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Quantifying and Rejecting Outliers: The Grubbs Test01:02

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
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MOF: an R function to detect outlier microarray.

Song Yang1, Xiang Guo, Hai Hu

  • 1Windber Research Institute, Windber, PA 15963, USA.

Genomics, Proteomics & Bioinformatics
|March 31, 2009
PubMed
Summary
This summary is machine-generated.

We created a new R function called microarray outlier filter (MOF) to identify failed microarray data. This tool helps improve data quality by flagging unreliable arrays for removal from analysis.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Microarray technology is crucial for gene expression analysis.
  • Ensuring the quality of microarray data is essential for reliable downstream analysis.
  • Identifying and removing failed arrays can prevent skewed results.

Purpose of the Study:

  • To develop a user-friendly R function for identifying failed microarray data.
  • To provide a tool for quality control in microarray experiments.
  • To facilitate the exclusion of unreliable datasets from further analysis.

Main Methods:

  • Developed an R function named "microarray outlier filter" (MOF).
  • Utilized statistical indices: correlation coefficient and percentage of outlier spots.
  • Implemented a method for sorting arrays based on failure likelihood.

Main Results:

  • The MOF function effectively assists in identifying potentially failed arrays.
  • The function provides statistical indices to quantify array quality.
  • MOF enables systematic quality monitoring of microarray datasets.

Conclusions:

  • The microarray outlier filter (MOF) is a valuable tool for microarray data quality control.
  • MOF aids researchers in troubleshooting and removing low-quality datasets.
  • The function is freely available, promoting wider adoption in bioinformatics research.