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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: May 2, 2026

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

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

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Last Updated: May 2, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

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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.