---
title: Starting work with scRepertoire.
author: 
  name: Nick Borcherding
  email: ncborch@gmail.com
  affiliation: Washington University in St. Louis, School of Medicine, St. Louis, MO, USA
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
output:
  BiocStyle::html_document:
    toc_float: true
package: scRepertoire
vignette: >
  %\VignetteIndexEntry{Using scRepertoire}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8} 
---

```{r, echo=FALSE, results="hide", message=FALSE, warning=FALSE}
knitr::opts_chunk$set(error=FALSE, message=FALSE, warning=FALSE, tidy = FALSE)
library(BiocStyle)
library(scater)
library(Seurat)
library(igraph)
```

# Introduction

scRepertoire is designed to take filter contig outputs from the 10x Genomics Cell Ranger pipeline, process that data to assign clonotype based on two TCR or Ig chains and analyze the clonotype dynamics. The latter can be separated into 1) clonotype-only analysis functions, such as unique clonotypes or clonal space quantification, and 2) interaction with mRNA expression data using Seurat or SingleCellExperiment packages.

## Loading Libraries

```{r}
suppressMessages(library(scRepertoire))
```

## Loading Data

### What data to load into scRepertoire?

`scRepertoire` primarily functions using the `filtered_contig_annotations.csv` output generated by 10x Genomics Cell Ranger. This file is typically found in the ./outs/ directory of your VDJ alignment folder. 

### Demonstrating Manual Data Loading (10x Genomics)

To prepare data for scRepertoire from 10x Genomics outputs, you would:

* Load the `filtered_contig_annotations.csv` file for each of your samples.
* Combine these loaded data frames into a single list in your R environment.

```{r, eval=FALSE}
S1 <- read.csv(".../Sample1/outs/filtered_contig_annotations.csv")
S2 <- read.csv(".../Sample2/outs/filtered_contig_annotations.csv")
S3 <- read.csv(".../Sample3/outs/filtered_contig_annotations.csv")
S4 <- read.csv(".../Sample4/outs/filtered_contig_annotations.csv")

contig_list <- list(S1, S2, S3, S4)
```

## Other alignment workflows

Beyond the default 10x Genomics Cell Ranger pipeline outputs, `scRepertoire` supports various other single-cell immune receptor sequencing formats through the `loadContigs()` function.

### Supported Formats and Expected File Names

* `10X`: "filtered_contig_annotations.csv"
* `AIRR`: "airr_rearrangement.tsv"
* `BD`: "Contigs_AIRR.tsv"
* `Dandelion`: "all_contig_dandelion.tsv"
* `Immcantation`: "_data.tsv" (or similar)
* `JSON`: ".json"
* `MiXCR`: "clones.tsv"
* `ParseBio`: "barcode_report.tsv"
* `TRUST4`: "barcode_report.tsv"
* `WAT3R`: "barcode_results.csv"

Key Parameter(s) for `loadContigs()`

* `input`: A directory path containing your contig files (the function will recursively search) or a list/data frame of pre-loaded contig data.
* `format`: A string specifying the data format (e.g., `10X`, `TRUST4`, `WAT3R`). If set to "auto", the function will attempt to automatically detect the format based on file names or data structure.

You can provide `loadContigs()` with a directory where your sequencing experiments are located, and it will recursively load and process the contig data based on the file names:

```{r, eval=FALSE, tidy = FALSE}
# Directory example
contig.output <- c("~/Documents/MyExperiment")
contig.list <- loadContigs(input = contig.output, 
                           format = "TRUST4")
```

Alternatively, `loadContigs()` can be given a list of pre-loaded data frames and process the contig data based on the specified format:

```{r eval = FALSE, tidy = FALSE}
# List of data frames example
S1 <- read.csv("~/Documents/MyExperiment/Sample1/outs/barcode_results.csv")
S2 <- read.csv("~/Documents/MyExperiment/Sample2/outs/barcode_results.csv")
S3 <- read.csv("~/Documents/MyExperiment/Sample3/outs/barcode_results.csv")
S4 <- read.csv("~/Documents/MyExperiment/Sample4/outs/barcode_results.csv")

contig.list <- list(S1, S2, S3, S4)
contig.list <- loadContigs(input = contig.list, 
                           format = "WAT3R")
```

## Multiplexed Experiment

It is now easy to create the contig list from a multiplexed experiment by first generating a single-cell RNA object (either Seurat or Single Cell Experiment), loading the filtered contig file, and then using `createHTOContigList()`. This function will return a list separated by the `group.by` variable(s).

### Important Considerations for `createHTOContigList()`

* This function depends on the match of barcodes between the single-cell object and contigs. If there is a prefix or different suffix added to the barcode, this will result in no contigs being recovered.
* It is currently recommended to perform this step before integration workflows, as integration commonly alters the barcodes.
* There is a multi.run variable that can be used on an integrated object. However, it assumes you have modified the barcodes with the Seurat pipeline (automatic addition of _# to the end), and your contig list is in the same order.

To create a contig list separated by HTO (Hash Tag Oligo) IDs from a single-cell object:

```{r, eval = F, tidy = FALSE}
contigs <- read.csv(".../outs/filtered_contig_annotations.csv")

contig.list <- createHTOContigList(contigs, 
                                   Seurat.Obj, 
                                   group.by = "HTO_maxID")
```

## Example Data in `scRepertoire`

`scRepertoire` includes a built-in example dataset to demonstrate the functionality of the R package. This dataset consists of T cells derived from four patients with acute respiratory distress with paired peripheral-blood (B) and bronchoalveolar lavage (L), effectively creating 8 distinct runs for T cell receptor (TCR) enrichment. More information on the data set can be found in the corresponding [manuscript](https://pubmed.ncbi.nlm.nih.gov/33622974/). 

The built-in example data is derived from the 10x Cell Ranger pipeline, so it is ready to go for downstream processing and analysis. 

To load and preview the example data built into `scRepertoire`:

```{r tidy = FALSE}
data("contig_list") #the data built into scRepertoire

head(contig_list[[1]])
```

***

# Combining Contigs into Clones

The `combineTCR()` function processes a list of TCR sequencing results, consolidating them to the level of individual cell barcodes. It handles potential issues with repeated barcodes by adding prefixes from `samples` and `ID` parameters. The output includes combined reads into clone calls by nucleotide sequence (`CTnt`), amino acid sequence (`CTaa`), VDJC gene sequence (`CTgene`), or a combination of nucleotide and gene sequence (`CTstrict`).

Key Parameter(s) for `combineTCR()`

* `input.data`: A list of filtered contig annotations (e.g., filtered_contig_annotations.csv from 10x Cell Ranger) or outputs from `loadContigs()`.
* `samples`: Labels for your samples (recommended).
* `ID`: Additional sample labels (optional).
* `removeNA`: If `TRUE`, removes any cell barcode with an NA value in at least one chain (default is `FALSE`).
* `removeMulti`: If `TRUE`, removes any cell barcode with more than two immune receptor chains (default is `FALSE`).
* `filterMulti`: If `TRUE`, isolates the top two expressed chains in cell barcodes with multiple chains (default is `FALSE`).
* `filterNonproductive`: If `TRUE`, removes non-productive chains if the variable exists in the contig data (default is `TRUE`).

To combine TCR contigs from `contig_list` and apply sample prefixes:

```{r tidy = FALSE}
combined.TCR <- combineTCR(contig_list, 
                           samples = c("P17B", "P17L", "P18B", "P18L", 
                                            "P19B","P19L", "P20B", "P20L"),
                           removeNA = FALSE, 
                           removeMulti = FALSE, 
                           filterMulti = FALSE)

head(combined.TCR[[1]])
```

`combineTCR()` is the essential first step for organizing raw TCR sequencing data into a structured format for `scRepertoire` analyses. It allows for flexible handling of single and paired chains, barcode disambiguation, and initial filtering, producing a list of data frames where each row represents a single cell and its associated TCR clonotypes.

## combineBCR

The ```combineBCR()``` function is the primary tool for processing raw B cell receptor contig data into a format ready for analysis. It is analogous to ```combineTCR()``` but includes specialized logic for handling the complexities of BCRs, such as somatic hypermutation. The function consolidates contigs into a single data frame per sample, with each row representing a unique cell.

By default `(call.related.clones = TRUE)`, ```combineBCR()``` groups B cells into clones based on the similarity of their CDR3 sequences.

### How `combineBCR()` Groups Related Clones

* Internally calling ```clonalCluster()``` to build a network of related sequences.
* Using the `threshold` parameter to define connections. The `threshold` is a normalized Levenshtein distance, where a value closer to 1.0 requires higher sequence similarity. The default of 0.85 is a good starting point.
* Assigning a cluster-based ID to the CTstrict column.

Additionally, the `group.by` argument allows you to constrain the clustering analysis to only occur within distinct categories in your metadata. For example, using `group.by = "sample"` ensures that sequences from different samples are never compared or clustered together, even if they are identical.

Key Parameter(s) for `combineBCR()`

* `call.related.clones`: If `TRUE` (default), uses `clonalCluster()` to identify related clones based on sequence similarity.
* `group.by`: The column header used to group clones for clustering (if `NULL`, clusters will be calculated across all samples).
* `threshold`: The similarity threshold for `clonalCluster()` (default: 0.85).
* `dist_type`: The metric for calculating difference: "levenshtein" (default), "hamming", "nw" (Needleman-Wunsch), or "sw" (Smith-Waterman).
* `dist_mat:` The substitution matrix used if dist_type is "nw" or "sw" (e.g., "BLOSUM62", "PAM30")
* `normalize`: How to handle the threshold: "none", "length", or "maxlen".
* `chain`: The chain to use for clustering when call.related.clones = TRUE (default: `both`).
* `sequence`: The sequence type (`nt` or `aa`) for clustering (default: `nt`).
* use.V, use.J: If `TRUE`, sequences must share the same V/J gene to be clustered (default: `TRUE`)
* `cluster.method`: The clustering algorithm to apply to the edit-distanc network (default: `components`).

First, load the example BCR contig data:

```{r tidy = FALSE}
# Load example BCR contig data
BCR.contigs <- read.csv("https://www.borch.dev/uploads/contigs/b_contigs.csv")
```

Then, combine BCR contigs using the default similarity clustering:

```{r tidy = FALSE}

# Combine using the default similarity clustering
combined.BCR.clustered <- combineBCR(BCR.contigs, 
                                     samples = "Patient1", 
                                     threshold = 0.85)

# The CTstrict column contains cluster IDs (e.g., "cluster.1")
head(combined.BCR.clustered[[1]][, c("barcode", "CTstrict", "IGH", "cdr3_aa1")])
```

### Advanced: Grouping by Alignment

For more biological accuracy—specifically when analyzing amino acid sequences—you can use alignment metrics. This allows the clustering to penalize conservative amino acid changes less than radical changes.

Here we use Needleman-Wunsch alignment with the BLOSUM80 substitution matrix:

```{r}
combined.BCR.aligned <- combineBCR(BCR.contigs, 
                                   samples = "Patient1",
                                   sequence = "aa",        
                                   dist_type = "nw",      
                                   dist_mat = "BLOSUM80",  
                                   threshold = 0.85)

head(combined.BCR.aligned[[1]][, c("barcode", "CTstrict", "IGH", "cdr3_aa1")])
```


### Filtering and Cleaning Data

`combineBCR()` includes several arguments to filter and clean the contig data during processing.
* `filterNonproductive = TRUE` (Default): Removes any contigs that are not classified as productive, ensuring that only functional receptor chains are included in the analysis.
* `filterMulti = TRUE` (Default): For cells with more than one heavy or light chain detected, this automatically selects the chain with the highest UMI count (read abundance) and discards the others. This helps resolve cellular multiplets or technical artifacts.

Here is an example applying these filters (though they are on by default):

```{r}
cleaned.BCR <- combineBCR(BCR.contigs,
                          samples = "Patient1",
                          filterNonproductive = TRUE,
                          filterMulti = TRUE)

head(cleaned.BCR[[1]])
```

`combineBCR()` is designed for processing B cell repertoire data, going beyond simple contig aggregation to incorporate advanced clustering based on CDR3 sequence similarity. This enables the identification of clonally related B cells, crucial for studying B cell development, affinity maturation, and humoral immune responses. Its filtering options further ensure the quality and interpretability of the processed data.

***

# Additional Processing

## addVariable: Adding Variables for Plotting

What if there are more variables to add than just sample and ID? We can add them by using the `addVariable()` function. For each element, the function will add a column (labeled by `variable.name`) with the `variable`. The length of the `variables` parameter needs to match the length of the combined object.

Key Parameter(s) for `addVariable()`

* `variable.name`: A character string that defines the new variable to add (e.g., "Type", "Treatment").
* `variables`: A character vector defining the desired column value for each list element. Its length must match the number of elements in the input.data list.

As an example, here we add the Type in which the samples were processed and sequenced to the `combined.TCR` object:

```{r tidy = FALSE}
combined.TCR <- addVariable(combined.TCR, 
                            variable.name = "Type", 
                            variables = rep(c("B", "L"), 4))

head(combined.TCR[[1]])
```

## subsetClones: Filter Out Clonal Information

Likewise, we can remove specific list elements after `combineTCR()` or `combineBCR()` using the `subsetClones()` function. In order to subset, we need to identify the column header we would like to use for subsetting (`name`) and the specific values to include (`variables`).

Key Parameter(s) for `subsetClones()`

* `name`: The column header/name in the metadata of input.data to use for subsetting (e.g., "sample", "Type").
* `variables`: A character vector of the specific values within the chosen name column to retain in the subsetted data.

Below, we isolate just the two sequencing results from "P18L" and "P18B" samples:

```{r, tidy = FALSE}
subset1 <- subsetClones(combined.TCR, 
                        name = "sample", 
                        variables = c("P18L", "P18B"))

head(subset1[[1]][,1:4])
```

Alternatively, we can also just select the list elements after `combineTCR()` or `combineBCR()`.

```{r}
subset2 <- combined.TCR[c(3,4)]
head(subset2[[1]][,1:4])
```

## exportClones: Save Clonal Data  

After assigning the clone by barcode, we can export the clonal information using `exportClones()` to save for later use or to integrate with other bioinformatics pipelines. This function supports various output formats tailored for different analytical needs.

Key Parameter(s) for `exportClones()`
* `format`: The desired output format for the clonal data.
  * `airr`: Exports data in an Adaptive Immune Receptor Repertoire (AIRR) Community-compliant format, with each row representing a single receptor chain.
   * `immunarch`: Exports a list containing a data frame and metadata formatted for use with the `immunarch` package. 
  * `paired`: Exports a data frame with paired chain information (amino acid, nucleotide, genes) per barcode. This is the default.
  * `TCRMatch`: Exports a data frame specifically for the TCRMatch algorithm, containing TRB chain amino acid sequence and clonal frequency.
  * `tcrpheno`: Exports a data frame compatible with the `tcrpheno` pipeline, with TRA and TRB chains in separate columns.
* `write.file`: If `TRUE` (default), saves the output to a CSV file. If `FALSE`, returns the data frame or list to the R environment.
* `dir`: The directory where the output file will be saved. Defaults to the current working directory.
* `file.name`: The name of the CSV file to be saved.

To export the combined clonotypes as a `paired` data frame and save it to a specified directory:

```{r, eval = FALSE, tidy = FALSE}
exportClones(combined, 
             write.file = TRUE,
             dir = "~/Documents/MyExperiment/Sample1/"
             file.name = "clones.csv")
```

To return an `immunarch`-formatted data frame directly to your R environment without saving a file:

```{r}
immunarch <- exportClones(combined.TCR, 
                          format = "immunarch", 
                          write.file = FALSE)
head(immunarch[[1]][[1]])
```


## annotateInvariant

The `annotateInvariant()` function enables the identification of mucosal-associated invariant T (`MAIT`) cells and invariant natural killer T (`iNKT`) cells in single-cell sequencing datasets. These specialized T-cell subsets are defined by their characteristic TCR usage, making them distinguishable within single-cell immune profiling data. The function extracts TCR chain information from the provided single-cell dataset and evaluates it against known invariant TCR criteria for either MAIT or iNKT cells. Each cell is assigned a score indicating the presence (1) or absence (0) of the specified invariant T-cell population.

Key Parameter(s) for `annotateInavriant()`

* `type`: Character string specifying the type of invariant T cell to annotate (`MAIT` or `iNKT`).
* `species`: Character string specifying the species (`mouse` or `human`).

```{r, eval = FALSE, tidy = FALSE}
combined <- annotateInvariant(combined, 
                              type = "MAIT", 
                              species = "human")

combined <- annotateInvariant(combined, 
                              type = "iNKT", 
                              species = "human")
```

***

# Basic Clonal Visualizations

## cloneCall: How to call clones.

* `gene` - use the VDJC genes comprising the TCR/Ig  
* `nt` - use the nucleotide sequence of the CDR3 region  
* `aa` - use the amino acid sequence of the CDR3 region  
* `strict` - use the VDJC genes comprising the TCR + the nucleotide sequence of the CDR3 region. This is the [proper definition of clonotype](https://www.ncbi.nlm.nih.gov/pubmed/19568742). For ```combineBCR()``` strict refers to the edit distance clusters + Vgene of the Ig. 

It is important to note that the clonotype is called using essentially the combination of genes or nt/aa CDR3 sequences for both loci. As of this implementation of scRepertoire, clonotype calling is not incorporating small variations within the CDR3 sequences. As such the `gene` approach will be the most sensitive, while the use of `nt` or `aa` is moderately so, and the most specific for clonotypes being `strict`. Additionally, the clonotype call is trying to incorporate both loci, *i.e.*, both **TCRA** and **TCRB** chains and if a single cell barcode has multiple sequences identified (*i.e.*, 2 TCRA chains expressed in one cell). Using the 10x approach, there is a subset of barcodes that only return one of the immune receptor chains. The unreturned chain is assigned an **NA** value.

## clonalQuant: Quantifying Unique Clones

The `clonalQuant()` function is used to explore the clones by returning the total or relative numbers of unique clones.

Key Parameter(s) for `clonalQuant()`

* `scale`: If `TRUE`, converts the output to the relative percentage of unique clones scaled by the total repertoire size; if `FALSE` (default), reports the total number of unique clones.

To visualize the relative percent of unique clones across all chains (`"both"`) using the `strict` clone definition:

```{r tidy = FALSE}
clonalQuant(combined.TCR, 
            cloneCall="strict", 
            chain = "both", 
            scale = TRUE)
```

Another option is to define the visualization by data classes using the `group.by` parameter. Here, we'll use the `"Type"` variable, which was previously added to the combined.TCR list.

```{r}
clonalQuant(combined.TCR, 
            cloneCall="gene", 
            group.by = "Type", 
            scale = TRUE)
```

## clonalAbundance: Distribution of Clones by Size

`clonalAbundance()` allows for the examination of the relative distribution of clones by abundance. It produces a line graph showing the total number of clones at specific frequencies within a sample or group.

Key Parameter(s) for `clonalAbundance()`

* `scale`: If `TRUE`, converts the graphs into density plots to show relative distributions; if `FALSE` (default), displays raw counts.

To visualize the raw clonal abundance using the `gene` clone definition:

```{r tidy = FALSE}
clonalAbundance(combined.TCR, 
                cloneCall = "gene", 
                scale = FALSE)
```

`clonalAbundance()` output can also be converted into a density plot, which may allow for better comparisons between different repertoire sizes, by setting `scale = TRUE`.

```{r}
clonalAbundance(combined.TCR, 
                cloneCall = "gene", 
                scale = TRUE)
```

## clonalLength: Distribution of Sequence Lengths

`clonalLength()` allows you to look at the length distribution of the CDR3 sequences. Importantly, unlike the other basic visualizations, the `cloneCall` can only be `nt` (nucleotide) or `aa` (amino acid). Due to the method of calling clones as outlined previously (e.g., using NA for unreturned chain sequences or multiple chains within a single barcode), the length distribution may reveal a multimodal curve.

To visualize the amino acid length distribution for both chains ("both"):

```{r tidy = FALSE}
clonalLength(combined.TCR, 
             cloneCall="aa", 
             chain = "both") 
```

To visualize the amino acid length distribution for the `TRA` chain, scaled as a density plot:

```{r tidty = FALSE}
clonalLength(combined.TCR, 
             cloneCall="aa", 
             chain = "TRA", 
             scale = TRUE) 
```

## clonalCompare: Clonal Dynamics Between Categorical Variables

`clonalCompare()` allows you to look at clones between samples and changes in dynamics. It is useful for tracking how the proportions of top clones change between conditions.

Key Parameters for `clonalCompare()`

* `samples`: A character vector to isolate specific samples by their list element name.
* `clones`: A character vector of specific clonal sequences to visualize. If used, `top.clones` will be ignored.
*` top.clones`: The top n number of clones to graph, calculated based on the frequency within individual samples.
* `highlight.clones`: A character vector of specific clonal sequences to color; all other clones will be greyed out.
* `relabel.clones`: If `TRUE`, simplifies the legend by labeling isolated clones numerically (e.g., "Clone: 1").
* `graph`: The type of plot to generate; `alluvial` (default) or `area`.
* `proportion`: If `TRUE` (default), the y-axis represents proportional abundance; if `FALSE`, it represents raw clone counts.

To compare the top 10 clones between samples "P17B" and "P17L" using amino acid sequences as an alluvial plot:

```{r tidy = FALSE}
clonalCompare(combined.TCR, 
                  top.clones = 10, 
                  samples = c("P17B", "P17L"), 
                  cloneCall="aa", 
                  graph = "alluvial")
```

We can also choose to highlight specific clones, such as in the case of *"CVVSDNTGGFKTIF_CASSVRRERANTGELFF"* and *"NA_CASSVRRERANTGELFF"* using the `highlight.clones` parameter. In addition, we can simplify the plot to label the clones as "Clone: 1", "Clone: 2", etc., by setting `relabel.clones = TRUE`.

```{r tidy = FALSE}
clonalCompare(combined.TCR, 
              top.clones = 10,
              highlight.clones = c("CVVSDNTGGFKTIF_CASSVRRERANTGELFF", "NA_CASSVRRERANTGELFF"),
              relabel.clones = TRUE,
              samples = c("P17B", "P17L"), 
              cloneCall="aa", 
              graph = "alluvial")
```

Alternatively, if we only want to show specific clones, we can use the `clones` parameter.

```{r}
clonalCompare(combined.TCR, 
              clones = c("CVVSDNTGGFKTIF_CASSVRRERANTGELFF", "NA_CASSVRRERANTGELFF"),
              relabel.clones = TRUE,
              samples = c("P17B", "P17L"), 
              cloneCall="aa", 
              graph = "alluvial")
```


## clonalScatter: Scatterplot of Two Variables

clonalScatter() organizes two repertoires, quantifies their relative clone sizes, and produces a scatter plot comparing the two samples. Clones are categorized by counts into singlets or expanded, either exclusively present or shared between the selected samples.

Key Parameter(s) for `clonalScatter()`

* `x.axis`, `y.axis`: Names of the list elements or meta data variable to place on the x-axis and y-axis.
* `dot.size`: Specifies how dot size is determined; `total` (default) displays the total number of clones between the x- and y-axis, or a specific list element name for size calculation.
* `graph`: The type of graph to display; `proportion` for the relative proportion of clones (default) or `count` for the total count of clones by sample.

To compare samples "P18B" and "P18L" based on `gene` clone calls, with dot size representing the total number of clones, and plotting clone proportions:

```{r tidy = FALSE}
clonalScatter(combined.TCR, 
              cloneCall ="gene", 
              x.axis = "P18B", 
              y.axis = "P18L",
              dot.size = "total",
              graph = "proportion")
```

***

# Visualizing Clonal Dynamics

## clonalHomeostasis: Examining Clonal Space

By examining the clonal space, we effectively look at the relative space occupied by clones at specific proportions. Another way to think about this would be to consider the total immune receptor sequencing run as a measuring cup. In this cup, we will fill liquids of different viscosity—or different numbers of clonal proportions. Clonal space homeostasis asks what percentage of the cup is filled by clones in distinct proportions (or liquids of different viscosity, to extend the analogy). The proportional cut points are set under the `cloneSize` variable in the function and can be adjusted.

Default `cloneSize` Bins

* **Rare**: 0 to 0.0001
* **Small**: 0.0001 to 0.001
* **Medium**: 0.001 to 0.01
* **Large**: 0.01 to 0.1
* **Hyperexpanded**: 0.1 to 1

To visualize clonal homeostasis using gene clone calls with default `cloneSize` bins:

```{r tidy = FALSE}
clonalHomeostasis(combined.TCR, 
                  cloneCall = "gene")

```

We can reassign the proportional cut points for `cloneSize`, which can drastically alter the visualization and analysis 

```{r tidy = FALSE}
clonalHomeostasis(combined.TCR, 
                  cloneCall = "gene",
                  cloneSize = c(Rare = 0.001, Small = 0.01, Medium = 0.1, 
                                Large = 0.3, Hyperexpanded = 1))
```

In addition, we can use the `group.by` parameter to look at the relative proportion of clones between groups, such as by tissue type. First, ensure the "Type" variable is added to `combined.TCR`:

```{r tidy = FALSE}
combined.TCR <- addVariable(combined.TCR, 
                            variable.name = "Type", 
                            variables = rep(c("B", "L"), 4))
```

Now, visualize clonal homeostasis grouped by "Type":

```{r tidy = FALSE}
clonalHomeostasis(combined.TCR, 
                  group.by = "Type",
                  cloneCall = "gene")
```

`clonalHomeostasis()` provides an assessment of how different "sizes" of clones (based on their proportional abundance) contribute to the overall repertoire. This visualization helps to identify shifts in repertoire structure, such as expansion of large clones in response to infection or contraction in chronic conditions, offering insights into immune system activity and health.

## clonalProportion: Examining Space Occupied by Ranks of Clones

Like clonal space homeostasis, `clonalProportion()` also categorizes clones into separate bins. The key difference is that instead of looking at the relative proportion of the clone to the total, `clonalProportion()` ranks the clones by their total count or frequency of occurrence and then places them into predefined bins.

The `clonalSplit` parameter represents the ranking of clonotypes. For example, 1:10 refers to the top 10 clonotypes in each sample. The default bins are set under the `clonalSplit` variable and can be adjusted. 

Default `clonalSplit` Bins

* **10** (top 1-10 clones)
* **100**: (top 11-100 clones)
* **1000**: (top 101-1000 clones)
* **10000**: (top 1001-10000 clones)
* **30000**: (top 10001-30000 clones)
* **100000**: (top 30001-100000 clones)
 
To visualize the clonal proportion using `gene` clone calls with default `clonalSplit` bins:

```{r tidy = FALSE}
clonalProportion(combined.TCR, 
                 cloneCall = "gene") 
```

To visualize clonal proportion using `nt` (nucleotide) clone calls with custom `clonalSplit` bins:

```{r}
clonalProportion(combined.TCR, 
                 cloneCall = "nt",
                 clonalSplit = c(1, 5, 10, 100, 1000, 10000)) 
```

`clonalProportion()` complements `clonalHomeostasis()` by providing a perspective on how the "richest" (most abundant) clones contribute to the overall repertoire space. By segmenting clones into rank-based bins, it helps identify whether a few highly expanded clones or a larger number of moderately expanded clones dominate the immune response, offering distinct insights into repertoire structure and dynamics.

***

# Summarizing Repertoires

## percentGeneUsage

Gene quantification and visualization has been redesigned to offer a more robust and translatable function under `percentGeneUsage()`. We have maintained the functionality of the previous functions for gene-level quantification under the aliases `vizGenes()`, `percentGenes()`, and `percentVJ()`.

### vizGenes: Flexible Gene Usage Visualization

The `vizGenes()` function offers a highly adaptable approach to visualizing the relative usage of TCR or BCR genes. It acts as a versatile alias for `percentGeneUsage()`, allowing for comparisons across different chains, scaling of values, and selection between bar charts and heatmaps.

* `x.axis`: Specifies the gene segment to display along the x-axis (e.g., "TRBV", "TRBD", "IGKJ").
* `y.axis`
  * Another gene segment (e.g., "TRAV", "TRBJ") for paired gene analysis. When x.axis and y.axis are both gene segments, vizGenes() internally calls percentGeneUsage() with genes = c(x.axis, y.axis), resulting in a heatmap.
  * A categorical variable (e.g., "sample", "orig.ident") to visualize gene usage across different groups. When y.axis is a categorical variable, vizGenes() maps it to the group.by parameter of percentGeneUsage(), creating facets for each category.
* `plot`: Determines the visualization type:
  * "barplot": Ideal for visualizing the distribution of a single gene segment.
  * "heatmap": Suitable for single gene usage (with a group.by or y.axis categorical variable) or for paired gene analysis.
* `summary.fun`: (Inherited from `percentGeneUsage`) Defines the statistic to display: "percent" (default), "proportion", or "count". This implicitly handles the scaling of values.

```{r tidy = FALSE}
vizGenes(combined.TCR,
         x.axis = "TRBV",
         y.axis = NULL, # No specific y-axis variable, will group all samples
         plot = "barplot",
         summary.fun = "proportion") 
```

This plot shows the proportion of each *TRBV* gene segment observed across the entire combined.TCR dataset. Since `y.axis` is NULL, samples are grouped by the list element of the combined.TCR data.

`vizGenes()` is particularly useful for examining gene pairings. Let's look at the differences in *TRBV* and *TRBJ* usage between peripheral blood and lung samples from your dataset. We'll subset combined.TCR for this analysis.

```{r tidy = FALSE}
# Peripheral Blood Samples
vizGenes(combined.TCR[c("P17B", "P18B", "P19B", "P20B")],
         x.axis = "TRBV",
         y.axis = "TRBJ",
         plot = "heatmap",
         summary.fun = "percent") # Display percentages

# Lung Samples
vizGenes(combined.TCR[c("P17L", "P18L", "P19L", "P20L")],
         x.axis = "TRBV",
         y.axis = "TRBJ",
         plot = "heatmap",
         summary.fun = "percent") # Display percentages
```

In these examples, by providing both `x.axis` and `y.axis` as gene segments ("TRBV" and "TRBJ"), `vizGenes()` automatically performs a paired gene analysis, generating a heatmap where the intensity reflects the percentage of each V-J pairing.

Beyond V-J pairings within a single chain, `vizGenes()` can also visualize gene usage across different chains. For instance, to examine *TRBV* and *TRAV* pairings for patient P17's samples:

```{r tidy = FALSE}
vizGenes(combined.TCR[c("P17B", "P17L")],
         x.axis = "TRBV",
         y.axis = "TRAV",
         plot = "heatmap",
         summary.fun = "count") 
```

### percentGenes: Quantifying Single Gene Usage

The `percentGenes()` function is a specialized alias for `percentGeneUsage()` designed to quantify the usage of a single V, D, or J gene locus for a specified immune receptor chain. By default, it returns a heatmap visualization.

Key Parameters for `percentGenes()`:

* `chain`: Specifies the immune receptor chain (e.g., "TRB", "TRA", "IGH", "IGL"). 
* `gene`: Indicates the gene locus to quantify: "Vgene", "Dgene", or "Jgene".
* `group.by`, `order.by`, `summary.fun`, `exportTable`, `palette`: These parameters are directly passed to `percentGeneUsage()`.

To quantify and visualize the percentage of TRBV gene usage across your samples:

```{r}
percentGenes(combined.TCR,
             chain = "TRB",
             gene = "Vgene",
             summary.fun = "percent")
```

This generates a heatmap showing the percentage of each TRBV gene segment within each sample, allowing for easy visual comparison of gene usage profiles across your samples.

The raw data returned by `percentGenes()` (when `exportTable` = TRUE) can be a powerful input for further downstream analysis, such as dimensionality reduction techniques like Principal Component Analysis. This allows you to summarize the complex gene usage patterns and identify samples with similar or distinct repertoires.

```{r}
df.genes <- percentGenes(combined.TCR,
                         chain = "TRB",
                         gene = "Vgene",
                         exportTable = TRUE,
                         summary.fun = "proportion") 

# Performing PCA on the gene usage matrix
pc <- prcomp(t(df.genes) )

# Getting data frame to plot from
df_plot <- as.data.frame(cbind(pc$x[,1:2], colnames(df.genes)))
colnames(df_plot) <- c("PC1", "PC2", "Sample")
df_plot$PC1 <- as.numeric(df_plot$PC1)
df_plot$PC2 <- as.numeric(df_plot$PC2)

ggplot(df_plot, aes(x = PC1, y = PC2)) +
  geom_point(aes(fill = Sample), shape = 21, size = 5) +
  guides(fill=guide_legend(title="Samples")) +
  scale_fill_manual(values = hcl.colors(nrow(df_plot), "inferno")) +
  theme_classic() +
  labs(title = "PCA of TRBV Gene Usage")
```

This PCA plot visually clusters samples based on their *TRBV* gene usage profiles, helping to identify underlying patterns or relationships between samples.

### percentVJ: Quantifying V-J Gene Pairings

The `percentVJ()` function is another specialized alias for `percentGeneUsage()`, specifically designed to quantify the proportion or percentage of V and J gene segments paired together within individual repertoires. It always produces a heatmap visualization.

Key Parameters for `percenVJ()`:

* `chain`: Specifies the immune receptor chain (e.g., **"TRB"**, **"TRA"**, **"IGH"**, **"IGL"**). This dictates which V and J gene segments are analyzed (e.g., **TRBV** and **TRBJ** for `chain` = "TRB")

```{r}
percentVJ(combined.TCR[1:2],
          chain = "TRB",
          summary.fun = "percent")
```

This heatmap displays the percentage of each *TRBV-TRBJ* pairing for each sample, providing a detailed view of the V-J recombination landscape.

Similar to `percentGenes()`, the quantitative output of `percentVJ()` can be used for dimensionality reduction to summarize V-J pairing patterns across samples.

```{r}
df.vj <- percentVJ(combined.TCR,
                   chain = "TRB",
                   exportTable = TRUE,
                   summary.fun = "proportion") # Export proportions for PCA

# Performing PCA on the V-J pairing matrix
pc.vj <- prcomp(t(df.vj))

# Getting data frame to plot from
df_plot_vj <- as.data.frame(cbind(pc.vj$x[,1:2], colnames(df.vj)))
colnames(df_plot_vj) <- c("PC1", "PC2", "Sample")
df_plot_vj$PC1 <- as.numeric(df_plot_vj$PC1)
df_plot_vj$PC2 <- as.numeric(df_plot_vj$PC2)

# Plotting the PCA results
ggplot(df_plot_vj, aes(x = PC1, y = PC2)) +
  geom_point(aes(fill = Sample), shape = 21, size = 5) +
  guides(fill=guide_legend(title="Samples")) +
  scale_fill_manual(values = hcl.colors(nrow(df_plot_vj), "inferno")) +
  theme_classic() +
  labs(title = "PCA of TRBV-TRBJ Gene Pairings")
```

## percentAA: Amino Acid Composition by Residue

Quantify the proportion of amino acids along the CDR3 sequence with `percentAA()`. By default, the function will pad the sequences with NAs up to the maximum of `aa.length`. Sequences longer than aa.length will be removed before visualization (default `aa.length` = 20). 

Key Parameter(s) for `percentAA()`

* `aa.length`: The maximum length of the CDR3 amino acid sequence to consider.

To visualize the relative percentage of amino acids at each position of the CDR3 sequences for the TRB chain, up to a length of 20 amino acids:

```{r, message = FALSE, tidy = FALSE}
percentAA(combined.TCR, 
          chain = "TRB", 
          aa.length = 20)
```

This plot displays the relative proportion of each amino acid at different positions across the CDR3 sequence for the specified chain. It provides insights into the amino acid composition and variability at each residue, which can be indicative of functional constraints or selection pressures on the CDR3

## positionalEntropy: Entropy across CDR3 Sequences

We can also quantify the level of entropy/diversity across amino acid residues along the CDR3 sequence. `positionalEntropy()` combines the quantification by residue of `percentAA()` with diversity calculations. Positions without variance will have a value reported as 0 for the purposes of comparison.

Key Parameter(s) for `positionalEntropy()`

* `method`  
  * `shannon` - Shannon Index
  * `inv.simpson` - Inverse Simpson Index
  * `gini.simpson` - Gini-Simpson Index
  * `norm.entropy` - Normalized Entropy 
  * `pielou` - Pielou's Evenness
  * `hill1`, `hill2`, `hill3` - Hill Numbers 
  
To visualize the normalized entropy across amino acid residues for the TRB chain, up to a length of 20 amino acids:

```{r tidy = FALSE}
positionalEntropy(combined.TCR, 
                  chain = "TRB", 
                  aa.length = 20)
```

The plot generated by `positionalEntropy()` illustrates the diversity or entropy at each amino acid position within the CDR3 sequence. Higher entropy values indicate greater variability in amino acid usage at that position, suggesting less selective pressure or more promiscuous binding, while lower values suggest conserved positions critical for structural integrity or antigen recognition.

## positionalProperty: Amino Acid Properties across CDR3 Sequence

Like ```positionalEntropy()```, we can also examine a series of amino acid properties along the CDR3 sequences using ```positionalProperty()```. Important differences for ```positionalProperty()``` is dropping NA values as they would void the mean calculation and displaying a ribbon with the 95% confidence interval surrounding the mean value for the selected properties.

Key Parameter(s) for `positionalEntropy()`

* `method`
  * `atchleyFactors`: [citation](https://pubmed.ncbi.nlm.nih.gov/15851683/)
  * `crucianiProperties`: [citation](https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/cem.856)
  * `FASGAI`: [citation](https://pubmed.ncbi.nlm.nih.gov/18318694/)
  * `kideraFactors`: [citation](https://link.springer.com/article/10.1007/BF01025492)
  * `MSWHIM`: [citation](https://pubs.acs.org/doi/10.1021/ci980211b)
  * `ProtFP`: [citation](https://pubmed.ncbi.nlm.nih.gov/24059694/)
  *  `stScales`: [citation](https://pubmed.ncbi.nlm.nih.gov/19373543/)
  *  `tScales`: [citation](https://www.sciencedirect.com/science/article/abs/pii/S0022286006006314)
  *  `VHSE`: [citation](https://pubmed.ncbi.nlm.nih.gov/15895431/)
  *  `zScales`: [citation](https://pubmed.ncbi.nlm.nih.gov/9651153/)
  
To examine the Atchley factors of amino acids across the CDR3 sequence for the first two samples:

```{r tidy = FALSE}
positionalProperty(combined.TCR[c(1,2)], 
                  chain = "TRB", 
                  aa.length = 20, 
                  method = "atchleyFactors") + 
  scale_color_manual(values = hcl.colors(5, "inferno")[c(2,4)])
```

This function provides a detailed view of how physicochemical properties of amino acids change along the CDR3 sequence. By visualizing the mean property value and its confidence interval, one can identify positions with distinct characteristics or significant variations between groups, offering insights into structural and functional aspects of the CDR3.

## percentKmer: Motif Quantification 

Another quantification of the composition of the CDR3 sequence is to define motifs by sliding across the amino acid or nucleotide sequences at set intervals, resulting in substrings or kmers.

Key Parameter(s) for `percentKmer()`:

* `cloneCall`: Defines the clonal sequence grouping; only accepts `aa` (amino acids) or `nt` (nucleotides)
* `motif.length`: The length of the kmer to analyze.
* `top.motifs`: Displays the n most variable motifs as a function of median absolute deviation.

To visualize the percentage of the top 25 most variable 3-mer amino acid motifs for the TRB chain:

```{r tidy = FALSE}
percentKmer(combined.TCR, 
            cloneCall = "aa",
            chain = "TRB", 
            motif.length = 3, 
            top.motifs = 25)
```

To perform the same analysis but for nucleotide motifs:

```{r}
percentKmer(combined.TCR, 
            cloneCall = "nt",
            chain = "TRB", 
            motif.length = 3, 
            top.motifs = 25)
```

The heatmaps generated by `percentKmer()` illustrate the relative composition of frequently occurring k-mer motifs across samples or groups. This analysis can highlight recurrent sequence patterns in the CDR3, which may be associated with specific antigen recognition, disease states, or processing mechanisms. By examining the most variable motifs, researchers can identify key sequence features that differentiate immune repertoires.

***

# Comparing Clonal Diversity and Overlap

## clonalDiversity: Clonal Diversity Quantification

Diversity can be measured for samples or by other variables. `clonalDiversity()` calculates a specified diversity metric for groups within a dataset. To control for variations in library size, the function can perform bootstrapping with downsampling. It resamples each group to the size of the smallest group and calculates the diversity metric across multiple iterations, returning the mean values.

### How `clonalDiversity()` Handles Downsampling and Bootstrapping

* It determines the number of clones in the smallest group.
* For each group, it performs `n.boots` iterations (default = 100).
* In each iteration, it randomly samples the clones (with replacement) down to the size of the smallest group.
* It calculates the selected diversity metric on this downsampled set.
* The final reported diversity value is the mean of the results from all bootstrap iterations.
* This process can be disabled by setting `skip.boots = TRUE`.

### Available Diversity Metrics (`metric`)

| **Metric** | **What it measures** | **Typical scale** | **Higher value indicates…** |
| :------------- | :---------------------------------------------------------------------------------------------------------------- | :---------------- | :--------------------------------------------------------------------------------- |
| `shannon`      | Joint *richness + evenness* of clonotypes; moderately sensitive to rare clones (entropy of the frequency distribution). | 0 → ∞             | **More distinct clonotypes** *and* a more even clone-size distribution.            |
| `inv.simpson`  | Evenness-weighted diversity (inverse Simpson’s D); heavily penalises dominance by a few large clones.             | 1 → ∞             | **Few highly dominant clones**; diversity spread across many mid-abundance clones. |
| `norm.entropy` | Shannon entropy divided by ln *S* (where *S* = no. clonotypes); removes library-size dependence.                   | 0 → 1             | Same interpretation as `shannon`, but **comparable across samples of different depths**. |
| `gini.simpson` | 1 – Simpson’s D; probability that two reads drawn at random come from **different** clonotypes.                     | 0 → 1             | **Greater heterogeneity** (higher chance the two reads represent distinct clones). |
| `chao1`        | Estimated *richness only* (number of clonotypes), correcting for unseen singletons/doubletons.                    | 0 → ∞             | **More unique clonotypes**, regardless of their relative abundances.               |
| `ACE`          | Abundance-based Coverage Estimator—alternative richness estimator robust when many clones are rare.               | 0 → ∞             | **More unique clonotypes**, especially when the repertoire contains many low-frequency clones. |
| `gini`         | Inequality of clone size distribution (Gini Coefficient); measures how unevenly clone sizes are distributed.      | 0 → 1             | **Greater inequality**; a few highly abundant clones dominate the repertoire.      |
| `d50`          | Repertoire dominance; how many of the top clones are needed to constitute 50% of the total library.               | 1 → ∞             | **Less dominance** by top clones; the repertoire is more evenly distributed.       |
| `hill0`        | Richness (Hill number q=0); the total number of unique clonotypes observed in the sample.                         | 0 → ∞             | **More unique clonotypes** (greater raw richness).                                 |
| `hill1`        | Effective number of abundant clonotypes (Hill number q=1); the exponential of Shannon entropy.                    | 1 → ∞             | **Higher diversity** of common clonotypes, balancing richness and evenness.        |
| `hill2`        | Effective number of dominant clonotypes (Hill number q=2); equivalent to the inverse Simpson index.              | 1 → ∞             | **Higher diversity** among the most dominant clonotypes in the repertoire.       |


Key Parameters for `clonalDiversity()`

* `metric`: The diversity metric to calculate.
* `x.axis`: An additional metadata variable to group samples along the x-axis.
* `return.boots`: If `TRUE`, returns all bootstrap values instead of the mean.
* `skip.boots`: If `TRUE`, disables downsampling and bootstrapping.
* `n.boots`: The number of bootstrap iterations to perform (default is 100).

To calculate Shannon diversity, grouped by sample:

```{r tidy = FALSE}
clonalDiversity(combined.TCR, 
                cloneCall = "gene")
```

There are two options for grouping in `clonalDiversity()`: `group.by` and `x.axis`.

* `group.by`: Reorganizes the clone information into new groups that the calculation will be based on.
* `x.axis`: Keeps the organization of the clone information the same, but plots along the x-axis for improved visibility or grouping.

First, add a "Patient" variable to combined.TCR

```{r tidy = FALSE}
combined.TCR <- addVariable(combined.TCR, 
                            variable.name = "Patient", 
                             variables = c("P17", "P17", "P18", "P18", 
"P19","P19", "P20", "P20"))
```

Now, calculate Inverse Simpson diversity, grouped by "Patient":

```{r tidy = FALSE}
clonalDiversity(combined.TCR, 
                cloneCall = "gene", 
                group.by = "Patient", 
                metric = "inv.simpson")
```

Calculate Inverse Simpson diversity with "Patient" on the x-axis, keeping the original grouping:

```{r tidy = FALSE}
clonalDiversity(combined.TCR, 
                cloneCall = "gene", 
                x.axis = "Patient", 
                metric = "inv.simpson")
```

`clonalDiversity()` functions in quantifying and comparing the diversity of immune repertoires across different samples or experimental conditions. By implementing bootstrapping and offering a wide range of diversity metrics, it provides robust and comparable measures of clonal richness and evenness, which are fundamental for understanding immune responses and disease states

## clonalRarefaction: Sampling-based Extrapolation

`clonalRarefaction()` uses Hill numbers to estimate rarefaction, or estimating species richness, based on the abundance of clones across groupings. The underlying rarefaction calculation uses the observed receptor abundance to compute diversity. This function relies on the `iNEXT` R package.

### Hill Numbers and Their Interpretation

* `0`: Species richness
* `1`: Shannon Diversity
* `2`: Simpson Diversity

### plot.type Options

* `1`: Sample-size-based rarefaction/extrapolation curve
* `2`: Sample completeness curve
* `3`: Coverage-based rarefaction/extrapolation curve

Key Parameter(s) for `clonalRarefaction()`

* `plot.type`: The type of plot to generate.
* `hill.numbers`: The Hill numbers to be plotted (0, 1, or 2).
* `n.boots`: The number of bootstrap replicates used to derive confidence intervals (default is 20).

This function relies on the [iNEXT](https://cran.r-project.org/web/packages/iNEXT/index.html) with the accompanying [manuscript](http://chao.stat.nthu.edu.tw/wordpress/paper/120_pdf_appendix.pdf). Like the other wrapping functions in scRepertoire, please cite the original work. The sample completeness curve (**plot.type** = 2), may not show full sample coverage due to the size/diversity of the input data.

### Rarefaction using Species Richness (q = 0)

```{r, message=FALSE, tidy = FALSE}
clonalRarefaction(combined.TCR,
                  plot.type = 1,
                  hill.numbers = 0,
                  n.boots = 2)

clonalRarefaction(combined.TCR,
                  plot.type = 2,
                  hill.numbers = 0,
                  n.boots = 2)

clonalRarefaction(combined.TCR,
                  plot.type = 3,
                  hill.numbers = 0,
                  n.boots = 2)
```

### Rarefaction using Shannon Diversity (q = 1)

```{r tidy = FALSE}
clonalRarefaction(combined.TCR,
                  plot.type = 1,
                  hill.numbers = 1,
                  n.boots = 2)

clonalRarefaction(combined.TCR,
                  plot.type = 2,
                  hill.numbers = 1,
                  n.boots = 2)

clonalRarefaction(combined.TCR,
                  plot.type = 3,
                  hill.numbers = 1,
                  n.boots = 2)
```

`clonalRarefaction()` provides robust estimates of immune repertoire diversity, allowing for comparison of richness and effective diversity across samples, even with varying sequencing depths. By visualizing rarefaction and extrapolation curves, researchers can assess the completeness of their sampling and make fair comparisons of diversity, which is essential for understanding immune complexity.

## clonalSizeDistribution: Modeling Clonal Composition

Another method for modeling the repertoire distribution is a discrete gamma-GPD spliced threshold model, proposed by [Koch et al.](https://pubmed.ncbi.nlm.nih.gov/30485278/) The spliced model models the repertoire and allows for the application of a power law distribution for larger clonal-expanded sequences and a Poisson distribution for smaller clones. After fitting the models, repertoires can be compared using Euclidean distance. If using this function, please read/cite [Koch et al.](https://pubmed.ncbi.nlm.nih.gov/30485278/) and check out the [powerTCR](https://bioconductor.org/packages/release/bioc/html/powerTCR.html) R package.

Key Parameter(s) for ```clonalSizeDistribution()```

* `method`: The agglomeration method for hierarchical clustering (e.g., "ward.D2").

To model the clonal size distribution using amino acid clone calls and hierarchical clustering with the "ward.D2" method:

```{r tidy = FALSE}
clonalSizeDistribution(combined.TCR, 
                       cloneCall = "aa", 
                       method= "ward.D2")
```

`clonalSizeDistribution()` offers a sophisticated approach to modeling immune repertoire composition, distinguishing between the distribution of rare and expanded clones. By applying a spliced statistical model, it provides a more accurate representation of the repertoire's underlying clonal architecture, enabling robust comparisons and a deeper understanding of immune system dynamics.

## clonalOverlap: Exploring Sequence Overlap

If you are interested in measures of similarity between the samples loaded into scRepertoire, using `clonalOverlap()` can assist in the visualization. The underlying `clonalOverlap()` calculation varies by the `method` parameter.

### `method` Parameters for `clonalOverlap()`

* `overlap` - Overlap coefficient 
* `morisita` - Morisita's overlap index
* `jaccard` - Jaccard index
* `cosine` - Cosine similarity
* `raw` - Exact number of overlapping clones

To calculate and visualize the Morisita overlap index using strict clone calls:

```{r tidy = FALSE}
clonalOverlap(combined.TCR, 
              cloneCall = "strict", 
              method = "morisita")
```

To calculate and visualize the raw number of overlapping clones using strict clone calls:

```{r tidy = FALSE}
clonalOverlap(combined.TCR, 
              cloneCall = "strict", 
              method = "raw")
```

`clonalOverlap()` is a tool for assessing the similarity and shared clonotypes between different immune repertoires. By offering various quantitative methods and a clear heatmap visualization, it allows researchers to identify the degree of overlap, providing insights into shared immune responses, cross-reactivity, or the impact of different treatments on clonal composition.

***

# Combining Clones and Single-Cell Objects

The data in the scRepertoire package is derived from a [study](https://pubmed.ncbi.nlm.nih.gov/33622974/) of acute respiratory stress disorder in the context of bacterial and COVID-19 infections. The internal single cell data (`scRep_example()`) built in to scRepertoire is randomly sampled 500 cells from the fully integrated Seurat object to minimize the package size. We will use both Seurat and Single-Cell Experiment (SCE) with scater to perform further visualizations in tandem. 

## Preprocessed Single-Cell Object

```{r tidy = FALSE}
scRep_example <- get(data("scRep_example"))

#Making a Single-Cell Experiment object
sce <- Seurat::as.SingleCellExperiment(scRep_example)

#Adding patient information
scRep_example$Patient <- substr(scRep_example$orig.ident, 1,3)

#Adding type information
scRep_example$Type <- substr(scRep_example$orig.ident, 4,4)
```

## Note on Dimensional Reduction

In single-cell RNA sequencing workflows, dimensional reduction is typically performed by first identifying highly variable features. These features are then used directly for UMAP/tSNE projection or as inputs for principal component analysis. The same approach is commonly applied to clustering as well.

However, in immune-focused datasets, VDJ genes from TCR and BCR are often among the most variable genes. This variability arises naturally due to clonal expansion and diversity within lymphocytes. As a result, UMAP projections and clustering outcomes may be influenced by clonal information rather than broader transcriptional differences across cell types.

To mitigate this issue, a common strategy is to exclude VDJ genes from the set of highly variable features before proceeding with clustering and dimensional reduction. We introduce a set of functions that facilitate this process by removing VDJ-related genes from either a Seurat Object or a vector of gene names (useful for SCE-based workflows).

### Functions to Exclude VDJ Genes

* `quietVDJgenes()` – Removes both TCR and BCR VDJ genes.
* `quietTCRgenes()` – Removes only TCR VDJ genes.
* `quietBCRgenes()` – Removes only BCR VDJ genes, but retains BCR VDJ pseudogenes in the variable features.

Let's first check the top 10 variable features in the scRep_example Seurat object before any removal:

```{r tidy = FALSE}
# Check the first 10 variable features before removal
VariableFeatures(scRep_example)[1:10]
```

Now, we'll remove TCR VDJ genes from the scRep_example object:

```{r tidy = FALSE}
# Remove TCR VDJ genes
scRep_example <- quietTCRgenes(scRep_example)

# Check the first 10 variable features after removal
VariableFeatures(scRep_example)[1:10]
```

By applying these functions, you can ensure that clustering and dimensional reduction are driven by broader transcriptomic differences across cell types rather than being skewed by the inherent variability due to clonal expansion. This provides a more accurate representation of cellular heterogeneity independent of clonal lineage.

## Preprocessed Single-Cell Object

The data in the `scRepertoire` package is derived from a [study](https://pubmed.ncbi.nlm.nih.gov/33622974/) of acute respiratory stress disorder in the context of bacterial and COVID-19 infections. The internal single cell data (`scRep_example()`) built in to scRepertoire is randomly sampled 500 cells from the fully integrated Seurat object to minimize the package size. However, for the purpose of the vignette we will use the full single-cell object with 30,000 cells. We will use both Seurat and Single-Cell Experiment (SCE) with `scater` to perform further visualizations in tandem. 

## combineExpression

After processing the contig data into clones via `combineBCR()` or `combineTCR()`, we can add the clonal information to the single-cell object using `combineExpression()`. 

**Importantly**, the major requirement for the attachment is matching contig cell barcodes and barcodes in the row names of the metadata of the Seurat or Single-Cell Experiment object. If these do not match, the attachment will fail. We suggest making changes to the single-cell object barcodes for ease of use.

### Calculating `cloneSize`

Part of `combineExpression()` is calculating the clonal frequency and proportion, placing each clone into groups called `cloneSize`. The default `cloneSize` argument uses the following bins: `c(Rare = 1e-4, Small = 0.001, Medium = 0.01, Large = 0.1, Hyperexpanded = 1)`, which can be modified to include more/less bins or different names.

Clonal frequency and proportion are dependent on the repertoires being compared. You can modify the calculation using the `group.by` parameter, such as grouping by a Patient variable. If `group.by` is not set, `combineExpression()` will calculate clonal frequency, proportion, and `cloneSize` as a function of individual sequencing runs. Additionally, `cloneSize` can use the frequency of clones when `proportion = FALSE`. 

Key Parameter(s) for `combineExpression()`

* `input.data`: The product of `combineTCR()`, `combineBCR()`, or a list containing both.
* `sc.data`: The Seurat or Single-Cell Experiment (SCE) object to attach the clonal data to.
* `proportion`: If `TRUE` (default), calculates the proportion of the clone; if `FALSE`, calculates total frequency.
* `cloneSize`: Bins for grouping based on proportion or frequency. If proportion is `FALSE` and cloneSize bins are not set high enough, the upper limit will automatically adjust.
* `filterNA`: Method to subset the Seurat/SCE object of barcodes without clone information.
* `addLabel`: Adds a label to the frequency header, useful for trying multiple group.by variables or recalculating frequencies after subsetting.

We can look at the default cloneSize groupings using the Single-Cell Experiment object we just created, with group.by set to the sample variable used in combineTCR():

```{r tidy = FALSE}
sce <- combineExpression(combined.TCR, 
                         sce, 
                         cloneCall="gene", 
                         group.by = "sample", 
                         proportion = TRUE)

#Define color palette 
colorblind_vector <- hcl.colors(n=7, palette = "inferno", fixup = TRUE)

plotUMAP(sce, colour_by = "cloneSize") +
    scale_color_manual(values=rev(colorblind_vector[c(1,3,5,7)]))
```

Alternatively, if we want `cloneSize` to be based on the frequency of the clone, we can set proportion = FALSE and will need to change the `cloneSize` bins to integers. If we haven't inspected our clone data, setting the upper limit of the clonal frequency might be difficult; `combineExpression()` will automatically adjust the upper limit to fit the distribution of the frequencies.

```{r tidy = FALSE}
scRep_example <- combineExpression(combined.TCR, 
                                   scRep_example, 
                                   cloneCall="gene", 
                                   group.by = "sample", 
                                   proportion = FALSE, 
                                   cloneSize=c(Single=1, Small=5, Medium=20, Large=100, Hyperexpanded=500))

# Bug in Seurat 5.3.1 no longer handles NA for DimPlot
# Will Update in Future Commits

# Seurat::DimPlot(scRep_example, group.by = "cloneSize") +
#    scale_color_manual(values=rev(colorblind_vector[c(1,3,4,5,7)]))
```

### Combining both TCR and BCR 

If you have both TCR and BCR enrichment data, or wish to include information for both gamma-delta and alpha-beta T cells, you can create a single list containing the outputs of `combineTCR()` and `combineBCR()` and then use `combineExpression()`. 

**Major Note**: If there are duplicate barcodes (e.g., if a cell has both Ig and TCR information), the immune receptor information will not be added for those cells. It might be worth checking cluster identities and removing incongruent barcodes in the products of `combineTCR()` and `combineBCR()`. As an anecdote, the  [testing data](https://support.10xgenomics.com/single-cell-vdj/datasets/6.0.1/SC5v2_Melanoma_5Kcells_Connect_single_channel_SC5v2_Melanoma_5Kcells_Connect_single_channel) used to improve this function showed 5-6% barcode overlap.

```{r, eval=FALSE, tidy = FALSE}
#This is an example of the process, which will not be evaluated during knit
TCR <- combineTCR(...)
BCR <- combineBCR(...)
list.receptors <- c(TCR, BCR)


seurat <- combineExpression(list.receptors, 
                            seurat, 
                            cloneCall="gene", 
                            proportion = TRUE)
```

`combineExpression()` is a core function in `scRepertoire` that bridges the immune repertoire data with single-cell gene expression data. It enriches your Seurat or SCE object with crucial clonal information, including calculated frequencies and proportions, allowing for integrated analysis of cellular identity and clonal dynamics. Its flexibility in defining `cloneSize` and handling various grouping scenarios makes it adaptable to diverse experimental designs, even accommodating the integration of both TCR and BCR data.

*** 

# Visualizations for Single-Cell Objects

## clonalOverlay 

Using the dimensional reduction graphs as a reference, `clonalOverlay()` generates an overlay of the positions of clonally-expanded cells. It highlights areas of high clonal frequency or proportion on your UMAP or tSNE plots.

Key Parameters for clonalOverlay()

* `sc.data`: The single-cell object after` combineExpression()`.
* `reduction`: The dimensional reduction to visualize (e.g., "umap", "pca"). Default is "pca".
* `cut.category`: The metadata variable to use for filtering the overlay (e.g., "clonalFrequency" or "clonalProportion").
* `cutpoint`: The lowest clonal frequency or proportion to include in the contour plot.
* `bins`: The number of contours to draw.

`clonalOverlay()` can be used to look across all cells or faceted by a metadata variable using `facet.by`. The overall dimensional reduction will be maintained as we facet, while the contour plots will adjust based on the `facet.by` variable. The coloring of the dot plot is based on the active identity of the single-cell object.

This visualization was authored by Dr. Francesco Mazziotta and inspired by Drs. Carmona and Andreatta and their work with [ProjectTIL](https://github.com/carmonalab/ProjecTILs), a pipeline for annotating T cell subtypes.

```{r tidy = FALSE}
clonalOverlay(scRep_example, 
              reduction = "umap", 
              cutpoint = 1, 
              bins = 10, 
              facet.by = "Patient") + 
              guides(color = "none")
```

## clonalNetwork 

Similar to `clonalOverlay()`, `clonalNetwork()` visualizes the network interaction of clones shared between clusters along the single-cell dimensional reduction. This function shows the relative proportion of clones flowing from a starting node, with the ending node indicated by an arrow

### Filtering Options for clonalNetwork()

* `filter.clones`:
  * Select a number to isolate the clones comprising the top n number of cells (e.g., `filter.clones = 2000`).
  * Select `min` to scale all groups to the size of the minimum group.
* `filter.identity`: For the identity chosen to visualize, show the "to" and "from" network connections for a single group.
* `filter.proportion`: Remove clones from the network that comprise less than a certain proportion of clones in groups.
* `filter.graph`: Remove reciprocal edges from one half of the graph, allowing for cleaner visualization.
                          
Now, visualize the clonal network with no specific identity filter:

```{r tidy = FALSE}
#ggraph needs to be loaded due to issues with ggplot
library(ggraph)

#No Identity filter
clonalNetwork(scRep_example, 
              reduction = "umap", 
              group.by = "seurat_clusters",
              filter.clones = NULL,
              filter.identity = NULL,
              cloneCall = "aa")
```

We can look at the clonal relationships relative to a single cluster using the `filter.identity` parameter. For example, focusing on Cluster 3:

```{r, tidy = FALSE}
#Examining Cluster 3 only
clonalNetwork(scRep_example, 
              reduction = "umap", 
              group.by = "seurat_clusters",
              filter.identity = 3,
              cloneCall = "aa")
```

You can also use the `exportClones` parameter to quickly get clones that are shared across multiple identity groups, along with the total number of clone copies in the dataset.

```{r tidy = FALSE}
shared.clones <- clonalNetwork(scRep_example, 
                               reduction = "umap", 
                               group.by = "seurat_clusters",
                               cloneCall = "aa", 
                               exportClones = TRUE)
head(shared.clones)
```

## highlightClones

The `highlightClones()` function allows you to specifically visualize the distribution of particular clonal sequences on your single-cell dimensional reduction plots. This helps in tracking the location and expansion of clones of interest.

Key Parameters for `highlightClones()`

* `cloneCall`: The type of sequence to use for highlighting (e.g., "aa", "nt", "strict").
* `sequence`: A character vector of the specific clonal sequences to highlight.

To highlight the most prominent amino acid sequences: *CAERGSGGSYIPTF_CASSDPSGRQGPRWDTQYF* and *CARKVRDSSYKLIF_CASSDSGYNEQFF*:

```{r tidy = FALSE}
scRep_example <- highlightClones(scRep_example, 
                    cloneCall= "aa", 
                    sequence = c("CAERGSGGSYIPTF_CASSDPSGRQGPRWDTQYF", 
                                 "CARKVRDSSYKLIF_CASSDSGYNEQFF"))

# Bug in Seurat 5.3.1 no longer handles NA for DimPlot
# Will Update in Future Commits

# Seurat::DimPlot(scRep_example, group.by = "highlight") + 
#   guides(color=guide_legend(nrow=3,byrow=TRUE)) + 
#   ggplot2::theme(plot.title = element_blank(), 
#                 legend.position = "bottom")
```

## clonalOccupy

`clonalOccupy()` visualizes the count of cells by cluster, categorized into specific clonal frequency ranges. It uses the cloneSize metadata variable (generated by `combineExpression()`) to plot the number of cells within each clonal expansion designation, using a secondary variable like cluster. Credit for the idea goes to Drs. Carmona and Andreatta.

Key Parameters for `clonalOccupy()`
* `x.axis`: The variable in the metadata to graph along the x-axis (e.g., "seurat_clusters", "ident").
* `label`: If TRUE, includes the number of clones in each category by x.axis variable.
* `proportion`: If TRUE, converts the stacked bars into relative proportions.
* `na.include`: If TRUE, visualizes NA values.

To visualize the count of cells by `seurat_clusters` based on `cloneSize` groupings:

```{r tidy = FALSE}
clonalOccupy(scRep_example, 
              x.axis = "seurat_clusters")
```

To visualize the proportion of cells by `ident` (active identity), without labels:

```{r}
clonalOccupy(scRep_example, 
             x.axis = "ident", 
             proportion = TRUE, 
             label = FALSE)
```

## alluvialClones

After the metadata has been modified with clonal information, `alluvialClones()` allows you to look at clones across multiple categorical variables, enabling the examination of the interchange between these variables. Because this function produces a graph with each clone arranged by called stratification, it may take some time depending on the size of the repertoire.  

Key Parameters for `alluvialClones()`

* `y.axes`: The columns that will separate the proportional visualizations.
* `color`: The column header or clone(s) to be highlighted.
* `facet`: The column label to separate facets.
* `alpha`: The column header to have gradated opacity.

To visualize clonal flow across "Patient", "ident", and "Type", highlighting specific amino acid clones:

```{r tidy = FALSE}
alluvialClones(scRep_example, 
               cloneCall = "aa", 
               y.axes = c("Patient", "ident", "Type"), 
               color = c("CVVSDNTGGFKTIF_CASSVRRERANTGELFF", "NA_CASSVRRERANTGELFF")) + 
    scale_fill_manual(values = c("grey", colorblind_vector[3]))
```

To visualize clonal flow across "Patient", "ident", and "Type", coloring by "ident":

```{r tidy = FALSE}
alluvialClones(scRep_example, 
                   cloneCall = "gene", 
                   y.axes = c("Patient", "ident", "Type"), 
                   color = "ident") 
```

`alluvialClones()` provides a visual representation of clonal distribution and movement across multiple categorical annotations. It is particularly effective for tracking how specific clones or clonal groups transition between different states, tissues, or cell types, offering a dynamic perspective on immune repertoire evolution and function.

## getCirclize

Like alluvial graphs, we can also visualize the interconnection of clusters using chord diagrams from the [circlize R package](https://jokergoo.github.io/circlize_book/book/). The first step is to get the data frame output to feed into the` chordDiagram()` function in `circlize`, which can be done using `getCirclize()`.

This function calculates the relative number of unique and shared clones based on the `group.by` variable using the product of `combineExpression()`. It creates a matrix the size of the `group.by` variable and then simplifies it into instructions readable by the `circlize` R package. The output represents the total number of unique and shared clones by the group.by variable. If using the downstream `circlize` R package, please read and cite the following [manuscript](https://pubmed.ncbi.nlm.nih.gov/24930139/). 

Key Parameters for getCirclize()

* `proportion`: If `TRUE`, normalizes the relationship by proportion; if `FALSE` (default), uses unique clone counts.
* `include.self`: If `TRUE`, includes counting clones within a single group.by comparison.

To get data for a chord diagram showing shared clones between `seurat_clusters`:

```{r tidy = FALSE}
library(circlize)
library(scales)

circles <- getCirclize(scRep_example, 
                       group.by = "seurat_clusters")

#Just assigning the normal colors to each cluster
grid.cols <- hue_pal()(length(unique(scRep_example$seurat_clusters)))
names(grid.cols) <- unique(scRep_example$seurat_clusters)

#Graphing the chord diagram
chordDiagram(circles, self.link = 1, grid.col = grid.cols)
```

This can also be used if we want to explore just the lung-specific T cells by subsetting the single-cell object. For the sake of this vignette, we can also look at setting `proportion = TRUE` to get a scaled output.

```{r}
subset <- subset(scRep_example, Type == "L")

circles <- getCirclize(subset, group.by = "ident", proportion = TRUE)

grid.cols <- scales::hue_pal()(length(unique(subset@active.ident)))
names(grid.cols) <- levels(subset@active.ident)

chordDiagram(circles, 
             self.link = 1, 
             grid.col = grid.cols, 
             directional = 1, 
             direction.type =  "arrows",
             link.arr.type = "big.arrow")
```

`getCirclize()` facilitates the creation of visually striking and informative chord diagrams to represent shared clonal relationships between distinct groups within your single-cell data. By providing a flexible way to quantify and format clonal overlap, it enables researchers to effectively illustrate complex clonal connectivity patterns, which are crucial for understanding immune communication and migration.

***

# Quantifying Clonal Bias

## StartracDiversity

From the excellent work by [Zhang et al. (2018, Nature)](https://www.nature.com/articles/s41586-018-0694-x), the authors introduced new methods for looking at clones by cellular origins and cluster identification. We strongly recommend you read and cite their publication when using this function.

To use the ```StartracDiversity()``` function, you need the output of the ```combineExpression()``` function and a column in your metadata that specifies the tissue of origin. 

### Indices Output from `StartracDiversity()`

*  `expa` - Clonal Expansion. Measures the degree of clonal proliferation within a given cell cluster. It is calculated as `1 - normalized Shannon entropy`, where a higher value indicates that a few clones dominate the cluster.  
*  `migr` - Cross-tissue Migration. Quantifies the movement of clonal T cells between different tissues, as defined by the `type` parameter. This index is based on the entropy of a single clonotype's distribution across the specified tissues.  
*  `tran` - State Transition. Measures the developmental transition of T cell clones between different functional clusters. This index is calculated from the entropy of a single clonotype's distribution across the cell clusters identified in the data.

Key Parameters for `StartracDiversity()`

* `type`: The variable in the metadata that provides tissue type.
* `group.by`: A column header in the metadata to group the analysis by (e.g., "sample", "treatment"). 

By default, `StartracDiversity()` will calculate all three indices (`expa`, `migr`, and `tran`) for each cluster and group you define. This provides a comprehensive overview of the clonal dynamics in your dataset. The output is a three-paneled plot, with each panel representing one index.

In the example data, `type` corresponds to the "Type" column, which includes "P" (peripheral blood) and "L" (lung) classifiers. The analysis is grouped by the "Patient" column.

```{r}
# Calculate and plot all three STARTRAC indices
StartracDiversity(scRep_example, 
                  type = "Type", 
                  group.by = "Patient")
```

### Calculating a Single Index

If you're only interested in one aspect of clonal dynamics, you can specify it using the `index` parameter. For example, to only calculate and plot clonal expansion:

```{r}
# Calculate and plot only the clonal expansion index
StartracDiversity(scRep_example, 
                  type = "Type", 
                  group.by = "Patient",
                  index = "expa")
```

### Pairwise Migration Analysis

Another feature of `StartracDiversity()` is the ability to perform pairwise comparisons. To specifically quantify the migration between two tissues (e.g., Lung vs. Periphery), set `index = "migr"` and tell the function which column to use for the comparison with `pairwise = "Type"`.

```{r}
# # Calculate pairwise migration between tissues
StartracDiversity(scRep_example, 
                  type = "Type", 
                  group.by = "Patient",
                  index = "migr",
                  pairwise = "Type")
```

## clonalBias

A new metric proposed by [Massimo et al](https://pubmed.ncbi.nlm.nih.gov/35829695/), ```clonalBias()```, like STARTRAC, is a clonal metric that seeks to quantify how individual clones are skewed towards a specific cellular compartment or cluster. A clone bias of `1` indicates that a clone is composed of cells from a single compartment or cluster, while a clone bias of `0` matches the background subtype distribution. Please read and cite the linked manuscript if using `clonalBias()`

Key Parameter(s) for `clonalBias()`

* `group.by`: A column header in the metadata that bias will be based on.
* `split.by`: The variable to use for calculating the baseline frequencies (e.g., "Type" for lung vs peripheral blood comparison)
* `n.boots`: Number of bootstraps to downsample.
* `min.expand`: Clone frequency cut-off for the purpose of comparison (default = 10).

Here we calculate and plot clonal bias using `aa` clone calls, splitting by "Patient" and grouping by "seurat_clusters", with a minimum expansion of 5 and 10 bootstraps:

```{r, message = FALSE, tidy = FALSE}
clonalBias(scRep_example, 
           cloneCall = "aa", 
           split.by = "Patient", 
           group.by = "seurat_clusters",
           n.boots = 10, 
           min.expand =5)
```

***

# Clustering by Edit Distance

## clonalCluster: Cluster by Sequence Similarity

The `clonalCluster()` function provides a powerful method to group clonotypes based on sequence similarity. It calculates the edit distance between CDR3 sequences and uses this information to build a network, identifying closely related clusters of T or B cell receptors. This functionality allows for a more nuanced definition of a "clone" that extends beyond identical sequence matches.

### Core Concepts

The clustering process follows these key steps:

* **Sequence Selection**: The function selects either the nucleotide (`sequence = "nt"`) or amino acid (`sequence = "aa"`) CDR3 sequences for a specified chain.
* **Distance Calculation**: It calculates the edit distance between all pairs of sequences. By default, it also requires sequences to share the same V gene (`use.v = TRUE`).
* **Network Construction**: An edge is created between any two sequences that meet the similarity threshold, forming a network graph.
* **Clustering**: A graph-based clustering algorithm is run to identify connected components or communities within the network. By default, it identifies all directly or indirectly connected sequences as a single cluster (`cluster.method = "components"`).
* **Output**: The function can either add the resulting cluster IDs to the input object, return an `igraph` object for network analysis, or export a sparse adjacency matrix.

### Understanding the `threshold` Parameter

The behavior of the threshold parameter is critical for controlling cluster granularity:

* **Normalized Similarity (threshold < 1)**: When the threshold is a value between 0 and 1 (e.g., `0.85`), it represents the normalized edit distance (Levenshtein distance / mean sequence length). A higher value corresponds to a stricter similarity requirement. This is useful for comparing sequences of varying lengths.
* **Raw Edit Distance (threshold >= 1)**: When the threshold is a whole number (e.g., 2), it represents the maximum raw edit distance allowed. A lower value corresponds to a stricter similarity requirement. This is useful when you want to allow a specific number of mutations.

Key Parameter(s) for ```clonalCluster()```

* `sequence`: Specifies whether to cluster based on `aa` (amino acid) or `nt` (nucleotide) sequences.
* `threshold`: The similarity threshold for clustering. Values `< 1` are normalized similarity, while values `>= 1` are raw edit distance.
* `group.by`: A column header in the metadata or lists to group the analysis by (e.g., "sample", "treatment"). If `NULL`, clusters are calculated across all sequences.
* `use.V`: If `TRUE`, sequences must share the same V gene to be clustered together.
* `use.J`: If `TRUE`, sequences must share the same J gene to be clustered together.
* `cluster.method`: The clustering algorithm to use. Defaults to `components`, which finds connected subgraphs.
* `cluster.prefix`: A character prefix to add to the cluster names (e.g., "cluster.").
* `exportGraph`: If `TRUE`, returns an igraph object of the sequence network.
* `exportAdjMatrix`: If `TRUE`, returns a sparse adjacency matrix (dgCMatrix) of the network.

### Demonstrating Basic Use

To run clustering on the first two samples for the TRA chain, using amino acid sequences with a normalized similarity threshold of 0.85:

```{r tidy = FALSE}
# Run clustering on the first two samples for the TRA chain
sub_combined <- clonalCluster(combined.TCR[c(1,2)], 
                              chain = "TRA", 
                              sequence = "aa", 
                              threshold = 0.85)

# View the new cluster column
head(sub_combined[[1]][, c("barcode", "TCR1", "TRA.Cluster")])
```

### Demonstrating Clustering with a Single-Cell Object

You can calculate clusters based on specific metadata variables within a single-cell object by using the `group.by` parameter. This is useful for analyzing clusters on a per-sample or per-patient basis without subsetting the data first.

First, add patient and type information to the `scRep_example` Seurat object:

```{r tidy = FALSE}
#Adding patient information
scRep_example$Patient <- substr(scRep_example$orig.ident, 1,3)

#Adding type information
scRep_example$Type <- substr(scRep_example$orig.ident, 4,4)
```

Now, run clustering on the `scRep_example` Seurat object, grouping calculations by "Patient":

```{r}
# Run clustering, but group calculations by "Patient"
scRep_example <- clonalCluster(scRep_example, 
                               chain = "TRA", 
                               sequence = "aa", 
                               threshold = 0.85, 
                               group.by = "Patient")

#Define color palette 
num_clusters <- length(unique(na.omit(scRep_example$TRA.Cluster)))
cluster_colors <- hcl.colors(n = num_clusters, palette = "inferno")

# Bug in Seurat 5.3.1 no longer handles NA for DimPlot
# Will Update in Future Commits

# DimPlot(scRep_example, group.by = "TRA.Cluster") +
#  scale_color_manual(values = cluster_colors, na.value = "grey") + 
#  NoLegend()
```

### Returning an igraph Object:

Instead of modifying the input object, ```clonalCluster()``` can export the underlying network structure for advanced analysis. Set `exportGraph = TRUE` to get an igraph object consisting of the networks of barcodes by the indicated clustering scheme. 

```{r tidy = FALSE}
set.seed(42)
#Clustering Patient 19 samples
igraph.object <- clonalCluster(combined.TCR[c(5,6)],
                               chain = "TRB",
                               sequence = "aa",
                               group.by = "sample",
                               threshold = 0.85, 
                               exportGraph = TRUE)

#Setting color scheme
col_legend <- factor(igraph::V(igraph.object)$group)
col_samples <- hcl.colors(2,"inferno")[as.numeric(col_legend)]
color.legend <- factor(unique(igraph::V(igraph.object)$group))
sample.vertices <- V(igraph.object)[sample(length(igraph.object), 500)]

subgraph.object <- induced_subgraph(igraph.object, vids = sample.vertices)
V(subgraph.object)$degrees <- igraph::degree(subgraph.object)
edge_alpha_color <- adjustcolor("gray", alpha.f = 0.3)

#Plotting
plot(subgraph.object,
     layout = layout_nicely(subgraph.object),
     vertex.label = NA,
     vertex.size = sqrt(igraph::V(subgraph.object)$degrees), 
     vertex.color = col_samples[sample.vertices],
     vertex.frame.color = "white", 
     edge.color = edge_alpha_color,
     edge.arrow.size = 0.05,
     edge.curved = 0.05, 
     margin = -0.1)
legend("topleft", 
       legend = levels(color.legend), 
       pch = 16, 
       col = unique(col_samples), 
       bty = "n")
```

### Returning a Sparse Adjacency Matrix

For computational applications, you can export a sparse adjacency matrix using `exportAdjMatrix = TRUE`. This matrix represents the connections between all barcodes in the input data, with the edit distance that meet the threshold in places of connection.

```{r}
# Generate the sparse matrix
adj.matrix <- clonalCluster(combined.TCR[c(1,2)],
                            chain = "TRB",
                            exportAdjMatrix = TRUE)

# View the dimensions and a snippet of the matrix
dim(adj.matrix)
print(adj.matrix[1:10, 1:10])
```

### Using Both Chains

You can analyze the combined network of both TRA/TRB or IGH/IGL chains by setting `chain = "both"`. This will create a single cluster column named `Multi.Cluster`.

```{r}
# Cluster using both TRB and TRA chains simultaneously
clustered_both <- clonalCluster(combined.TCR[c(1,2)], 
                                chain = "both")

# View the new "Multi.Cluster" column
head(clustered_both[[1]][, c("barcode", "TCR1", "TCR2", "Multi.Cluster")])
```

### Using Different Clustering Algorithms

While the default `cluster.method = "components"` is robust, you can use other algorithms from the igraph package, such as `walktrap` or `louvain`, to potentially uncover different community structures.

```{r}
# Cluster using the walktrap algorithm
graph_walktrap <- clonalCluster(combined.TCR[c(1,2)],
                                cluster.method = "walktrap",
                                exportGraph = TRUE)

# Compare the number of clusters found
length(unique(V(graph_walktrap)$cluster))
```

Overall, `clonalCluster()` is a versatile function for defining and analyzing clonal relationships based on sequence similarity. It allows researchers to move beyond exact sequence matches, providing a more comprehensive understanding of clonal families. The ability to customize parameters like `threshold`, `chain` selection, and `group.by` ensures adaptability to diverse research questions. Furthermore, the option to export `igraph` objects or sparse adjacency matrices provides advanced users with the tools for in-depth network analysis.

***

# Conclusion  

This has been a general overview of the capabilities of scRepertoire from the initial processing and visualization to attach to the mRNA expression values in a single-cell object. If you have any questions, comments, or suggestions, please visit the [GitHub repository](https://github.com/BorchLab/scRepertoire).

### Session Info

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





