In KaiAragaki/amplify: Automate PCR Tasks Reproducibly

library(ggplot2)
library(knitr)
library(dplyr)

df <- params$data

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pcr_lib_qc_plot_dil(df)

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::: {style="flex-basis: 100px; flex-grow: 1"} Theoretical (perfectly diluted) standards are in gray, true dilutions are in blue. Samples are in red. Dilutions between standards shown in text. :::

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pcr_lib_qc_plot_outliers(df)

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::: {style="flex-basis: 100px; flex-grow: 1"} CT-values as represented by Z-score. Blue dots represent samples not considered outliers. Shaded blue region is +/- three standard deviations, which is considered the cutoff for outliers. Black dots are samples considered outliers. Far outliers (greater than 11 standard deviations) are represented by arrows (>>>) with the true value displayed beside it. Z scores are calculated after removing the outlier, if any. :::

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pcr_lib_qc_plot_conc(df)

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::: {style="flex-basis: 100px; flex-grow: 1"} Library concentrations across samples. Sample concentrations before outlier removal are in gray, after outlier removal are in blue. :::

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pcr_lib_qc_plot_slope(df)

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::: {style="flex-basis: 100px; flex-grow: 1"} Log of library quantities vs CT values. An optimal slope is -3.32. An optimal efficiency is 100%. Acceptable efficiencies are >80%. :::

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