Background: A run chart is a line graph of a measure plotted over time with the median as a horizontal line. The main purpose of the run chart is to identify process improvement or degradation, which may be detected by statistical tests for non-random patterns in the data sequence.
Methods: We studied the sensitivity to shifts and linear drifts in simulated processes using the shift, crossings and trend rules for detecting non-random variation in run charts.
Results: The shift and crossings rules are effective in detecting shifts and drifts in process centre over time while keeping the false signal rate constant around 5% and independent of the number of data points in the chart. The trend rule is virtually useless for detection of linear drift over time, the purpose it was intended for.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4244133 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0113825 | PLOS |
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Spatially resolved transcriptomics has made it possible to study the subcellular organization of mRNA, a critical aspect of cellular function. However, there is a dearth of analytical tools to identify and interpret the functional significance of subcellular spatial distribution patterns. To address this, we present CellSP, a computational framework for identifying, visualizing, and characterizing consistent subcellular spatial patterns of mRNA.
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Genet Sel Evol
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