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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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...
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

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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...
Ribosome Profiling02:24

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
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Related Experiment Video

Updated: Jul 16, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Variational sparse Gaussian-process method for detecting spatially variable genes and cellular interactions in

Zhicong Wang1,2,3, Jing Li1, Liqing Xie3,4

  • 1School of Computer Science, Northwestern Polytechnical University, 1 Dongxiang Rd., Chang'an Dist., Xi'an, Shaanxi, 710129, China.

Briefings in Bioinformatics
|July 14, 2026
PubMed
Summary

We developed a new computational framework, VISGP, to analyze spatial transcriptomics data. VISGP identifies spatially variable genes and cell interactions, offering deeper biological insights into tissue heterogeneity and disease.

Keywords:
cellular interactionsligand–receptor interactionssparse Gaussian processspatial transcriptomicsspatially variable genesvariational inference

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Area of Science:

  • Genomics
  • Computational Biology
  • Systems Biology

Background:

  • Spatially resolved transcriptomic (SRT) technologies capture gene expression within tissue context.
  • Understanding context-dependent gene regulation and cellular communication is crucial.

Purpose of the Study:

  • To introduce a novel computational framework, VISGP, for analyzing SRT data.
  • To identify spatially variable genes (SVGs) and infer cell-cell communication.
  • To address computational and memory limitations of existing methods.

Main Methods:

  • Developed a variational inference-assisted sparse Gaussian-process (VISGP) framework.
  • Combined sparse Gaussian-process approximations with variational inference.
  • Utilized inducing variables to reduce computational costs and enable gene-specific adaptation.

Main Results:

  • VISGP identified more SVGs compared to existing methods across simulated and real SRT datasets.
  • Identified 85 spatially constrained ligand-receptor pairs missed by alternative approaches.
  • Demonstrated reduced computational and memory requirements.

Conclusions:

  • VISGP offers a scalable and statistically robust method for analyzing SRT data.
  • The framework facilitates decoding spatial gene regulation and cell-cell communication.
  • VISGP provides valuable biological insights into cellular heterogeneity and cancer pathology.