---
title: "Overview of the tidySummarizedExperiment package"
author: "Stefano Mangiola"
date: "`r Sys.Date()`"
package: tidySummarizedExperiment
output:
BiocStyle::html_document:
toc_float: true
bibliography: tidySummarizedExperiment.bib
vignette: >
%\VignetteEngine{knitr::knitr}
%\VignetteIndexEntry{Overview of the tidySummarizedExperiment package}
%\usepackage[UTF-8]{inputenc}
---
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[](https://www.tidyverse.org/lifecycle/#maturing)
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**Brings SummarizedExperiment to the tidyverse!**
website: [stemangiola.github.io/tidySummarizedExperiment/](https://stemangiola.github.io/tidySummarizedExperiment/)
Please also have a look at
- [tidySingleCellExperiment](https://stemangiola.github.io/tidySingleCellExperiment/) for tidy manipulation of SingleCellExperiment objects
- [tidyseurat](https://stemangiola.github.io/tidyseurat/) for tidy manipulation of Seurat objects
- [tidybulk](https://stemangiola.github.io/tidybulk/) for tidy analysis of RNA sequencing data
- [nanny](https://github.com/stemangiola/nanny) for tidy high-level data analysis and manipulation
- [tidygate](https://github.com/stemangiola/tidygate) for adding custom gate information to your tibble
- [tidyHeatmap](https://stemangiola.github.io/tidyHeatmap/) for heatmaps produced with tidy principles
```{r, echo=FALSE, include=FALSE}
library(knitr)
knitr::opts_chunk$set(warning=FALSE, message=FALSE)
```
# Introduction
tidySummarizedExperiment provides a bridge between Bioconductor [SummarizedExperiment](https://bioconductor.org/packages/release/bioc/html/SummarizedExperiment.html) [@morgan2020summarized] and the tidyverse [@wickham2019welcome]. It creates an invisible layer that enables viewing the
Bioconductor *SummarizedExperiment* object as a tidyverse tibble, and provides SummarizedExperiment-compatible *dplyr*, *tidyr*, *ggplot* and *plotly* functions. This allows users to get the best of both Bioconductor and tidyverse worlds.
## Functions/utilities available
SummarizedExperiment-compatible Functions | Description
------------ | -------------
`all` | After all `tidySummarizedExperiment` is a SummarizedExperiment object, just better
tidyverse Packages | Description
------------ | -------------
`dplyr` | Almost all `dplyr` APIs like for any tibble
`tidyr` | Almost all `tidyr` APIs like for any tibble
`ggplot2` | `ggplot` like for any tibble
`plotly` | `plot_ly` like for any tibble
Utilities | Description
------------ | -------------
`as_tibble` | Convert cell-wise information to a `tbl_df`
## Installation
```{r, eval=FALSE}
if (!requireNamespace("BiocManager", quietly=TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("tidySummarizedExperiment")
```
From Github (development)
```{r, eval=FALSE}
devtools::install_github("stemangiola/tidySummarizedExperiment")
```
Load libraries used in the examples.
```{r}
library(ggplot2)
library(tidySummarizedExperiment)
```
# Create `tidySummarizedExperiment`, the best of both worlds!
This is a SummarizedExperiment object but it is evaluated as a tibble. So it is fully compatible both with SummarizedExperiment and tidyverse APIs.
```{r}
pasilla_tidy <- tidySummarizedExperiment::pasilla
```
**It looks like a tibble**
```{r}
pasilla_tidy
```
**But it is a SummarizedExperiment object after all**
```{r}
assays(pasilla_tidy)
```
# Tidyverse commands
We can use tidyverse commands to explore the tidy SummarizedExperiment object.
We can use `slice` to choose rows by position, for example to choose the first row.
```{r}
pasilla_tidy %>%
slice(1)
```
We can use `filter` to choose rows by criteria.
```{r}
pasilla_tidy %>%
filter(condition == "untreated")
```
We can use `select` to choose columns.
```{r}
pasilla_tidy %>%
select(.sample)
```
We can use `count` to count how many rows we have for each sample.
```{r}
pasilla_tidy %>%
count(.sample)
```
We can use `distinct` to see what distinct sample information we have.
```{r}
pasilla_tidy %>%
distinct(.sample, condition, type)
```
We could use `rename` to rename a column. For example, to modify the type column name.
```{r}
pasilla_tidy %>%
rename(sequencing=type)
```
We could use `mutate` to create a column. For example, we could create a new type column that contains single
and paired instead of single_end and paired_end.
```{r}
pasilla_tidy %>%
mutate(type=gsub("_end", "", type))
```
We could use `unite` to combine multiple columns into a single column.
```{r}
pasilla_tidy %>%
unite("group", c(condition, type))
```
We can also combine commands with the tidyverse pipe `%>%`.
For example, we could combine `group_by` and `summarise` to get the total counts for each sample.
```{r}
pasilla_tidy %>%
group_by(.sample) %>%
summarise(total_counts=sum(counts))
```
We could combine `group_by`, `mutate` and `filter` to get the transcripts with mean count > 0.
```{r}
pasilla_tidy %>%
group_by(.feature) %>%
mutate(mean_count=mean(counts)) %>%
filter(mean_count > 0)
```
# Plotting
```{r}
my_theme <-
list(
scale_fill_brewer(palette="Set1"),
scale_color_brewer(palette="Set1"),
theme_bw() +
theme(
panel.border=element_blank(),
axis.line=element_line(),
panel.grid.major=element_line(size=0.2),
panel.grid.minor=element_line(size=0.1),
text=element_text(size=12),
legend.position="bottom",
aspect.ratio=1,
strip.background=element_blank(),
axis.title.x=element_text(margin=margin(t=10, r=10, b=10, l=10)),
axis.title.y=element_text(margin=margin(t=10, r=10, b=10, l=10))
)
)
```
We can treat `pasilla_tidy` as a normal tibble for plotting.
Here we plot the distribution of counts per sample.
```{r plot1}
pasilla_tidy %>%
ggplot(aes(counts + 1, group=.sample, color=`type`)) +
geom_density() +
scale_x_log10() +
my_theme
```
# Session Info
```{r}
sessionInfo()
```
# References