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...
Read More »Analysis of single-cell rna-seq data
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...
Read More »AllenDigger – spatial expression data visualization, spatial heterogeneity delineation, and single-cell registration based on the Allen Brain Atlas
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...
Read More »scAnnotate – an automated cell type annotation tool for single-cell RNA-sequencing data
Single-cell RNA-sequencing (scRNA-seq) technology enables researchers to investigate a genome at the cellular level with unprecedented resolution...
Read More »scANANSE – gene regulatory network and motif analysis of single-cell clusters
The recent development of single-cell techniques is essential to unravel complex biological systems. By measuring the transcriptome and the accessible...
Read More »SRTsim – spatial pattern preserving simulations for spatially resolved transcriptomics
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 ...
Read More »Single-cell gene set enrichment analysis and transfer learning for functional annotation of scRNA-seq data
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...
Read More »SpliceTools – a suite of downstream RNA splicing analysis tools to investigate mechanisms and impact of alternative splicing
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...
Read More »scPrisma – inference of topological signals in single-cell data using spectral template matching
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 ...
Read More »GraphST – spatially informed clustering, integration, and deconvolution of spatial transcriptomics
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...
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