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Related Concept Videos

Quartile01:15

Quartile

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Quartiles are numbers that separate the data into quarters. Quartiles may or may not be part of the data. To find the quartiles, first, find the median or second quartile. The first quartile, Q1, is the middle value of the lower half of the data, and the third quartile, Q3, is the middle value, or median, of the upper half of the data. To get the idea, consider the same data set:
1; 1; 2; 2; 4; 6; 6.8; 7.2; 8; 8.3; 9; 10; 10; 11.5
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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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Modified Boxplots00:57

Modified Boxplots

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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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Relative Frequency Distribution00:55

Relative Frequency Distribution

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A relative frequency distribution is the proportion or fraction of times a value occurs in a data set. To find the relative frequencies, one can divide each frequency by the total number of data points in the sample. It is very similar to a regular frequency distribution, except that instead of reporting how many data values fall in a class, a relative frequency distribution reports the fraction of data values that fall in a class. These fractions or proportions are called relative frequencies...
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Boxplot01:12

Boxplot

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Box plots (also called box-and-whisker plots or box-whisker plots) give an excellent graphical image of the concentration of the data. They also show how far the extreme values are from most data. A box plot is constructed from five values: the minimum value, the first quartile, the median, the third quartile, and the maximum value. We use these values to compare how close other data values are to them. To construct a box plot, use a horizontal or vertical number line and a rectangular box. The...
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Review and Preview01:10

Review and Preview

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In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
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Visualizing 'omic feature rankings and log-ratios using Qurro.

Marcus W Fedarko1,2, Cameron Martino2,3, James T Morton4

  • 1Department of Computer Science and Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.

NAR Genomics and Bioinformatics
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Summary

New tool Qurro (QUality-Ratio Explorer) simplifies analysis of omics data. It links feature rankings to log-ratios, improving reproducibility and efficiency for researchers.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Omics Data Analysis

Background:

  • Omics data analysis often involves ranking features based on their association with variation or covariates.
  • Exploratory analysis of these rankings, particularly for log-ratios, has been cumbersome and prone to errors using custom code.

Purpose of the Study:

  • Introduce Qurro, an interactive tool designed to streamline the exploration of compositional omics data.
  • To provide a reproducible and efficient method for visualizing feature rankings and associated log-ratios.

Main Methods:

  • Qurro integrates a 'rank plot' of feature rankings with a 'sample plot' of selected log-ratios.
  • Interactive controls allow users to select features from the rank plot, dynamically updating both plots.

Main Results:

  • Demonstrates Qurro's ability to facilitate simple and effective exploration of feature rankings and log-ratios.
  • Highlights the tool's utility in addressing the limitations of previous custom-coded approaches.

Conclusions:

  • Qurro offers a novel and user-friendly interface for omics data exploration.
  • The tool enhances reproducibility and efficiency in analyzing compositional data by linking feature rankings to log-ratios.