This functions allows for the calculation and visualizations of
various overlap metrics for clones. The methods include overlap
coefficient (**overlap**), Morisita's overlap index
(**morisita**), Jaccard index (**jaccard**), cosine
similarity (**cosine**) or the exact number of clonal
overlap (**raw**).

```
clonalOverlap(
input.data,
cloneCall = "strict",
method = NULL,
chain = "both",
group.by = NULL,
order.by = NULL,
exportTable = FALSE,
palette = "inferno"
)
```

- input.data
The product of

`combineTCR`

,`combineBCR`

, or`combineExpression`

- cloneCall
How to call the clone - VDJC gene (

**gene**), CDR3 nucleotide (**nt**), CDR3 amino acid (**aa**), VDJC gene + CDR3 nucleotide (**strict**) or a custom variable in the data- method
The method to calculate the "overlap", "morisita", "jaccard", "cosine" indices or "raw" for the base numbers

- chain
indicate if both or a specific chain should be used - e.g. "both", "TRA", "TRG", "IGH", "IGL"

- group.by
The variable to use for grouping

- order.by
A vector of specific plotting order or "alphanumeric" to plot groups in order

- exportTable
Returns the data frame used for forming the graph

- palette
Colors to use in visualization - input any hcl.pals

ggplot of the overlap of clones by group

The formulas for the indices are as follows:

**Overlap Coefficient:**
$$overlap = \frac{\sum \min(a, b)}{\min(\sum a, \sum b)}$$

**Raw Count Overlap:**
$$raw = \sum \min(a, b)$$

**Morisita Index:**
$$morisita = \frac{\sum a b}{(\sum a)(\sum b)}$$

**Jaccard Index:**
$$jaccard = \frac{\sum \min(a, b)}{\sum a + \sum b - \sum \min(a, b)}$$

**Cosine Similarity:**
$$cosine = \frac{\sum a b}{\sqrt{(\sum a^2)(\sum b^2)}}$$

Where:

\(a\) and \(b\) are the abundances of species \(i\) in groups A and B, respectively.

```
#Making combined contig data
combined <- combineTCR(contig_list,
samples = c("P17B", "P17L", "P18B", "P18L",
"P19B","P19L", "P20B", "P20L"))
clonalOverlap(combined,
cloneCall = "aa",
method = "jaccard")
```