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doc: reduce build size
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@js2264
js2264 committed Jan 9, 2024
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63 changes: 34 additions & 29 deletions vignettes/tidyCoverage.Rmd
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Expand Up @@ -20,15 +20,20 @@ knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
crop = NULL,
width = 180
width = 180,
dpi = 72,
fig.align = "center",
fig.width = 5,
fig.asp = 0.7,
dev = 'jpeg'
)
```

```{r warning = FALSE, include = FALSE, echo = FALSE, message = FALSE, results = FALSE}
library(tidyCoverage)
```

## Introduction
# Introduction

Genome-wide assays provide powerful methods to profile the composition, the
conformation and the activity of the chromatin. Linear "coverage" tracks
Expand All @@ -49,7 +54,7 @@ classes built on top of the `SummarizedExperiment` class. These classes
formalize the extraction and aggregation of coverage tracks over
sets of genomic features of interests.

## Installation
# Installation

`tidyCoverage` package can be installed from Bioconductor using the following
command:
Expand All @@ -61,9 +66,9 @@ if (!require("BiocManager", quietly = TRUE))
BiocManager::install("tidyCoverage")
```

## `CoverageExperiment` and `AggregatedCoverage` classes
# `CoverageExperiment` and `AggregatedCoverage` classes

### `CoverageExperiment`
## `CoverageExperiment`

`tidyCoverage` package defines the `CoverageExperiment`, directly extending
the `SummarizedExperiment` class. This means that all standard methods
Expand Down Expand Up @@ -106,15 +111,15 @@ assay(ce, 'coverage')[1, 1][[1]] |> class()
assay(ce, 'coverage')[1, 1][[1]] |> dim()
## Compare this to `rowData(ce)$n` and `width(ce)`
# Compare this to `rowData(ce)$n` and `width(ce)`
rowData(ce)$n
width(ce)
assay(ce[1, 1], 'coverage')[[1]][1:10, 1:10]
```

### `AggregatedCoverage`
## `AggregatedCoverage`

`AggregatedCoverage` also directly extends the `SummarizedExperiment` class.

Expand Down Expand Up @@ -146,9 +151,9 @@ assay(ac[1, 1], 'mean')[[1]] |> length()
assay(ac[1, 1], 'mean')[[1]][1:10]
```

## Manipulate `CoverageExperiment` objects
# Manipulate `CoverageExperiment` objects

### Create a `CoverageExperiment` object
## Create a `CoverageExperiment` object

One can use `CoverageExperiment()` constructor along with:

Expand Down Expand Up @@ -196,7 +201,7 @@ CoverageExperiment(
)
```

### Bin a `CoverageExperiment` object
## Bin a `CoverageExperiment` object

By default, `CoverageExperiment` objects store _per-base_ track coverage.
This implies that any cell from the `coverage` assay has as many columns
Expand Down Expand Up @@ -233,7 +238,7 @@ CE3
identical(CE2, CE3)
```

### Expand a `CoverageExperiment` object
## Expand a `CoverageExperiment` object

The `expand` method from the `tidyr` package is adapted to `CoverageExperiment`
objects to return a tidy `tibble`. This reformated object contains several
Expand All @@ -259,7 +264,7 @@ the `coord` and `coord.scaled` are handled correspondingly.
expand(CE3)
```

### Plot coverage of a set of tracks over a single genomic locus
## Plot coverage of a set of tracks over a single genomic locus

To illustrate how to visualize coverage tracks from a `CoverageExperiment`
object over a single genomic locus of interest,
Expand Down Expand Up @@ -307,9 +312,9 @@ In this plot, each facet represents the coverage of a different genomic track
over a single region of interest (`chrII:450001-475000`). Each facet has
independent scaling thanks to `facet_grid(..., scales = free)`.

## Manipulate `AggregatedCoverage` objects
# Manipulate `AggregatedCoverage` objects

### Aggregate a `CoverageExperiment` into an `AggregatedCoverage` object
## Aggregate a `CoverageExperiment` into an `AggregatedCoverage` object

It is often useful to `aggregate()` genomic `tracks` coverage over a set of
genomic `features`.
Expand Down Expand Up @@ -339,25 +344,25 @@ Note that the `coarsen-then-aggregate` or `aggregate-by-bin` are **NOT**
equivalent. This is due to the certain operations being not commutative with `mean` (e.g. `sd`, `min`/`max`, ...).

```{r}
## Coarsen `CoverageExperiment` with `window = ...` then per-bin `aggregate`:
# Coarsen `CoverageExperiment` with `window = ...` then per-bin `aggregate`:
CoverageExperiment(
tracks = import(bw_file, as = "Rle"), features = import(bed_file),
width = 3000
) |>
coarsen(window = 20) |> ### FIRST COARSEN...
aggregate() |> ### ... THEN AGGREGATE
coarsen(window = 20) |> ## FIRST COARSEN...
aggregate() |> ## ... THEN AGGREGATE
as_tibble()
## Per-base `CoverageExperiment` then `aggregate` with `bin = ...`:
# Per-base `CoverageExperiment` then `aggregate` with `bin = ...`:
CoverageExperiment(
tracks = import(bw_file, as = "Rle"), features = import(bed_file),
width = 3000
) |>
aggregate(bin = 20) |> ### DIRECTLY AGGREGATE BY BIN
aggregate(bin = 20) |> ## DIRECTLY AGGREGATE BY BIN
as_tibble()
```

### `AggregatedCoverage` over multiple tracks / feature sets
## `AggregatedCoverage` over multiple tracks / feature sets

As en example for the rest of this vignette, we compute an `AggregatedCoverage`
object using multiple genomic track files and multiple sets of genomic ranges.
Expand Down Expand Up @@ -389,7 +394,7 @@ AC <- aggregate(CE)
AC
```

### Plot aggregated coverages with `ggplot2`
## Plot aggregated coverages with `ggplot2`

Because `AggregatedCoverage` objects can be easily coerced into `tibble`s,
the full range of `ggplot2` functionalities can be exploited to plot
Expand Down Expand Up @@ -437,7 +442,7 @@ AC |>
theme(legend.position = 'top')
```

## Use a tidy grammar
# Use a tidy grammar

`tidySummarizedExperiment` package implements native `tidyverse` functionalities
to `SummarizedExperiment` objects and their extensions. It tweaks the way
Expand Down Expand Up @@ -476,7 +481,7 @@ AC |>
**Note:** To read more about the `tidySummarizedExperiment` package and the overall
`tidyomics` project, read the preprint [here](https://www.biorxiv.org/content/10.1101/2023.09.10.557072v2).

### Example workflow using tidy grammar
## Example workflow using tidy grammar

```{r}
CoverageExperiment(tracks, features, width = 5000, scale = TRUE, center = TRUE) |>
Expand All @@ -491,9 +496,9 @@ CoverageExperiment(tracks, features, width = 5000, scale = TRUE, center = TRUE)
theme(legend.position = 'top')
```

## Example use case: `AnnotationHub` and `TxDb` resources
# Example use case: `AnnotationHub` and `TxDb` resources

### Recover TSSs of forward human genes
## Recover TSSs of forward human genes

Let's first fetch features of interest from the human `TxDb` resources.

Expand All @@ -507,7 +512,7 @@ TSSs <- GenomicFeatures::genes(txdb) |>

These 1bp-wide `GRanges` correspond to forward TSSs genomic positions.

### Recover H3K4me3 coverage track from ENCODE
## Recover H3K4me3 coverage track from ENCODE

Let's also fetch a real-life ChIP-seq dataset (e.g. `H3K4me3`)
from ENCODE stored in the `AnnotationHub`:
Expand All @@ -521,7 +526,7 @@ H3K4me3_bw <- ah[['AH34904']]
H3K4me3_bw
```

### Compute the aggregated coverage of H3K4me3 ± 3kb around the TSSs of forward human genes
## Compute the aggregated coverage of H3K4me3 ± 3kb around the TSSs of forward human genes

We can now extract the coverage of `H3K4me3` over all the human forward TSSs
(± 3kb) and aggregate this coverage.
Expand All @@ -544,7 +549,7 @@ CoverageExperiment(

We obtain the typical profile of enrichment of `H3K4me3` over the +1 nucleosome.

### With more genomic tracks
## With more genomic tracks

This more complex example fetches a collection of 15 different ChIP-seq genomic
tracks to check their profile of enrichment over human forward TSSs.
Expand Down Expand Up @@ -594,7 +599,7 @@ AC |>

![](../man/figures/PTMs-TSSs.png)

## Session info
# Session info

```{r}
sessionInfo()
Expand Down

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