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Related Concept Videos

RNA-seq03:21

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 microarray-based...

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SCNT: an R package for data analysis and visualization of single-cell and spatial transcriptomics.

Jianbo Qing1, Jialu Wu2, Yafeng Li3

  • 1Department of Nephrology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China.

BMC Bioinformatics
|July 18, 2025
PubMed
Summary

The SCNT package streamlines single-cell (SC) and spatial transcriptomics (ST) data analysis and visualization. This R-based tool simplifies complex workflows, making SC and ST data more accessible for researchers.

Keywords:
RSCNTSingle-cell sequencingSpatial transcriptomicsggplot2

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Single-cell (SC) and spatial transcriptomics (ST) technologies offer unprecedented insights into gene expression within complex tissues.
  • Analyzing and visualizing SC and ST data presents significant challenges due to data complexity and diverse analytical platforms.

Purpose of the Study:

  • To develop an efficient and user-friendly R-based package, SCNT (Single-Cell, Single-Nucleus, and Spatial Transcriptomics Analysis and Visualization Tools), for processing, analyzing, and visualizing SC and ST data.
  • To address the challenges in SC and ST data analysis and visualization.

Main Methods:

  • SCNT integrates established tools like Seurat and ggplot2, facilitating format conversion between Seurat and H5ad.
  • The package supports high-resolution spatial visualization, including customizable gene expression and clustering plots.
  • SCNT simplifies critical analysis steps: quality control, dimensionality reduction, and doublet detection.

Main Results:

  • The SCNT package demonstrated effectiveness in processing and visualizing SC and ST data.
  • Tested on PBMC, Visum, and Visium HD human kidney datasets, SCNT enhanced workflow efficiency.
  • Seamless integration and simplified analysis steps were highlighted.

Conclusions:

  • SCNT provides a valuable, flexible, and user-friendly tool for SC and ST data exploration.
  • The package streamlines workflows for both novice and advanced researchers.
  • Future work will expand ST platform support and multi-omics integration.