From dd1703843e0717929ba7509e8655c9c1d13f63e3 Mon Sep 17 00:00:00 2001 From: Cole Date: Mon, 14 Mar 2022 12:13:32 -0500 Subject: [PATCH] rebuild after recent error fixes --- .Rbuildignore | 1 + .gitignore | 1 + doc/mxnorm-vignette.html | 2 +- 3 files changed, 3 insertions(+), 1 deletion(-) diff --git a/.Rbuildignore b/.Rbuildignore index 8c86fc7..6bb12f3 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -6,3 +6,4 @@ R/.create_sample_data.R R/.create_metrics.R ^Meta$ ^doc/ +^doc$ diff --git a/.gitignore b/.gitignore index 89a2478..bd17d72 100644 --- a/.gitignore +++ b/.gitignore @@ -5,3 +5,4 @@ .DS_Store inst/doc /Meta/ +/doc/ diff --git a/doc/mxnorm-vignette.html b/doc/mxnorm-vignette.html index a7ea173..6c8288d 100644 --- a/doc/mxnorm-vignette.html +++ b/doc/mxnorm-vignette.html @@ -471,7 +471,7 @@

Otsu discordance scores with run_otsu_discordance()

In the above plot we observe that not only are the density curves for each marker in the analysis far better aligned after normalization, we also see that the Otsu thresholds (ticks on the x-axis) have moved far closer than in the raw data. In general, also note that all plots generated using mxnorm are ggplot2 plots and can be adjusted and adapted as needed given the ggplot2 framework. We can also visualize the results of the threshold discordance analysis stratified by slide and marker, with slide means indicated by the white diamonds:

plot_mx_discordance(mx_data)
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+

Note that for each slide and marker pair in the dataset (denoted as colored points in the above plot), we see a reduction in threshold discordance in the normalized data compared to the raw data. Further, we also see dramatic improvements in the mean threshold discordance denoted by the white diamonds for the normalized data.