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Related Concept Videos

Principal Stresses in a Beam01:11

Principal Stresses in a Beam

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In prismatic beams subject to arbitrary transverse loading, It is essential to analyze the interaction between shear forces and bending moments in order to understand stress distribution and ensure structural integrity. The highest normal or bending stress occurs at the outer fibers of the beam, decreasing linearly to zero at the neutral axis. In contrast, shear stress peaks at the neutral axis and diminishes toward the outer surfaces.
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Principal Stresses01:24

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The graphical depiction of normal and shearing stress equations is represented by a circle, demonstrating the interplay between these stresses under different angular conditions. The center of this circle C, located on the vertical axis, represents the average normal stress, while its radius shows the range of stress variations. At points A and B, where the circle intersects the horizontal axis, the maximum and minimum normal stresses are observed, occurring without shearing stress. These...
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Distance Corrections01:15

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To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead 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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Updated: Jan 29, 2026

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
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Correction of Copy Number Variation Data Using Principal Component Analysis.

Jiayu Chen1, Jingyu Liu1,2, Vince D Calhoun1,2

  • 1Dept. of Electrical Engineering, University of New Mexico, Albuquerque, NM.

IEEE International Conference on Bioinformatics and Biomedicine Workshops. IEEE International Conference on Bioinformatics and Biomedicine
|February 5, 2019
PubMed
Summary
This summary is machine-generated.

Principal component analysis (PCA) improves copy number variation (CNV) detection from SNP array data. This method reduces noise and enhances accuracy for reliable genetic variation analysis.

Keywords:
Log R Ratiocopy number variationprincipal component analysis

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Copy number variation (CNV) detection from SNP array data is hindered by low signal-to-noise ratios.
  • Confounding factors introduce significant variance, complicating accurate CNV identification.

Purpose of the Study:

  • To develop and validate a novel method for improving CNV detection accuracy.
  • To address the challenges posed by low signal-to-noise and confounding factors in SNP array data analysis.

Main Methods:

  • Application of principal component analysis (PCA) for data correction.
  • Utilizing PCA to eliminate variance attributed to confounding factors in CNV data.

Main Results:

  • Simulations demonstrated a substantial increase in CNV detection accuracy post-correction.
  • Real SNP array data analysis showed significant improvements in data quality after applying the PCA-based correction.

Conclusions:

  • PCA-based correction is an effective strategy for enhancing CNV detection in SNP array data.
  • The proposed method improves both the accuracy of CNV identification and the overall quality of genomic data.