DoRothEA

https://saezlab.github.io/dorothea/
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DoRothEA is a gene regulatory network containing signed transcription factor-target gene...
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### Install DoRothEA Package

Source: https://saezlab.github.io/dorothea/index.html

Instructions for installing the package from Bioconductor or the development version from GitHub.

```R
## To install the package from Bioconductor
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("dorothea")

## To install the development version from the Github repo:
devtools::install_github("saezlab/dorothea")
```

--------------------------------

### Running VIPER with DoRothEA Regulons

Source: https://saezlab.github.io/dorothea/reference/run_viper.html

Example demonstrating how to load DoRothEA regulons and execute the run_viper function on a gene expression dataset.

```R
# use example gene expression matrix from bcellViper package
library(bcellViper)
#> Loading required package: Biobase
#> Loading required package: BiocGenerics
#> 
#> Attaching package: ‘BiocGenerics’
#> The following objects are masked from ‘package:stats’:
#> 
#>     IQR, mad, sd, var, xtabs
#> The following objects are masked from ‘package:base’:
#> 
#>     Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
#>     as.data.frame, basename, cbind, colnames, dirname, do.call,
#>     duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
#>     lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
#>     pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
#>     tapply, union, unique, unsplit, which.max, which.min
#> Welcome to Bioconductor
#> 
#>     Vignettes contain introductory material; view with
#>     'browseVignettes()'. To cite Bioconductor, see
#>     'citation("Biobase")', and for packages 'citation("pkgname")'.
data(bcellViper, package = "bcellViper")
# acessing (human) dorothea regulons
# for mouse regulons: data(dorothea_mm, package = "dorothea")
data(dorothea_hs, package = "dorothea")
# run viper
tf_activities <- run_viper(dset, dorothea_hs,
                          options =  list(method = "scale", minsize = 4,
                          eset.filter = FALSE, cores = 1,
                          verbose = FALSE))
#> Warning: This function is deprecated, please check the package decoupleR to infer activities.
```

--------------------------------

### Pipe Operator Example

Source: https://saezlab.github.io/dorothea/reference/pipe.html

Demonstrates passing a vector to the mean function using the pipe operator.

```R
c(1:10) %>% mean()
#> [1] 5.5
```

--------------------------------

### Load R Packages

Source: https://saezlab.github.io/dorothea/articles/dorothea.html

Load the necessary R packages for DoRothEA and related analysis. Ensure these packages are installed before running.

```r
library(dorothea)
library(decoupleR)
library(ggplot2)
library(dplyr)
```

--------------------------------

### Get CollecTRI GRN

Source: https://saezlab.github.io/dorothea/articles/dorothea.html

Retrieves the CollecTRI GRN. Use `split_complexes = FALSE` to keep complexes intact, which is the recommended default.

```R
net <- decoupleR::get_collectri(split_complexes = FALSE)
head(net)
```

--------------------------------

### Function Signature for run_viper

Source: https://saezlab.github.io/dorothea/reference/run_viper.html

The signature for the run_viper function, defining input parameters and configuration options.

```R
run_viper(input, regulons, options = list(), tidy = FALSE, assay_key = "RNA")
```

--------------------------------

### DoRothEA Datasets

Source: https://saezlab.github.io/dorothea/reference/index.html

Reference for the main datasets available in the DoRothEA package.

```APIDOC
## Datasets

### `dorothea_hs`

#### Description
Human DoRothEA.

### `dorothea_hs_pancancer`

#### Description
Human DoRothEA (pancancer).

### `dorothea_mm`

#### Description
Mouse DoRothEA.

### `dorothea_mm_pancancer`

#### Description
Mouse DoRothEA (pancancer).

### `entire_database`

#### Description
Entire database with associated meta data.
```

--------------------------------

### DoRothEA Functions

Source: https://saezlab.github.io/dorothea/reference/index.html

Reference for the main functions available in the DoRothEA package.

```APIDOC
## Functions

### `df2regulon()`

#### Description
Construction of dorothea regulons for viper analysis.

### `run_viper()`

#### Description
VIPER wrapper.
```

--------------------------------

### run_viper Function

Source: https://saezlab.github.io/dorothea/reference/run_viper.html

Executes the VIPER algorithm to infer TF activities based on provided gene expression data and DoRothEA regulons.

```APIDOC
## run_viper

### Description
A convenient wrapper for the viper function using DoRothEA regulons to compute TF activity scores.

### Parameters
- **input** (matrix/data.frame/ExpressionSet/Seurat/SingleCellExperiment) - Required - Gene expression matrix with genes in rows and samples in columns.
- **regulons** (table) - Required - DoRothEA regulons in table format.
- **options** (list) - Optional - Named options to pass to viper (e.g., minsize, method). Do not include 'eset' or 'regulon'.
- **tidy** (logical) - Optional - Whether to return TF activity scores in a tidy format.
- **assay_key** (string) - Optional - Assay name to extract data from if input is a Seurat object.

### Response
- **Returns** (matrix/object) - A matrix of normalized enrichment scores, or the input object with added TF activities (e.g., a new assay in Seurat).

### Request Example
run_viper(dset, dorothea_hs, options = list(method = "scale", minsize = 4, eset.filter = FALSE, cores = 1, verbose = FALSE))
```

--------------------------------

### Convert DoRothEA Regulons to viper Format

Source: https://saezlab.github.io/dorothea/reference/df2regulon.html

Use this function to convert DoRothEA regulons stored in a table to the format required by the viper function. Ensure the dorothea package is loaded to access the regulon data.

```R
data(dorothea_hs, package = "dorothea")
# convert to the format required by viper
viper_regulons = df2regulon(dorothea_hs)
```

--------------------------------

### Visualize Proportion of Positive Edges per TF

Source: https://saezlab.github.io/dorothea/articles/dorothea.html

Generate a density plot to visualize the distribution of the percentage of positive edges for each transcription factor. This helps identify activators versus repressors.

```r
prop <- net %>%
  mutate(mor = case_when(mor < 0 ~ -1, mor > 0 ~ 1)) %>%
  group_by(source, mor) %>%
  summarize(n = n()) %>%
  mutate(freq = n / sum(n)) %>%
  filter(mor == 1)

ggplot(data=prop, aes(x=freq)) + 
  geom_density() + 
  theme(text = element_text(size=12)) + 
  xlab('% of positive edges') + 
  ylab('densities') + 
  theme_bw() + 
  theme(legend.position = "none")
```

--------------------------------

### Visualize Number of Target Genes per TF

Source: https://saezlab.github.io/dorothea/articles/dorothea.html

Generate a density plot to visualize the distribution of the number of target genes per transcription factor. This helps understand the scale of regulons.

```r
n_genes <- net %>%
  group_by(source) %>%
  summarize(n = n())

ggplot(data=n_genes, aes(x=n)) + 
  geom_density() + 
  theme(text = element_text(size=12)) + 
  xlab('Number of target genes') + 
  ylab('densities') + 
  theme_bw() + 
  theme(legend.position = "none")
```

--------------------------------

### Visualize Edges per Confidence Level

Source: https://saezlab.github.io/dorothea/articles/dorothea.html

Create a bar plot showing the number of TF-target relationships (edges) for each confidence level. This illustrates the contribution of each confidence level to the network.

```r
n_edges <- net %>%
  group_by(confidence) %>%
  summarize(n = n())

ggplot(data=n_edges, aes(x=confidence, y=log10(n), color=confidence, fill=confidence)) + 
  geom_bar(stat="identity") + 
  theme(text = element_text(size=12)) + 
  xlab('Confidence') + 
  ylab('log10(Number of edges)') + 
  theme_bw() + 
  theme(legend.position = "none")
```

--------------------------------

### Mouse DoRothEA (Pancancer) Data Format

Source: https://saezlab.github.io/dorothea/reference/dorothea_mm_pancancer.html

Describes the structure and fields of the Mouse DoRothEA (pancancer) dataset.

```APIDOC
## Mouse DoRothEA (Pancancer) Data Format

### Description
A table of mouse TF-target interactions.

### Fields
- **tf** (string) - TF identifier as MGI symbols
- **confidence** (string) - Summary confidence score classifying regulons based on their quality (A to E).
- **target** (string) - Target identifier as MGI symbols.
- **mor** (string) - Mode of regulation indicating the effect of a TF on the target.
```

--------------------------------

### Pipe Operator Usage

Source: https://saezlab.github.io/dorothea/reference/pipe.html

Basic syntax for using the pipe operator to pass the left-hand side object to the right-hand side function.

```R
lhs %>% rhs
```

--------------------------------

### df2regulon Function

Source: https://saezlab.github.io/dorothea/reference/df2regulon.html

Converts DoRothEA regulons stored in a table to the format required by the viper function.

```APIDOC
## df2regulon Function

### Description
This function converts DoRothEA's regulons that are stored in a table to the format required by the `viper` function.

### Arguments 

*   **df** (table) - A regulon table from dorothea package.

### Value
Regulons in the `viper` format.

### Examples
```R
# acessing (human) dorothea regulons
# for mouse regulons: data(dorothea_mm, package = "dorothea")
data(dorothea_hs, package = "dorothea")
# convert to the format required by viper
viper_regulons = df2regulon(dorothea_hs)
```
```

--------------------------------

### Retrieve DoRothEA Regulons

Source: https://saezlab.github.io/dorothea/articles/dorothea.html

Retrieve DoRothEA regulons for human with specified confidence levels. The output is a tibble showing TF, confidence, target gene, and mode of regulation.

```r
net <- decoupleR::get_dorothea(levels = c('A', 'B', 'C', 'D'))
head(net)
```

--------------------------------

### Pipe Operator Usage

Source: https://saezlab.github.io/dorothea/reference/pipe.html

The pipe operator allows for chaining operations by passing the left-hand side (lhs) as the first argument to the right-hand side (rhs).

```APIDOC
## Pipe Operator (%>%) 

### Description 
Provides a mechanism to chain functions by passing the result of the left-hand side expression as the first argument to the right-hand side function. 

### Usage 
`lhs %>% rhs` 

### Value 
The result of the right-hand side function applied to the left-hand side input. 

### Examples 
```r 
c(1:10) %>% mean() 
#> [1] 5.5 
```
```
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