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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: Jun 10, 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

Statistical batch-aware embedded integration, dimension reduction, and alignment for spatial transcriptomics.

Yanfang Li¹, Shihua Zhang^1,2,3

¹NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.

Bioinformatics (Oxford, England)

|October 14, 2024

Summary

This summary is machine-generated.

STADIA, a new spatial transcriptomics model, effectively reduces batch effects and identifies spatial domains across multiple tissue slices. This joint modeling approach enhances biological pattern extraction and outperforms existing methods for integrated analysis.

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Last Updated: Jun 10, 2025

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Published on: July 6, 2022

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

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Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

Area of Science:

Single-cell biology
Computational biology
Genomics

Background:

Spatial transcriptomics (ST) offers insights into cellular molecular characteristics by integrating gene expression with spatial location.
Integrating multiple ST slices is challenging due to limited biological variation per slice and significant batch effects.
Separate analyses for integration, dimensionality reduction, and downstream tasks yield suboptimal results, conflating technical artifacts with biological signals.

Purpose of the Study:

To develop a joint modeling approach for spatial transcriptomics data integration.
To simultaneously reduce batch effects, extract common biological patterns, and identify spatial domains across multiple ST slices.
To improve the understanding of the interplay between technical artifacts and biological signals in ST data.

Main Methods:

Proposed STADIA, a hierarchical hidden Markov random field model for spatial transcriptomics data.
Implemented a joint modeling strategy integrating multi-slice integration, dimensionality reduction, and spatial domain identification.
Validated STADIA on five diverse datasets across species, organs, and platforms (10x Visium, ST, Slice-seqV2).

Main Results:

STADIA effectively reduces batch effects and identifies common biological patterns and spatial domains across multiple ST slices.
The model captures conserved tissue structures while preserving slice-specific biological signals.
STADIA outperformed competing methods (PRECAST, fastMNN, Harmony, STAGATE, GraphST) in balancing batch effect correction and spatial domain identification.

Conclusions:

Joint modeling in STADIA provides a more accurate and insightful analysis of spatial transcriptomics data compared to separate analytical steps.
STADIA offers a robust framework for multi-slice integration and spatial domain discovery in diverse ST datasets.
The developed R-based source code is publicly available for the research community.