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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

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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...
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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
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The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...
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The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
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Related Experiment Video

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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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A Survey and Comparative Study of Statistical Tests for Identifying Differential Expression from Microarray Data.

Sanghamitra Bandyopadhyay, Saurav Mallik, Anirban Mukhopadhyay

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |September 11, 2015
    PubMed
    Summary

    This study surveys statistical tests for differential expression analysis in DNA microarray data. It compares methods using real and simulated data to guide the selection of robust transcript analysis techniques.

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    Area of Science:

    • Bioinformatics
    • Genomics
    • Statistical Genetics

    Background:

    • DNA microarrays enable simultaneous measurement of thousands of transcripts (genes/miRNAs) across samples.
    • Differential expression analysis is crucial for identifying transcripts with significant expression changes between conditions.
    • Various statistical testing methods exist for differential expression analysis.

    Purpose of the Study:

    • To provide a comprehensive survey of parametric and non-parametric statistical testing methods for differential expression in microarray data.
    • To compare the performance of these methods using real and simulated expression datasets.
    • To offer guidance on selecting appropriate statistical tests for analyzing transcriptomic data.

    Main Methods:

    • Survey of parametric and non-parametric statistical tests for differential expression.
    • Performance comparison using real miRNA and mRNA expression datasets.
    • Validation of differentially expressed miRNAs against the PhenomiR 2.0 database.
    • Power analysis using simulated datasets of varying sample sizes (10-100 per group).

    Main Results:

    • Comparative analysis of statistical test performances on real and simulated microarray data.
    • Validation of identified differentially expressed miRNAs using PhenomiR 2.0.
    • Assessment of statistical test power based on data distribution assumptions.
    • Identification of advantages and limitations for each statistical test.

    Conclusions:

    • The study provides a comparative evaluation of statistical methods for differential expression analysis in microarray data.
    • Findings aid in selecting robust tests based on data characteristics and study objectives.
    • Highlights areas for future research in transcriptomic data analysis.