Semi-structured, raw scRNA-seq data from public repositories are difficult to retrieve and integrate together for cell-type and cell-function annotation exercises. Performing a literature search for cell-type markers from each cluster can be a time-consuming and error-prone process. OVERVIEW To enable effective targeting of disease mechanics, knowledge of cellular heterogeneity and dynamics is essential. In the data processing protocols of scRNA-seq experiments, cell type identification is a vital step for subsequent analysis. While the human body is estimated to contain ~ 100 trillion cells, identifying distinct cell types from cluster-based experiments remains a challenge. Despite emerging advances in annotation methods of single- cell experiments, a consensus amongst R&D teams is that m

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