Integrated analysis of multimodal single-cell data
‘weighted-nearest neighbor’ analysis is introduced, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities.
Abstract
ezSingleCell is an interactive and easy-to-use application for the analysis of multiple single-cell and spatial omics data types for biologists. It combines the best-performing publicly available methods and in-house novel algorithms for in-depth data analysis, and interactive data visualization. . ezSingleCell consists of five modules to handle different data types and analysis tasks. In addition, ezSingleCell allows crosstalk between different modules in a unified interface. Acceptable input data can be in a variety of formats, while the output consists of publication ready figures and tables. Users can customize the relevant parameters to customise data analysis to suit their analysis aims with the help of in-depth manuals and video tutorials. ezSingleCell’s streamlined interface can analyse a standard scRNA-seq dataset containing 3000 cells in less than five mins. ezSingleCell is available in two forms: an installation-free web application (https://immunesinglecell.org/ezsc/) or a software package with a shinyApp interface (https://github.com/JinmiaoChenLab/ezSingleCell2) for offline analysis.