From 92057d31c500c6fd1391e7692f170b30a8f66bac Mon Sep 17 00:00:00 2001 From: altuna akalin Date: Fri, 9 Oct 2020 02:20:40 +0200 Subject: [PATCH] fixed issues in the bs-seq for pdf compilation --- 10-bs-seq-analysis.Rmd | 2 +- 11-multiomics-analysis.Rmd | 3 ++- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/10-bs-seq-analysis.Rmd b/10-bs-seq-analysis.Rmd index 5acb5be..e9e8adc 100644 --- a/10-bs-seq-analysis.Rmd +++ b/10-bs-seq-analysis.Rmd @@ -339,7 +339,7 @@ res=methSeg(mbw,minSeg=10,G=1:4, ``` In this case, we know that BIC does not improve much after 4 segment classes. Now, we will not have a look at the characteristics of the segment classes. We are going to plot the mean methylation value and the length of the segment as a scatter plot; the result of this plot is shown in Figure \@ref(fig:segplot). -```{r segplot, fig.cap="Scatter plot of segment mean, methylation values versus segment length. Each dot is a segment identified by the _methSeg()_ function."} +```{r segplot, fig.cap="Scatter plot of segment mean, methylation values versus segment length. Each dot is a segment identified by the methSeg() function."} # plot plot(res$seg.mean, log10(width(res)),pch=20, diff --git a/11-multiomics-analysis.Rmd b/11-multiomics-analysis.Rmd index 463010e..ad396e6 100644 --- a/11-multiomics-analysis.Rmd +++ b/11-multiomics-analysis.Rmd @@ -775,4 +775,5 @@ ggplot2::ggplot(cov_factor, ggplot2::aes(x=cimp, y=factor2, group=cimp)) + ggplo 4. Microsatellite instability (MSI) is associated with hyper-mutated tumors. As seen in Figure \@ref(fig:momutationsHeatmap), one of the subtypes has tumors with significantly more mutations than the other. Which subtype is that? Which NMF factor is associated with that subtype? And which NMF factor is associated with MSI? [Difficulty: **Advanced**] -[^mfamca]: When dealing with categorical variables, MFA uses MCA (Multiple Correspondence Analysis). This is less relevant to biological data analysis and will not be discussed here. \ No newline at end of file +[^mfamca]: When dealing with categorical variables, MFA uses MCA (Multiple Correspondence Analysis). This is less relevant to biological data analysis and will not be discussed here. +