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Related Concept Videos

Sampling Methods: Overview01:06

Sampling Methods: Overview

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 sampling...
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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...
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In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.

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ResA3: a web tool for resampling analysis of arbitrary annotations.

Aaron Ruhs1, Franz Cemic, Thomas Braun

  • 1Biomolecular Mass Spectrometry and Cardiac Development and Remodeling, Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany. Aaron.Ruhs@mpi-bn.mpg.de

Plos One
|February 6, 2013
PubMed
Summary

ResA is a new tool for analyzing biological annotations, offering statistical significance for enrichment and regulation. This resampling analysis method supports large-scale transcriptomics and proteomics datasets.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Statistical significance of annotations is crucial for interpreting large-scale biological datasets.
  • Existing tools may not adequately handle diverse annotation sources or multiple analysis types simultaneously.
  • Interpreting enrichment and regulation in transcriptomics and proteomics data requires robust analytical methods.

Purpose of the Study:

  • To introduce ResA (Resampling Analysis of Arbitrary Annotations), a novel tool for empirical, non-parametric statistical analysis of biological annotations.
  • To facilitate the analysis of enrichment and regulation from various online resources like KEGG, Gene Ontology, and Pfam.
  • To provide a user-friendly platform for evaluating large-scale omics datasets.

Main Methods:

  • Resampling algorithms are employed for non-parametric statistical significance testing.
  • The ResA tool integrates with multiple annotation databases (KEGG, Gene Ontology, Pfam).
  • It supports the analysis of multiple annotation types in a single run, including a dedicated Gene Ontology feature.

Main Results:

  • ResA provides readily accessible navigable table views with statistical inference information.
  • The tool was successfully validated using a dataset measuring stable isotope incorporation rates into proteins in intact animals.
  • It demonstrated capability in analyzing diverse annotations and supporting complex biological data.

Conclusions:

  • ResA serves as a valuable complement to existing bioinformatics tools for annotation analysis.
  • The tool is well-suited for the increasing volume and complexity of transcriptomics and proteomics data.
  • It enhances the ability to determine statistical significance in various experimental settings.