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Related Concept Videos

Test for Homogeneity01:23

Test for Homogeneity

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The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
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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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Hypothesis Test for Test of Independence01:16

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The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
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The Kruskal-Wallis test, also known as the Kruskal-Wallis H test, serves as a nonparametric alternative to the one-way ANOVA, offering a solution for analyzing the differences across three or more independent groups based on a single, ordinal-dependent variable. This statistical test is particularly valuable in scenarios where the data does not meet the normal distribution assumption required by its parametric counterparts. Kruskal-Wallis test is designed typically to handle ordinal data or...
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Introduction to Test of Independence01:21

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In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
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One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
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Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
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A fast small-sample kernel independence test for microbiome community-level association analysis.

Xiang Zhan1, Anna Plantinga2, Ni Zhao3

  • 1Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, U.S.A.

Biometrics
|March 16, 2017
PubMed
Summary
This summary is machine-generated.

Researchers developed a new Kernel RV (KRV) test to analyze the complex relationship between host gene expression and microbiome composition. This method effectively identifies associations, offering new insights into diseases like inflammatory bowel disease.

Keywords:
KernelMicrobiome compositionMultivariate association testOmnibus testRV coefficient

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Area of Science:

  • Microbiome research
  • Genomics
  • Systems biology

Background:

  • Understanding the microbiome's role in health and disease requires analyzing its relationship with host genomic data.
  • High dimensionality and complex interactions between host genomics and microbiota present significant analytical challenges.

Purpose of the Study:

  • To introduce and validate a novel statistical method, the Kernel RV (KRV) coefficient test.
  • To evaluate the overall association between host gene expression and microbiome composition.

Main Methods:

  • Application of the Kernel RV (KRV) coefficient test, which captures nonlinear correlations and general dependency.
  • Development of strategies for adjusting confounding effects and selecting optimal kernels.
  • Utilizing simulation studies to assess the KRV test's performance in statistical independence testing.

Main Results:

  • The KRV test effectively identifies associations between microbiome composition and host genomic data in finite samples.
  • The method demonstrates power in detecting existing associations while controlling type I error rates.
  • KRV analysis of an inflammatory bowel disease (IBD) cohort provided new biological insights into host transcriptome-microbiome interactions.

Conclusions:

  • The KRV test is a powerful and reliable tool for analyzing the complex interplay between host genomics and microbiome composition.
  • This approach facilitates formal statistical inference and enhances biological understanding in microbiome-host interaction studies.
  • The KRV test offers a robust framework for exploring microbiome-related diseases and host responses.