Recently developed spatial gene expression technologies such as the SpatialTranscriptomics and Visium platforms allow for comprehensive measurement of transcriptomic profiles while retaining spatial context. However, existing methods for analyzing spatial gene expression data often do not efficiently leverage the spatial information and fail to address the limited resolution of the technology. Here, we introduce BayesSpace, a fully Bayesian statistical method for clustering analysis and resolution enhancement of spatial transcriptomics data that seamlessly integrates into current transcriptomics analysis workflows. We show that BayesSpace improves the identification of transcriptionally distinct tissues from spatial transcriptomics samples of the brain, of melanoma, and of squamous cell carcinoma. In particular, BayesSpace's improved resolution allows the identification of tissue structure that is not detectable at the original resolution and thus not recovered by other methods. Using an in silico dataset constructed from scRNA-seq, we demonstrate that BayesSpace can spatially resolve expression patterns to near single-cell resolution without the need for external single-cell sequencing data.In all, our results illustrate the utility BayesSpace has in facilitating the discovery of biological insights from a variety of spatial transcriptomics datasets.

BackgroundNonmotor symptoms are common among patients with Parkinson’s disease (PD) and some may precede disease diagnosis.MethodsWe conducted a meta-analysis on the prevalence of selected nonmotor symptoms before and after PD diagnosis, using random-effect models. We searched PubMed (1965 through October/November 2012) for the following symptoms: hyposmia, constipation, rapid eye movement sleep behavior disorder, excessive daytime sleepiness, depression, and anxiety. Eligible studies were publications in English with original data on one or more of these symptoms.ResultsThe search generated 2,373 non-duplicated publications and 332 met the inclusion criteria, mostly (n = 320) on symptoms after PD diagnosis. For all symptoms, the prevalence was substantially higher in PD cases than in controls, each affecting over a third of the patients. Hyposmia was the most prevalent (75.5% in cases vs. 19.1% in controls), followed by constipation (50% vs. 17.7%), anxiety (39.9% vs. 19.1%), rapid eye movement sleep behavior disorder (37.0% vs. 7.0%), depression (36.6% vs. 14.9%), and excessive daytime sleepiness (33.9% vs. 10.5%). We observed substantial heterogeneities across studies and meta-regression analyses suggested that several factors might have contributed to this. However, the prevalence estimates were fairly robust in several sensitivity analyses. Only 20 studies had data on any symptoms prior to PD diagnosis, but still the analyses revealed higher prevalence in future PD cases than in controls.ConclusionThese symptoms are common among PD patients both before and after diagnosis. Further studies are needed to understand the natural history of nonmotor symptoms in PD and their etiological and clinical implications.Electronic supplementary materialThe online version of this article (doi:10.1186/2047-9158-4-1) contains supplementary material, which is available to authorized users.