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Related Concept Videos

Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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...
What Are Outliers?01:12

What Are Outliers?

Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
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Outliers and Influential Points01:08

Outliers and Influential Points

An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the vertical...
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
Gene Duplication and Divergence02:37

Gene Duplication and Divergence

The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was  generated by gene duplication and divergence, indicating its critical role in evolution.
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Modified Boxplots00:57

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A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
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Related Experiment Video

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Competitive Genomic Screens of Barcoded Yeast Libraries
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DigOut: viewing differential expression genes as outliers.

Hui Yu1, Kang Tu, Lu Xie

  • 1Bioinformatics Center, Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P R China. yuhui@scbit.org

Journal of Bioinformatics and Computational Biology
|December 15, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces multivariate outlier analysis for identifying differentially expressed (DE) genes in non-replicated, multi-conditional gene expression datasets. This method outperforms the limit fold change (LFC) model, offering improved stability and satisfactory results.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Identifying differentially expressed (DE) genes is crucial in microarray data analysis.
  • Existing methods are well-established for replicated, two-conditional datasets.
  • However, analyzing non-replicated, multi-conditional datasets presents significant challenges.

Purpose of the Study:

  • To address the limitations of current methods for DE gene selection in non-replicated, multi-conditional microarray datasets.
  • To evaluate the performance of multivariate outlier analysis compared to the limit fold change (LFC) model.

Main Methods:

  • Application of multivariate outlier analysis to non-replicated, multi-conditional microarray datasets.
  • Comparative analysis using simulated datasets against the LFC model.
  • Validation on manipulated real gene expression datasets and a real non-replicated dataset series.

Main Results:

  • Multivariate outlier analysis demonstrated significantly better performance than the LFC model in simulated experiments.
  • The proposed method showed improved stability against sample variations in real datasets.
  • Satisfactory results were obtained upon reanalysis of a real non-replicated multi-conditional expression dataset.

Conclusions:

  • Multivariate outlier analysis, exemplified by algorithms like DigOut, is highly effective for DE gene selection.
  • This approach is particularly valuable for non-replicated, multi-conditional gene expression datasets where traditional methods fall short.