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Related Concept Videos

Cell Specific Gene Expression01:58

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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...
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...

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Related Experiment Video

Updated: May 11, 2026

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations
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Batch correcting single-cell spatial transcriptomics count data with Crescendo improves visualization and detection

Nghia Millard1,2,3,4,5,6,7, Jonathan H Chen6,7,8,9, Mukta G Palshikar2,3,6

  • 1Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Boston, MA, USA.

Genome Biology
|February 25, 2025
PubMed
Summary
This summary is machine-generated.

Batch effects obscure gene patterns in spatial transcriptomics. Crescendo algorithm corrects these effects, enabling accurate gene expression visualization across multiple samples and technologies.

Keywords:
Batch correctionCrescendoLigand-receptor interactionsPatternsSingle-cellSpatial transcriptomics

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Spatial transcriptomics enables gene expression analysis within anatomical context.
  • Batch effects present a significant challenge for visualizing and integrating gene expression patterns across different samples.
  • Accurate cross-sample analysis is crucial for understanding complex biological systems.

Purpose of the Study:

  • To develop a computational method for correcting batch effects in spatial transcriptomics data.
  • To enable accurate visualization and analysis of gene expression patterns across multiple samples.
  • To enhance downstream analyses such as gene colocalization and ligand-receptor interaction detection.

Main Methods:

  • Development of the Crescendo algorithm for batch effect correction at the gene expression level.
  • Application of Crescendo to spatial and single-cell RNA sequencing datasets.
  • Evaluation of Crescendo's performance on datasets ranging from 170,000 to 7 million cells.

Main Results:

  • Crescendo effectively corrects for batch effects, allowing for accurate gene expression visualization across samples.
  • The algorithm demonstrates utility and scalability across diverse datasets and technologies.
  • Corrected data enhances the detection of gene colocalization and ligand-receptor interactions.

Conclusions:

  • Crescendo is a robust algorithm for mitigating batch effects in spatial transcriptomics.
  • The method facilitates reliable cross-sample and cross-technology data integration.
  • Crescendo significantly improves the power of spatial transcriptomics for biological discovery.