Recently developed technologies to generate single-cell genomic data have made a revo-lutionary impact in the field of biology.Multi-omics assays offer even greater opportunities to understand cellular states and biological processes.The problem of integrating different omics data with very different dimensionality and statistical properties remains,however,quite challenging.A growing body of computational tools is being developed for this task,leveraging ideas ranging from machine translation to the theory of networks,and represents another frontier on the interface of biology and data science.Our goal in this review is to provide a comprehensive,up-to-date survey of computational techniques for the integration of single-cell multi-omics data,while making the concepts behind each algorithm approachable to a non-expert audience.

We present GranatumX, a next-generation software environment for single-cell RNA sequencing (scRNA-seq) data analysis. GranatumX is inspired by the interactive webtool Granatum. GranatumX enables biologists to access the latest scRNA-seq bioinformatics methods in a web-based graphical environment. It also offers software developers the opportunity to rapidly promote their own tools with others in customizable pipelines. The architecture of GranatumX allows for easy inclusion of plugin modules, named Gboxes, which wrap around bioinformatics tools written in various programming languages and on various platforms. GranatumX can be run on the cloud or private servers and generate reproducible results. It is a community-engaging, flexible, and evolving software ecosystem for scRNA-seq analysis, connecting developers with bench scientists. GranatumX is freely accessible at http://garmiregroup.org/granatumx/app.