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Related Concept Videos

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Longitudinal Studies

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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
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Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
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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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Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
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Statistical challenges in longitudinal microbiome data analysis.

Saritha Kodikara1, Susan Ellul2, Kim-Anh Lê Cao1

  • 1Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne, Royal Parade, 3052, Victoria, Australia.

Briefings in Bioinformatics
|July 13, 2022
PubMed
Summary
This summary is machine-generated.

Longitudinal microbiome studies reveal temporal microbial dynamics. This review analyzes methods for differential abundance, clustering, and network modeling, offering R tutorials for reproducible research.

Keywords:
16Sclusteringcompositionalitydifferential abundancenetworksrelative abundanceshotgun sequencing

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Statistical Modeling

Background:

  • The microbiome is a complex microbial community interacting with its host and environment.
  • Longitudinal studies are crucial for understanding temporal microbiome variations.
  • Current statistical methods face limitations with complex longitudinal microbiome data.

Purpose of the Study:

  • To identify key analytical objectives for longitudinal microbiome studies.
  • To review and compare existing statistical methods for analyzing temporal microbiome data.
  • To highlight areas for methodological development and provide reproducible R tutorials.

Main Methods:

  • Exploration of strengths and limitations of current statistical methods.
  • Comparison of different methods via simulation and case studies for differential abundance and clustering.
  • Identification of network modeling approaches for temporal microbial relationships.

Main Results:

  • Current methods have limitations in addressing the complexities of longitudinal microbiome data.
  • Comparative analyses demonstrate varying performance of methods for differential abundance and clustering.
  • Opportunities for advancing statistical methodologies in microbiome research are identified.

Conclusions:

  • There is a need for improved statistical approaches to analyze longitudinal microbiome data.
  • Methodological advancements can enhance our understanding of microbial dynamics over time.
  • Reproducible research through provided R tutorials facilitates further investigation.