Community-wide hackathons to identify central themes in single-cell multi-omics

[...]dimension reduction approaches can extract and combine latent components of global variance that are shared between data modalities [8], thereby learning novel cellular and molecular pathways associated with biological processes directly from the data. [...]biological discovery of the regulatory processes that span molecular scales is an active area of biological research and a key motivation for generating multi-modal single-cell datasets. [...]gene expression depends on gene regulatory element activity and thus requires an experimental design that must also account for spatial and temporal elements for a given cell. [...]defining a specific data integration task, and benchmarking the computational performance of the method for assessment relies on multi-modal data with specific study designs. [...]we can validate analysis approaches by benchmarking several algorithms and methods on the same dataset, allowing for open comparison of both standard and new methodologies.