Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Targeting CCR1 remodels the tumor microenvironment and relieves immune suppression in pancreatic cancer.

Cancer immunology research·2026

Same author

Asynchronous evolution of epithelium and stroma differentiates precursor lesions from pancreatic cancer.

Cancer discovery·2026

Same author

Rewiring Oncogenic Transcriptional Complexes with Domain-ALTeration Chimeras (DALTACs) in Prostate Cancer.

bioRxiv : the preprint server for biology·2026

Same author

ISPAT-3D: Spatially Varying Conditional Volumetric Network Estimation for 3D Tumor Imaging.

bioRxiv : the preprint server for biology·2026

Same author

Evaluation of Murine Tissue Architecture and Cellular Spatial Relationships in Murine Tumor Microenvironments Utilizing Multiplex Fluorescent Immunohistochemistry.

Methods in molecular biology (Clifton, N.J.)·2026

Same author

Mapping the Tumor Microenvironment with Integrative Single-Cell RNA Sequencing and Spatial Proteomics: Uncovering Mechanisms of Disease and Therapeutic Resistance.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Isolation of Mesenchymal Stem Cell-Derived Extracellular Vesicles.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Modeling Melanoma Immune Surveillance by CAR-T Cells in Human Skin Organoids.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Stepwise Optimization of a Matrigel-Based In Vitro Angiogenesis Assay for Reproducible and Quantifiable 2D-Tube Formation Using HUVECs.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Quantifying Mechanical Properties of Fresh Ovarian Tissue with Optical Brillouin Microscopy.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

3D Chromatin Architecture During Early Development: New Methods and New Findings.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Metabolic Plasticity in Embryogenesis Throughout the Lens of NAD<sup></sup>.

Methods in molecular biology (Clifton, N.J.)·2026

See all related articles

Search research articles

Home
From Raw Data To Biological Insights: A Practical Guide For Spatial Transcriptomics Analysis In R And Python.

Home
From Raw Data To Biological Insights: A Practical Guide For Spatial Transcriptomics Analysis In R And Python.

Related Experiment Video

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

From Raw Data to Biological Insights: A Practical Guide for Spatial Transcriptomics Analysis in R and Python.

Ahmed M Elhossiny¹, Reva Kulkarni¹, Arvind Rao¹

¹Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.

Methods in Molecular Biology (Clifton, N.J.)

|April 30, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

Spatial transcriptomics preserves tissue architecture for gene expression analysis. This chapter details R and Python tools for analyzing spatial data, from preprocessing to biological insights.

Keywords:

Clustering Data analysis Data objects Deconvolution Giotto Python R Seurat Spatial feature experiment Spatial transcriptomics Squidpy

More Related Videos

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Related Experiment Videos

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Area of Science:

Molecular biology
Genomics
Bioinformatics

Background:

Spatial transcriptomics captures gene expression within tissue architecture, preserving spatial context lost in traditional methods.
This enables the study of cellular organization, neighborhood interactions, and functional niches.
Analyzing this complex data requires specialized containers integrating spatial coordinates and molecular expression.

Purpose of the Study:

To provide a comprehensive overview of spatial transcriptomics data analysis.
To describe essential preprocessing steps for accurate tissue characterization.
To detail downstream analyses including deconvolution, domain identification, and cell-type-specific investigations.

Main Methods:

Utilizing specialized data containers for spatial transcriptomics.

Implementing rigorous preprocessing workflows for tissue characterization.

Applying downstream analyses such as deconvolution, spatial domain identification, and cell-cell communication analysis.

Main Results:

Detailed descriptions of major R and Python object classes for spatial transcriptomics.
Support for comprehensive workflows from data management to biological insight derivation.
Enabling advanced analyses like cell-type-specific differential expression and neighborhood analysis.

Conclusions:

Spatial transcriptomics offers profound biological insights by integrating gene expression with tissue architecture.
Specialized R and Python tools are crucial for managing and analyzing complex spatial transcriptomics data.
This work supports researchers in deriving significant biological understanding from spatial omics data.