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Full-Text Articles in Genetics and Genomics
Towards Understanding The Function Of An Ets-Like Gene In Nematostella Vectensis: Generation Of A Knockout Mutant Line And A Transgenic Reporter Line, Emily Bullock
Biological Sciences Undergraduate Honors Theses
Due to their unique phylogenic position as sister to Bilateria, Cnidaria are often credited with the utility of allowing for reconstruction of ancestral biology based on characteristics shared with bilaterians and other animals. This factor makes investigation into the nervous systems of cnidarians critical in understanding early neural evolution. Wamides, a class of neuropeptides, have been shown to play a regulatory role in life cycle transitions across many different species. The cnidarian specific Wamide neuropeptide, GLWamide, has previously been identified to play an accelerator role in the metamorphic timing of a specific species of sea anemone, Nematostella vectensis. However, …
Sccad: Cluster Decomposition-Based Anomaly Detection For Rare Cell Identification In Single-Cell Expression Data, Yunpei Xu, Shaokai Wang, Qilong Feng, Jiazhi Xia, Yaohang Li, Hong-Dong Li, Jianxin Wang
Sccad: Cluster Decomposition-Based Anomaly Detection For Rare Cell Identification In Single-Cell Expression Data, Yunpei Xu, Shaokai Wang, Qilong Feng, Jiazhi Xia, Yaohang Li, Hong-Dong Li, Jianxin Wang
Computer Science Faculty Publications
Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for characterizing cellular landscapes within complex tissues. Large-scale single-cell transcriptomics holds great potential for identifying rare cell types critical to the pathogenesis of diseases and biological processes. Existing methods for identifying rare cell types often rely on one-time clustering using partial or global gene expression. However, these rare cell types may be overlooked during the clustering phase, posing challenges for their accurate identification. In this paper, we propose a Cluster decomposition-based Anomaly Detection method (scCAD), which iteratively decomposes clusters based on the most differential signals in each cluster to effectively separate …