remove warning messages

OHCA.scss

@@ -77,7 +77,7 @@ h1, .h1, h2, .h2, h3, .h3, h4, .h4, h5, .h5 {
 /* Put a border around session info print ------------------------------------------------ */
 
 #session-info code, p+ pre code {
-  border: 1px solid black
+  // border: 1px solid black
 }
 
 /* Code colors ------------------------------------------------------------------------- */

index.qmd

@@ -21,10 +21,6 @@ starts with key concepts important for the analysis of chromatin conformation ca
 presents `Bioconductor` tools that can be leveraged to process, analyze, explore and visualize 
 Hi-C data. 
 
-::: {.callout-caution}
-This project is currently under active development. Several sections of this book may not be accurate, as the packages introduced in this book are still maturing. 
-:::
-
 ::: {.content-hidden when-format="pdf"}
 
 # Table of contents {-}

topological-features.qmd

@@ -5,11 +5,6 @@ bibliography: bibliography.bib
 
 # Finding topological features in Hi-C 
 
-::: {.callout-warning}
-## Page under construction
-A number of sections in this page still need to be completed. 
-:::
-
 ```{r}
 #| echo: false
 #| results: "hide"
@@ -108,6 +103,7 @@ file), the `export` function can be used:
 
 ```{r}
 library(GenomicRanges)
+library(rtracklayer)
 coverage(metadata(microC_compts)$eigens, weight = 'eigen') |> export('microC_eigen.bw')
 topologicalFeatures(microC_compts, "compartments") |> export('microC_compartments.gff3')
 ```
@@ -155,8 +151,7 @@ pairwise eigenvector quantiles and plotted in a 2D heatmap.
 
 ```{r}
 library(BiocParallel)
-bpparam <- SerialParam(progressbar = FALSE)
-plotSaddle(microC_compts, nbins = 25, BPPARAM = bpparam)
+plotSaddle(microC_compts, nbins = 25, BPPARAM = SerialParam(progressbar = FALSE))
 ```
 
 Here, the top-left small corner represents average O/E scores between strong B