  Recently developed technologies to generate single-cell genomic data have
made a revolutionary impact in the field of biology. Multi-omics assays offer
even greater opportunities to understand cellular states and biological
processes. However, the problem of integrating different -omics data with very
different dimensionality and statistical properties remains quite challenging.
A growing body of computational tools are being developed for this task,
leveraging ideas ranging from machine translation to the theory of networks and
representing a new frontier on the interface of biology and data science. Our
goal in this review paper is to provide a comprehensive, up-to-date survey of
computational techniques for the integration of multi-omics and alignment of
multiple modalities of genomics data in the single cell research field.
