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Related Concept Videos

RNA-seq03:21

RNA-seq

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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...
12.3K

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Updated: Mar 9, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
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LEAP: constructing gene co-expression networks for single-cell RNA-sequencing data using pseudotime ordering.

Alicia T Specht, Jun Li

    Bioinformatics (Oxford, England)
    |December 21, 2016
    PubMed
    Summary

    We developed LEAP, a new algorithm for gene co-expression network construction from single-cell RNA sequencing data. LEAP identifies time-delayed gene interactions using cell pseudotime, enhancing biological insights.

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    Area of Science:

    • Computational Biology
    • Genomics
    • Bioinformatics

    Background:

    • Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of cellular heterogeneity.
    • Gene co-expression networks are crucial for understanding gene regulatory mechanisms.
    • Inferring time-delayed interactions in dynamic biological processes remains challenging.

    Purpose of the Study:

    • To introduce LEAP, a novel algorithm for constructing gene co-expression networks from scRNA-seq data.
    • To leverage estimated cell pseudotime for identifying time-lagged gene co-expression relationships.
    • To provide a computational tool for analyzing dynamic gene expression patterns.

    Main Methods:

    • Development of the LEAP algorithm for gene co-expression network inference.
    • Utilizing single-cell RNA sequencing data as input.
    • Incorporating estimated pseudotime to model temporal dynamics in gene expression.
    • Implementation as an R package available on CRAN.

    Main Results:

    • LEAP successfully constructs gene co-expression networks by accounting for time delays.
    • The algorithm effectively identifies gene interactions that evolve over pseudo-time.
    • Demonstrates a novel approach to inferring dynamic regulatory relationships from static scRNA-seq snapshots.

    Conclusions:

    • LEAP offers a powerful method for uncovering time-delayed gene co-expression in scRNA-seq data.
    • The R package facilitates the application of this method in biological research.
    • This approach enhances the understanding of gene regulatory networks in dynamic cellular states.