Index between observed results of distinct analytical methods were calculated for optimization. Optimization through thresholding effect-size and expression-level reduced the greatest discordances between distinct methods' analytical results and resulted in a 65% increase in concordance. Conclusions: We have demonstrated that comparing accuracies of different single-subject analysis methods for clinical optimization in transcriptomics requires a new evaluation framework. Reliable and robust reference standards, independent of the evaluated method, can be obtained under a limited number of parameter combinations: Fold change (FC) ranges thresholds, expression level cutoffs, and exclusion of the tested method from the RS development process. When applying anticonservative reference standard frameworks (e.g., using the same method for RS development and prediction), most of the concordant signal between prediction and Gold Standard (GS) cannot be confirmed by other methods, which we conclude as biased results. Statistical tests to determine DEGs from a single-subject study generate many biased results requiring subsequent filtering to increase reliability. Conventional single-subject studies pertain to one or a few patient's measures over time and require a substantial conceptual framework extension to address the numerous measures in genome-wide analyses of gene products. The proposed referenceNof1 framework addresses some of the inherent challenges for improving transcriptome scale single-subject analyses by providing a robust approach to constructing reference standards.

Graded regulation of cellular quiescence depth between proliferation and senescence by a lysosomal dimmer switch.

The reactivation of quiescent cells to proliferate is fundamental to tissue repair and homeostasis in the body. Often referred to as the G0 state, quiescence is, however, not a uniform state but with graded depth. Shallow quiescent cells exhibit a higher tendency to revert to proliferation than deep quiescent cells, while deep quiescent cells are still fully reversible under physiological conditions, distinct from senescent cells. Cellular mechanisms underlying the control of quiescence depth and the connection between quiescence and senescence are poorly characterized, representing a missing link in our understanding of tissue homeostasis and regeneration. Here we measured transcriptome changes as rat embryonic fibroblasts moved from shallow to deep quiescence over time in the absence of growth signals. We found that lysosomal gene expression was significantly up-regulated in deep quiescence, and partially compensated for gradually reduced autophagy flux. Reducing lysosomal function drove cells progressively deeper into quiescence and eventually into a senescence-like irreversibly arrested state; increasing lysosomal function, by lowering oxidative stress, progressively pushed cells into shallower quiescence. That is, lysosomal function modulates graded quiescence depth between proliferation and senescence as a dimmer switch. Finally, we found that a gene-expression signature developed by comparing deep and shallow quiescence in fibroblasts can correctly classify a wide array of senescent and aging cell types in vitro and in vivo, suggesting that while quiescence is generally considered to protect cells from irreversible arrest of senescence, quiescence deepening likely represents a common transition path from cell proliferation to senescence, related to aging.

Effect of Intermittent Versus Continuous Low-Dose Aspirin on Nasal Epithelium Gene Expression in Current Smokers: A Randomized, Double-Blinded Trial.

A chemopreventive effect of aspirin (ASA) on lung cancer risk is supported by epidemiologic and preclinical studies. We conducted a randomized, double-blinded study in current heavy smokers to compare modulating effects of intermittent versus continuous low-dose ASA on nasal epithelium gene expression and arachidonic acid (ARA) metabolism. Fifty-four participants were randomized to intermittent (ASA 81 mg daily for one week/placebo for one week) or continuous (ASA 81 mg daily) for 12 weeks. Low-dose ASA suppressed urinary prostaglandin E2 metabolite (PGEM; change of -4.55 ± 11.52 from baseline 15.44 ± 13.79 ng/mg creatinine for arms combined, P = 0.02), a surrogate of COX-mediated ARA metabolism, but had minimal effects on nasal gene expression of nasal or bronchial gene-expression signatures associated with smoking, lung cancer, and chronic obstructive pulmonary disease. Suppression of urinary PGEM correlated with favorable changes in a smoking-associated gene signature (P < 0.01). Gene set enrichment analysis (GSEA) showed that ASA intervention led to 1,079 enriched gene sets from the Canonical Pathways within the Molecular Signatures Database. In conclusion, low-dose ASA had minimal effects on known carcinogenesis gene signatures in nasal epithelium of current smokers but results in wide-ranging genomic changes in the nasal epithelium, demonstrating utility of nasal brushings as a surrogate to measure gene-expression responses to chemoprevention. PGEM may serve as a marker for smoking-associated gene-expression changes and systemic inflammation. Future studies should focus on NSAIDs or agent combinations with broader inhibition of pro-inflammatory ARA metabolism to shift gene signatures in an anti-carcinogenic direction.

Phase II Trial of Chemopreventive Effects of Levonorgestrel on Ovarian and Fallopian Tube Epithelium in Women at High Risk for Ovarian Cancer: An NRG Oncology Group/GOG Study.

A large body of epidemiologic evidence has shown that use of progestin-containing preparations lowers ovarian cancer risk. The purpose of the current study was to gather further preclinical evidence supporting progestins as cancer chemopreventives by demonstrating progestin-activation of surrogate endpoint biomarkers pertinent to cancer prevention in the genital tract of women at increased risk of ovarian cancer. There were 64 women enrolled in a multi-institutional randomized trial who chose to undergo risk-reducing bilateral salpingo-oophorectomy (BSO) and to receive the progestin levonorgestrel or placebo for 4 to 6 weeks prior to undergoing BSO. The ovarian and fallopian tube epithelia (FTE) were compared immunohistochemically for effects of levonorgestrel on apoptosis (primary endpoint). Secondary endpoints included TGFß isoform expression, proliferation, and karyometric features of nuclear abnormality. In both the ovary and fallopian tube, levonorgestrel did not confer significant changes in apoptosis or expression of the TGFß1, 2, or 3 isoforms. In the ovarian epithelium, treatment with levonorgestrel significantly decreased the proliferation index. The mean ovarian Ki-67 value in the placebo arm was 2.027 per 100 cells versus 0.775 per 100 cells in the levonorgestrel arm (two-sided P value via Mann-Whitney U test = 0.0114). The karyometric signature of nuclei in both the ovarian and FTE deviated significantly from normal controls (women at average risk of ovarian cancer), but was significantly less abnormal in women treated with levonorgestrel. These karyometric data further support the idea that progestins may clear genetically abnormal cells and act as chemopreventive agents against ovarian and fallopian tube cancer.

Semiparametric Single-Index Model for Estimating Optimal Individualized Treatment Strategy.

Different from the standard treatment discovery framework which is used for finding single treatments for a homogenous group of patients, personalized medicine involves finding therapies that are tailored to each individual in a heterogeneous group. In this paper, we propose a new semiparametric additive single-index model for estimating individualized treatment strategy. The model assumes a flexible and nonparametric link function for the interaction between treatment and predictive covariates. We estimate the rule via monotone B-splines and establish the asymptotic properties of the estimators. Both simulations and an real data application demonstrate that the proposed method has a competitive performance.

Exit from quiescence displays a memory of cell growth and division.

Reactivating quiescent cells to proliferate is critical to tissue repair and homoeostasis. Quiescence exit is highly noisy even for genetically identical cells under the same environmental conditions. Deregulation of quiescence exit is associated with many diseases, but cellular mechanisms underlying the noisy process of exiting quiescence are poorly understood. Here we show that the heterogeneity of quiescence exit reflects a memory of preceding cell growth at quiescence induction and immediate division history before quiescence entry, and that such a memory is reflected in cell size at a coarse scale. The deterministic memory effects of preceding cell cycle, coupled with the stochastic dynamics of an Rb-E2F bistable switch, jointly and quantitatively explain quiescence-exit heterogeneity. As such, quiescence can be defined as a distinct state outside of the cell cycle while displaying a sequential cell order reflecting preceding cell growth and division variations.The quiescence-exit process is noisy even in genetically identical cells under the same environmental conditions. Here the authors show that the heterogeneity of quiescence exit reflects a memory of preceding cell growth at quiescence induction and immediate division history prior to quiescence entry.

N-of-1-pathways MixEnrich: advancing precision medicine via single-subject analysis in discovering dynamic changes of transcriptomes.

BACKGROUND: Transcriptome analytic tools are commonly used across patient cohorts to develop drugs and predict clinical outcomes. However, as precision medicine pursues more accurate and individualized treatment decisions, these methods are not designed to address single-patient transcriptome analyses. We previously developed and validated the N-of-1-pathways framework using two methods, Wilcoxon and Mahalanobis Distance (MD), for personal transcriptome analysis derived from a pair of samples of a single patient. Although, both methods uncover concordantly dysregulated pathways, they are not designed to detect dysregulated pathways with up- and down-regulated genes (bidirectional dysregulation) that are ubiquitous in biological systems. RESULTS: We developed N-of-1-pathways MixEnrich, a mixture model followed by a gene set enrichment test, to uncover bidirectional and concordantly dysregulated pathways one patient at a time. We assess its accuracy in a comprehensive simulation study and in a RNA-Seq data analysis of head and neck squamous cell carcinomas (HNSCCs). In presence of bidirectionally dysregulated genes in the pathway or in presence of high background noise, MixEnrich substantially outperforms previous single-subject transcriptome analysis methods, both in the simulation study and the HNSCCs data analysis (ROC Curves; higher true positive rates; lower false positive rates). Bidirectional and concordant dysregulated pathways uncovered by MixEnrich in each patient largely overlapped with the quasi-gold standard compared to other single-subject and cohort-based transcriptome analyses. CONCLUSION: The greater performance of MixEnrich presents an advantage over previous methods to meet the promise of providing accurate personal transcriptome analysis to support precision medicine at point of care.

kMEn: Analyzing noisy and bidirectional transcriptional pathway responses in single subjects.

MOTIVATION: Understanding dynamic, patient-level transcriptomic response to therapy is an important step forward for precision medicine. However, conventional transcriptome analysis aims to discover cohort-level change, lacking the capacity to unveil patient-specific response to therapy. To address this gap, we previously developed two N-of-1-pathways methods, Wilcoxon and Mahalanobis distance, to detect unidirectionally responsive transcripts within a pathway using a pair of samples from a single subject. Yet, these methods cannot recognize bidirectionally (up and down) responsive pathways. Further, our previous approaches have not been assessed in presence of background noise and are not designed to identify differentially expressed mRNAs between two samples of a patient taken in different contexts (e.g. cancer vs non cancer), which we termed responsive transcripts (RTs). METHODS: We propose a new N-of-1-pathways method, k-Means Enrichment (kMEn), that detects bidirectionally responsive pathways, despite background noise, using a pair of transcriptomes from a single patient. kMEn identifies transcripts responsive to the stimulus through k-means clustering and then tests for an over-representation of the responsive genes within each pathway. The pathways identified by kMEn are mechanistically interpretable pathways significantly responding to a stimulus. RESULTS: In ∼9000 simulations varying six parameters, superior performance of kMEn over previous single-subject methods is evident by: (i) improved precision-recall at various levels of bidirectional response and (ii) lower rates of false positives (1-specificity) when more than 10% of genes in the genome are differentially expressed (background noise). In a clinical proof-of-concept, personal treatment-specific pathways identified by kMEn correlate with therapeutic response (p-value<0.01). CONCLUSION: Through improved single-subject transcriptome dynamics of bidirectionally-regulated signals, kMEn provides a novel approach to identify mechanism-level biomarkers.

Principal weighted support vector machines for sufficient dimension reduction in binary classification.

Sufficient dimension reduction is popular for reducing data dimensionality without stringent model assumptions. However, most existing methods may work poorly for binary classification. For example, sliced inverse regression (Li, 1991) can estimate at most one direction if the response is binary. In this paper we propose principal weighted support vector machines, a unified framework for linear and nonlinear sufficient dimension reduction in binary classification. Its asymptotic properties are studied, and an efficient computing algorithm is proposed. Numerical examples demonstrate its performance in binary classification.