projectR: an R/Bioconductor package for transfer learning via PCA, NMF, correlation and clustering

Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution

(2019) Samuel G. Rodriques et al.   SCIENCE

Decomposing Cell Identity for Transfer Learning across Cellular Measurements, Platforms, Tissues, and Species

(2019) Genevieve L. Stein-O’Brien et al.   Cell Systems

MultiPLIER: A Transfer Learning Framework for Transcriptomics Reveals Systemic Features of Rare Disease

(2019) Jaclyn N. Taroni et al.   Cell Systems

Comprehensive Integration of Single-Cell Data

(2019) Tim Stuart et al.   CELL

TheDrosophilaembryo at single-cell transcriptome resolution

(2017) Nikos Karaiskos et al.   SCIENCE

Dimension reduction techniques for the integrative analysis of multi-omics data

(2016) Chen Meng et al.   BRIEFINGS IN BIOINFORMATICS

CoGAPS: an R/C++ package to identify patterns and biological process activity in transcriptomic data

(2010) Elana J. Fertig et al.   BIOINFORMATICS

A Quantitative Spatiotemporal Atlas of Gene Expression in the Drosophila Blastoderm

(2008) Charless C. Fowlkes et al.   CELL