### Load Example Data: cis-eQTLs

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

Loads the 'eqtls' dataset, which contains lists of cis-eQTL genes from different brain regions.

```r
data(eqtls)
```

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

### Analyze Cancer Gene Sets with SuperExactTest

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

This example demonstrates how to analyze cancer gene sets using the `supertest` function. It requires loading the `Cancer` dataset and specifies a background population size `n`. The results can then be summarized and visualized.

```R
#Analyze the cancer gene sets
data(Cancer)
Result=supertest(Cancer, n=20687)
summary(Result)
plot(Result,degree=2:7,sort.by='size')
```

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

### Load Example Data

Source: https://cran.r-project.org/web/packages/SuperExactTest/vignettes/set_html.html

Load the pre-compiled cis-eQTL gene sets data included with the SuperExactTest package. This dataset is used for demonstrating the package's functionality.

```r
data("eqtls")
```

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

### Cancer Census Dataset

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

An example dataset containing a list of seven cancer predisposition gene sets.

```APIDOC
## Cancer Census Dataset

### Description
This example dataset contains a list of seven cancer predisposition gene sets.

### Usage
```R
data(Cancer)
```

### Details
The seven cancer predisposition gene sets are:
  * NRG (Rahman, N. Realizing the promise of cancer predisposition genes. Nature 2014, 505:302-308);
  * NBG (Tamborero, D. et al. Comprehensive identification of mutational cancer driver genes across 12 tumor types. Scientific reports 2013, 3:2650);
  * LDG (Kandoth, C. et al. Mutational landscape and significance across 12 major cancer types. Nature 2013, 502:333-339);
  * GGG (Lawrence, M. S. et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 2014, 505:495-501);
  * ELG (Garraway, L. A. & Lander, E. S. Lessons from the cancer genome. Cell 2013, 153:17-37);
  * CCG (Futreal, P. A. et al. A census of human cancer genes. Nature reviews. Cancer 2004, 4:177-183);
  * BVG (Vogelstein, B. et al. Cancer genome landscapes. Science 2013, 339:1546-1558).

### References
Minghui Wang, Yongzhong Zhao, and Bin Zhang (2015). Efficient Test and Visualization of Multi-Set Intersections. _Scientific Reports_ 5: 16923.

### See Also
`supertest`
```

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

### GAWS Catalog Dataset

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

An example dataset containing a list of gene sets associated with six types of clinical traits curated in the GWAS Catalog.

```APIDOC
## GAWS Catalog Dataset

### Description
This example dataset contains a list of gene sets associated with six types of clinical traits curated in the GWAS Catalog.

### Usage
```R
data(GWAS)
```

### Details
The six clinical traits are:
  * NEU (Bipolar disorder and schizophrenia, Schizophrenia, Major depressive disorder, Alzheimer's disease, Parkinson's disease, Cognitive performance, Bipolar disorder);
  * INF (Crohn's disease, Ulcerative colitis, Inflammatory bowel disease, Rheumatoid arthritis, Multiple sclerosis, Systemic lupus erythematosus);
  * CVD (Type 2 diabetes, Coronary heart disease, Blood pressure, total Cholesterol, HDL cholesterol, Triglycerides);
  * HT (height);
  * IgG (IgG glycosylation);
  * OB (obesity, obesity related traits).

### References
Minghui Wang, Yongzhong Zhao, and Bin Zhang (2015). Efficient Test and Visualization of Multi-Set Intersections. _Scientific Reports_ 5: 16923.

### See Also
`supertest`
```

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

### Access Summary Help

Source: https://cran.r-project.org/web/packages/SuperExactTest/vignettes/set_html.html

Access the help documentation for the summary function specific to msets objects. This provides detailed information on the function's arguments and return values.

```r
?summary.msets
```

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

### Load SuperExactTest Package

Source: https://cran.r-project.org/web/packages/SuperExactTest/vignettes/set_html.html

Load the SuperExactTest package into the R session. This is a prerequisite for using any of its functions.

```r
library("SuperExactTest")
```

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

### Tabulate Summary Results to CSV

Source: https://cran.r-project.org/web/packages/SuperExactTest/vignettes/set_html.html

Write the 'Table' component of the summary result to a CSV file. Ensure row names are not included in the output file.

```r
write.csv(summary(res)$Table, file="summary.table.csv", row.names=FALSE)
```

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

### Summarize msets Object

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

Generate summary statistics for a 'msets' object. This is useful for inspecting the observed and expected intersection sizes, p-values, and overlap elements.

```R
## Not run: 
#set up fake data
x=list(S1=letters[1:20], S2=letters[10:26], S3=sample(letters,10), S4=sample(letters,10))
obj=supertest(x,n=26)
summary(obj)

## End(Not run)
```

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

### Inspect Data Structure

Source: https://cran.r-project.org/web/packages/SuperExactTest/vignettes/set_html.html

Check the structure of the loaded 'cis.eqtls' object. This helps in understanding the format and content of the gene sets.

```r
str(cis.eqtls)
```

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

### Visualize Intersection Analysis Results (Circular Layout)

Source: https://cran.r-project.org/web/packages/SuperExactTest/vignettes/set_html.html

Plot the results of the `supertest` analysis in a circular layout. Intersections can be sorted by size, and color scales and legend positions can be customized.

```R
plot(res, sort.by="size", margin=c(2,2,2,2), color.scale.pos=c(0.85,1), legend.pos=c(0.9,0.15))
```

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

### Calculate Probability Density of Set Intersections

Source: https://cran.r-project.org/web/packages/SuperExactTest/vignettes/set_html.html

Use `dpsets` to calculate the probability density for a given intersection size. Requires intersection size, a vector of set sizes, and the total population size.

```R
dpsets(num.observed.overlap, length.gene.sets, n=total)
```

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

### Visualize Intersection Analysis Results (Landscape Layout)

Source: https://cran.r-project.org/web/packages/SuperExactTest/vignettes/set_html.html

Plot the results in a landscape (matrix) layout, optionally filtering for intersections among a specific range of set degrees. This layout is useful for visualizing presence/absence of sets and intersection sizes.

```R
plot(res, Layout="landscape", degree=2:4, sort.by="size", margin=c(0.5,5,1,2))
```

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

### Summarize an msets Object

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

Outputs summary statistics of a msets object, including observed and expected intersection sizes, p-values, and overlap elements.

```APIDOC
## summary(object, ...)

### Description
This function outputs summary statistics of a msets object.

### Usage
```R
## S3 method for class 'msets'
summary(object, degree=NULL, ...)
```

### Arguments
`object` |  a `msets` object.  
`degree` |  a vector of intersection degrees to pull out.  
`...` |  additional arguments (not implemented).  

### Value
A list:
`Barcode` |  a vector of 0/1 character strings, representing the set composition of each intersection.
`otab` |  a vector of observed intersection size between any combination of sets.
`etab` |  a vector of expected intersection size between any combination of sets if background population size is specified.
`set.names` |  set names.
`set.sizes` |  set sizes.
`n` |  background population size.
`P.value` |  upper tail p value for each intersection if background population size n is specified.
`Table` |  a data.frame containing degree, otab, etab, fold change, p value and the overlap elements.

### Author(s)
Minghui Wang <minghui.wang@mssm.edu>, Bin Zhang <bin.zhang@mssm.edu>

### References
Minghui Wang, Yongzhong Zhao, and Bin Zhang (2015). Efficient Test and Visualization of Multi-Set Intersections. _Scientific Reports_ 5: 16923.

### See Also
`msets`

### Examples
```R
#set up fake data
x=list(S1=letters[1:20], S2=letters[10:26], S3=sample(letters,10), S4=sample(letters,10))
obj=supertest(x,n=26)
summary(obj)
```
```

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

### Perform Intersection and Statistical Test Simultaneously

Source: https://cran.r-project.org/web/packages/SuperExactTest/vignettes/set_html.html

Use the `MSET` function to perform intersection operations and probability calculations in a single step. It returns a list containing fold enrichment and p-value.

```R
fit=MSET(cis.eqtls, n=total, lower.tail=FALSE)
```

```R
fit$FE
```

```R
fit$p.value
```

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

### Multi-Set Intersection Probability Functions

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

Provides density and distribution functions for multi-set intersection tests. `dpsets` calculates the density, and `cpsets` calculates the distribution function, with options for simulation-based p-values.

```APIDOC
## Density and distribution function of multi-set intersection test.

### Usage
```R
dpsets(x,L,n,log.p =FALSE)
cpsets(x,L,n,lower.tail=TRUE,log.p=FALSE,
       simulation.p.value=FALSE,number.simulations=1000000)
```

### Arguments
`x` |  integer, number of elements overlap among all sets.  
`L` |  vector, set sizes.  
`n` |  integer, background population size.  
`lower.tail` |  logical; if TRUE, probability is `P[overlap <= x]`, otherwise, `P[overlap > x]`.  
`log.p` |  logical; if TRUE, probability p is given as `log(p)`.  
`simulation.p.value` |  logical; if TRUE, probability p is computed from simulation.  
`number.simulations` |  integer; number of simulations.  

### Value
`dpsets` gives the density and `cpsets` gives the distribution function.

### Author(s)
Minghui Wang <minghui.wang@mssm.edu>

### References
Minghui Wang, Yongzhong Zhao, and Bin Zhang (2015). Efficient Test and Visualization of Multi-Set Intersections. _Scientific Reports_ 5: 16923. 

### See Also
`supertest`, `MSET`
```

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

### Set Operations (Union and Intersection)

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

Extends the base R `union` and `intersect` functions to handle more than two input vectors, performing set union and intersection on multiple input vectors.

```APIDOC
## Set Operations

### Description
Performs set union and intersection on multiple input vectors. 

### Usage
```R
union(x, y, ...)
intersect(x, y, ...)
```

### Arguments
`x`, `y`, `...` |  vectors (of the same mode) containing a sequence of items (conceptually) with no duplicated values.  

### Details
These functions extend the the same functions in the `base` package to handle more than two input vectors. 

### Value
A vector of the same mode as x or y for intersect, and of a common mode for union. 

### Author(s)
Minghui Wang <minghui.wang@mssm.edu>, Bin Zhang <bin.zhang@mssm.edu>

### References
Minghui Wang, Yongzhong Zhao, and Bin Zhang (2015). Efficient Test and Visualization of Multi-Set Intersections. _Scientific Reports_ 5: 16923. 

### Examples
```R
##not run##
```
```

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

### Load Cancer Census Dataset in R

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

Loads the Cancer Census dataset, which contains seven cancer predisposition gene sets. This dataset is useful for demonstrating multi-set intersection analysis.

```r
data(Cancer)
```

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

### Plotting SuperExactTest Results

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

Visualizes the results of the SuperExactTest, showing intersections between sets. Allows for customization of various plot elements like labels, colors, and layout.

```APIDOC
## plot(x, ...)

### Description
Visualizes the results of the SuperExactTest.

### Usage
```R
plot(x, ...)
```

### Details
The plot canvas has coordinates 0~1 for both x and y axes. Additional optional plot parameters include:
  * `ylab`, a character string of y axis label.
  * `circle.radii`, radii size of the circles in `landscape` Layout. Default 0.5.
  * `heatmapColor`, a vector of customized heat colors.
  * `show.expected.overlap`, whether to show expected overlap in `landscape` Layout. Default 'FALSE'.
  * `expected.overlap.style`, one of c("hatchedBox","horizBar","box"). Default 'hatchedBox'.
  * `expected.overlap.lwd`, line width for expected.overlap "horizBar" and "box". Default 2.
  * `color.expected.overlap`, color for showing expected overlap in hatched lines. Default 'grey'.
  * `alpha.expected.overlap`, alpha channel for transparency for showing expected overlap hatched lines. Default 1 (normalized to the range 0 to 1).
  * `cex`, scale of text font size.
  * `cex.lab`, scale of axis label text font size.
  * `show.track.id`, logic, whether to show the track id in the `circular` layout. Default `TRUE`.
  * `phantom.tracks`, number of phantom tracks in the middle in the `circular` layout. Default 2.
  * `gap.within.track`, ratio of gap width over block width on the same track. Default 0.1.
  * `gap.between.track`, ratio of gap width over track width. Default 0.1.
  * `bar.split`, a vector of two values specifying a continuous range that will be cropped in the y axis with the `landscape` layout.
  * `elements.list`, a data.frame or matrix such as the one generated by the `summary` function from a `msets` object, with row names matching the barcodes of intersection combinations and at least one column named "Elements" listing the elements to be displayed (the elements should be concatenated by separator ", ").
  * `elements.cex`, numeric; specifying the amount by which intersection element text should be magnified. Default 0.9.
  * `elements.rot`, numeric; the angle to rotate the text of intersection elements. Default 45.
  * `elements.col`, colour for intersection element text. Default black.
  * `elements.maximum`, maximum number of elements to show.
  * `intersection.size.rotate`, logic, whether to rotate the text of intersection size.
  * `flip.vertical`, logic, whether to flip the bars to downwards in `landscape` Layout. Default 'FALSE'.
  * `title`, figure title. Default NULL.
  * `cex.title`, scale of title text font size. Default 1.

### Value
No return.

### Author(s)
Minghui Wang <minghui.wang@mssm.edu>, Bin Zhang <bin.zhang@mssm.edu>

### References
Minghui Wang, Yongzhong Zhao, and Bin Zhang (2015). Efficient Test and Visualization of Multi-Set Intersections. _Scientific Reports_ 5: 16923.

### See Also
`msets`

### Examples
```R
#set up fake data
x=list(S1=letters[1:20], S2=letters[10:26], S3=sample(letters,10), S4=sample(letters,10))
obj=supertest(x,n=26)
plot(obj)
```
```

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

### Find Intersections and Assign Elements

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

Identifies intersection combinations within a list of sets and assigns each element to its corresponding combination. Can enforce mutual exclusivity.

```r
intersectElements(sets,mutual.exclusive=TRUE)
```

```r
intersectElements(sets,mutual.exclusive=FALSE)
```

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

### Calculate Intersections Among Multiple Sets and Perform Statistical Tests

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

Calculates intersection sizes among multiple sets and performs statistical tests of the intersections.

```APIDOC
## supertest(x, n = NULL, ...)

### Description
This function calculates intersection sizes among multiple sets and performs statistical tests of the intersections. 

### Usage
```R
supertest(x, n=NULL, degree=NULL, ...)
```

### Arguments
`x` |  a list of sets.
`n` |  background population size. If NULL, only observed sizes are calculated.
`degree` |  a vector of intersection degrees to pull out. If NULL, all intersections are calculated.
`...` |  additional arguments passed to other methods.

### Value
A `msets` object containing information about the intersections and statistical tests.

### Author(s)
Minghui Wang <minghui.wang@mssm.edu>, Bin Zhang <bin.zhang@mssm.edu>

### References
Minghui Wang, Yongzhong Zhao, and Bin Zhang (2015). Efficient Test and Visualization of Multi-Set Intersections. _Scientific Reports_ 5: 16923.

### See Also
`msets`
```

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

### Load GAWS Catalog Dataset in R

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

Loads the GAWS Catalog dataset, which contains gene sets associated with six types of clinical traits. This dataset can be used for similar multi-set intersection analyses.

```r
data(GWAS)
```

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

### Summarize Intersection Analysis Results

Source: https://cran.r-project.org/web/packages/SuperExactTest/vignettes/set_html.html

Use the generic `summary` function to obtain detailed statistics for the intersection analysis, including observed and expected overlaps, fold enrichment (FE), and P-values for each intersection.

```R
summary(res)
```

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

### Calculate Gene Set Lengths

Source: https://cran.r-project.org/web/packages/SuperExactTest/vignettes/set_html.html

Calculate and display the number of genes in each of the cis-eQTL gene sets. This is useful for understanding the scale of the input data.

```r
(length.gene.sets=sapply(cis.eqtls,length))
```

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

### Compute Probability Density of Intersection Sizes

Source: https://cran.r-project.org/web/packages/SuperExactTest/vignettes/set_html.html

Calculate the probability density distribution for all possible intersection sizes (from 0 to 101) among the four gene sets using the `dpsets` function. This function implements the exact probability calculation for multi-set intersections.

```r
(p=sapply(0:101,function(i) dpsets(i, length.gene.sets, n=total)))
```

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

### MSET Function

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

Calculate FE and significance of intersection among multiple sets.

```APIDOC
## Exact Test of Multi-Set Intersection

### Description
Calculate FE and significance of intersection among multiple sets.

### Usage
```R
MSET(x,n,lower.tail=TRUE,log.p=FALSE)

```

### Arguments
`x` |  list; a collection of sets.  
`n` |  integer; background population size.
`lower.tail` |  logical; if TRUE, probability is `P[overlap < m]`, otherwise, `P[overlap >= m]`, where `m` is the number of elements overlap between all sets.  
`log.p` |  logical; if TRUE, probability p is given as log(p).  

### Details
This function implements an efficient statistical test for multi-set intersections. The algorithm behind this function was described in Wang et al 2015.

### Value
A list with the following elements: 
`intersects` |  a vector of intersect items.  
`FE` |  fold enrichment of the intersection.  
`p.value` |  one-tail probability of observing equal to or larger than the number of intersect items.  

### Author(s)
Minghui Wang <minghui.wang@mssm.edu>, Bin Zhang <bin.zhang@mssm.edu>

### References
Minghui Wang, Yongzhong Zhao, and Bin Zhang (2015). Efficient Test and Visualization of Multi-Set Intersections. _Scientific Reports_ 5: 16923.

### See Also
`supertest`, `cpsets`, `dpsets`

### Examples
```R
## Not run: 
#set up fake data
x=list(S1=letters[1:20], S2=letters[10:26], S3=sample(letters,10), S4=sample(letters,10))
MSET(x, 26, FALSE)

## End(Not run)

```
```

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

### Calculate Intersections and Perform Statistical Tests

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

Calculate intersection sizes among multiple sets and perform statistical tests. This function is the core of the package for analyzing set overlaps.

```R
supertest(x, n=NULL, degree=NULL, ...)
```

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

### supertest Function

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

The `supertest` function calculates intersection sizes between multiple sets and performs statistical tests if a background population size is provided. It returns an object of class `msets`.

```APIDOC
## supertest Function

### Description
Calculates intersection sizes between multiple sets and performs statistical tests of the intersections if a background population size `n` is specified.

### Arguments
- `x` (list): A collection of sets.
- `n` (integer): Background population size. Required for computing the statistical significance of intersections.
- `degree` (vector): A vector of intersection degrees for overlap analysis. If `NULL`, all possible intersections are computed.
- `...` (additional arguments): Not implemented.

### Value
An object of class `msets`.

### Examples
```R
# Analyze the cancer gene sets
data(Cancer)
Result <- supertest(Cancer, n = 20687)
summary(Result)
plot(Result, degree = 2:7, sort.by = 'size')
```
```

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

### Plot Multi-Set Intersections

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

Draws multi-set intersections. Various layout and display options are available. Customize parameters like 'sort.by' and 'min.intersection.size' for tailored visualizations.

```R
plot(x, Layout=c('circular','landscape'), degree=NULL,
	keep.empty.intersections=TRUE,
	sort.by=c('set','size','degree','p-value'),
	min.intersection.size=0, max.intersection.size=Inf,
	ylim=NULL, log.scale=FALSE, yfrac=0.8, margin=NULL,
	color.scale.pos=c(0.85, 0.9), legend.pos=c(0.85,0.25),
	legend.col=2, legend.text.cex=1, color.scale.cex=1, 
	color.scale.title=expression(paste(-Log[10],'(',italic(P),')')), 
	color.on='#2EFE64', color.off='#EEEEEE', 
	show.overlap.size=TRUE, show.fold.enrichment=FALSE, show.set.size=TRUE,
	overlap.size.cex=0.9, track.area.range=0.3, bar.area.range=0.2,
	new.gridPage=TRUE, minMinusLog10PValue=0,
	maxMinusLog10PValue=NULL, show.elements=FALSE, ...)
```

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

### Plot SuperExactTest Results

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

Visualize the results of a supertest object. This function generates a plot based on the calculated intersections and their statistical significance.

```R
## Not run: 
#set up fake data
x=list(S1=letters[1:20], S2=letters[10:26], S3=sample(letters,10), S4=sample(letters,10))
obj=supertest(x,n=26)
plot(obj)

## End(Not run)
```

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

### Find Intersection of Multiple Gene Sets

Source: https://cran.r-project.org/web/packages/SuperExactTest/vignettes/set_html.html

Use the base R `intersect` function to find the common elements among multiple vectors. Note: The package provides a re-implementation for handling more than two input vectors.

```R
common.genes=intersect(cis.eqtls[[1]], cis.eqtls[[2]], cis.eqtls[[3]],
 cis.eqtls[[4]])
```

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

### Calculate Fold Enrichment

Source: https://cran.r-project.org/web/packages/SuperExactTest/vignettes/set_html.html

Calculate the fold enrichment (FE) by dividing the number of observed overlaps by the number of expected overlaps.

```R
(FE=num.observed.overlap/num.expcted.overlap)
```

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

### Calculate Jaccard Index

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

Calculates Jaccard indices between pairs of sets provided as a list.

```APIDOC
## Calculate Jaccard Index

### Description
This function calculates Jaccard indices between pairs of sets. 

### Usage
```R
jaccard(x)
```

### Arguments
`x` |  list, a collect of sets.  

```

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

### Perform Intersection Analysis with supertest

Source: https://cran.r-project.org/web/packages/SuperExactTest/vignettes/set_html.html

Use `supertest` to enumerate all possible intersections for a given list of input vectors (gene sets) and perform statistical tests. The function returns an object of class `msets`.

```R
res=supertest(cis.eqtls, n=total)
```

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

### Calculate Multi-Set Intersection Density

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

Computes the density of the multi-set intersection test. Requires specifying the number of overlapping elements, set sizes, and background population size.

```r
(d=dpsets(x,c(A,B,C,D),n))
```

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

### Calculate Jaccard Indices

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

Calculates pairwise Jaccard indices for a list of sets. Ensure data is set up correctly before calling the function.

```R
#set up fake data
x=list(S1=letters[1:20], S2=letters[10:26], S3=sample(letters,10), S4=sample(letters,10))
jaccard(x)
```

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

### Calculate Expected Overlap Size

Source: https://cran.r-project.org/web/packages/SuperExactTest/vignettes/set_html.html

Compute the expected number of overlapping genes among the four cis-eQTL gene sets, assuming they were independently and randomly sampled from a larger population of genes.

```r
total=18196
(num.expcted.overlap=total*do.call(prod,as.list(length.gene.sets/total)))
```

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

### msets Class

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

Represents data regarding the intersections between multiple sets, typically created by the supertest function.

```APIDOC
## Class to Contain Multi-Set Intersections
### Description
This object contains data regarding the intersections between multiple sets. This object is usually created by the `supertest` function. 
### Details
Intersection combination is denoted by a `barcode` string of '0' and '1', where a value of '1' in the `i`th position of the string indicates that the intersection is involved with the `i`th set, 0 otherwise. E.g., string '000101' indicates that the intersection is an overlap between the 4th and 6th sets. Function `deBarcode` can be used to decrypt the barcode. Generic `summary` and `plot` functions can be applied to extract and visualize the results. 
### Value
`x` |  a list of sets from input.  
---|---
`set.names` |  names of the sets. If the input sets do not have names, they will be automatically named as SetX where X is an integer from 1 to the total number of sets.  
`set.sizes` |  a vector of set sizes.  
`n` |  background population size.  
`overlap.sizes` |  a named vector of intersection sizes. Each intersection component is named by a barcoded character string of '0' and '1'. See `Details` for barcode.  
`overlap.expected` |  a named vector of expected intersection sizes when item `n` is available.  
`P.value` |  a vector of p values for the intersections when item `n` is available.  
### Author(s)
Minghui Wang <minghui.wang@mssm.edu>, Bin Zhang <bin.zhang@mssm.edu>
### References
Minghui Wang, Yongzhong Zhao, and Bin Zhang (2015). Efficient Test and Visualization of Multi-Set Intersections. _Scientific Reports_ 5: 16923. 
### See Also
`supertest`, `summary.msets`, `plot.msets`, `deBarcode`
```

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

### plot.msets function

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

Draws intersections among multiple sets. This is an S3 method for the 'msets' class.

```APIDOC
## Draw Multi-Set Intersections
### Description
This function draws intersections among multiple sets. 
### Usage
```R
## S3 method for class 'msets'
plot(x, Layout=c('circular','landscape'), degree=NULL,
	keep.empty.intersections=TRUE,
	sort.by=c('set','size','degree','p-value'),
	min.intersection.size=0, max.intersection.size=Inf,
	ylim=NULL, log.scale=FALSE, yfrac=0.8, margin=NULL,
	color.scale.pos=c(0.85, 0.9), legend.pos=c(0.85,0.25),
	legend.col=2, legend.text.cex=1, color.scale.cex=1, 
	color.scale.title=expression(paste(-Log[10],'(',italic(P),')'))
	color.on='#2EFE64', color.off='#EEEEEE', 
	show.overlap.size=TRUE, show.fold.enrichment=FALSE, show.set.size=TRUE,
	overlap.size.cex=0.9, track.area.range=0.3, bar.area.range=0.2,
	new.gridPage=TRUE, minMinusLog10PValue=0,
	maxMinusLog10PValue=NULL, show.elements=FALSE, ...) 
```
```

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

### Decrypt Barcode

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

Decrypts barcode information, which are character strings of '0' and '1' indicating the presence or absence of sets in an intersection combination.

```APIDOC
## Decrypt Barcode

### Description
Decrypt barcode information. 

### Usage
```R
deBarcode(barcode, setnames, collapse=' & ')
```

### Arguments
`barcode` |  a vector of character strings, encoding the intersection combination.  
`setnames` |  set names.  
`collapse` |  an optional character string to separate the results. See `paste`.  

### Details
`barcode` are character strings of '0' and '1', indicating absence or presence of each set in a intersection combination. 

### Value
A vector. 

### Author(s)
Minghui Wang <minghui.wang@mssm.edu>

### Examples
```R
deBarcode(c('01011','10100'), c('S1','S2','S3','S4','S5'))
```
```

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

### Calculate Cumulative Probability of Set Intersections

Source: https://cran.r-project.org/web/packages/SuperExactTest/vignettes/set_html.html

Use `cpsets` to calculate the cumulative probability of observing a certain number of intersection genes or more. Requires the observed intersection size minus one, a vector of set sizes, the total population size, and a `lower.tail` argument.

```R
cpsets(num.observed.overlap-1, length.gene.sets, n=total, lower.tail=FALSE)
```

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

### Calculate Multi-Set Intersection Cumulative Probability

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

Computes the cumulative distribution function for the multi-set intersection test. Can optionally use simulations for p-value calculation.

```r
(p=cpsets(x,c(A,B,C,D),n,lower.tail=FALSE))
```

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

### Calculate Jaccard Index for Sets

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

Computes the Jaccard indices between pairs of sets provided in a list.

```r
jaccard(x)
```

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

### Decrypt Barcode Information

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

Decodes barcode strings representing set intersections into human-readable labels. Allows customization of the separator.

```r
deBarcode(c('01011','10100'), c('S1','S2','S3','S4','S5'))
```

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

### jaccard function

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

Calculates pairwise Jaccard indices for a list of sets.

```APIDOC
## Value
A matrix of pairwise Jaccard indices.
### Author(s)
Minghui Wang <minghui.wang@mssm.edu>
### Examples
```R
## Not run: 
#set up fake data
x=list(S1=letters[1:20], S2=letters[10:26], S3=sample(letters,10), S4=sample(letters,10))
jaccard(x)

## End(Not run)
```
```

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

### Find Intersection Membership

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

Finds intersections and assigns elements to their respective intersection combinations. The `mutual.exclusive` argument controls how elements are assigned when they belong to multiple intersections.

```APIDOC
## Find Intersection Membership

### Description
Find intersections and assign element to intersection combinations. 

### Usage
```R
intersectElements(x, mutual.exclusive=TRUE)
```

### Arguments
`x` |  list; a collection of sets.  
`mutual.exclusive` |  logical; see `Details`.  

### Details
See example below for the use of `mutual.exclusive`. 

### Value
A data.frame with two columns: 
`Entry` |  set elements.  
`barcode` |  intersection combination that each entry belongs to.  

### Author(s)
Minghui Wang <minghui.wang@mssm.edu>

### Examples
```R
set.seed(123)
sets=list(S1=sample(letters,10), S2=sample(letters,5), S3=sample(letters,7))
intersectElements(sets,mutual.exclusive=TRUE)
intersectElements(sets,mutual.exclusive=FALSE)
```
```

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

### Calculate Exact Test for Multi-Set Intersection in R

Source: https://cran.r-project.org/web/packages/SuperExactTest/refman/SuperExactTest.html

Calculates the fold enrichment (FE) and significance of intersections among multiple sets. Use this function to assess the statistical significance of overlapping elements across several sets.

```r
MSET(x, n, lower.tail = FALSE)
```

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