fix: execute all chunks in centros workflow

inst/pages/workflow-centros.qmd

@@ -34,31 +34,30 @@ We leverage two yeast datasets in this notebook.
 
 ## Importing Hi-C data and plotting contact matrices
 
-```{r eval = FALSE}
+```{r}
+library(HiContactsData)
 library(HiContacts)
 library(purrr)
 library(ggplot2)
 hics <- list(
-    'G1' = import('/home/rsg/repos/OHCA-data/S288c_G1.mcool', resolution = 4000),
-    'G2M' = import('/home/rsg/repos/OHCA-data/S288c_G2M.mcool', resolution = 4000)
+    'G1' = import(HiContactsData('yeast_g1', 'mcool'), format = 'cool', resolution = 4000),
+    'G2M' = import(HiContactsData('yeast_g2m', 'mcool'), format = 'cool', resolution = 4000)
 )
 imap(hics, ~ plotMatrix(
     .x, use.scores = 'balanced', limits = c(-4, -1), caption = FALSE
 ) + ggtitle(.y))
 ```
 
-![](images/20230523141745.jpg)
-
 We can visually appreciate that inter-chromosomal interactions, notably 
 between centromeres, are less prominent in G2/M.
 
 ## Checking P(s) and cis/trans interactions ratio
 
-```{r eval = FALSE}
+```{r}
 library(dplyr)
 pairs <- list(
-    'G1' = PairsFile('/home/rsg/repos/OHCA-data/S288c_G1.pairs'),
-    'G2M' = PairsFile('/home/rsg/repos/OHCA-data/S288c_G2M.pairs') 
+    'G1' = PairsFile(HiContactsData('yeast_g1', 'pairs')),
+    'G2M' = PairsFile(HiContactsData('yeast_g2m', 'pairs')) 
 )
 ps <- imap_dfr(pairs, ~ distanceLaw(.x, by_chr = TRUE) |> 
     mutate(sample = .y) 
@@ -69,12 +68,10 @@ plotPsSlope(ps, ggplot2::aes(x = binned_distance, y = slope, group = interaction
     scale_color_manual(values = c('black', 'red'))
 ```
 
-![](images/20230523144800.png)
-
 This confirms that interactions in cells synchronized in G2/M are enriched 
 for 10-30kb-long interactions. 
 
-```{r eval = FALSE}
+```{r}
 ratios <- imap_dfr(hics, ~ cisTransRatio(.x) |> mutate(sample = .y))
 ggplot(ratios, aes(x = chr, y = trans_pct, fill = sample)) + 
     geom_col() + 
@@ -83,37 +80,34 @@ ggplot(ratios, aes(x = chr, y = trans_pct, fill = sample)) +
     facet_grid(~sample)
 ```
 
-![](images/20230523152700.png)
-
 We can also highlight that trans (inter-chromosomal) interactions are proportionally 
 decreasing in G2/M-synchronized cells. 
 
 ## Centromere virtual 4C profiles
 
-```{r eval = FALSE}
+```{r}
 data(centros_yeast)
-v4c_centro <- imap_dfr(hics, ~ virtual4C(.x, resize(centros_yeast[2], 8000)) |> 
+v4c_centro <- imap_dfr(hics, ~ virtual4C(.x, GenomicRanges::resize(centros_yeast[2], 8000)) |> 
     as_tibble() |> 
     mutate(sample = .y) |> 
     filter(seqnames == 'IV')
 ) 
-ggplot(v4c_centro, aes(x = start, y = score, colour = sample)) +
-    geom_line() +
+ggplot(v4c_centro, aes(x = start, y = score, fill = sample)) +
+    geom_area() +
     theme_bw() +
     labs(
         x = "chrIV position", 
         y = "Contacts with chrII centromere", 
         title = "Interaction profile of chrII centromere"
-    )
+    ) + 
+    coord_cartesian(ylim = c(0, 0.015))
 ```
 
-![](images/20230523153300.png)
-
 ## Aggregated 2D signal over all pairs of centromeres
 
 We can start by computing all possible pairs of centromeres. 
 
-```{r eval = FALSE}
+```{r}
 centros_pairs <- lapply(1:length(centros_yeast), function(i) {
     lapply(1:length(centros_yeast), function(j) {
         S4Vectors::Pairs(centros_yeast[i], centros_yeast[j])
@@ -125,28 +119,11 @@ centros_pairs <- lapply(1:length(centros_yeast), function(i) {
 centros_pairs <- centros_pairs[anchors(centros_pairs, 'first') != anchors(centros_pairs, 'second')]
 
 centros_pairs
-## GInteractions object with 240 interactions and 0 metadata columns:
-##         seqnames1       ranges1     seqnames2       ranges2
-##             <Rle>     <IRanges>         <Rle>     <IRanges>
-##     [1]         I 151583-151641 ---        II 238361-238419
-##     [2]         I 151583-151641 ---       III 114322-114380
-##     [3]         I 151583-151641 ---        IV 449879-449937
-##     [4]         I 151583-151641 ---         V 152522-152580
-##     [5]         I 151583-151641 ---        VI 147981-148039
-##     ...       ...           ... ...       ...           ...
-##   [236]       XVI 556255-556313 ---        XI 440229-440287
-##   [237]       XVI 556255-556313 ---       XII 151366-151424
-##   [238]       XVI 556255-556313 ---      XIII 268222-268280
-##   [239]       XVI 556255-556313 ---       XIV 628588-628646
-##   [240]       XVI 556255-556313 ---        XV 326897-326955
-##   -------
-##   regions: 16 ranges and 0 metadata columns
-##   seqinfo: 17 sequences (1 circular) from R64-1-1 genome
 ```
 
 Then we can aggregate the Hi-C signal over each pair of centromeres.
 
-```{r eval = FALSE}
+```{r}
 aggr_maps <- purrr::imap(hics, ~ {
     aggr <- aggregate(.x, centros_pairs, maxDistance = 1e999)
     plotMatrix(
@@ -155,43 +132,33 @@ aggr_maps <- purrr::imap(hics, ~ {
         caption = FALSE
     ) + ggtitle(.y)
 })
-## Going through preflight checklist...
-## Parsing the entire contact matrix as a sparse matrix...
-## Modeling distance decay...
-## Filtering for contacts within provided targets...
-## Going through preflight checklist...
-## Parsing the entire contact matrix as a sparse matrix...
-## Modeling distance decay...
-## Filtering for contacts within provided targets...
 
 cowplot::plot_grid(plotlist = aggr_maps, nrow = 1)
 ```
 
-![](images/20230523180300.png)
-
 ## Aggregated 1D interaction profile of centromeres 
 
 One can generalize the previous virtual 4C plot, by extracting the interaction profile 
 between all possible pairs of centromeres in each dataset. 
 
-```{r eval = FALSE}
+```{r}
 df <- map_dfr(1:{length(centros_yeast)-1}, function(i) {
-    centro1 <- resize(centros_yeast[i], fix = 'center', 8000)
+    centro1 <- GenomicRanges::resize(centros_yeast[i], fix = 'center', 8000)
     map_dfr({i+1}:length(centros_yeast), function(j) {
-        centro2 <- resize(centros_yeast[j], fix = 'center', 80000)
-        gi <- GInteractions(centro1, centro2)
+        centro2 <- GenomicRanges::resize(centros_yeast[j], fix = 'center', 80000)
+        gi <- InteractionSet::GInteractions(centro1, centro2)
         imap_dfr(hics, ~ .x[gi] |> 
             interactions() |> 
             as_tibble() |>
             mutate(
                 sample = .y, 
-                center = center2 - start(resize(centro2, fix = 'center', 1))
+                center = center2 - start(GenomicRanges::resize(centro2, fix = 'center', 1))
             ) |> 
             select(sample, seqnames1, seqnames2, center, balanced)
         )
     })
 }) 
-p <- ggplot(df, aes(x = center/1e3, y = balanced)) + 
+ggplot(df, aes(x = center/1e3, y = balanced)) + 
     geom_line(aes(group = interaction(seqnames1, seqnames2)), alpha = 0.03, col = "black") + 
     geom_smooth(col = "red", fill = "red") + 
     theme_bw() + 
@@ -203,8 +170,6 @@ p <- ggplot(df, aes(x = center/1e3, y = balanced)) +
     facet_grid(~sample)
 ```
 
-![](images/20230523180000.png)
-
 # Session info {-}
 
 ::: {.callout-note collapse="true"}

inst/pages/workflow-yeast.qmd

@@ -40,18 +40,17 @@ We leverage seven yeast datasets in this notebook. They are all available from S
 
 :::
 
-## Recovering data from SRA
-
-The easiest for this is to directly fetch files from SRA from their FTP server. We can do so using the base `download.file` function. 
-
 ::: {.callout-note}
 
-The next two code chunks illustrate how to do download and process Hi-C reads from SRA, but 
-they are not actually executed when rendering this website as it would take a 
-significant amount of time. 
+This notebook does not execute code when it's rendered. Instead, the results 
+and the plots have been locally computed and manually embedded in the notebook. 
 
 :::
 
+## Recovering data from SRA
+
+The easiest for this is to directly fetch files from SRA from their FTP server. We can do so using the base `download.file` function. 
+
 ```{r eval = FALSE}
 # !! This code is not actually executed !!
 dir.create('data')
@@ -413,7 +412,7 @@ results
 ```
 
 The `results()` output is not very informative as it is. It requires a little 
-bit of reformating to be able to extract valuable insights from it. 
+bit of reformatting to be able to extract valuable insights from it. 
 
 ```{r eval = FALSE}
 df <- left_join(results@hic_table, results(results)) |>