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Related Concept Videos

Sampling Methods: Overview01:06

Sampling Methods: Overview

4.0K
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

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Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
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Random Sampling Method01:09

Random Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
15.8K
Downsampling01:20

Downsampling

806
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
806
Sample Handling01:02

Sample Handling

3.0K
Transportation of samples from the collection point to the laboratory, as well as storage and preservation techniques, are crucial for maintaining sample integrity and ensuring accurate and reliable test results.
Samples should be transported carefully from collection points to the laboratory. They should be properly sealed and clearly labeled to prevent cross-contamination. To preserve the sample integrity, optimal temperature conditions during transport are essential. This could involve using...
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Analysis of Multidimensional Microscopy Data Using Cell-ACDC
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A robust Correntropy-based method for analyzing multisample aCGH data.

Majid Mohammadi1, Ghosheh Abed Hodtani2, Maryam Yassi3

  • 1Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.

Genomics
|August 7, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a robust Correntropy-based method for analyzing array comparative genomic hybridization (aCGH) data. The new approach effectively handles noise and analyzes multiple profiles simultaneously for improved genomic insights.

Keywords:
CancerCorrentropyDNA copy numberHalf-Quadratic programmingaCGH

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Area of Science:

  • Genomics
  • Bioinformatics
  • Signal Processing

Background:

  • Array comparative genomic hybridization (aCGH) is crucial for detecting genomic copy number variations.
  • Existing aCGH data analysis methods can be sensitive to noise and corruptions.
  • Simultaneous analysis of multiple aCGH profiles can enhance detection accuracy.

Purpose of the Study:

  • To develop a novel, robust method for aCGH data analysis.
  • To improve the resilience of aCGH analysis against various noise types, including non-Gaussian noise.
  • To enable simultaneous analysis of all aCGH profiles within a dataset.

Main Methods:

  • A new formulation for aCGH data analysis using low-rank properties and Correntropy.
  • Application of the Half-Quadratic method for solving the proposed formulation.
  • Comparative analysis against existing methods using simulated and real-world data.

Main Results:

  • The proposed Correntropy-based method demonstrates superior robustness against high levels of noise and data corruption.
  • The method effectively handles non-Gaussian noise, outperforming traditional approaches.
  • Simultaneous analysis of all aCGH profiles leads to more comprehensive and accurate results.

Conclusions:

  • The Correntropy-based method offers a significant advancement in aCGH data analysis.
  • This approach provides a more reliable tool for genomic variation detection, especially in noisy datasets.
  • The simultaneous analysis capability enhances the overall utility of aCGH in research and diagnostics.