Joint dimension reduction and clustering analysis of single-cell RNA-seq and spatial transcriptomics data.

Dimension reduction and (spatial) clustering is usually performed sequentially; however, the low-dimensional embeddings estimated in the dimension-reduction step may not be relevant to the class labels inferred in the clustering step. We therefore developed a computation method, Dimension-Reduction Spatial-Clustering (DR-SC), that can simultaneously perform dimension reduction and (spatial) clustering within a unified framework. Joint analysis by DR-SC produces accurate (spatial) clustering results and ensures the effective extraction of biologically informative low-dimensional features. DR-SC is applicable to spatial clustering in spatial transcriptomics that characterizes the spatial organization of the tissue by segregating it into multiple tissue structures. Here, DR-SC relies on a latent hidden Markov random field model to encourage the spatial smoothness of the detected spatial cluster boundaries. Underlying DR-SC is an efficient expectation-maximization algorithm based on an iterative conditional mode. As such, DR-SC is scalable to large sample sizes and can optimize the spatial smoothness parameter in a data-driven manner. With comprehensive simulations and real data applications, we show that DR-SC outperforms existing clustering and spatial clustering methods: it extracts more biologically relevant features than conventional dimension reduction methods, improves clustering performance, and offers improved trajectory inference and visualization for downstream trajectory inference analyses.

Center-augmented ℓ2 -type regularization for subgroup learning.

The existing methods for subgroup analysis can be roughly divided into two categories: finite mixture models (FMM) and regularization methods with an ℓ1 -type penalty. In this paper, by introducing the group centers and ℓ2 -type penalty in the loss function, we propose a novel center-augmented regularization (CAR) method; this method can be regarded as a unification of the regularization method and FMM and hence exhibits higher efficiency and robustness and simpler computations than the existing methods. In particular, its computational complexity is reduced from the O ( n 2 ) $O(n^2)$ of the conventional pairwise-penalty method to only O ( n K ) $O(nK)$ , where n is the sample size and K is the number of subgroups. The asymptotic normality of CAR is established, and the convergence of the algorithm is proven. CAR is applied to a dataset from a multicenter clinical trial, Buprenorphine in the Treatment of Opiate Dependence; a larger R2 is produced and three additional significant variables are identified compared to those of the existing methods.

Functional data analysis with covariate-dependent mean and covariance structures.

Functional data analysis has emerged as a powerful tool in response to the ever-increasing resources and efforts devoted to collecting information about response curves or anything that varies over a continuum. However, limited progress has been made with regard to linking the covariance structures of response curves to external covariates, as most functional models assume a common covariance structure. We propose a new functional regression model with covariate-dependent mean and covariance structures. Particularly, by allowing variances of random scores to be covariate-dependent, we identify eigenfunctions for each individual from the set of eigenfunctions that govern the variation patterns across all individuals, resulting in high interpretability and prediction power. We further propose a new penalized quasi-likelihood procedure that combines regularization and B-spline smoothing for model selection and estimation and establish the convergence rate and asymptotic normality of the proposed estimators. The utility of the developed method is demonstrated via simulations, as well as an analysis of the Avon Longitudinal Study of Parents and Children concerning parental effects on the growth curves of their offspring, which yields biologically interesting results.

Supervised structural learning of semiparametric regression on high-dimensional correlated covariates with applications to eQTL studies.

Expression quantitative trait loci (eQTL) studies utilize regression models to explain the variance of gene expressions with genetic loci or single nucleotide polymorphisms (SNPs). However, regression models for eQTL are challenged by the presence of high dimensional non-sparse and correlated SNPs with small effects, and nonlinear relationships between responses and SNPs. Principal component analyses are commonly conducted for dimension reduction without considering responses. Because of that, this non-supervised learning method often does not work well when the focus is on discovery of the response-covariate relationship. We propose a new supervised structural dimensional reduction method for semiparametric regression models with high dimensional and correlated covariates; we extract low-dimensional latent features from a vast number of correlated SNPs while accounting for their relationships, possibly nonlinear, with gene expressions. Our model identifies important SNPs associated with gene expressions and estimates the association parameters via a likelihood-based algorithm. A GTEx data application on a cancer related gene is presented with 18 novel eQTLs detected by our method. In addition, extensive simulations show that our method outperforms the other competing methods in bias, efficiency, and computational cost.

Cluster non-Gaussian functional data.

Gaussian distributions have been commonly assumed when clustering functional data. When the normality condition fails, biased results will follow. Additional challenges occur as the number of the clusters is often unknown a priori. This paper focuses on clustering non-Gaussian functional data without the prior information of the number of clusters. We introduce a semiparametric mixed normal transformation model to accommodate non-Gaussian functional data, and propose a penalized approach to simultaneously estimate the parameters, transformation function, and the number of clusters. The estimators are shown to be consistent and asymptotically normal. The practical utility of the methods is confirmed via simulations as well as an application of the analysis of Alzheimer's disease study. The proposed method yields much less classification error than the existing methods. Data used in preparation of this paper were obtained from the Alzheimer's Disease Neuroimaging Initiative database.

Covariance-Insured Screening.

Modern bio-technologies have produced a vast amount of high-throughput data with the number of predictors far greater than the sample size. In order to identify more novel biomarkers and understand biological mechanisms, it is vital to detect signals weakly associated with outcomes among ultrahigh-dimensional predictors. However, existing screening methods, which typically ignore correlation information, are likely to miss weak signals. By incorporating the inter-feature dependence, a covariance-insured screening approach is proposed to identify predictors that are jointly informative but marginally weakly associated with outcomes. The validity of the method is examined via extensive simulations and a real data study for selecting potential genetic factors related to the onset of multiple myeloma.

A single-index threshold Cox proportional hazard model for identifying a treatment-sensitive subset based on multiple biomarkers.

In this paper, we introduce a single-index threshold Cox proportional hazard model to select and combine biomarkers to identify patients who may be sensitive to a specific treatment. A penalized smoothed partial likelihood is proposed to estimate the parameters in the model. A simple, efficient, and unified algorithm is presented to maximize this likelihood function. The estimators based on this likelihood function are shown to be consistent and asymptotically normal. Under mild conditions, the proposed estimators also achieve the oracle property. The proposed approach is evaluated through simulation analyses and application to the analysis of data from two clinical trials, one involving patients with locally advanced or metastatic pancreatic cancer and one involving patients with resectable lung cancer.

Component-wise gradient boosting and false discovery control in survival analysis with high-dimensional covariates.

MOTIVATION: Technological advances that allow routine identification of high-dimensional risk factors have led to high demand for statistical techniques that enable full utilization of these rich sources of information for genetics studies. Variable selection for censored outcome data as well as control of false discoveries (i.e. inclusion of irrelevant variables) in the presence of high-dimensional predictors present serious challenges. This article develops a computationally feasible method based on boosting and stability selection. Specifically, we modified the component-wise gradient boosting to improve the computational feasibility and introduced random permutation in stability selection for controlling false discoveries. RESULTS: We have proposed a high-dimensional variable selection method by incorporating stability selection to control false discovery. Comparisons between the proposed method and the commonly used univariate and Lasso approaches for variable selection reveal that the proposed method yields fewer false discoveries. The proposed method is applied to study the associations of 2339 common single-nucleotide polymorphisms (SNPs) with overall survival among cutaneous melanoma (CM) patients. The results have confirmed that BRCA2 pathway SNPs are likely to be associated with overall survival, as reported by previous literature. Moreover, we have identified several new Fanconi anemia (FA) pathway SNPs that are likely to modulate survival of CM patients. AVAILABILITY AND IMPLEMENTATION: The related source code and documents are freely available at https://sites.google.com/site/bestumich/issues. CONTACT: yili@umich.edu.

Levodopa alone compared with levodopa-sparing therapy as initial treatment for Parkinson's disease: a meta-analysis.

To assess the long-term use of L-dopa alone vs L-dopa-sparing therapy, as initial treatment, provides the most efficient long-term control of symptoms and best quality of life for people with early Parkinson's disease (PD). PubMed; Google scholar; Cochrane Central Register of Controlled Trials and the Web of Science were searched for randomised, placebo-controlled trials (RCTs) on L-dopa alone and L-dopa sparing as initial treatment in early PD patients. We used a random effects model rather than a fixed effects model because of this takes into account heterogeneity between multi-studies. Eleven RCTs were included. The results showed that L-dopa alone could evidently improve the UPDRS part I (p = 0.005), part II (p < 0.0001), part III (p < 0.0001) and UPDRS total score (p = 0.004) compared with L-dopa-sparing therapy in PD patients. Meanwhile, a reduced risk of dyskinesia (p < 0.0001, RR = 1.88, 95 % CI 1. 37-2.59) and wearing-off phenomenon (p < 0.00001, RR = 1.36, 95 % CI 1. 20-1.55) in patients treated initially with L-dopa-sparing therapy compared to L-dopa has been consistently reported. What is more, we found more patients on aL-dopa-sparing therapy were more than triple as likely to discontinue treatment prematurely due to adverse events than L-dopa treatment patients (43.7 vs 15.8 %). L-Dopa alone is the most effective medication available for treating the motor symptoms of PD patients, despite the greater incidence of involuntary movements. Meanwhile, more patients on dopamine agonists or MAOBI were more likely to discontinue treatment prematurely than L-dopa alone treatment patients within the long follow-up period.

A systematic review and meta-analysis of cognitive behavioral and psychodynamic therapy for depression in Parkinson's disease patients.

Numerous practice guidelines have recommended cognitive behavioral therapy (CBT) and psychodynamic therapy as a treatment of choice for depression in Parkinson's disease (PD). However, no recent meta-analysis has examined the effects of brief psychotherapy (which includes both CBT and psychodynamic therapy) for adult depression in PD. We decided to conduct such a systematic review and meta-analysis. We included randomized controlled trials (RCTs) examining the effects of brief psychotherapy compared with control groups, other support nursing, or pharmacotherapy. The quality of included studies was strictly evaluated. Twelve studies including 766 patients met all inclusion criteria. The result showed that brief psychotherapy could evidently improve the HAMD (p < 0.00001) and Moca scale (p = 0.006). There was no statistical significance in PDQ-39 scale (p = 0.31). In the subgroup analysis by types of brief psychotherapy, the efficacy of psychodynamic psychotherapy was better than CBT (SMD = -2.02 vs SMD = -0.90) for the outcome measure according to HAMD scale. Meanwhile, we found brief psychotherapy in China was more effective than in US (SMD = -1.54 vs SMD = -1.23), and in low quality studies was more efficacious than in high quality studies (SMD = -1.50 vs SMD = -1.33). Time of brief psychotherapy treatment above 6 weeks was superior to studies with less than 6 weeks treatment. We found brief psychotherapy is probable effective in the management of depression in PD patients. But one reason to undermine the validity of findings is high clinical heterogeneity and low methodological quality of the included trials.