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Related Concept Videos

Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
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Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...
Uncertainty: Overview00:59

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Biostatistics: Overview

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A Reporter Assay to Analyze Intronic microRNA Maturation in Mammalian Cells
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puma: a Bioconductor package for propagating uncertainty in microarray analysis.

Richard D Pearson1, Xuejun Liu, Guido Sanguinetti

  • 1School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. richard.pearson@well.ox.ac.uk

BMC Bioinformatics
|July 11, 2009
PubMed
Summary
This summary is machine-generated.

The puma package improves Affymetrix GeneChip data analysis by incorporating uncertainty propagation for gene expression, enhancing differential expression detection, principal component analysis, and clustering. This R package offers broader scope and faster computation than previous methods.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Traditional microarray data analysis often overlooks expression level uncertainties.
  • Previous uncertainty propagation methods were fragmented, computationally expensive, and limited in scope.

Purpose of the Study:

  • To introduce puma, a unified Bioconductor package for Affymetrix GeneChip data analysis.
  • To enhance downstream analyses by incorporating uncertainty propagation methods.
  • To provide a computationally efficient and user-friendly tool for gene expression analysis.

Main Methods:

  • Developed the puma Bioconductor package in R.
  • Implemented uncertainty propagation for differential expression detection (extended to multi-factorial designs).
  • Integrated uncertainty propagation for principal component analysis and clustering.
  • Parallelized computations for increased speed.

Main Results:

  • puma provides a comprehensive suite of uncertainty propagation methods in a single package.
  • Significantly improved differential expression detection, PCA, and clustering results.
  • Extended differential expression analysis to multi-factorial experimental designs.
  • Achieved substantial speed improvements through parallelization.

Conclusions:

  • puma democratizes access to advanced uncertainty propagation techniques for microarray analysis.
  • Offers improved scope, speed, and ease of use compared to prior implementations.
  • Recommended for researchers using Affymetrix GeneChip for gene expression analysis.