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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...
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Sample Size Calculation01:19

Sample Size Calculation

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Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

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Sample size calculation based on generalized linear models for differential expression analysis in RNA-seq data.

Chung-I Li, Yu Shyr

    Statistical Applications in Genetics and Molecular Biology
    |November 21, 2016
    PubMed
    Summary
    This summary is machine-generated.

    Determining the optimal sample size for RNA sequencing (RNA-seq) studies is crucial. This new method uses a generalized linear model to estimate sample size, accommodating covariates and simplifying experimental design.

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    Area of Science:

    • Genomics
    • Bioinformatics
    • Statistical Genetics

    Background:

    • RNA sequencing (RNA-seq) is rapidly advancing, leading to exponential growth in sample data.
    • Proteomic studies are increasingly multivariate and quantitative, necessitating robust experimental design.
    • Existing sample size calculation methods, based on hypothesis testing and specific distributions, struggle with covariate accommodation.

    Purpose of the Study:

    • To propose a novel, user-friendly method for determining optimal sample size in RNA-seq studies.
    • To address limitations of current methods in handling covariates.
    • To provide a practical tool for researchers designing RNA-seq experiments.

    Main Methods:

    • Developed an estimating procedure utilizing a generalized linear model.
    • Constructed a representative exemplary dataset to estimate conditional power.
    • Avoided complex mathematical approximations, offering an easy-to-use approach.
    • Ensured downstream analysis compatibility with existing R/Bioconductor packages.

    Main Results:

    • The proposed method effectively estimates conditional power without complex formulas.
    • Demonstrated the practicability and efficiency of the method through application to three real-world RNA-seq studies.
    • Developed an online calculator for determining optimal RNA-seq sample size.

    Conclusions:

    • The generalized linear model-based approach offers a significant improvement for RNA-seq sample size determination.
    • This method is practical, efficient, and accommodates covariates, enhancing experimental design.
    • The provided online calculator facilitates optimal sample size calculation for RNA-seq researchers.