Bertrand Z. Yeung

TL;DR: Weighted-nearest neighbor analysis as mentioned in this paper is an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities.

TL;DR: ‘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.

TL;DR: Cell Hashing is introduced, where oligo-tagged antibodies against ubiquitously expressed surface proteins uniquely label cells from distinct samples, which can be subsequently pooled and can robustly identify cross-sample multiplets.

TL;DR: ASAP-seq as discussed by the authors uses a bridging approach that repurposes antibody:oligonucleotide conjugates designed for existing technologies that pair protein measurements with single-cell RNA sequencing.

TL;DR: An approach for multiplexing snRNA-seq is presented, using sample-barcoded antibodies to uniquely label nuclei from distinct samples, and a computational tool to assign the sample of origin is developed.