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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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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...
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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...
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Statistical Methods to Analyze Parametric Data: ANOVA01:12

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Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
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In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
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Precision Measurements and Parametric Models of Vertebral Endplates
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CLOUD: a non-parametric detection test for microbiome outliers.

Emmanuel Montassier1,2, Gabriel A Al-Ghalith3, Benjamin Hillmann3

  • 1MiHAR lab, Université de Nantes, 44000, Nantes, France. emmanuel.montassier@univ-nantes.fr.

Microbiome
|August 8, 2018
PubMed
Summary
This summary is machine-generated.

A new CLOUD test identifies outlier gut microbiome profiles, assessing patient microbiome similarity and stability against healthy individuals. This method helps in understanding dysbiosis in various diseases.

Keywords:
ConformityDysbiosisFecal microbiota transplantationMicrobiomeOutlierStability

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Area of Science:

  • Microbiome research
  • Computational biology
  • Human health

Background:

  • Gut microbiome dysbiosis is linked to numerous diseases but shows high inter-individual variation.
  • Individual microbiomes vary over time, forming a personal microbiome cloud.
  • Existing tests lack robustness for detecting outlier microbiome profiles in healthy populations.

Purpose of the Study:

  • To develop a robust non-parametric test for detecting outlier human gut microbiome profiles.
  • To assess microbiome health based on conformity and stability relative to healthy individuals.
  • To account for wide variations in microbiome composition and temporal changes.

Main Methods:

  • Proposed the CLOUD (Conformity and Stability) test for outlier detection.
  • Utilized locally linear embedded ecological distances to handle diverse microbiome compositions.
  • Incorporated intra-individual temporal variation for increased test robustness.

Main Results:

  • Applied the CLOUD test to patient cohorts (Clostridium difficile colitis) and healthy cohorts.
  • Demonstrated high concordance between CLOUD conformity/stability indices and clinical outcomes.
  • Validated the test's ability to identify outlier microbiome profiles.

Conclusions:

  • The CLOUD test provides a framework for evaluating suspected dysbiosis.
  • It can be incorporated into diagnostic and prognostic assessments for various diseases.
  • Further research can explore its role in understanding disease mechanisms.