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Related Concept Videos

Relative Frequency Histogram01:14

Relative Frequency Histogram

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The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
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One-Way ANOVA: Unequal Sample Sizes01:15

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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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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
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Sample Proportion and Population Proportion01:20

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Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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propr: An R-package for Identifying Proportionally Abundant Features Using Compositional Data Analysis.

Thomas P Quinn1,2, Mark F Richardson3,4, David Lovell5

  • 1Deakin University, Bioinformatics Core Research Group, Geelong, Victoria, Australia. contacttomquinn@gmail.com.

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|November 28, 2017
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Summary
This summary is machine-generated.

Analyzing relative abundance data requires special methods. Proportionality offers a valid alternative to correlation for assessing pairwise associations in compositional data, with a new R package providing efficient analysis tools.

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Area of Science:

  • Life Sciences
  • Bioinformatics
  • Biostatistics

Background:

  • Many life science assays generate compositional data, representing relative abundances of sample components.
  • Standard statistical methods like correlation can yield misleading results when applied to relative data.
  • There is a growing need for appropriate analytical techniques for compositional data.

Purpose of the Study:

  • To introduce proportionality as a robust alternative to correlation for analyzing relative abundance data.
  • To present a computationally efficient R package implementing three proportionality measures.
  • To advance the understanding and application of proportionality analysis in the life sciences.

Main Methods:

  • Review of the mathematical underpinnings of proportionality.
  • Development and implementation of an R package for proportionality analysis.
  • Application of proportionality measures to genomic datasets.

Main Results:

  • The R package provides efficient computation of three distinct proportionality measures.
  • Demonstration of proportionality analysis on real-world genomic data.
  • Identification of ongoing challenges and considerations in relative abundance data analysis.

Conclusions:

  • Proportionality is a statistically sound method for assessing pairwise associations in compositional data.
  • The developed R package facilitates practical and efficient application of proportionality analysis.
  • Further research is needed to address remaining challenges in analyzing relative abundance data.