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Related Concept Videos

Longitudinal Studies01:26

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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When an object's velocity changes over time, the total distance traveled can be determined by summing small displacement intervals over short increments. This approach approximates the true distance through numerical summation and the use of integral calculus. An estimate of the total displacement can be obtained by measuring velocity at regular intervals and multiplying each value by the corresponding time step.If a runner accelerates over the first three seconds of a race, speed measurements...
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Correlations

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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
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In geometry, measuring the direct distance between two points on a plane is essential in various practical and theoretical applications. Whether in navigation, engineering, or computer graphics, determining the shortest path between two locations involves using the distance formula. This formula is derived from the Pythagorean Theorem, which relates the lengths of the sides of a right triangle. On a coordinate plane, the horizontal and vertical distances between two points serve as the legs of...
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Distance-correlation based gene set analysis in longitudinal studies.

Jiehuan Sun1, Jose D Herazo-Maya2, Xiu Huang3

  • 1Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA.

Statistical Applications in Genetics and Molecular Biology
|February 5, 2018
PubMed
Summary
This summary is machine-generated.

A new method, Distance-Correlation based Gene Set Analysis (dcGSA), analyzes longitudinal gene expression data to find gene sets linked to disease progression. This robust approach identifies more disease-related gene sets than existing methods.

Keywords:
distance correlationgene set analysislongitudinal gene expression study

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Longitudinal gene expression data aids in monitoring disease progression and understanding etiology.
  • Identifying gene sets with coordinated changes linked to clinical outcomes offers insights into disease mechanisms and potential treatments.

Purpose of the Study:

  • To introduce a novel non-parametric method, Distance-Correlation based Gene Set Analysis (dcGSA), for analyzing longitudinal gene expression data.
  • To capture both linear and nonlinear relationships between gene sets and clinical outcomes.
  • To address subject heterogeneity and confounding effects in longitudinal studies.

Main Methods:

  • Developed a Distance-Correlation based Gene Set Analysis (dcGSA) method.
  • Employed a non-parametric statistical approach.
  • Designed to handle subject heterogeneity and unobserved covariates.

Main Results:

  • dcGSA demonstrated higher power in detecting relevant genes compared to existing methods in simulations.
  • The method successfully identified more disease-related gene sets in a real-world systemic lupus erythematosus dataset.
  • dcGSA can identify related gene sets with differing effects across subjects and assess associations with multiple outcomes.

Conclusions:

  • dcGSA is a powerful and robust tool for gene set analysis in longitudinal gene expression studies.
  • The method provides significant insights into the molecular basis of disease progression.
  • dcGSA offers an advancement over traditional gene set analysis techniques for complex biological data.