01-intro.Rmd

# Introduction {#intro}

Suppose we have a list of genes.

```{r}
genes <- paste0("gene",1:1000)
set.seed(20210302)
gene_list <- list(A = sample(genes,100),
                  B = sample(genes,200),
                  C = sample(genes,300),
                  D = sample(genes,200))
library(dplyr)
```

## `VennDiagram`

`VennDiagram` [@R-VennDiagram] is currently the most popular Venn plot tool in R environment.

```{r}
library(VennDiagram)
VennDiagram <- venn.diagram(x = gene_list, filename = NULL)
cowplot::plot_grid(VennDiagram)
```

It provide basic functions to draw Euler plot.

```{r}
venn.plot <- draw.triple.venn(
	area1 = 65,
	area2 = 75,
	area3 = 85,
	n12 = 35,
	n23 = 15,
	n13 = 25,
	n123 = 5,
	category = c("First", "Second", "Third"),
	fill = c("blue", "red", "green"),
	lty = "blank",
	cex = 2,
	cat.cex = 2,
	cat.col = c("blue", "red", "green"),
	euler.d = TRUE
	)
cowplot::plot_grid(venn.plot)
```

helper function to get Venn partitions.

```{r}
get.venn.partitions(gene_list) %>% dplyr::as_tibble()
```



```{r include=FALSE}
# delete log file
unlink("VennDiagram*.log")
```

## `colorfulVennPlot`

[@R-colorfulVennPlot]

This package can specify the filled color for each region, but the first required parameter is a numeric vector of length 15, with specific names in suitable order, which makes it is very complicated to setup and use.

Besides, the filled color need to be specified one by one, which is very complicated as well.

```{r}
library(colorfulVennPlot)
Colors <- c('red', 'yellow', 'green', 'pink', 'darkgreen','blue','lightblue','tan', 
  'yellowgreen','orange','purple','white','grey','plum','brown')
regions <- seq(15)
names(regions) <- c('1000', '0100', '1100', '0010', '1010', '0110', '1110', '0001', 
  '1001', '0101', '1101', '0011', '1011', '0111', '1111')
plotVenn4d(regions, Colors=Colors)
```

## `venn`

[@R-venn]

Using `venn` is very simple.

```{r}
library(venn)
venn(gene_list)
```

`venn` also support different shapes.

```{r}
venn(x = gene_list, ellipse = TRUE)
```


`venn` support `ggplot`, but it depends on `ggpolypath`, which is not popular.

```{r}
venn::venn(gene_list, ellipse = TRUE, ggplot = TRUE)
```

`venn` plots Venn diagram up to 7 sets.


```{r}
venn::venn(5)
```

```{r}
venn::venn(6)
```

```{r}
venn::venn(7)
```

`venn` accepts multiple format of input. 

```{r}
intersections <- c('~A~B~C~D', '~A~B~CD', '~A~BC~D', '~A~BCD', '~AB~C~D', '~AB~CD', '~ABC~D', '~ABCD', 'A~B~C~D', 'A~B~CD', 'A~BC~D', 'A~BCD', 'AB~C~D', 'AB~CD', 'ABC~D', 'ABCD')
venn(intersections, zcol = colors()[sample(1:657, 16)])

venn("A*D, A*B*~C + B*C*~D", zcol = c("blue", "red"))

venn("AD, AB~C + BC~D", zcol = c("blue", "red"))

venn("1-----")
venn("100 + 110 + 101 + 111")
```



## `nVennR`

`nVennR` provides an interface for the `nVenn` algorithm [@perez-silvaNVennGeneralizedQuasiproportional2018]. This algorithm works for any number of sets, and usually yields pleasing and informative Venn diagrams with proportionality information.

```{r message=FALSE, warning=FALSE}
library(nVennR)
myNV <- plotVenn(gene_list)
```

```{r results="asis"}
cat(myNV$svg)
```


## `eulerr`

[@R-eulerr]

`eulerr` generates area-proportional euler diagrams that display set relationships (intersections, unions, and disjoints) with circles or ellipses.

Unlike Venn diagrams, which show all possible relations between different sets, the Euler diagram shows only relevant relationships.

Venn diagrams are a more restrictive form of Euler diagrams. A Venn diagram must contain all $2^n$ logically possible zones of overlap between its $n$ curves, representing all combinations of inclusion/exclusion of its constituent sets.

```{r}
library(eulerr)
venn_plot <- venn(gene_list)
plot(venn_plot)
```

```{r}
euler_plot <- euler(gene_list)
plot(euler_plot)
```


At the time of March 9th, 2021, it is possible to provide input to eulerr as either

a named numeric vector with set combinations as disjoint set combinations or unions (depending on how the argument type is set in euler()),
a matrix or data frame of logicals with columns representing sets and rows the set relationships for each observation,
a list of sample spaces, or
a table.

```{r}
# Input as a matrix of logicals
set.seed(1)
mat <- cbind(
  A = sample(c(TRUE, TRUE, FALSE), 50, TRUE),
  B = sample(c(TRUE, FALSE), 50, TRUE),
  C = sample(c(TRUE, FALSE, FALSE, FALSE), 50, TRUE)
)
(fit2 <- euler(mat))
```

The diagnose function is good.

```{r}
error_plot(fit2)
```



## `venneuler`

[@R-venneuler]

`venneulerr` depends on JAVA runtime environment, which makes its installation is unbearable for non-JAVA users.

Besides, it only has one exported function, which doesn't support list input.

```{r}
library(venneuler)
combinations <- c(A=0.3, B=0.3, C=1.1, "A&B"=0.1, "A&C"=0.2, "B&C"=0.1 ,"A&B&C"=0.1,"D"=0.2,"C&D"=0.1)
vd <- venneuler(combinations)
plot(vd)
```

## `RVenn`

[@R-RVenn]

`RVenn` is a package for dealing with multiple sets, while the base R functions (`intersect`, `union` and `setdiff`) only work with two sets.

The functions overlap, unite and discern abstract away the details, so one can just construct the universe and choose the sets to operate by index or set name.

`RVenn` provide a `ggvenn()` function to draw 2-3 sets Venn diagram. In this case, 
its advantages are mostly rely on set operation functions. 

```{r}
library(RVenn)
ggvenn(Venn(gene_list[1:3]))
```

## `gplots`

[@R-gplots]

`gplots` provides various R programming tools for plotting data. It supports Venn plot up to five sets.

```{r}
library(gplots)
venn(gene_list)
```

## `ggVennDiagram`

[@R-ggVennDiagram]

```{r}
library(ggVennDiagram)
ggVennDiagram(gene_list)
```


## `ggvenn`

[@R-ggvenn]

```{r}
library(ggvenn)
ggvenn(gene_list)
```
`ggvenn` can show elements in polygon regions.

```{r}
a <- list(A = c("apple", "pear", "peach"),
          B = c("apple", "lemon"))
ggvenn(a, show_elements = TRUE)

ggvenn(a, show_elements = TRUE, label_sep = "\n")  # show elements in line
```