Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods,
together with associated computational tools and the growing availability of public data …

The recent boom in microfluidics and combinatorial indexing strategies, combined with low
sequencing costs, has empowered single-cell sequencing technology. Thousands—or even …

Spatial transcriptomics approaches have substantially advanced our capacity to detect the
spatial distribution of RNA transcripts in tissues, yet it remains challenging to characterize …

Single-cell RNA sequencing (scRNA-seq) distinguishes cell types, states and lineages
within the context of heterogeneous tissues. However, current single-cell data cannot …

Single-cell atlases often include samples that span locations, laboratories and conditions,
leading to complex, nested batch effects in data. Thus, joint analysis of atlas datasets …

Background Large-scale single-cell transcriptomic datasets generated using different
technologies contain batch-specific systematic variations that present a challenge to batch …

The high-dimensional data created by high-throughput technologies require visualization
tools that reveal data structure and patterns in an intuitive form. We present PHATE, a …

The rapid progress of protocols for sequencing single-cell transcriptomes over the past
decade has been accompanied by equally impressive advances in the computational …

Chemotherapy resistance is a critical contributor to cancer mortality and thus an urgent
unmet challenge in oncology. To characterize chemotherapy resistance processes in high …

Motivation Increasing numbers of large scale single cell RNA-Seq projects are leading to a
data explosion, which can only be fully exploited through data integration. A number of …