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Stars1
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LanguageR
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License
MIT License -
Created over 4 years ago
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Updated almost 2 years ago
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Repository Details
The goal of iCTC is to detect whether peripheral blood cells have CTCs (circulating tumor cell) or not.
More Repositories
1
The-cellular-basis-of-the-loss-of-smell-in-2019-nCoV-infected-individuals
A prominent clinical symptom of 2019-nCoV infection is hyposmia/anosmia (decrease or loss of sense of smell), along with general symptoms such as fatigue, shortness of breath, fever, and cough. The identity of the cell lineages that underpin infection associated with loss of olfaction could be critical for the diagnostics/clinical management of 2019-nCoV infected individuals. Angiotensin I Converting Enzyme 2 (ACE2), and Transmembrane protease serine 2 (TMPRSS2) are emerging as associated host receptors vital for viral entry. Accordingly, the ongoing medical examinations and the autopsy reports of the deceased individuals strongly corroborate with the organ/cell-type-specific expression of ACE2, TMPRSS2, and other putative viral entry-associated genes. To determine the cellular basis of anosmia upon 2019-nCoV infection, we employed a targeted bioinformatic analysis of single-cell expression profiles of human olfactory epithelium cell-types. Our results underscored selective expression of these viral entry-associated genes in a subset of sustentacular cells, Bowmanโs gland cells, and stem cells of the olfactory epithelium. Co-expression analysis of ACE2 and TMPRSS2 and protein-protein interaction among the host and viral proteins elected regulatory cytoskeleton protein-enriched sustentacular cells as the most vulnerable cell-type of the olfactory epithelium. Furthermore, expression, structural and docking analyses of these viral-entry moieties revealed the potential risk of olfactory dysfunction in four additional mammalian species, revealing an evolutionarily conserved susceptibility. In summary, our findings suggest the molecular and cellular rationale of loss of smell in 2019-nCoV infected patients.R
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ROSeq
ROSeq - A rank based approach to modeling gene expression with filtered and normalized read count matrix. Takes in the complete filtered and normalized read count matrix, the location of the two sub-populations and the number of cores to be used.R
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