steveyu323/SEURATEXT:

The package aims to extend the functionality of Seurat package for the general biological science community to address a variety of downstream analysis. Specifically, the package aims to address following biological question related to single cell sequencing data: 1) In Silico simulation of cell cytometry based on two cell markers of interest. It is always necessary but tiring to inspect and separate out different cell types within the sc-seq data to perform downstream analysis or reclustering. The cell cytometry module is designed to take in a Seurat V3.0 object and inspect whether the 2 selected markers could well-separate cell populations into four quadrants. The user can then assign cells into four quadrants based on the 2 markers and user-defined threshold for the presence of the markers. A quadrant graph for population distribution and a quadrant graph for mapping back to the Seurat UMAP Dimplot can be plotted.The user could easily extract out a specific quadrant of cell to build a new seurat object containing only the desired cells for downstream reclustering/analysis.The pipeline imitates a flow cytometry process to select out the desired cluster of cells for later use based on unique cell markers, and is especially helpful when conducting research on immune cell populations. 2) Automated Cell Type identification based on reference table. Currently, researchers widely proceed identification of cell type of the sub-populations in clustering results manually using empirical knowledge. However, the lack of comprehensive knowledge the labour related to the process is tedious. The package provided an efficient algorithm for identification of cell types based on a given reference table of cell markers available online, and the user can apply a customized marker table for the identification 3) An interactive session for visualizing Control VS Stim DE analysis. The DE analysis usually involves the inspection of clustering plot, the table of gene of interests with fold change and statistical significance, and Differential expression plots such as violin plot. The package aims to resolve the intricacy and inconvenience while inspecting DE data through an interactive ShinyApp integrating both clustering plot, marker genes, cell population, and differential expression. 4) Extension and Customization module on CellPhoneDB outputs. CellPhoneDB is a powerful tool that has gained popularity to inspect inter-cellular ligand-receptor interaction. However, the online server itself does not come with functions to filter out undesired clusters and to plot the cell type interaction graph using Cytoscape. The package will provide detailed tutorial on building a publishable intercellular interaction graph, as well as various plotting functions to aim the understanding of cell-cell interactions

install.packages("remotes")
remotes::install_github("steveyu323/SEURATEXT")