doc: remove useless callout boxes

_common.R

@@ -17,7 +17,7 @@ options(
   cli.num_colors = 0,
   cli.hyperlink = FALSE,
   pillar.bold = TRUE,
-  width = 77 # 80 - 3 for #> comment
+  width = 9999 # 80 - 3 for #> comment
 )
 
 ggplot2::theme_set(ggplot2::theme_gray(12))

data-representation.qmd

@@ -238,7 +238,7 @@ involve comparisons *between* ranges **in a single** GRanges object.
 ::: {.callout-note}
 ## Note 
 Compared to previous section, the result of each function described below 
-depends on the entire set of ranges in the input `GRanges` object.
+depends on the **entire set of ranges** in the input `GRanges` object.
 :::
 
 - Computing the "inverse" genomic ranges, i.e. ranges in-between the input ranges:
@@ -345,18 +345,13 @@ none of the remaining ranges from `query` overlap with ranges from `subject`.
 
 To directly **subset** ranges from `query` overlapping with ranges from a 
 `subject` (e.g. to only keep *peaks* overlapping a *TSS*), we can use the 
-`subsetByOverlaps` function. 
+`subsetByOverlaps` function. The output of `subsetByOverlaps` is a subset of the original `GRanges` object
+provided as a `query`, with retained ranges being unmodified. 
 
 ```{r}
 subsetByOverlaps(peaks, TSSs)
 ```
 
-::: {.callout-note}
-## Note 
-The output of `subsetByOverlaps` is a subset of the original `GRanges` object
-provided as a `query`, with retained ranges being unmodified. 
-:::
-
 - Counting overlaps between a query and a subject
 
 Finally, the `countOverlaps` is used to count, for each range in a `query`, how 
@@ -506,8 +501,6 @@ The way `GInteractions` objects are printed in an R console mimics that of
 `GRanges`, but pairs two "ends" (a.k.a. *anchors*) of an interaction together, 
 each end being represented as a separate `GRanges` range. 
 
-::: {.callout-note}
-## Notes
 - Note that it is possible to have interactions joining two identical anchors. 
 
 ```{r}
@@ -525,7 +518,6 @@ gi[4]
 ```{r}
 gi[5]
 ```
-:::
 
 ### `GInteractions` specific slots
 
@@ -650,9 +642,7 @@ Note that for "trans" inter-chromosomal interactions, i.e. interactions with anc
 different chromosomes, the notion of genomic distance is meaningless and for 
 this reason, `pairdist` returns a `NA` value. 
 
-::: {.callout-tip}
-## Advanced `pairdist` arguments
-The `type` argument can be tweaked to specify which type of "distance" should 
+The `type` argument of the `pairdist()` function can be tweaked to specify which type of "distance" should 
 be computed:  
 
 - `mid`: The distance between the midpoints of the two regions
@@ -665,7 +655,6 @@ be computed:
 - `diag`: The difference between the anchor indices is returned.
           This corresponds to a diagonal on the interaction space when
           bins are used in the 'regions' slot of 'x'.
-:::
 
 #### `GInteractions` overlap methods
 
@@ -929,8 +918,6 @@ to describe important topological features.
 the contact matrix has been created. This is often useful to estimate some Hi-C metrics. 
 - `metadata` is a `list` to further describe the experiment. 
 
-::: {.callout-tip}
-## `HiCExperiment` slots
 These pieces of information are called `slots`. They can be directly accessed 
 using `getter` functions, bearing the same name than the slot. 
 
@@ -953,10 +940,6 @@ pairsFile(hic)
 
 metadata(hic)
 ```
-:::
-
-::: {.callout-note}
-## Notes
 
 `import` also works for other types of `ContactFile` (`HicFile`, 
 `HicproFile`, `PairsFile`), e.g. 
@@ -977,17 +960,19 @@ pf <- PairsFile(pairsf)
 pairs <- import(pf)
 pairs
 ```
-:::
 
 #### Customizing the `import`
 
 To reduce the `import` to only parse the data that is relevant to the study,
 two arguments can be passed to `import`, along with a `ContactFile`. 
 
-::: {.callout-tip}
-## Key `import` arguments:
+::: {.callout-warning icon='true'}
+
+##### Key `import` arguments:
+
 - `focus`: This can be used to **only parse data for a specific genomic location**.
 - `resolution`: This can be used to choose which resolution to parse the contact matrix at (this is ignored if the `ContactFile` is not multi-resolution, e.g. `.cool` or HiC-Pro generated matrices)
+
 :::
 
 - Import interactions within a single chromosome:
@@ -1059,12 +1044,9 @@ The imported genomic interactions can be directly **exposed** using the
 interactions(yeast_hic)
 ```
 
-::: {.callout-note}
-## Note 
 Because genomic interactions are actually stored as `GInteractions`, `regions` 
 and `anchors` work on `HiCExperiment` objects just as they work with 
 `GInteractions`!
-:::
 
 ```{r}
 regions(yeast_hic)
@@ -1119,8 +1101,6 @@ head(scores(yeast_hic, "count"))
 head(scores(yeast_hic, "balanced"))
 ```
 
-::: {.callout-tip}
-## Tip
 Calling `interactions(hic)` returns a `GInteractions` with `scores` already 
 stored in extra columns. This short-hand allows one to dynamically check `scores` 
 directly from the `interactions` output.
@@ -1130,7 +1110,6 @@ interactions(yeast_hic)
 
 head(interactions(yeast_hic)$count)
 ```
-:::
 
 #### topologicalFeatures
 
@@ -1173,8 +1152,7 @@ pairsFile(yeast_hic)
 readLines(pairsFile(yeast_hic), 25)
 ```
 
-::: {.callout-important}
-## Importing a `PairsFile`
+#### Importing a `PairsFile`
 The `.pairs` file linked to a `HiCExperiment` object can itself be imported in 
 a `GInteractions` object: 
 
@@ -1185,7 +1163,6 @@ import(pairsFile(yeast_hic), format = 'pairs')
 Note that these `GInteractions` are **not** binned, contrary to `interactions` 
 extracted from a `HiCExperiment`. Anchors of the interactions listed in the 
 `GInteractions` imported from a disk-stored `.pairs` file are all of width `1`. 
-:::
 
 ## Visual summary of the `HiCExperiment` data structure
 

interactions-centric.qmd

@@ -115,15 +115,12 @@ pf <- PairsFile(pairsf)
 pf
 ```
 
-::: {.callout-note}
-## Reminder!
-`PairsFile` connections can be imported directly into a `GInteractions` object 
-with `import()`: 
+If needed, `PairsFile` connections can be imported directly into a `GInteractions` object 
+with `import()`. 
 
 ```{r}
 import(pf)
 ```
-:::
 
 We can compute a P(s) per chromosome from this `.pairs` file using the `distanceLaw` function.
 
@@ -133,15 +130,6 @@ ps <- distanceLaw(pf, by_chr = TRUE)
 ps
 ```
 
-::: {.callout-note}
-## Note
-Because this is a toy dataset, contacts are only provided for the chromosome `II`. 
-
-```{r}
-table(ps$chr)
-```
-:::
-
 The `plotPs()` and `plotPsSlope()` functions are convenient `ggplot2`-based functions
 with pre-configured settings optimized for P(s) visualization.
 
@@ -198,13 +186,8 @@ plotPsSlope(ps_from_hic, aes(x = binned_distance, y = slope))
 
 The ratio between cis interactions and trans interactions is often used to 
 assess the overall quality of a Hi-C dataset. It can be computed *per chromosome* 
-using the `cisTransRatio()` function. 
-
-::: {.callout-tip}
-## Tip!
-You will need to provide a genome-wide `HiCExperiment` to estimate
+using the `cisTransRatio()` function. You will need to provide a **genome-wide** `HiCExperiment` to estimate
 cis/trans ratios!
-:::
 
 ```{r}
 full_hic <- import(cf, resolution = 2000)
@@ -223,13 +206,10 @@ ggplot(ct, aes(x = chr, y = cis_pct)) +
     labs(x = 'Chromosomes', y = '% of cis contacts')
 ```
 
-::: {.callout-important}
-## Watch out
 Cis/trans contact ratios will greatly vary **depending on the cell cycle 
 phase the sample is in!** For instance, chromosomes during the mitosis phase 
 of the cell cycle have very little trans contacts, due to their structural 
 organization and individualization. 
-:::
 
 ## Virtual 4C profiles
 

matrix-centric.qmd

@@ -114,20 +114,15 @@ data can be normalized using matrix balancing approaches
 disk-stored matrices using out-of-memory strategies (e.g. with 
 `cooler balance <.cool>`). However, if contact matrix files are imported 
 into a `HiCExperiment` object but no `balanced` scores are available, 
-in-memory balancing can be performed using the `normalize` function.
+in-memory balancing can be performed using the `normalize` function. This 
+adds an extra `ICE` element in `scores` list (while the `interactions` 
+themselves are unmodified).
 
 ```{r}
 normalized_hic <- normalize(hic)
 normalized_hic
 ```
 
-::: {.callout-note}
-## Note
-The only change done to the `HiCExperiment` object by the `normalize` function is 
-the addition of a single extra `ICE` in `scores` list. The `interactions` 
-themselves are unmodified. 
-:::
-
 It is possible to plot the different `scores` of the resulting object to 
 visualize the newly computed `scores`. In this example, `ICE` scores 
 should be nearly identical to `balanced` scores, which were originally imported 
@@ -161,23 +156,14 @@ This is sometimes called "detrending", as it
 effectively removes the average polymer behavior from the balanced matrix. 
 
 The `detrend` function performs this operation on a given `HiCExperiment` object. 
+It adds two extra elements in `scores` list: `expected` and `detrended` metrics
+(while the `interactions` themselves are unmodified). 
 
 ```{r}
 detrended_hic <- detrend(hic)
 detrended_hic
 ```
 
-::: {.callout-note}
-## Note
-The only change done to the `HiCExperiment` object by the `detrend` function is 
-the addition of two extra `scores`: 
-
-1. `expected`
-2. `detrended`
-
-The `interactions` themselves are unmodified. 
-:::
-
 Topological features will be visually more prominent in the O/E `detrended` Hi-C map. 
 
 :::{.column-page-right}

parsing.qmd

@@ -212,9 +212,6 @@ telomere <- GRanges("II:700001-813184")
 subsetByOverlaps(hic, telomere) |> interactions()
 ```
 
-::: {.callout-important icon='true'}
-## `type` argument
-
 By default, `subsetByOverlaps(hic, telomere)` will only recover interactions 
 **constrained** within `telomere`, i.e. interactions for which both ends are 
 in `telomere`. 
@@ -225,7 +222,6 @@ at least one of their anchors within `telomere`.
 ```{r}
 subsetByOverlaps(hic, telomere, type = "any") |> interactions()
 ```
-:::
 
 #### `<HiCExperiment>["..."]`
 
@@ -287,10 +283,9 @@ hic["II:300001-320000|IV:1-100000"]
 ```{r}
 hic[c('II', 'III', 'IV')]
 ```
-:::
 
-::: {.callout-note}
-## Note
+Some notes: 
+
 - This last example (subsetting for a vector of several chromosomes) is the 
 only scenario for which `[`-based in-memory subsetting of pre-imported data is 
 the only way to go, as such subsetting is not possible with `focus` 
@@ -301,6 +296,7 @@ efficiently using the `focus` argument when `import`ing data into a
 - However, keep in mind that subsetting preserves extra data, e.g. added `scores`, 
 `topologicalFeatures`, `metadata` or `pairsFile`, whereas this information is 
 lost using `focus` with `import`.
+
 :::
 
 ### Zooming on a `HiCExperiment`
@@ -431,8 +427,6 @@ topologicalFeatures(hic, 'loops')
 hic
 ```
 
-::: {.callout-note}
-## Note
 All these objects can be used in `*Overlap` methods, as they all 
 extend the `GRanges` class of objects. 
 
@@ -444,7 +438,6 @@ countOverlaps(
     subject = topologicalFeatures(hic, 'loops')
 )
 ```
-:::
 
 #### `pairsFile`