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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Sep 11, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

spVelo: RNA velocity inference for multi-batch spatial transcriptomics data.

Wenxin Long¹, Tianyu Liu^2,3, Lingzhou Xue⁴

¹Department of Statistics, The Pennsylvania State University, University Park, 16802, PA, USA.

|August 12, 2025

Summary

spVelo offers a scalable RNA velocity framework for spatial transcriptomics, integrating spatial data and handling multiple batches. This tool enhances understanding of transcriptional dynamics and biological mechanisms in complex tissues.

Keywords:

RNA velocity Spatial transcriptomics Trajectory inference Transcriptomics dynamics Variational inference

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Last Updated: Sep 11, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

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Published on: July 6, 2022

Application of Laser Microdissection to Uncover Regional Transcriptomics in Human Kidney Tissue

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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

RNA velocity analyzes transcriptional dynamics from snapshot data.
Existing methods do not leverage spatial transcriptomics data or scale to multi-batch datasets.

Purpose of the Study:

Introduce spVelo, a scalable framework for RNA velocity inference in multi-batch spatial transcriptomics.
Enable downstream analyses like uncertainty quantification and gene regulatory network inference.

Main Methods:

Developed a scalable computational framework, spVelo.
Integrated spatial information into RNA velocity calculations.
Designed for multi-batch spatial transcriptomics data.

Main Results:

spVelo provides scalable RNA velocity inference for spatial transcriptomics.
The framework supports diverse applications including trajectory inference and cell-cell communication analysis.
Facilitates deeper insights into tissue organization and biological mechanisms.

Conclusions:

spVelo advances RNA velocity analysis by incorporating spatial data and scalability.
The framework has broad potential for uncovering spatially resolved biological mechanisms.
Enables comprehensive analysis of complex tissue dynamics.