---
title: "Analyzing Regulatory Impact Factors and Partial Correlation and Information Theory"
author: "Carlos Alberto Oliveira de Biagi Junior, Ricardo Perecin Nociti, Breno Osvaldo Funicheli, João Paulo Bianchi Ximenez, Patrícia de Cássia Ruy, Marcelo Gomes de Paula, Rafael dos Santos Bezerra and Wilson Araújo da Silva Junior"
date: "`r format(Sys.Date(), '%m/%d/%Y')`"
output:
  rmarkdown::html_document:
    highlight: pygments
    toc: true
    fig_width: 6
    fig_height: 5
    fig_retina: NULL
    fig_caption: yes
bibliography: library.bib
vignette: >
  %\VignetteIndexEntry{Analyzing Regulatory Impact Factors and Partial Correlation and Information Theory}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
  %\usepackage[utf8]{inputenc}
---

```{r setup, echo=FALSE, results="hide"}
knitr::opts_chunk$set(tidy = FALSE,
                      cache = FALSE,
                      dev = "png",
                      message = FALSE, error = FALSE, warning = TRUE)
```

# Introduction
This vignette provides the necessary instructions for performing the Partial 
Correlation coefficient with Information Theory (PCIT) [@reverter2008combining] 
and Regulatory Impact Factors (RIF) [@reverter2010regulatory] algorithm.

The PCIT algorithm identifies meaningful correlations to define edges in a 
weighted network and can be applied to any correlation-based network including 
but not limited to gene co-expression networks, while the RIF algorithm identify 
critical transcript factors (TF) from gene expression data. 

These two algorithms when combined provide a very relevant layer of information 
for gene expression studies (Microarray, RNA-seq and single-cell RNA-seq data).


## Regulatory Information Factors (RIF)
A gene expression data from microarray, RNA-seq or single-cell RNA-seq spanning 
two biological conditions of interest (e.g. normal/tumor, healthy/disease, 
malignant/nonmalignant) is subjected to standard normalization techniques and 
significance analysis to identify the target genes whose expression is 
differentially expressed (DE) between the two conditions. Then, the regulators 
(e.g. Transcript Factors genes) are identified in the data. The TF genes can be 
obtained from the literature [@wang2015regulator][@vaquerizas2009census].
Next, the co-expression correlation between each TF and the DE genes is computed 
for each of the two conditions. This allows for the computation of the 
differential wiring (DW) from the difference in co-expression correlation 
existing between a TF and a DE genes in the two conditions. As a result, RIF 
analysis assigns an extreme score to those TF that are consistently most 
differentially co-expressed with the highly abundant and highly DE genes (case 
of RIF1 score), and to those TF with the most altered ability to act as 
predictors of the abundance of DE genes (case of RIF2 score). A given TF may not 
show a change in expression profile between the two conditions to score highly 
by RIF as long as it shows a big change in co-expression with the DE genes. To 
this particular, the profile of the TF gene (triangle, solid line) is identical 
in both conditions (slightly downwards). Instead, the DE gene (circle, dashed 
line) is clearly over-expressed in condition B. Importantly, the expression of 
the TF and the DE gene shows a strong positive correlation in condition A, and a 
strong negative correlation in condition B.

<center>
![A schematic diagram of the RIF analysis. (A) Gene expression data is 
normalized and statistically assessed to identify differentially expressed (DE) 
genes and differentially PIF genes (represented by circles) which together are 
deemed as the Target genes; Simultaneously, (B) transcription factors (TF, 
represented by triangles) included in the microarray are collected and (C) their 
co-expression correlation with the target genes computed for each of the two 
conditions of interest; Finally, (D) the way in which TF and target genes are 
differentially co-expressed between the two conditions is used to compute the 
relevance of each TF according to RIF1 and RIF2. 

```{r fig1, fig.align = 'center', out.width = "50%", fig.cap = "Reverter et al. 2010", echo = F}
knitr::include_graphics("fig1.jpg")
```

## Partial Correlation with Information Theory (PCIT)
The proposed PCIT algorithm contains two distinct steps as follows:

#### **Step 1 - Partial correlations**
For every trio of genes in x, y and z, the three first-order partial correlation 
coefficients are computed by:

$$r_{xy.z} = \frac{r_{xy} - r_{xz} r_{yz}}{\sqrt{(1-r^{2}_{xz})(1-r^{2}_{yz})}}$$, 
and similarly for $r_{xz.y}$ and $r_{yz.x}$.

The partial correlation coefficient between *x* and *y* given *z* (here denoted 
by $r_{xy.z}$) indicates the strength of the linear relationship between *x* and 
*y* that is independent of (uncorrelated with) *z*. Calculating the ordinary (or 
unconditional or zero-order) correlation coefficient and comparing it with the 
partial correlation, we might see that the association between the two variables 
has been sharply reduced after eliminating the effect of the third variable.

#### **Step 2 - Information theory**
We invoke the Data Processing Inequality (DPI) theorem of Information Theory 
which states that 'no clever manipulation of the data can improve the inference 
that can be made from the data' [@cover2012elements]. For every trio of genes, 
and in order to obtain the tolerance level ($\varepsilon$) to be used as the 
local threshold for capturing significant associations, the average ratio of 
partial to direct correlation is computed as follows:

$$\varepsilon = (\frac{r_{xy.z}}{r_{xy}} + \frac{r_{xz.y}}{r_{xz}} + \frac{r_{yz.x}}{r_{yz}})$$
In the context of our network reconstruction, a connection between genes *x* 
and *y* is discarded if:

$$|r_{xy}| \le |\varepsilon r_{xz}| and |r_{xy}| \le |\varepsilon r_{yz}|$$
Otherwise, the association is defined as significant, and a connection between 
the pair of genes is established in the reconstruction of the GCN. To ascertain 
the significance of the association between genes *x* and *y*, the above 
mentioned Steps 1 and 2 are repeated for each of the remaining *n−2* genes 
(denoted here by *z*).


# Installation
To install, just type:
```{r install, eval=FALSE}
BiocManager::install("cbiagii/CeTF")
```
for Linux users is necessary to install **libcurl4-openssl-dev**, 
**libxml2-dev** and **libssl-dev** dependencies.


# Workflow
There are many ways to perform the analysis. The following sections will be 
splited by steps, and finishing with the complete analysis with visualization. 
We will use the **airway** [@himes2014rna] dataset in the following sections. 
This dataset provides a RNA-seq count data from four human ASM cell lines that 
were treated with dexamenthasone - a potent synthetic glucocorticoid. Briefly, 
this dataset has 4 samples untreated and other 4 samples with the treatment.

## PCIT
The first option is to perform the PCIT analysis. The output will be a list with 
3 elements. The first one contains a dataframe with the pairwise correlation 
between genes (corr1) and the significant pairwise correlation (corr2 $\neq$ 0). 
The second element of the list stores the adjacency matrix with all correlation. 
And the last element contains the adjacency matrix with only the significant 
values:
```{r PCIT, eval=TRUE, warning=FALSE, message=FALSE}
# Loading packages
library(CeTF)
library(airway)
library(kableExtra)
library(knitr)

# Loading airway data
data("airway")

# Creating a variable with annotation data
anno <- as.data.frame(colData(airway))
anno <- anno[order(anno$dex, decreasing = TRUE), ]
anno <- data.frame(cond = anno$dex, 
                   row.names = rownames(anno))

# Creating a variable with count data
counts <- assay(airway)

# Sorting count data samples by conditions (untrt and trt)
counts <- counts[, rownames(anno)]
colnames(counts) <- paste0(colnames(counts), c(rep("_untrt", 4), rep("_trt", 4)))

# Differential Expression analysis to use only informative genes
DEGenes <- expDiff(exp = counts,
                   anno = anno,
                   conditions = c('untrt', 'trt'),
                   lfc = 4.5,
                   padj = 0.05, 
                   diffMethod = "Reverter")

# Selecting only DE genes from counts data
counts <- counts[rownames(DEGenes$DE_unique), ]

# Converting count data to TPM
tpm <- apply(counts, 2, function(x) {
            (1e+06 * x)/sum(x)
        })

# Count normalization
PCIT_input <- normExp(tpm)

# PCIT input for untrt
PCIT_input_untrt <- PCIT_input[,grep("_untrt", colnames(PCIT_input))]

# PCIT input for trt
PCIT_input_trt <- PCIT_input[,grep("_trt", colnames(PCIT_input))]

# Performing PCIT analysis for untrt condition
PCIT_out_untrt <- PCIT(PCIT_input_untrt, tolType = "mean")

# Performing PCIT analysis for trt condition
PCIT_out_trt <- PCIT(PCIT_input_trt, tolType = "mean")

# Printing first 10 rows for untrt condition
kable(PCIT_out_untrt$tab[1:10, ]) %>% 
  kable_styling(bootstrap_options = "striped", full_width = FALSE)

# Printing first 10 rows for trt condition
kable(PCIT_out_trt$tab[1:10, ]) %>% 
  kable_styling(bootstrap_options = "striped", full_width = FALSE)

# Adjacency matrix: PCIT_out_untrt$adj_raw or PCIT_out_trt$adj_raw
# Adjacency matrix only with the significant values: PCIT_out_untrt$adj_sig or PCIT_out_trt$adj_sig
```


## Histogram of connectivity distribution
After performing the PCIT analysis, it is possible to verify the histogram 
distribution of the clustering coefficient of the adjacency matrix with the 
significant values:
```{r hist, eval=TRUE, fig.align="center"}
# Example for trt condition
histPlot(PCIT_out_trt$adj_sig)
```

## Density Plot of raw correlation and significant PCIT
It's possible to generate the density plot with the significance values of 
correlation. We'll use the raw adjacency matrix and the adjacency matrix with 
significant values. It is necessary to define a cutoff of the correlation module 
(values between -1 and 1) that will be considered as significant:
```{r densitySig, eval=TRUE, fig.align="center"}
# Example for trt condition
densityPlot(mat1 = PCIT_out_trt$adj_raw, 
           mat2 = PCIT_out_trt$adj_sig, 
           threshold = 0.5)
```

## RIF
To perform the RIF analysis we will need the count data, an annotation table and 
a list with the Transcript Factors of specific organism (*Homo sapiens* in this 
case) and follow the following steps in order to get the output (dataframe with 
the average expression, RIF1 and RIF2 metrics for each TF):
```{r RIF, eval=TRUE, warning=FALSE, message=FALSE}
# Loading packages
library(CeTF)
library(airway)
library(kableExtra)
library(knitr)

# Loading airway data
data("airway")

# Creating a variable with annotation data
anno <- as.data.frame(colData(airway))
anno <- anno[order(anno$dex, decreasing = TRUE), ]
anno <- data.frame(cond = anno$dex, 
                   row.names = rownames(anno))

# Creating a variable with count data
counts <- assay(airway)

# Sorting count data samples by conditions (untrt and trt)
counts <- counts[, rownames(anno)]
colnames(counts) <- paste0(colnames(counts), c(rep("_untrt", 4), rep("_trt", 4)))

# Differential Expression analysis to use only informative genes
DEGenes <- expDiff(exp = counts,
                   anno = anno,
                   conditions = c('untrt', 'trt'),
                   lfc = 2,
                   padj = 0.05, 
                   diffMethod = "Reverter")

# Selecting only DE genes from counts data
counts <- counts[rownames(DEGenes$DE_unique), ]

# Converting count data to TPM
tpm <- apply(counts, 2, function(x) {
            (1e+06 * x)/sum(x)
        })

# Count normalization
Clean_Dat <- normExp(tpm)

# Loading the Transcript Factors (TFs) character
data("TFs")

# Verifying which TFs are in the subsetted normalized data
TFs <- rownames(Clean_Dat)[rownames(Clean_Dat) %in% TFs]

# Selecting the Target genes
Target <- setdiff(rownames(Clean_Dat), TFs)

# Ordering rows of normalized count data
RIF_input <- Clean_Dat[c(Target, TFs), ]

# Performing RIF analysis
RIF_out <- RIF(input = RIF_input,
               nta = length(Target),
               ntf = length(TFs),
               nSamples1 = 4,
               nSamples2 = 4)

# Printing first 10 rows
kable(RIF_out[1:10, ]) %>% 
  kable_styling(bootstrap_options = "striped", full_width = FALSE)
```

## Whole analysis of Regulatory Impact Factors (RIF) and Partial Correlation and Information Theory analysis (PCIT)
Finally, it is possible to run the entire analysis all at once. The output will 
be a CeTF object with all results generated between the different steps. To 
access the CeTF object is recommended to use the accessors from **CeTF** 
class:
```{r all, eval=TRUE}
# Loading packages
library(airway)
library(CeTF)

# Loading airway data
data("airway")

# Creating a variable with annotation data
anno <- as.data.frame(colData(airway))

# Creating a variable with count data
counts <- assay(airway)

# Sorting count data samples by conditions (untrt and trt)
counts <- counts[, order(anno$dex, decreasing = TRUE)]

# Loading the Transcript Factors (TFs) character
data("TFs")

# Performing the complete analysis
out <- runAnalysis(mat = counts, 
                   conditions=c("untrt", "trt"),
                   lfc = 5,
                   padj = 0.05,
                   TFs = TFs,
                   nSamples1 = 4,
                   nSamples2= 4,
                   tolType = "mean",
                   diffMethod = "Reverter", 
                   data.type = "counts")
```


## Getting some graphical outputs
Based on CeTF class object resulted from **runAnalysis** function is possible 
to get a plot for Differentially Expressed (DE) genes that shows the relationship 
between log(baseMean) and Difference of Expression or log2FoldChange, enabling 
the visualization of the distribution of DE genes and TF in both conditions. 
These two first plot provides a initial graphical visualization of results:
```{r SmearPlotDE, eval=TRUE, fig.align="center"}
# Using the runAnalysis output (CeTF class object)
SmearPlot(object = out, 
          diffMethod = 'Reverter', 
          lfc = 1.5, 
          conditions = c('untrt', 'trt'), 
          type = "DE")
```

Then is possible to generate the same previous plot but now for a single TF. So, 
if you have a specific TF that you want to visualize, this is the recommended 
plot:
```{r SmearPlotTF, eval=TRUE, fig.align="center"}
# Using the runAnalysis output (CeTF class object)
SmearPlot(object = out,
          diffMethod = 'Reverter',
          lfc = 1.5,
          conditions = c('untrt', 'trt'),
          TF = 'ENSG00000185917',
          label = TRUE, 
          type = "TF")
```

The next output is a network for both conditions resulted from \emph{runAnalysis}:
```{r netConditionsPlot, eval=TRUE, fig.align="center"}
# Using the runAnalysis output (CeTF class object)
netConditionsPlot(out)
```

Then we will associate the genes in both conditions with Gene Ontology (GO) and 
other databases. *getGroupGO* function will be performed where a set of genes 
are counted within all possible ontologies, without statistical power. 
This function is capable of perform groupGO for any organism as long as it's 
annotation package exists (i.e. org.Hs.eg.db, org.Bt.eg.db, org.Rn.eg.db, etc). 
In this example we will perform only for first condition:
```{r getGroupGO, eval=TRUE, fig.align="center"}
# Loading Homo sapiens annotation package
library(org.Hs.eg.db)

# Accessing the network for condition 1
genes <- unique(c(as.character(NetworkData(out, "network1")[, "gene1"]), 
                  as.character(NetworkData(out, "network1")[, "gene2"])))

# Performing getGroupGO analysis
cond1 <- getGroupGO(genes = genes, 
                    ont = "BP", 
                    keyType = "ENSEMBL", 
                    annoPkg = org.Hs.eg.db, 
                    level = 3)
```

Alternatively, it is possible to perform the enrichment analysis with a 
statistical power. In this case, there are many databases options to perform 
this analysis (i.e. GO, KEGG, REACTOME, etc). The *getEnrich* function will
perform the enrichment analysis and returns which pathways are enriched in 
a given set of genes. This analysis requires a reference list of genes with 
all genes that will be considered to enrich. In this package there is a function, 
*refGenes* that has these references genes for ENSEMBL and SYMBOL nomenclatures 
for 5 organisms: Human (**Homo sapiens**), Mouse (**Mus musculus**), Zebrafish 
(**Danio rerio**), Cow (**Bos taurus**) and Rat (**Rattus norvegicus**). If the 
user wants to use a different reference set, simply inputs a character vector 
with the genes.
```{r getEnrich, eval=FALSE, fig.align="center"}
# Accessing the network for condition 1
genes <- unique(c(as.character(NetworkData(out, "network1")[, "gene1"]), 
                  as.character(NetworkData(out, "network1")[, "gene2"])))

# Performing getEnrich analysis
enrich <- getEnrich(organism='hsapiens', database='geneontology_Biological_Process', 
                   genes=genes, GeneType='ensembl_gene_id', 
                   refGene=refGenes$Homo_sapiens$ENSEMBL, 
                   fdrMethod = "BH", fdrThr = 0.05, minNum = 5, maxNum = 500)
```

In the next steps we will use the *getGroupGO* results, but it is worth 
mentioning that you can use the results of the *getEnrich* function. To run 
the following steps with the *getEnrich* results is recommended to change the 
*lfc* parameter in aboce *runAnalysis* function by 3. That way there will be 
more pathways to enrich. Remembering that this will require more processing time.

After the association of GOs and genes, we are able to plot the network of 
previously results. It's possible to choose which variables ("pathways", "TFs" 
or "genes") we want to group. All these variables are inputted by the user, i.e. 
the user can choose the pathways, TFs or genes returned from analysis from the 
runAnalysis function and CeTF class object, or can also choose by specific 
pathways, TFs or genes of interest. The table used to plot the networks are 
stored in a list resulted from netGOTFPlot function. To get access is needed to 
use the *object$tab*. The list will be splitted by groupBy argument choice:
```{r netGOTFPlot1, eval=TRUE, fig.align="center"}
# Subsetting only the first 12 Ontologies with more counts
t1 <- head(cond1$results, 12)

# Subsetting the network for the conditions to make available only the 12 nodes subsetted
t2 <- subset(cond1$netGO, cond1$netGO$gene1 %in% as.character(t1[, "ID"]))

# generating the GO plot grouping by pathways
pt <- netGOTFPlot(netCond = NetworkData(out, "network1"),
                  resultsGO = t1,
                  netGO = t2,
                  anno = NetworkData(out, "annotation"),
                  groupBy = 'pathways',
                  type = 'GO')
pt$plot
head(pt$tab$`GO:0006807`)

# generating the GO plot grouping by TFs
TFs <- NetworkData(out, "keytfs")$TF[1:4]
pt <- netGOTFPlot(netCond = NetworkData(out, "network1"),
                  resultsGO = t1,
                  netGO = t2,
                  anno = NetworkData(out, "annotation"),
                  groupBy = 'TFs',
                  TFs = TFs, 
                  type = 'GO')
pt$plot
head(pt$tab$ENSG00000011465)

# generating the GO plot grouping by specifics genes
genes <- c('ENSG00000011465', 'ENSG00000026025', 'ENSG00000075624', 'ENSG00000077942')
pt <- netGOTFPlot(netCond = NetworkData(out, "network1"),
                  resultsGO = t1,
                  netGO = t2,
                  anno = NetworkData(out, "annotation"),
                  groupBy = 'genes',
                  genes = genes,
                  type = 'GO')
pt$plot
head(pt$tab$ENSG00000011465)
```

Finally, we will merge the results from the network of condition 1 (Gene-Gene 
and TF-Gene interaction) with groupGO results (GO-Gene or GO-TF interaction). 
The final results has the objective to integrate all these data in order to 
results in a complete view of the data:
```{r netGOTFPlot2, eval=TRUE, fig.align="center"}
pt <- netGOTFPlot(netCond = NetworkData(out, "network1"),
                  netGO = t2,
                  keyTFs = NetworkData(out, "keytfs"), 
                  type = 'Integrated')

pt$plot
head(pt$tab)
```


## Using accessors to access results
Is possible to use the accessors from CeTF class object to access the 
outputs generated by the *runAnalysis* function. These outputs can be used as 
input in **Cytoscape** [@shannon2003cytoscape]. The accessors are:

* **getData**: returns the raw, tpm and normalized data;
* **getDE**: returns the DE genes and TFs;
* **InputData** returns the input matrices used to perform RIF and PCIT in 
*runAnalysis* function;
* **OutputData** returns the output matrices and lists that output from RIF and 
PCIT in *runAnalysis* function;
* **NetworkData** returns the network of interactions between gene and key TFs 
for both conditions, the keyTFs and the annotation for each gene and TF.

All accessors can be further explored by looking in more detail at the documentation.


# Additional Features
This package has some additional features to plot the results. Let's use the
gene set from *airway* data previously used. The features are:

## Network diffusion analysis
Network propagation is an important and widely used algorithm in systems biology, 
with applications in protein function prediction, disease gene prioritization, 
and patient stratification. Propagation provides a robust estimate of network 
distance between sets of nodes. Network propagation uses the network of 
interactions to find new genes that are most relevant to a set of well 
understood genes. To perform this analysis is necessary to install the latest
Cytoscape software version and know the path that will be installed the 
software. After running the diffusion analysis is possible to perform the enrichment for
the subnetwork resulted. Finally, ih this vignette we'll not perform the 
diffusion analysis because this requires Cytoscape to be installed.
```{r diffusion, eval=FALSE, fig.align="center"}
result <- diffusion(object = out,
                    cond = "network1",
                    genes = c("ENSG00000185591", "ENSG00000179094"),
                    cyPath = "C:/Program Files/Cytoscape_v3.7.2/Cytoscape.exe",
                    name = "top_diffusion", 
                    label = T)
```

## Circos plot
This plot makes it possible to visualize the targets of specific TFs or genes.
The main idea is to identify which chromosomes are linked to the targets of a 
given gene or TF to infer whether there are cis (same chromosome) or trans 
(different chromosomes) links between them. The black links are between different
chromosomes while the red links are between the same chromosome.
```{r circos, eval=FALSE, fig.align="center"}
# Loading the CeTF object class demo
data("CeTFdemo")

CircosTargets(object = CeTFdemo, 
              file = "/path/to/gtf/file/specie.gtf", 
              nomenclature = "ENSEMBL", 
              selection = "ENSG00000185591", 
              cond = "condition1")
```
<center>
![](fig2.png){width=50%}
</center>

## RIF relationships plots
To visualize the relationship between RIF results (RIF1 and RIF2) is possible 
to generate the following plot:
```{r RIFPlot1, eval=TRUE, fig.align="center"}
RIFPlot(object = out,
        color  = "darkblue",
        type   = "RIF")
```

Then to get the relationship between RIF1/RIF2 and differential expressed genes:
```{r RIFPlot2, eval=TRUE, fig.align="center"}
RIFPlot(object = out,
        color  = "darkblue",
        type   = "DE")
```

## Enrichment plots
We provide some option to plot the results from **getEnrich** function. First of 
all we need to perform the enrichment analysis for a set of genes:
```{r enrichment, eval=TRUE, fig.align="center"}
# Selecting the genes related to the network for condition 1
genes <- unique(c(as.character(NetworkData(out, "network1")[, "gene1"]), 
                  as.character(NetworkData(out, "network1")[, "gene2"])))

# Performing enrichment analysis
cond1 <- getEnrich(organism='hsapiens', database='geneontology_Biological_Process', 
                   genes=genes, GeneType='ensembl_gene_id',
                   refGene=refGenes$Homo_sapiens$ENSEMBL,
                   fdrMethod = "BH", fdrThr = 0.05, minNum = 5, maxNum = 500)
```

After performing the enrichment analysis we have 4 options for viewing this data:

### Heatmap-like functional classification
```{r heatplot, eval=TRUE, fig.align="center"}
heatPlot(res = cond1$results,
         diff = getDE(out, "all"), 
         font_size = 4)
```

### Circle Barplot
```{r circle, eval=TRUE, fig.align="center"}
enrichPlot(res = cond1$results,
           type = "circle")
```

### Barplot
```{r barplot, eval=TRUE, fig.align="center"}
enrichPlot(res = cond1$results,
           type = "bar")
```

### Dotplot
```{r dotplot, eval=TRUE, fig.align="center"}
enrichPlot(res = cond1$results,
           type = "dot")
```


# Session info

```{r sessionInfo}
sessionInfo()
```


# References
