Data Analysis

In situ capturing technologies add tissue context to gene expression data, with the potential of providing a greater understanding of complex biological systems. However, splicing variants and full-length...

As single-cell RNA sequencing experiments continue to advance scientific discoveries across biological disciplines, an increasing number of analysis tools and workflows for analyzing the data have been developed. In this chapter, University of Florida...

Spatial transcriptomics can be used to capture cellular spatial organization and has facilitated new insights into different biological contexts, including developmental biology, cancer, and neuroscience. However, its wide application is still hindered by...

Single-cell RNA-sequencing (scRNA-seq) technology enables researchers to investigate a genome at the cellular level with unprecedented resolution...

The recent development of single-cell techniques is essential to unravel complex biological systems. By measuring the transcriptome and the accessible...

Spatially resolved transcriptomics (SRT)-specific computational methods are often developed, tested, validated, and evaluated in silico using simulated data. Unfortunately, existing simulated SRT data are often poorly documented, hard to reproduce, or unrealistic. Single-cell simulators are not directly applicable for SRT ...

Although an essential step, cell functional annotation often proves particularly challenging from single-cell transcriptional data. Several methods have been developed to accomplish this task. However, in most...

As a fundamental aspect of normal cell signaling and disease states, there is great interest in determining alternative splicing (AS) changes in physiologic, pathologic, and pharmacologic settings. High throughput...

Single-cell RNA sequencing has been instrumental in uncovering cellular spatiotemporal context. This task is challenging as cells simultaneously encode multiple, potentially cross-interfering, biological signals. Researchers from the Hebrew University of Jerusalem have developed scPrisma, a spectral computational method that uses ...

Spatial transcriptomics technologies generate gene expression profiles with spatial context, requiring spatially informed analysis tools for three key tasks, spatial clustering, multisample integration, and cell...