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Cell Specific Gene Expression01:58

Cell Specific Gene Expression

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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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Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
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A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then...
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Chromatin is the massive complex of DNA and proteins packaged inside the nucleus. The complexity of chromatin folding and how it is packaged inside the nucleus greatly influences  access to genetic information. Generally, the nucleus' periphery is considered transcriptionally repressive, while the cell's interior is considered a transcriptionally active area. 
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The structure and stability of mRNA molecules regulates gene expression, as mRNAs are a key step in the pathway from gene to protein. In eukaryotes, the half-life of mRNA varies from a few minutes up to several days. mRNA stability is essential in growth and development. The absence of the proteins regulating its stability, such as tristetraprolin in mice, can cause systemic issues, including bone marrow overgrowth, inflammation, and autoimmunity.
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Related Experiment Video

Updated: Feb 11, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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SigEMD: A powerful method for differential gene expression analysis in single-cell RNA sequencing data.

Tianyu Wang1, Sheida Nabavi2

  • 1Computer Science and Engineering Department, University of Connecticut, Storrs, CT, USA.

Methods (San Diego, Calif.)
|April 28, 2018
PubMed
Summary

This study introduces SigEMD, a novel method for differential gene expression analysis in single-cell RNA sequencing (scRNAseq). SigEMD accurately identifies key gene expression changes, overcoming challenges like data sparsity and zero counts.

Keywords:
Data imputationDifferential gene expression analysisMultimodal dataNonparametric modelsSingle-cell RNAseq

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Single-cell RNA sequencing (scRNAseq) presents unique challenges, including multimodality, high zero counts, and data sparsity, distinguishing it from bulk RNA sequencing.
  • These characteristics necessitate advanced methods for accurate differential gene expression (DE) analysis to identify cell-type-specific expression changes.

Purpose of the Study:

  • To develop a precise and efficient method, SigEMD, for identifying differentially expressed genes in scRNAseq data.
  • To address the technical challenges inherent in scRNAseq data, such as zero counts and multimodality, to improve DE gene detection.

Main Methods:

  • SigEMD integrates data imputation, a logistic regression model, and a nonparametric Earth Mover's Distance approach.
  • A logistic regression model and data imputation are employed to mitigate the impact of excessive zero counts.
  • A nonparametric method enhances sensitivity for detecting DE genes in multimodal scRNAseq data.

Main Results:

  • The SigEMD method demonstrated powerful performance in detecting differentially expressed genes.
  • Evaluation using simulated and real datasets showed high precision, sensitivity, and specificity compared to existing methods.
  • Incorporation of gene interaction network information further reduced false positives in DE gene identification.

Conclusions:

  • SigEMD offers a robust solution for differential gene expression analysis in scRNAseq.
  • The method effectively handles the complexities of scRNAseq data, leading to more reliable identification of DE genes.
  • SigEMD advances the field of scRNAseq data analysis by providing improved accuracy and efficiency.