Accurate cell classification is the groundwork for downstream analysis of single-cell sequencing data, yet how to identify marker genes to distinguish different cell types still remains as a big challenge. We developed COSG as a cosine similarity-based method for more accurate and scalable marker gene identification. COSG is applicable to single-cell RNA sequencing data, single-cell ATAC sequencing data and spatially resolved transcriptome data. COSG is fast and scalable for ultra-large datasets of million-scale cells. Application on both simulated and real experimental datasets demonstrates the superior performance of COSG in terms of both accuracy and efficiency as compared with other available methods. Marker genes or genomic regions identified by COSG are more indicative and with greater cell-type specificity.
High-precision transit photometry supplies ideal opportunities for detecting new exoplanets and characterizing their physical properties, which usually encode valuable information for unveiling the planetary structure, atmosphere and dynamical history. We present revised properties of three transiting systems (i.e. HAT-P-13, HAT-P-16 and WASP-32) through analyzing TESS photometry and ground-based transit observations, which were obtained at the 1m and 2.4m telescopes of Yunnan Observatories, China, and the 1.2m telescope of Hamburg Observatory, Germany, as well as the data in the literature. During modelling the transit light curves, Gaussian process is employed to account for the potential systematic errors. Through comprehensive timing analysis, we find that both HAT-P-13b and HAT-P-16b show significant timing variations (TTVs) that can be explained by apsidal precession. TTVs of WASP-32b may be led by a decaying orbit due to tidal dissipation or apsidal precession. However, the current observations can not rule out the origins of three systems’ TTVs from gravitational perturbations of close planetary companions conclusively.
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