Manhattan plot

Manhattan plot

A Manhattan plot is a scatterplot used in GWAS studies to display data with a large number of data points, where the x-axis represents m/z or time and the y-axis represents -log10(p). The plot is useful for visualizing significant features (p < .05) between paired groups, which are represented as colored dots. The plot is particularly insightful for data with a distribution of higher-magnitude values and many non-zero amplitude values.From: Microarray Image and Data Analysis [2019], An in vitro study on the differentiated metabolic mechanism of chloroquine-resistant Plasmodium falciparum using high-resolution metabolomics [2021]

After-Effects of Driver‑Paced Transitions of Control

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Published in Alexander Eriksson, Neville A. Stanton, Driver Reactions to Automated Vehicles, 2018

Alexander Eriksson, Neville A. Stanton

The performance metrics were tested in a time series, assessing the difference between conditions every 333.33 milliseconds for the full 20-second testing period by using t-tests. The tests were corrected using the Bonferroni method, resulting in an alpha of 0.00083, which is represented in the graphs as a red horizontal dashed line in the middle graph of each figure. An uncorrected alpha-level of 0.01 is shown as a horizontal black line in the same graph. The level of significance is represented through the negative base-10 logarithms of P, where large values represent small P-values in a similar fashion to the ‘Manhattan’ plot (previously used in Gibson, 2010; Petermeijer et al., 2017; Tanikawa et al., 2012). Cohen’s D was calculated to assess effect size between conditions in the following way:

Discussion of “Scaling-up process characterization”

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Published in Quality Engineering, 2018

William H. Woodall, Lata Kodali, Emmanuel Yashchin

With respect to change screening, the use of the –log(p-value) transformation is common in bioinformatics. One example is Li (2012) who also used the “volcano plot” that is based on this transformation. Another example is Gibson (2010) who used a “Manhattan plot” to display the single nucleotide polymorphisms (SNPs) of a genome wide association study (GWAS) for a “generic common disease.” This metric is also extensively used as a measure of severity of detected out of control conditions in monitoring systems, e.g., see Yashchin (2017).

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After-Effects of Driver‑Paced Transitions of Control

Discussion of “Scaling-up process characterization”