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Related Concept Videos

Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

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Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
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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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Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
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Manual Construction of a Tissue Microarray using the Tape Method and a Handheld Microarrayer
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Analyzing Microarray Data.

Jui-Hung Hung, Zhiping Weng

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    This protocol demonstrates analyzing microarray data with Babelomics, including normalization and differential gene expression analysis. It identifies genes affected by c-Myc oncogene transgenesis in mice, aiding cancer research.

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    Area of Science:

    • Bioinformatics
    • Genomics
    • Molecular Biology

    Background:

    • RNA-sequencing (RNA-seq) analysis lacks widely adopted graphical user interface (GUI) software.
    • Microarray data analysis remains a relevant approach in transcriptomics.

    Purpose of the Study:

    • To provide a protocol for analyzing microarray data using the Babelomics platform.
    • To demonstrate differential gene expression analysis and hierarchical clustering for biological insights.

    Main Methods:

    • Quantile normalization of microarray data.
    • Differential gene expression analysis to identify genes affected by c-Myc.
    • Hierarchical clustering using the Cluster program.
    • Visualization of results with TreeView.

    Main Results:

    • Identification of differentially expressed genes in mice with human c-Myc oncogene transgenesis.
    • Successful application of Babelomics for comprehensive microarray data analysis.
    • Clustering revealed patterns in gene expression related to c-Myc activity.

    Conclusions:

    • Babelomics offers a viable GUI-based solution for microarray data analysis.
    • The protocol effectively identifies genes modulated by oncogene expression.
    • This approach facilitates the study of oncogenic mechanisms in model organisms.