An interpretable Bayesian clustering approach with feature selection for analyzing spatially resolved transcriptomics data.

Recent breakthroughs in spatially resolved transcriptomics (SRT) technologies have enabled comprehensive molecular characterization at the spot or cellular level while preserving spatial information. Cells are the fundamental building blocks of tissues, organized into distinct yet connected components. Although many non-spatial and spatial clustering approaches have been used to partition the entire region into mutually exclusive spatial domains based on the SRT high-dimensional molecular profile, most require an ad hoc selection of less interpretable dimensional-reduction techniques. To overcome this challenge, we propose a zero-inflated negative binomial mixture model to cluster spots or cells based on their molecular profiles. To increase interpretability, we employ a feature selection mechanism to provide a low-dimensional summary of the SRT molecular profile in terms of discriminating genes that shed light on the clustering result. We further incorporate the SRT geospatial profile via a Markov random field prior. We demonstrate how this joint modeling strategy improves clustering accuracy, compared with alternative state-of-the-art approaches, through simulation studies and 3 real data applications.

iIMPACT: integrating image and molecular profiles for spatial transcriptomics analysis.

Current clustering analysis of spatial transcriptomics data primarily relies on molecular information and fails to fully exploit the morphological features present in histology images, leading to compromised accuracy and interpretability. To overcome these limitations, we have developed a multi-stage statistical method called iIMPACT. It identifies and defines histology-based spatial domains based on AI-reconstructed histology images and spatial context of gene expression measurements, and detects domain-specific differentially expressed genes. Through multiple case studies, we demonstrate iIMPACT outperforms existing methods in accuracy and interpretability and provides insights into the cellular spatial organization and landscape of functional genes within spatial transcriptomics data.

Automatic block-wise genotype-phenotype association detection based on hidden Markov model.

BACKGROUND: For detecting genotype-phenotype association from case-control single nucleotide polymorphism (SNP) data, one class of methods relies on testing each genomic variant site individually. However, this approach ignores the tendency for associated variant sites to be spatially clustered instead of uniformly distributed along the genome. Therefore, a more recent class of methods looks for blocks of influential variant sites. Unfortunately, existing such methods either assume prior knowledge of the blocks, or rely on ad hoc moving windows. A principled method is needed to automatically detect genomic variant blocks which are associated with the phenotype. RESULTS: In this paper, we introduce an automatic block-wise Genome-Wide Association Study (GWAS) method based on Hidden Markov model. Using case-control SNP data as input, our method detects the number of blocks associated with the phenotype and the locations of the blocks. Correspondingly, the minor allele of each variate site will be classified as having negative influence, no influence or positive influence on the phenotype. We evaluated our method using both datasets simulated from our model and datasets from a block model different from ours, and compared the performance with other methods. These included both simple methods based on the Fisher's exact test, applied site-by-site, as well as more complex methods built into the recent Zoom-Focus Algorithm. Across all simulations, our method consistently outperformed the comparisons. CONCLUSIONS: With its demonstrated better performance, we expect our algorithm for detecting influential variant sites may help find more accurate signals across a wide range of case-control GWAS.

Cu-catalyzed oxygenation of alkene-tethered amides with O2 via unactivated C[double bond, length as m-dash]C bond cleavage: a direct approach to cyclic imides.

The transformations of unactivated alkenes through C[double bond, length as m-dash]C bond double cleavage are always attractive but very challenging. We report herein a chemoselective approach to valuable cyclic imides by a novel Cu-catalyzed geminal amino-oxygenation of unactivated C[double bond, length as m-dash]C bonds. O2 was successfully employed as the oxidant as well as the O-source and was incorporated into alkenyl amides via C[double bond, length as m-dash]C bond cleavage for the efficient preparation of succinimide or glutarimide derivatives. Moreover, the present strategy under simple conditions can be used in the late-stage modification of biologically active compounds and the synthesis of pharmaceuticals, which demonstrated the potential application.

Selective Aerobic Oxygenation of Tertiary Allylic Alcohols with Molecular Oxygen.

Aerobic epoxidation of tertiary allylic alcohols remains a significant challenge. Reported here is an efficient and highly chemoselective copper-catalyzed epoxidation and semipinacol rearrangement reaction of tertiary allylic alcohols with molecular oxygen. The solvent 1,4-dioxane activates dioxygen, thereby precluding the addition of a sacrificial reductant.

Oxygenation of Simple Olefins through Selective Allylic C-C Bond Cleavage: A Direct Approach to Cinnamyl Aldehydes.

A novel metal-free allylic C-C σ-bond cleavage of simple olefins to give valuable cinnamyl aldehydes is reported. 1,2-Aryl or alkyl migration through allylic C-C bond cleavage occurs in this transformation, which is assisted by an alkyl azide reagent. This method enables O-atom incorporation into simple unfunctionalized olefins to construct cinnamyl aldehydes. The reaction features simple hydrocarbon substrates, metal-free conditions, and high regio- and stereoselectivity.

Cs2 CO3 -Catalyzed Aerobic Oxidative Cross-Dehydrogenative Coupling of Thiols with Phosphonates and Arenes.

An efficient Cs2 CO3 -catalyzed oxidative coupling of thiols with phosphonates and arenes that uses molecular oxygen as the oxidant is described. These reactions provide not only a novel alkali metal salt catalyzed aerobic oxidation, but also an efficient approach to thiophosphates and sulfenylarenes, which are ubiquitously found in pharmaceuticals and pesticides. The reaction proceeds under simple and mild reaction conditions, tolerates a wide range of functional groups, and is applicable to the late-stage synthesis and modification of bioactive molecules.