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Related Concept Videos

Normal Distribution01:11

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The normal, a continuous distribution, is the most important of all the distributions. Its graph is a bell-shaped symmetrical curve, which is observed in almost all disciplines. Some of these include psychology, business, economics, the sciences, nursing, and, of course, mathematics. Some instructors may use the normal distribution to help determine students’ grades. Most IQ scores are normally distributed. Often real-estate prices fit a normal distribution. The normal distribution is...
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Normal stress is a type of stress that occurs when forces act perpendicular, or normal, to a material's cross-sectional area. This stress often arises in structures when subjected to axial loading, which is the application of force along the axis of an object. A practical example of this can be found in bridge truss members.
When a rod is under axial loading, the internal forces and corresponding stress are normal to the plane of the section, so it is termed normal stress. It's important to...
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In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
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Standardized test scores often follow a symmetric distribution that can be modeled with the normal distribution, a fundamental concept in statistics. This distribution is particularly useful for interpreting test performance fairly across populations, as it provides a mathematical framework for understanding variability and central tendency in large datasets.From Histogram to Frequency DistributionRaw test data are often displayed using histograms, where the height of each bar represents the...
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When a beam is subjected to different loads, such as weight, pressure, or other external forces, internal forces are generated within the beam. These forces can have a significant impact on the overall stability and strength of the structure. Engineers use various methods to analyze and determine the magnitude and direction of these internal forces. One common technique used to determine internal forces in beams is the method of sections. This method involves considering an imaginary point or...
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Related Experiment Video

Updated: Feb 8, 2026

DNA Methylation: Bisulphite Modification and Analysis
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Meffil: efficient normalization and analysis of very large DNA methylation datasets.

J L Min1,2, G Hemani1,2, G Davey Smith1,2

  • 1MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.

Bioinformatics (Oxford, England)
|June 23, 2018
PubMed
Summary

We developed meffil, an R package for efficient analysis of large DNA methylation datasets. It improves quality control and association studies, handling distributed data for better meta-analyses.

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • DNA methylation datasets are rapidly increasing in size and scope.
  • Efficient computational tools are essential for managing and analyzing these large-scale epigenomic data.

Purpose of the Study:

  • To introduce meffil, an R package for the quality control, normalization, and epigenome-wide association studies (EWAS) of large DNA methylation datasets.
  • To provide a computational solution that minimizes memory usage and running time for large-scale EWAS.

Main Methods:

  • Developed meffil, an R package implementing a re-engineered functional normalization method.
  • Incorporated fixed and random effects and automated parameter estimation within functional normalization.
  • Enabled normalization of distributed datasets without sharing individual-level data.

Main Results:

  • The re-implemented functional normalization minimizes computational memory without increasing running time.
  • Reduced technical variation in DNA methylation levels, leading to lower false positive rates and improved statistical power.
  • Facilitated meta-analyses of EWAS by reducing heterogeneity in distributed datasets.

Conclusions:

  • meffil offers an efficient and scalable solution for analyzing large DNA methylation BeadChip microarray datasets.
  • The package enhances the accuracy and power of EWAS and supports distributed data analysis for meta-analyses.