view wgcna_construct_network.Rmd @ 4:a41eb67bce4c draft

planemo upload for repository https://github.com/statonlab/docker-GRReport/tree/master/my_tools/rmarkdown_wgcna commit d9ab791a7ce12362dc6e28c0a518a3f23dd581fe-dirty

---
title: 'WGCNA: construct network'
output:
    html_document:
      number_sections: true
      toc: true
      theme: cosmo
      highlight: tango
---

```{r setup, include=FALSE, warning=FALSE, message=FALSE}
knitr::opts_chunk$set(
  echo = ECHO
)
```

# Import workspace 

This step imports workspace from the **WGCNA: preprocessing** step.

```{r}
fcp = file.copy("PREPROCESSING_WORKSPACE", "deseq.RData")
load("deseq.RData")
```


# Processing outliers {.tabset}

## Before removing outliers

```{r}
plot(sampleTree, main = "Sample clustering to detect outliers", sub="", xlab="", cex.lab = 1.5,
     cex.axis = 1, cex.main = 1, cex = 0.5)
if(!is.na(HEIGHT_CUT)) {
  # plot a line to show the cut
  abline(h = HEIGHT_CUT, col = "red")
  # determine cluster under the line
  clust = cutreeStatic(sampleTree, cutHeight = HEIGHT_CUT, minSize = 10)
  keepSamples = (clust==1)
  expression_data = expression_data[keepSamples, ]
}
```

## After removing outliers

```{r}
sampleTree = hclust(dist(expression_data), method = "average");
plot(sampleTree, main = "Sample clustering to detect outliers", sub="", xlab="",
     cex.axis = 1, cex.main = 1, cex = 0.5)
```


# Trait data {.tabeset}

If trait data is provided, the first 100 rows from the data will be displayed here. A plot consisting of sample cluster dendrogram and trait heatmap will also be gerenated.

## Trait data table

```{r}
trait_data = data.frame()
if ("TRAIT_DATA" != 'None') {
  trait_data = read.csv("TRAIT_DATA", header = TRUE, row.names = 1)
  # form a data frame analogous to expression data that will hold the traits.
  sample_names = rownames(expression_data)
  trait_rows = match(sample_names, rownames(trait_data))
  trait_data = trait_data[trait_rows, ]
  datatable(head(trait_data, 100), style="bootstrap", filter = 'top',
          class="table-condensed", options = list(dom = 'tp', scrollX = TRUE))
}
```

## Dendrogram and heatmap

```{r fig.align='center', fig.width=8, fig.height=9}
if (nrow(trait_data) != 0) {
  traitColors = numbers2colors(trait_data, signed = FALSE)
  plotDendroAndColors(sampleTree, traitColors,
                      groupLabels = names(trait_data),
                      main = "Sample dendrogram and trait heatmap",
                      cex.dendroLabels = 0.5)
}
```


# The thresholding power

```{r}
powers = c(1:10, seq(12, 20, 2))
soft_threshold = pickSoftThreshold(expression_data, powerVector = powers, verbose = 5)
```

```{r fig.align='center'}
par(mfrow=c(1,2))
plot(soft_threshold$fitIndices[,1], -sign(soft_threshold$fitIndices[,3])*soft_threshold$fitIndices[,2],
     xlab="Soft Threshold (power)",
     ylab="Scale Free Topology Model Fit,signed R^2",type="n",
     main = paste("Scale independence"),
     cex.lab = 0.5);
text(soft_threshold$fitIndices[,1], -sign(soft_threshold$fitIndices[,3])*soft_threshold$fitIndices[,2],
     labels=powers,cex=0.5,col="red");

# calculate soft threshold power
y = -sign(soft_threshold$fitIndices[,3])*soft_threshold$fitIndices[,2]
r2_cutoff = 0.9
for(i in 1:length(powers)) {
  if(y[i] > r2_cutoff) {
    soft_threshold_power = soft_threshold$fitIndices[,1][i]
    r2_cutoff_new = y[i]
    break
  } 
  soft_threshold_power = soft_threshold$fitIndices[,1][length(powers)]
}
abline(h=r2_cutoff, col="red")
abline(v=soft_threshold_power, col="blue")
text(soft_threshold_power+1, r2_cutoff-0.1, 
     paste0('R^2 cutoff = ', round(r2_cutoff_new,2)),
     cex = 0.5, col = "red")

plot(soft_threshold$fitIndices[,1], soft_threshold$fitIndices[,5],
     xlab="Soft Threshold (power)",ylab="Mean Connectivity", type="n",
     main = paste("Mean connectivity"),
     cex.lab = 0.5)
text(soft_threshold$fitIndices[,1], soft_threshold$fitIndices[,5], labels=powers, cex=0.5,col="red")
par(mfrow=c(1,1))
```


# Construct network 

The gene network is constructed based on **soft threshold power = `r soft_threshold_power`**

```{r}
gene_network = blockwiseModules(expression_data, power = soft_threshold_power,
                                TOMType = "unsigned", minModuleSize = 30,
                                reassignThreshold = 0, mergeCutHeight = 0.25,
                                numericLabels = TRUE, pamRespectsDendro = FALSE,
                                verbose = 3)
```


# Gene modules {.tabset}

## Idenfity gene modules 

```{r}
modules = table(gene_network$colors)
n_modules = length(modules) - 1
module_size_upper = modules[2]
module_size_lower = modules[length(modules)]

module_table = data.frame(model_label = c(0, 1:n_modules),
                          gene_size = as.vector(modules))
datatable(t(module_table))
```

The results above indicates that there are **`r n_modules` gene modules**, labeled 1 through `r length(n_modules)` in order of descending size. The largest module has **`r module_size_upper` genes**, and the smallest module has **`r module_size_lower` genes**. The label 0 is reserved for genes outside of all modules. 


## Dendrogram and module plot

```{r}
# Convert labels to colors for plotting
module_colors = labels2colors(gene_network$colors)
# Plot the dendrogram and the module colors underneath
plotDendroAndColors(gene_network$dendrograms[[1]], module_colors[gene_network$blockGenes[[1]]],
                    "Module colors",
                    dendroLabels = FALSE, hang = 0.03,
                    addGuide = TRUE, guideHang = 0.05)
```


```{r echo=FALSE}
# save workspace
rm("opt")
save(list=ls(all.names = TRUE), file='CONSTRUCT_NETWORK_WORKSPACE')
```