---
title: "GRaNIE Workflow Example"
author: "Christian Arnold, Judith Zaugg, Rim Moussa"
date: "`r BiocStyle::doc_date()`"
package: "`r BiocStyle::pkg_ver('GRaNIE')`"
abstract: >
        In this vignette, we present GRaNIE (**G**ene **R**egul**a**tory **N**etwork **I**nference including **E**nhancers), a framework to reconstruct predictive enhancer-mediated regulatory network models that are based on integrating of expression and chromatin accessibility/activity pattern across individuals, and provide a comprehensive resource of cell-type specific gene regulatory networks for particular cell types. For an extended biological motivation, see the first section below. In the following, we summarize how to use the `GRaNIE` package in a real-world example, illustrate most features and how to work with a `GRaNIE` object. Importantly, this vignette will be continuously updated whenever new functionality becomes available or when we receive user feedback.
vignette: >
  %\VignetteIndexEntry{Workflow example}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
output:
  BiocStyle::html_document:
      code_folding: hide
---



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# Motivation and Summary

Genetic variants associated with diseases often affect non-coding regions, thus likely having a regulatory role. To understand the effects of genetic variants in these regulatory regions, identifying genes that are modulated by specific regulatory elements (REs) is crucial. The effect of gene regulatory elements, such as enhancers, is often cell-type specific, likely because the combinations of transcription factors (TFs) that are regulating a given enhancer have cell-type specific activity. This TF activity can be quantified with existing tools such as `diffTF` and captures differences in binding of a TF in open chromatin regions. Collectively, this forms a enhancer-mediated gene regulatory network (*eGRN*) with cell-type and data-specific TF-RE and RE-gene links. Here, we reconstruct such a *eGRN* using bulk RNA-seq and open chromatin (e.g., using ATAC-seq or ChIP-seq for open chromatin marks) and optionally TF activity data. Our network contains different types of links, connecting TFs to regulatory elements, the latter of which is connected to genes in the vicinity or within the same chromatin domain (*TAD*). We use a statistical framework to assign empirical FDRs and weights to all links using a permutation-based approach.

**For a more detailed description of the package, its mode of action, guidelines, recommendations, limitations, scope, etc,please see the [Package Details Vignette on the GRaNIE website](https://grp-zaugg.embl-community.io/GRaNIE/articles/GRaNIE_packageDetails.html).**

# Example data

Before we start with the package, let's retrieve some example data! For the purpose of this vignette, the data we will use is taken from [here](https://zenodo.org/record/1188300#.X370PXUzaSN) [1], has been minimally processed to meet the requirements of the `GRaNIE` package and consists of the following files:

* ATAC-seq peaks, raw counts (originally around 75,000, genome-wide, here filtered to around 60,500)
* RNA-Seq data, raw counts (originally for around 35,000 genes, here filtered to around 19,000)
* sample metadata with additional sample-specific information

In general, the dataset is from human macrophages (both naive and IFNg primed) of healthy individuals and various stimulations / infections (naive vs primed and infected with SL1344 vs not), with 4 groups in total: control/infected(SL1344) and naive/primed(IFNg)


Furthermore, the example dataset is accompanied by the following files:

* genome-wide transcription factor binding site predictions for 75 selected TFs, along with a translation table to link TF names to their corresponding Ensembl IDs. For each TF, a gzipped BED file has been created with predicted TF binding sites. The files have been generated with `PWMScan` and the `HOCOMOCO` database, see [2] for details.

# Example Workflow
<a name="section1"></a>

In the following example, you will use the example data to construct a *eGRN* from ATAC-seq, RNA-seq data as well transcription factor (TF) data.


```{r <knitr, echo=FALSE, message=FALSE, results="hide", class.output="scroll-200"}
library("knitr")
opts_chunk$set(
  tidy = TRUE,
  cache = FALSE,
  message = FALSE,
  fig.align="center",
  dpi=200,
  fig.height=8,
  results='hold')
```

First, let's load the required libraries. The `readr` package is already loaded and attached when loading the `GRaNIE` package, but we nevertheless load it here explicitly to highlight that we'll use various `readr`` functions for data import.

For reasons of brevity, we omit the output of this code chunk.

```{r loadLibaries2, echo=TRUE, message=FALSE, results="hide", warning=FALSE, class.output="scroll-200"}
library(readr)
library(GRaNIE)
```

## Install suggested, additional packages for full functionality

When installing *GRaNIE*, all required dependency packages are automatically installed. In addition, *GRaNIE* needs some additional packages for special functionality, packages that are not strictly necessary for the workflow but which enhance the functionality, may be required depending on certain parameters (such as your genome assembly version), or may be required only when using a particular functionality (such as the `robust` package for robust regression). The package will automatically check if any of these packages are missing during execution, and inform the user when a package is missing, along with a line to copy for pasting into R for installation.



## General notes

Each of the `GRaNIE` functions we mention here in this Vignette comes with sensible default parameters that we found to work well for most of the datasets we tested it with so far. For the purpose of this Vignette, however, and the resulting running times, we here try to provide a good compromise between biological necessity and computational efficacy.  However, **always check the validity and usefulness of the parameters before starting an analysis** to avoid unreasonable results.

## Reading the data required for the `GRaNIE` package

To set up a `GRaNIE` analysis, we first need to read in some data into `R`. 

For a more detailed description and list of the required and optional input data, please see the [Package Details Vignette on the GRaNIE website](https://grp-zaugg.embl-community.io/GRaNIE/articles/GRaNIE_packageDetails.html).

Briefly, the following data can be used for the `GRaNIE` package:

* open chromatin / peak data (from either ATAC-Seq, DNAse-Seq or ChIP-Seq data, for example), hereafter simply referred to as *enhancers*
* RNA-Seq data (gene expression counts for genes across samples)

The following data can be used optionally but are not required:

* sample metadata (e.g., sex, gender, age, sequencing batch, etc)
* TAD domains (bed file, not used here in this vignette)

So, let's import the enhancer and RNA-seq data as a data frame as well as some sample metadata. This can be done in any way you want as long as you end up with the right format. 

```{r importData, echo=TRUE, include=TRUE, class.output="scroll-200"}

# We load the example data directly from the web:
file_peaks = "https://www.embl.de/download/zaugg/GRaNIE/countsATAC.filtered.tsv.gz"
file_RNA   = "https://www.embl.de/download/zaugg/GRaNIE/countsRNA.filtered.tsv.gz"
file_sampleMetadata = "https://www.embl.de/download/zaugg/GRaNIE/sampleMetadata.tsv.gz"

countsRNA.df      = read_tsv(file_RNA, col_types = cols())
countsPeaks.df    = read_tsv(file_peaks, col_types = cols()) 
sampleMetadata.df = read_tsv(file_sampleMetadata, col_types = cols())


# Let's check how the data looks like
countsRNA.df
countsPeaks.df
sampleMetadata.df

# Save the name of the respective ID columns
idColumn_peaks = "peakID"
idColumn_RNA = "ENSEMBL"

```

While we recommend raw counts for both enhancers and RNA-Seq as input and offer several normalization choices in the pipeline, it is also possible to provide pre-normalized data. Note that the normalization method may have a large influence on the resulting *eGRN* network, so make sure the choice of normalization is reasonable. For more details, see the next sections.

As you can see, both enhancers and RNA-Seq counts must have exactly one ID column, with all other columns being numeric. For enhancers, this column may be called `peakID`, for example, but the exact name is not important and can be specified as a parameter later when adding the data to the object. The same applies for the RNA-Seq data, whereas a sensible choice here is `ensemblID`, for example. 

For the enhancer ID column, the required format is `chr:start-end`, with `chr` denoting the chromosome, followed by ":", and then `start`, `-`, and `end` for the enhancer start and end, respectively. As the coordinates for the enhancers are needed in the pipeline, the format must be exactly as stated here.

You may notice that the enhancers and RNA-seq data have different samples being included, and not all are overlapping. This is not a problem and as long as *some* samples are found in both of them, the `GRaNIE` pipeline can work with it. Note that only the shared samples between both data modalities are kept, however, so make sure that the sample names match between them and share as many samples as possible.


## Initialize a `GRaNIE` object

We got all the data in the right format, we can start with our `GRaNIE` analysis now! We start by specifying some parameters such as the genome assembly version the data have been produced with, as well as some optional object metadata that helps us to distinguish this `GRaNIE` object from others.

```{r initializeObject, echo=TRUE, include=TRUE, class.output="scroll-200"}
genomeAssembly = "hg38" #Either hg19, hg38 or mm10. Both enhancers and RNA data must have the same genome assembly

# Optional and arbitrary list with information and metadata that is stored within the GRaNIE object
objectMetadata.l = list(name                = paste0("Macrophages_infected_primed"),
                        file_peaks          = file_peaks,
                        file_rna            = file_RNA,
                        file_sampleMetadata = file_sampleMetadata,
                        genomeAssembly      = genomeAssembly)

dir_output = "."

GRN = initializeGRN(objectMetadata = objectMetadata.l,
                    outputFolder = dir_output,
                    genomeAssembly = genomeAssembly)

GRN
```
Initializing a `GRaNIE` object occurs in the function `initializeGRN()` and is trivial: All we need to specify is an output folder (this is where all the pipeline output is automatically being saved unless specified otherwise) and the genome assembly shortcut of the data. We currently support `hg19`, `hg38`, and `mm10`. Please contact us if you need additional genomes. The `objectMetadata` argument is recommended but optional and may contain an arbitrarily complex named list that is stored as additional metadata for the `GRaNIE` object. Here, we decided to specify a name for the `GRaNIE` object as well as the original paths for all 3 input files and the genome assembly.

For more parameter details, see the R help (`?initializeGRN`). 

At any time point, we can simply "print" a `GRaNIE` object by typing its name and a summary of the content is printed to the console.

## Add data

We are now ready to fill our empty object with data! After preparing the data beforehand, we can now use the data import function `addData()` to import both enhancers and RNA-seq data to the `GRaNIE` object. In addition to the count tables, we explicitly specify the name of the ID columns. As mentioned before, the sample metadata is optional but recommended if available.

An important consideration is data normalization for RNA and ATAC. We currently support three choices of normalization: `quantile`, `DESeq_sizeFactor` and `none` and refer to the R help for more details (`?addData`). The default for RNA-Seq is a quantile normalization, while for the open chromatin enhancer data, it is `DESeq_sizeFactor` (i.e., a "regular" `DESeq` size factor normalization). Importantly, `DESeq_sizeFactor` requires raw data, while `quantile` does not necessarily. We nevertheless recommend raw data as input, although it is also possible to provide pre-normalized data as input and then topping this up with another normalization method or "none". 


```{r addData, echo=TRUE, include=TRUE, eval = FALSE, class.output="scroll-200"}
GRN = addData(GRN, 
              counts_peaks = countsPeaks.df, normalization_peaks = "DESeq_sizeFactor", idColumn_peaks = idColumn_peaks,
              counts_rna = countsRNA.df, normalization_rna = "quantile", idColumn_RNA = idColumn_RNA,
              sampleMetadata = sampleMetadata.df,
              forceRerun = TRUE)
```

```{r loadObject, echo=FALSE, results = FALSE, eval = TRUE}
GRN = loadExampleObject()
```


Only overlapping samples between the two data modalities are kept in the `GRaNIE` object. Here, all 29 samples from the RNA data are kept because they are also found in the peak data, while only 29 out of 31 samples from the peak data are also found in the RNA data, resulting in 29 shared samples overall. The RNA counts are also permuted, which will be the basis for all analysis and plots in subsequent steps that repeat the analysis for permuted data in addition to the real, non-permuted data.

## Quality control 1: PCA plots

It is time for our first QC plots using the function `plotPCA_all()`! Now that we added peak and RNA data to the object, let's check with a *Principal Component Analysis* (PCA) for both peak and RNA-seq data as well as the original input and the normalized data (unless normalization has been set to none, in which case they are identical to the original data) where the variation in the data comes from. If sample metadata has been provided in the `addData()` function (something we strongly recommend), they are automatically added to the PCA plots by coloring the PCA results according to the provided metadata, so that potential batch effects can be examined and identified. For more details, see the R help (`?plotPCA_all`). 

Note that while this step is recommended to do, it is fully optional from a workflow point of view.


```{r runPCA, echo=TRUE, include=TRUE, eval = FALSE, class.output="scroll-200", collapse=FALSE, results="hold"}
GRN = plotPCA_all(GRN, data = c("rna"), topn = 500, type = "normalized", plotAsPDF = FALSE, pages = c(2,3,14), forceRerun = TRUE)
```

Depending on the parameters, multiple output files (and plots) may be produced, with up to two files for each of the specified `data` modalities (that is, RNA-Seq counts, as specified with `rna` here, as well as the peak counts, `peaks`, not done here for reasons of brevity). For each of them, PCA plots can be produced for both `raw` and `normalized` data (here: only `raw`). With `raw`, we here denote the original counts as given as input with the `addData()` function, irrespective of whether this was already pre-normalized or not. The `topn` argument specifies the number of top variable features to do PCA for - here 500.

There are more plots that are generated, make sure to examine these plots closely! For all details, which plots are produced and further comments on how to understand and interpret them, see the [Package Website](https://grp-zaugg.embl-community.io/GRaNIE/).


## Add TFs and TFBS and overlap with peak

Now it is time to add data for TFs and predicted TF binding sites (TFBS)! Our `GRaNIE` package requires pre-computed TFBS that need to be in a specific format. In brief, a 6-column bed file must be present for each TF, with a specific file name that starts with the name of the TF, an arbitrary and optional suffix (here: `_TFBS`) and a particular file ending (supported are `bed` or `bed.gz`; here, we specify the latter). All these files must be located in a particular folder that the `addTFBS()` functions then searches in order to identify those files that match the specified patterns. We provide example TFBS for the 3 genome assemblies we support. After setting this up, we are ready to overlap the TFBS and the peaks by calling the function `overlapPeaksAndTFBS()`.

For more parameter details, see the R help (`?addTFBS` and `?overlapPeaksAndTFBS`). 

```{r addTFBS, echo=TRUE, include=TRUE, eval = FALSE, class.output="scroll-200", results='hold'}

folder_TFBS_first50 = "https://www.embl.de/download/zaugg/GRaNIE/TFBS_selected.zip"
# Download the zip of all TFBS files. Takes too long here, not executed therefore
# download.file(folder_TFBS_first50, file.path("TFBS_selected.zip"), quiet = FALSE)
# unzip(file.path("TFBS_selected.zip"), overwrite = TRUE)
# motifFolder = tools::file_path_as_absolute("TFBS_selected")

GRN = addTFBS(GRN, motifFolder = motifFolder, TFs = "all", filesTFBSPattern = "_TFBS", fileEnding = ".bed.gz", forceRerun = TRUE)

GRN = overlapPeaksAndTFBS(GRN, nCores = 1, forceRerun = TRUE)
```

We see from the output (omitted here for brevity) that 6 TFs have been found in the specified input folder, and the number of TFBS that overlap our peaks for each of them. We successfully added our TFs and TFBS to the `GRaNIE` object"



## Filter data (optional)

Optionally, we can filter both peaks and RNA-Seq data according to various criteria using the function `filterData()`. 

For the open chromatin peaks, we currently support three filters: 

1. Filter by their normalized mean read counts (`minNormalizedMean_peaks`, default 5)
2. Filter by their size / width (in bp) and discarding peaks that exceed a particular threshold (`maxSize_peaks`, default: 10000 bp)
3. Filter by chromosome (only keep chromosomes that are provided as input to the function, `chrToKeep_peaks`)


For RNA-seq, we currently support the analogous filter as for open chromatin for normalized mean counts as explained above (`minNormalizedMeanRNA`).

The default values are usually suitable for bulk data and should result in the removal of very few peaks / genes; however, for single-cell data, lowering them may more reasonable. The output will print clearly how many peaks and genes have been filtered, so you can rerun the function with different values if needed.

For more parameter details, see the R help (`?filterData`). 

```{r filterData, echo=TRUE, include=TRUE, eval = TRUE, class.output="scroll-200", results='hold'}
# Chromosomes to keep for peaks. This should be a vector of chromosome names
chrToKeep_peaks = c(paste0("chr", 1:22), "chrX")
GRN = filterData(GRN, minNormalizedMean_peaks = 5, minNormalizedMeanRNA = 1, chrToKeep_peaks = chrToKeep_peaks, maxSize_peaks = 10000, forceRerun = TRUE)
```
We can see from the output that no peaks have been filtered due to their size and almost 11,000 have been filtered due to their small mean read counts, which collectively leaves around 64,000 peaks out of 75,000 originally. For the RNA data, almost half of the data has been filtered (16,211 out of around 35,000 genes). 

## Add TF-enhancer connections

We now have all necessary data in the object to start constructing our network. As explained elsewhere, we currently support two types of links for our `GRaNIE` approach:

1. TF - peak/enhancer
2. peak/enhancer - gene

Let's start with TF-enhancer links! For this, we employ the function `addConnections_TF_peak()`. By default, we use Pearson to calculate the correlations between TF expression and enhancer accessibility, but Spearman may sometimes be a better alternative, especially if the diagnostic plots show that the background is not looking as expected.

In addition to creating TF-enhancer links based on TF expression, we can also correlate enhancer accessibility with other measures. We call this the *connection type*, and `expression` is the default one in our framework. However, we implemented a flexible way of allowing also additional or other connection types. Briefly, this works as follows: Additional data has to be imported beforehand with a particular name (the name of the *connection type*). For example, measures that are related to so-called *TF activity* can be used in addition or as a replacement of TF expression. For each connection type that we want to include, we simply add it to the parameter `connectionTypes` along with the binary vector `removeNegativeCorrelation` that specifies whether or not negatively correlated pairs should be removed or not. For expression, the default is to not remove them, while removal may be more reasonable for measures related to TF activity.


Lastly, we offer a so called GC-correction that uses a GC-matching background to compare it with the foreground instead of using the full background as comparison. We are still investigating the plausibility and effects of this and therefore mark this feature as experimental as of now.

Note that the TF-enhancer links are constructed for both the original, non-permuted data (in the corresponding output plots that are produced, this is labeled as `original`) and permuted data (`permuted`). 
For more parameter options and parameter details, see the R help (`?addConnections_TF_peak`). 

```{r addTFPeakConnections, echo=TRUE, include=TRUE, eval = TRUE, class.output="scroll-200", results='hold'}
GRN = addConnections_TF_peak(GRN, plotDiagnosticPlots = FALSE,
                                     connectionTypes = c("expression"),
                                     corMethod = "pearson", forceRerun = TRUE)
```
From the output, we see that all of the 6 TFs also have RNA-seq data available and consequently will be included and correlated with the enhancer accessibility.

## Quality control 2: Diagnostic plots for TF-enhancer connections

After adding the TF-enhancer links to our `GRaNIE` object, let's look at some diagnostic plots. Depending on the user parameters, the plots are either directly plotted to the currently active graphics device or to PDF files as specified in the object or via the function parameters. If plotted to a PDF, within the specified or default output folder (when initializing the `GRaNIE` object) should contain two new files that are named `TF_peak.fdrCurves_original.pdf` and `TF_peak.fdrCurves_permuted.pdf`, for example. 

This function may run a while, and each time-consuming step has a built-in progress bar for the plot-related parts so the remaining time can be estimated. 

For reasons of brevity and organization, we fully describe their interpretation and meaning in detail elsewhere, however. In summary, TF-enhancer diagnostic plots are available for each TF, and each page summarizes the QC for each TF in two plots:  


```{r, echo=FALSE, include=TRUE, eval = TRUE, class.output="scroll-200", results='hold', fig.cap="<i>TF-enhancer diagnostic plots for EGR1.0.A</i>", results='hold', fig.height = 10}
GRN = plotDiagnosticPlots_TFPeaks(GRN, dataType = c("real", "permuted"), plotAsPDF = FALSE, pages = 4)
```

For this example, we can see a quite typical case: the TF-enhancer FDR for the various *EGR1.0.A* - enhancer pairs are above 0.2 for the wide majority correlation bins in both directions (that is, positive and negative), while a few bins towards more extreme correlation bins have a low FDR. The former indicate little statistical signal and confidence, while the latter are those connections we are looking for! Typically, however, only  few connections are in the more extreme bins, as indicated by the color. Here, correlation bin refers to the correlation of a particular *EGR1.0.A* - enhancer pair that has been discretized accordingly (i.e., a correlation of 0.07 would go into (0.05-0.10] correlation bin). Usually, depending on the mode of action of a TF, none or either one of the two directions may show a low FDR in particular areas of the plots, often close to more extreme correlation bins, but rarely both. For a better judgement and interpretation, we can also check how this looks like for permuted data:


We see that for permuted data, there is much less significant bins, as expected.

Here, in summary, a few positively correlated *EGR1.0.A* - enhancer pairs (with a correlation of above 0.5 or so) are statistically significant and may be retained for the final *eGRN* network (if corresponding genes connecting to the respective enhancers are found).


## Run the AR classification and QC (optional) {#output_AR}

Transcription factors (TFs) regulate many cellular processes and can therefore serve as readouts of the signaling and regulatory state. Yet for many TFs, the mode of action—repressing or activating transcription of target genes—is unclear. In analogy to our *diffTF* approach that we recently published to calculate differential TF activity,the classification of TFs into putative transcriptional activators or repressors can also be done from within the `GRaNIE` framework in an identical fashion. This can be achieved with the function `AR_classification_wrapper()`.  

**Note that this step is fully optional and can be skipped. The output of the function is not used for subsequent steps.**. To keep the memory footprint of the `GRaNIE` object low, we recommend to keep the function parameter default `deleteIntermediateData = TRUE`. Here, we specify to put all plots within the directory `plots`. However, for reasons of brevity, we do not actually run the code here.


```{r runARClassification, echo=TRUE, include=TRUE, eval = FALSE, class.output="scroll-200"}
GRN = AR_classification_wrapper(GRN, significanceThreshold_Wilcoxon = 0.05,
                                outputFolder = "plots", 
                                plot_minNoTFBS_heatmap = 100, plotDiagnosticPlots = TRUE,
                                forceRerun = TRUE)
```
The classification runs for both real and permuted data, as before. The contents of these plots are identical to and uses in fact practically the same code as our `diffTF` software, and we therefore do not include them here. We refer to the following links for more details as well as the [Package Details Vignette](packageDetails.html#output_AR):

1. [The official `diffTF` paper][1]
2. In general, the [ReadTheDocs documentaion][2], and in particular [this chapter][3]. In `File {comparisonType}.diagnosticPlotsClassification1.pdf:, pages 1-4`, the content of the files `TF_classification_stringencyThresholds` are explained in detail, while in `File {comparisonType}.diagnosticPlotsClassification2.pdf:, Page 20 - end` the contents of the files `TF_classification_summaryHeatmap` and `TF_classification_densityPlotsForegroundBackground` are elaborated upon.


[1]: <https://doi.org/10.1016/j.celrep.2019.10.106> "The original publication explaining the method and motivation in detail"
[2]: <https://difftf.readthedocs.io> "the *ReadTheDocs* help for *diffTF*"
[3]: <https://difftf.readthedocs.io/en/latest/chapter2.html#files-comparisontype-diagnosticplotsclassification1-pdf-and-comparisontype-diagnosticplotsclassification2-pdf> "the following part*"

For more parameter details, see also the R help (`?AR_classification_wrapper`). 


## Save `GRaNIE` object to disk (optional)

After steps that take up a bit of time, it may make sense to store the `GRaNIE` object to disk in order to be able to restore it at any time point. This can simply be done, for example, by saving it as an `rds` file using the built-in function `saveRDS` from R to save our `GRaNIE` object in a compressed rds format.  

```{r saveObject2, echo=TRUE, include=TRUE, eval=FALSE, class.output="scroll-200"}
GRN_file_outputRDS = paste0(dir_output, "/GRN.rds")
saveRDS(GRN, GRN_file_outputRDS)
```

You can then, at any time point, read it back into R with the following line:

`GRN = readRDS(GRN_file_outputRDS)`


## Add enhancer-gene connections

Let's add now the second type of connections, enhancer-genes, to connect our enhancers to genes! This can be done via the function `addConnections_peak_gene()`. This function has a few parameters, and we only touch upon a few of them here. Most importantly, the `promoterRange` specifies the *neighborhood* size, which denotes the maximum neighborhood size in bp for enhancers (for both upstream and downstream ) to find genes in vicinity and associate/correlate genes with enhancers. While the default is 250,000 bp, we here set it to just 10,000 bp for computational reasons. Also, we support the incorporation of *TADs* if available to replace the default fixed neighborhood-based approach by a more flexible, biologically-driven chromatin domain based approach. Here, we do not have TADs available, so we set it to `NULL`. For more parameter details, see the R help (`?addConnections_peak_gene`). 

```{r addPeakGeneConnections, echo=TRUE, include=TRUE, eval = TRUE, class.output="scroll-200"}
GRN = addConnections_peak_gene(GRN,
                               corMethod = "pearson", promoterRange = 10000, TADs = NULL,
                               nCores = 1, plotDiagnosticPlots = FALSE, plotGeneTypes = list(c("all")), forceRerun = TRUE)
```

We see from the output that almost 38,000 enhancer-gene links have been identified that match our parameters. However, only around 16,351 actually had corresponding RNA-seq data available, while RNA-seq data was missing or has been filtered for the other. This is a rather typical case, as not all known and annotated genes are included in the RNA-seq data in the first place. Similar to before, the correlations have also been calculated for the permuted data.


## Quality control 3: Diagnostic plots for enhancer-gene connections

Let's now check some diagnostic plots for the enhancer-gene connections. In analogy to the other diagnostic plots that we encountered already before, we describe their interpretation and meaning in more detail in the [Package Details Vignette](packageDetails.html)!


The following plot summarizes many key QC measures we implemented:
```{r, echo=TRUE, fig.cap="<i>Enhancer-gene diagnostic plots</i>", fig.height = 7, class.output="scroll-200"}
GRN = plotDiagnosticPlots_peakGene(GRN, gene.types = list(c("protein_coding", "lincRNA")), plotAsPDF = FALSE, pages = 1)
```

Without explaining the details here, we can see from mainly the upper left plot that the enhancer-gene connections show a good signal to noise ratio in the context of our framework and assumptions indeed!  

## Combine TF-enhancer and enhancer-gene connections and filter

Now that we added both TF-enhancers and enhancer-gene links to our `GRaNIE` object, we are ready to filter and combine them. So far, they are stored separately in the object for various reasons (see the Introductory Vignette for details), but ultimately, we aim for combining them to derive TF-enhancer-gene connections. To do so, we can simply run the `filterGRNAndConnectGenes()` function and filter the individual TF-enhancer and enhancer-gene links to our liking. The function has many more arguments, and we only specify a few in the example below. As before, we get a `GRaNIE` object back that now contains the merged and filtered TF-enhancer-gene connections that we can later extract. Some of the filters apply to the TF-enhancer links, some of them to the enhancer-gene links, the parameter name is intended to indicate that.

```{r combineAndFilter, echo=TRUE, include=TRUE, eval = TRUE, class.output="scroll-200"}
GRN = filterGRNAndConnectGenes(GRN, TF_peak.fdr.threshold = 0.2,
                               peak_gene.fdr.threshold = 0.2, peak_gene.fdr.method = "BH", 
                               gene.types = c("protein_coding", "lincRNA"),
                               allowMissingTFs = FALSE, allowMissingGenes = FALSE
                               )
```

The output shows the number of links before and after applying a particular filter that has been set for both real and permuted data. As expected and reassuringly, almost no connections remain for the permuted data, while the real data keeps around 2500 connections. 

Importantly, this filtered set of connections is now also saved in the `GRN` object and the basis for most if not all downstream functions that the package offers and that we explore now! It is important to keep that in mind: Re-running the `filterGRNAndConnectGenes()` method overwrites the `all.filtered` slot in the `GRN` object, and all downstream functions have to be re-run as well.

For more parameter details, see the R help (`?filterGRNAndConnectGenes`). 

## Add TF-gene correlations (optional)

Optionally, we can also include extra columns about the correlation of TF and genes directly. So far, only TF-enhancers and enhancer-genes have been correlated, but not directly TFs and genes. Based on a filtered set of TF-enhancer-gene connections, the function `add_TF_gene_correlation()` calculates the TF-gene correlation for each connection from the filtered set for which the TF is not missing.

```{r, include=TRUE, eval = TRUE, class.output="scroll-200"}
GRN = add_TF_gene_correlation(GRN, corMethod = "pearson", nCores = 1, forceRerun = TRUE)
```

As can be seen from the output, the Pearson correlation for 587 TF-gene pairs has been calculated. From the around 2500 connections we obtained above, since we set the parameter `allowMissingGenes = TRUE`, for the majority of the TF-enhancer-gene connections the gene is actually missing. That is, while a TF-enhancer connection below the specified significance threshold exists, no corresponding gene could be found that connects to the same enhancer, therefore setting the gene to *NA* rather than excluding the row altogether.

For more parameter details, see the R help (`?add_TF_gene_correlation`). 

Time to save our object again!
```{r saveObject, echo=TRUE, include=TRUE , eval=FALSE, class.output="scroll-200"}
GRN = deleteIntermediateData(GRN)
saveRDS(GRN, GRN_file_outputRDS)
```


        
## Retrieve filtered connections

We are now ready to retrieve the filtered connections, along with adding various additional metadata (optional). This can be done with the helper function `getGRNConnections()` that retrieves the filtered eGRN as a data frame from a `GRaNIE` object. Here, we specify `all.filtered`, as we want to retrieve all filtered connections (i.e., the eGRN). For more parameter details, see the R help (`getGRNConnections`). 

Here, for example, we add various additional information to the resulting data frame: TF-gene correlations, and gene metadata. We could also add TF and peak metadata as well as the results from running the `variancePartition` package, but this is not done here and we leave this as an exercise to the reader!

Note that the first time, we assign a different variable to the return of the function (i.e., `GRN_connections.all` and NOT `GRN` as before). Importantly, we have to select a new variable as we would otherwise overwrite our `GRN` object altogether! All `get` functions from the `GRaNIE` package return an element from within the object and NOT the object itself, so please keep that in mind and always check what the functions returns before running it. You can simply do so in the R help (`?getGRNConnections`). 

```{r, include=TRUE, eval = TRUE, class.output="scroll-200"}
GRN_connections.all = getGRNConnections(GRN, type = "all.filtered", include_TF_gene_correlations = TRUE, include_geneMetadata = TRUE)

GRN_connections.all
```

The table contains many columns, and the prefix of each column name indicates the part of the *eGRN* network that the column refers to (e.g., TFs, TF-enhancers, enhancers, enhancer-genes or genes, or TF-gene if the function `add_TF_gene_correlation()` has been run before). Data are stored in a format that minimizes the memory footprint (e.g., each character column is stored as a factor). This table can now be used for any downstream analysis, as it is just a normal data frame.


## Generate a connection summary for filtered connections

It is often useful to get a grasp of the general connectivity of a network and the number of connections that survive the filtering. This makes it possible to make an informed decision about which FDR to choose for TF-enhancer and enhancer-gene links, depending on how many links are retained and how many connections are needed for downstream analysis. To facilitate this and automate it, we offer the convenience function `generateStatsSummary()` that in essence iterates over different combinations of filtering parameters and calls the function `filterGRNAndConnectGenes()` once for each of them, and then records various connectivity statistics, and finally plots it by calling the function `plot_stats_connectionSummary()`. Note that running this function may take a while. Afterwards, we can graphically summarize this result in either a heatmap or a boxplot. For more parameter details, see the R help (`?generateStatsSummary` and `plot_stats_connectionSummary`). 

```{r connectionSummary, echo=TRUE, include=TRUE, eval = TRUE, out.width="50%", class.output="scroll-200"}
GRN = generateStatsSummary(GRN,
                           TF_peak.fdr = c(0.05, 0.1, 0.2), 
                           TF_peak.connectionTypes = "all",
                           peak_gene.fdr = c(0.1, 0.2),
                           peak_gene.r_range = c(0,1),
                           allowMissingGenes = c(FALSE, TRUE),
                           allowMissingTFs = c(FALSE),
                           gene.types = c("protein_coding", "lincRNA"),
                           forceRerun = TRUE)


GRN = plot_stats_connectionSummary(GRN, type = "heatmap", plotAsPDF = FALSE, pages = 3)
GRN = plot_stats_connectionSummary(GRN, type = "boxplot", plotAsPDF = FALSE, pages = 1)
```

Here, the output is less informative and just tells us about the current progress and parameter it iterates over. We can now check the two new PDF files that have been created!

Let's start with a connection summary in form of a heatmap! There are 3 heatmap classes, one for TFs, enhancers (labeled peaks) and genes, respectively. All of them compare the number of distinct TFs, enhancers, and genes that end up in the final `eGRN` in dependence of how stringently the connections are filtered (i.e., different FDR thresholds for both TF-enhancers and enhancer-genes). In addition, the same is repeated for the permuted data, which enables you to judge the connectivity of the real data as compared to what you can expect with random data!

For TFs, we see that the numbers are generally very small because we just run the analysis with few TFs. For the permuted data, depending on the random nature of it, none or almost none connections survive the filtering. You should see much bigger differences for full TF data and not just a few selected ones.


As the output plots show, alternatively, we can also represent the connectivity in form of a boxplot, which shows the connectivity for each node or connection type (i.e. TFs, enhancers, and genes, while enhancers are split between TF-enhancer and enhancer-gene depending on whether they connect with TFs or genes, respectively), and compares this again to the random version of the `eGRN`. The PDF contains many pages, and iterates over different FDR stringency thresholds. We here show two example pages:

Not all parameter combinations (such as FDR stringencies) result in connections! Sometimes, there is no `eGRN` as no connections survived the filtering.



## Construct the eGRN graph

For all network-related and visualization-related functions, we need to build a graph out of the filtered connections. For this, we provide a helper function that stores the graph-structure in the object, and it can be invoked as follows:

```{r buildGraph, echo=TRUE, include=TRUE, eval = TRUE, class.output="scroll-200"}

GRN = build_eGRN_graph(GRN, forceRerun = TRUE) 

```

As you can see, some details about the TF-peak-gene and TF-gene are shown as output. As mentioned before, the graph is constructed based on the filtered connections as calculated and stored by `filterGRNAndConnectGenes`. Importantly, the grah is reset whenever the function `filterGRNAndConnectGenes` is called either directly or indirectly via `generateStatsSummary`.


## Visualize the filtered *eGRN*

The `GRaNIE` package also offers a function to visualize a filtered *eGRN* network! It is very easy to invoke, but provides many options to customize the output and the way the graph is drawn. We recommend to explore the options in the R help (`?getGRNConnections`), and here just run the default visualization.


```{r visualizeGRN, echo=TRUE, include=TRUE, eval = TRUE, fig.cap="<i>eGRN example visualization</i>", fig.height = 9, class.output="scroll-200"}
GRN = visualizeGRN(GRN, plotAsPDF = FALSE)

```

We can see some highly connected TFs and that they actually seem to co-regulate some shared genes. The selection of TFs here for this toy dataset was based on highly connected TFs across all TF, though, so for a larger list of TFs, expect to see some TFs being not connected much or at all, though.

## Network and enrichment analyses for filtered connections

Lastly, our framework also supports various types of network and enrichment analyses that are fully integrated into the package. We offer these for the full filtered `eGRN` network as a whole (as produced by running the function `filterGRNAndConnectGenes()` before) as well as an enrichment per community. 

First, a proper graph (network) structure can be build with the function `build_eGRN_graph()`, which all network and enrichment functions use subsequently.

For both the general and the community statistics and enrichment, the package can:

- calculate and plot general structure and connectivity statistics for a filtered *eGRN* (function `plotGeneralGraphStats()`) and per community (functions `calculateCommunitiesStats()` and `plotCommunitiesStats()`) , 
- ontology enrichment and visualization for genes for the full network (functions `calculateGeneralEnrichment()` and `plotGeneralEnrichment()`) as well as per community (functions `calculateCommunitiesEnrichment()` and `plotCommunitiesEnrichment()`)

All functions can be called individually, adjusted flexibly and the data is stored in the `GRaNIE` object for ultimate flexibility. In the near future, we plan to expand this set of functionality to additional enrichment analyses such as other databases (specific diseases pathways etc), so stay tuned!
`calculateCommunitiesStats()`

**For the purpose of this vignette, let's run an enrichment analysis using `GO`. here, we run it with only `GO Biological Process` (`GO_BP`), while the other `GO` ontologies are also available (i.e., `GO Molecular Function`, abbreviated `GO_MF` in the plots). We also support other, more specialized enrichment analyses (`KEGG`, `Disease Ontology`, and `Reactome`). Lastly, users can select an arbitrary combination of all supported ontologies for ultimate flexibility! More are coming soon, stay tuned!**

For user convenience, all aforementioned functions can be called at once via a designated wrapper function `performAllNetworkAnalyses()`.
Many results are produces by this convenience function, and we here show only a few of them. The function is highly customizable, all or almost all of the available parameters from the individual functions (see above) are also available in this wrapper function, see the R help (`?performAllNetworkAnalyses`) for details. In order to invoke it and save all results to several PDF files using the default options, for example, you could simply type this:

```{r allNetworkAnalyses, echo=TRUE, include=TRUE, eval = FALSE, class.output="scroll-200"}
GRN = performAllNetworkAnalyses(GRN, ontology = c("GO_BP"), outputFolder = ".", forceRerun = TRUE)
```

As this functions needs a few minutes, for the purpose of the vignette, we do not include the output of this function here. Let's, however, go through all the functions that this wrapper executes so we have a better understanding of what is actually being done. We will also plot some of the results!

First, we have to create a network representation out of the filtered connections, and there are a few options for how the network structure should look like. We here keep the default options and refer to the R help for details (`?build_eGRN_graph`).


### General network statistics

Let's, however, check some of the results that are produced! Let's start with checking some general network statistics for the full network. From the various graphs that are produced, we here select only 2 of them for demonstration purposes. First we can check the vertex distribution and gene types for the overall network to get an idea of how the network looks like. Second, we can investigate the most important TFs and genes for the network for both the TF-enhancer-gene as well as TF-gene network. Here, we here show the results for the TF-gene network only:

```{r plotGraphStats, echo=FALSE, fig.cap="<i>General network statistics for the filtered network</i>", fig.width = 12, class.output="scroll-200"}

GRN = plotGeneralGraphStats(GRN, plotAsPDF = FALSE, pages = c(1,6))

```

First, we see the vertex degree of TF and genes, respectively:
We can also use algorithms for measuring the influence of a node in a network (*centrality*). Here, we show the results for both TFs and genes for two different measures of centrality, *eigenvector centrality* and centrality based on node degree:




### General network enrichment

Now that we have our eGRN network, we can do various enrichment analyses. Let's start with the most obvious one: Enrichment for the whole network. Again, we are not executing the function here for reasons of time, but you should do so of course when learning how to use the package!

```{r generalEnrichment, echo=FALSE, eval = FALSE, class.output="scroll-200"}

GRN = calculateGeneralEnrichment(GRN, ontology = "GO_BP")

```

We can now plot the enrichment for the full graph. In analogy to all the other `plot` functions, a PDF with all enrichment results is produced with the default setting, but by setting `plotAsPDF` to `FALSE`, we can also plot selected results / pages directly to the currently active graphics device. In this case here, as we select only one ontology, there is only one page:

```{r plotGeneralEnrichment, echo=TRUE, fig.cap="<i>General network enrichment for the filtered network</i>", fig.height=5, class.output="scroll-200"}

GRN = plotGeneralEnrichment(GRN, plotAsPDF = FALSE, pages = 1)

```

We can see that overall, cell cycle is the term with the most number of genes, and while it does not have the highest significance among all terms, it is still significant. Most of the other terms are more specialized, and point towards altered regulation of various epigenetic signaling alterations. The biological plausibility of them and how to continue after is now your challenge!



### Community network statistics and enrichment

Now, let's check whether we can identify communities within the whole network, along with community-specific enrichments.

```{r communityEnrichment, echo=FALSE, class.output="scroll-200"}

GRN = calculateCommunitiesStats(GRN)
GRN = calculateCommunitiesEnrichment(GRN, ontology = "GO_BP")

```

These functions may take a while, as enrichment is calculated for each community. Once finished, we are ready to plot the results! First, let's start with some general community statistics:

```{r plotCommunityStats, echo=FALSE, fig.height=9, fig.width = 10, fig.cap="<i>General statistics for the communities from the filtered network</i>", class.output="scroll-200"}

GRN = plotCommunitiesStats(GRN, plotAsPDF = FALSE, pages = c(1,3))

```

First, we see an overview across all communities and their network sizes, and whether the links belong to a TF or gene. Second, for a selected community, we summarize which genes and TFs are most relevant for this particular community. Because the example data is rather minimal, this looks very unspectacular here: Only one TF appears in the list, and all connected genes have the same node degree also. For real datasets, this will look more interesting and diverse.

Next, let's plot the community-specific enrichment:

```{r plotCommunityEnrichment, echo=FALSE, fig.height=7, fig.width = 9, fig.cap="<i>Community enrichment for 3 different communities</i>", class.output="scroll-200"}

GRN = plotCommunitiesEnrichment(GRN, plotAsPDF = FALSE, pages = c(1,2,3))

```

We also provide an overview across the whole network and all communities that lists all the enriched terms that appear in at least one enrichment analysis, so a direct comparison of the specificity and commonalities across communities and between the general network and any community is facilitated. This also shows that some terms, here more than with a full dataset, are only identified as being enriched for the full network but not within any of the communities individually. We offer this function for all terms as well as only the top 10 enriched terms per community, and we here show only the filtered version due to reasons of brevity:


```{r plotCommunityEnrichment2, echo=TRUE, fig.height=7, fig.width = 10,fig.cap="<i>Summary of the community enrichment</i>", class.output="scroll-200"}
GRN = plotCommunitiesEnrichment(GRN, plotAsPDF = FALSE, pages = c(5))


```


### TF enrichment analyses

In analogy to community enrichment, we can also calculate enrichment based on TFs via their target genes they are connected to. Running a TF enrichment analyses is straight forward, with the parameter `n` we can control the number of TFs to run the enrichment for - the function runs the enrichment for the top connected TFs. Thus, `n=3` equals running the enrichment for the top 3 connected TFs. Here, we show the results for one of the TFs, *EGR1.0.A*, as well as a summary across all top 3 connected TFs, in analogy the results for the community enrichment.

Let's first calculate the TF enrichment.This may take a while. We omit the output here.
```{r TFEnrichmentCal, echo=FALSE, eval = FALSE, class.output="scroll-200"}
GRN = calculateTFEnrichment(GRN, ontology = "GO_BP")
```

Now, we can plot it, similar to the enrichment results before:

```{r TFEnrichment, echo=TRUE, fig.cap="<i>Enrichment summary for EGR1.0.A</i>", fig.height=7, fig.width = 12, class.output="scroll-200"}
GRN = plotTFEnrichment(GRN, plotAsPDF = FALSE, n = 3, pages = c(1))
```

```{r TFEnrichment2, echo=TRUE, fig.cap="<i>Enrichment summary for selected TFs and the whole eGRN network</i>", fig.height=7, fig.width = 15, class.output="scroll-200"}
GRN = plotTFEnrichment(GRN, plotAsPDF = FALSE, n = 3, pages = c(5))
```
## Wrapping up

We are now finished with the main workflow, all that is left to do is to save our `GRaNIE` object to disk so we can load it at a later time point without having to repeat the analysis. We recommend to run the convenience function `deleteIntermediateData()` beforehand that aims to reduce its size by deleting some intermediate data that may still be stored within the object. For more parameter details, see the R help (`?deleteIntermediateData`).  Finally, as we did already in the middle of the workflow, we save the object finally in rds format.

```{r saveObject3, echo=TRUE, include=TRUE, eval=FALSE, class.output="scroll-200"}
GRN = deleteIntermediateData(GRN)
saveRDS(GRN, GRN_file_outputRDS)
```


# How to continue?
From here on, possibilities are endless, and you can further investigate patterns and trends in the data! We hope that the `GRaNIE` package is useful for your research and encourage you to contact us if you have any question or feature request!


# Session Info

```{r, class.output="scroll-200"}
 sessionInfo()
```

