Introduction

immGLIPH provides an R implementation of the GLIPH (Grouping of Lymphocyte Interactions by Paratope Hotspots) and GLIPH2 algorithms for clustering T cell receptors (TCRs) that are predicted to bind the same HLA-restricted peptide antigen.

The package identifies TCR specificity groups by detecting statistically enriched CDR3$\beta$ motifs (local similarity) and structurally similar CDR3$\beta$ sequences (global similarity), then clusters them into convergence groups and scores each group for biological significance.

immGLIPH is an R implementation of existing algorithms. Users should cite the original publications:

GLIPH: Glanville, J. et al. Identifying specificity groups in the T cell receptor repertoire. Nature 547, 94–98 (2017). doi:10.1038/nature22976
GLIPH2: Huang, H. et al. Analyzing the Mycobacterium tuberculosis immune response by T-cell receptor clustering with GLIPH2 and genome-wide antigen screening. Nature Biotechnology 38, 1194–1202 (2020). doi:10.1038/s41587-020-0505-4

Installation

immGLIPH can be installed from Bioconductor:

BiocManager::install("immGLIPH")

The reference repertoire data (~19 MB) is downloaded automatically the first time you run runGLIPH() and cached locally via BiocFileCache. You can pre-download the data with:

BiocManager::install("BiocFileCache")
ref <- getGLIPHreference()

Loading Package

library(immGLIPH)

Integration with the scRepertoire Ecosystem

immGLIPH integrates with the scRepertoire ecosystem through immApex. Both scRepertoire and immApex are Bioconductor packages and can be installed via BiocManager::install(). This means runGLIPH() can directly accept:

SingleCellExperiment objects with TCR information
combineTCR() output lists from scRepertoire
Standard data frames or character vectors

Quick Start

Loading the Example Data

immGLIPH includes built-in example data derived from the scRepertoire example dataset (Yost et al. 2019):

gliph_input_data: A data frame of 365 TRB CDR3 sequences with V-gene and patient annotations.
gliph_sce: A SingleCellExperiment object with TCR clonotype information in colData, demonstrating the single-cell workflow.

data("gliph_input_data")
head(gliph_input_data)

##               CDR3b     TRBV patient
## 1     CAISEQGKGELFF TRBV10-3    P17B
## 2 CASSVRRERANTGELFF    TRBV9    P17B
## 3 CASSVRRERANTGELFF    TRBV9    P17B
## 4   CASSSRQDSTDTQYF   TRBV27    P17B
## 5 CASSVRRERANTGELFF    TRBV9    P17B
## 6  CASSPRAGTPNTEAFF  TRBV4-1    P17B

dim(gliph_input_data)

## [1] 365   3

Input Data Format

runGLIPH() accepts a data frame with the following columns:

Column	Required	Description
CDR3b	Yes	CDR3$\beta$ amino acid sequences
TRBV	No	V-gene usage (e.g., “TRBV5-1”)
patient	No	Donor/sample identifier
HLA	No	HLA alleles, comma-separated
counts	No	Clone frequency

Alternative column names are automatically recognized (e.g., cdr3, v_gene, sample, clone_count).

Working with Single-Cell Data

When working with single-cell immune repertoire data, you can use scRepertoire to prepare your data and pass the output directly to immGLIPH.

library(scRepertoire)
data("contig_list")

# After processing with cellranger/etc, combine contigs
combined <- combineTCR(contig_list[seq_len(2)],
                       samples = c("P1", "P2"))

# Take a small slice so the example runs quickly. In real use, pass
# all samples and rely on the bundled reference downloaded by
# getGLIPHreference().
combined_small <- lapply(combined, function(x) x[seq_len(50), ])

# Use a small custom reference built from the bundled example data
small_ref <- gliph_input_data[, c("CDR3b", "TRBV")]

# Pass scRepertoire output directly to runGLIPH
results_sc <- runGLIPH(combined_small,
                       method     = "gliph2",
                       refdb_beta = small_ref,
                       sim_depth  = 100,
                       n_cores    = 1)

For SingleCellExperiment objects that already contain TCR metadata (e.g., added via scRepertoire::combineExpression()), immGLIPH extracts the receptor data automatically using immApex::getIR(). Here is an example using the bundled gliph_sce dataset:

library(SingleCellExperiment)
data("gliph_sce")

# SingleCellExperiment object with TCR info in colData
results_sce <- runGLIPH(gliph_sce,
                        method     = "gliph2",
                        chains     = "TRB",
                        refdb_beta = small_ref,
                        sim_depth  = 100,
                        n_cores    = 1)

The `runGLIPH()` Function

Key Arguments

Argument	Default	Description
`cdr3_sequences`	–	Input data (data frame, vector, SCE, or list)
`method`	`"gliph2"`	Algorithm preset: `"gliph1"`, `"gliph2"`, or `"custom"`
`sim_depth`	1000	Simulation depth (higher = more reproducible, slower)
`n_cores`	1	Number of parallel cores
`refdb_beta`	`"human_v2.0_CD48"`	Reference database (built-in name or custom data frame)
`local_similarities`	`TRUE`	Search for local (motif-based) similarities
`global_similarities`	`TRUE`	Search for global (structural) similarities
`structboundaries`	`TRUE`	Trim structural boundaries before comparison
`accept_CF`	`TRUE`	Filter to sequences starting with C, ending with F

Method Presets

The method parameter configures a coordinated set of algorithm choices:

Setting	`"gliph1"`	`"gliph2"`	`"custom"`
Local method	Repeated random sampling	Fisher’s exact test	User-defined
Global method	Hamming distance cutoff	Struct-based + Fisher	User-defined
Clustering	Connected components	Per-motif isolated	User-defined
Scoring	GLIPH1 formula	GLIPH2 formula	User-defined

Running GLIPH2 (Default)

By default runGLIPH() downloads a reference repertoire via BiocFileCache. For this vignette we supply a custom reference data frame directly using refdb_beta to avoid network access:

data("gliph_input_data")

# Use the input data itself as a small reference for demonstration
ref_df <- gliph_input_data[, c("CDR3b", "TRBV")]

res_gliph2 <- runGLIPH(
  cdr3_sequences = gliph_input_data[seq_len(200), ],
  method         = "gliph2",
  refdb_beta     = ref_df,
  sim_depth      = 100,
  n_cores        = 1
)

Running GLIPH1

res_gliph1 <- runGLIPH(
  cdr3_sequences = gliph_input_data[seq_len(200), ],
  method         = "gliph1",
  refdb_beta     = ref_df,
  sim_depth      = 100,
  n_cores        = 1
)

Understanding the Output

The output is a list with the following elements:

names(res_gliph2)

## [1] "sample_log"         "motif_enrichment"   "global_enrichment" 
## [4] "connections"        "cluster_properties" "cluster_list"      
## [7] "parameters"

Cluster Properties

The cluster_properties data frame summarizes each convergence group with enrichment scores:

head(res_gliph2$cluster_properties)

## NULL

Cluster Membership

The cluster_list is a named list where each element contains the member TCRs of a convergence group:

# Number of convergence groups
length(res_gliph2$cluster_list)

## [1] 0

# Members of the first cluster (if any found)
if (length(res_gliph2$cluster_list) > 0) {
  head(res_gliph2$cluster_list[[1]])
}

Motif Enrichment

The motif_enrichment element contains the locally enriched motifs:

# Significantly enriched motifs
if (!is.null(res_gliph2$motif_enrichment$selected_motifs)) {
  head(res_gliph2$motif_enrichment$selected_motifs)
}

## [1] motif      counts     num_in_ref avgRef     topRef     OvE        p.value   
## <0 rows> (or 0-length row.names)

Network Edges

The connections data frame contains the edge list representing the TCR similarity network:

if (!is.null(res_gliph2$connections)) {
  head(res_gliph2$connections)
}

Customizing the Analysis

Using `method = "custom"`

The "custom" method allows independent control over each algorithmic component:

res_custom <- runGLIPH(
  cdr3_sequences  = gliph_input_data[seq_len(200), ],
  method          = "custom",
  refdb_beta      = ref_df,
  local_method    = "fisher",    # or "rrs"
  global_method   = "cutoff",    # or "fisher"
  clustering_method = "GLIPH1.0", # or "GLIPH2.0"
  scoring_method  = "GLIPH2.0",  # or "GLIPH1.0"
  sim_depth       = 100,
  n_cores         = 1
)

Adjusting Significance Thresholds

For the Fisher-based local method (GLIPH2), you can adjust:

lcminp: Maximum p-value for a motif to be considered enriched (default 0.01)
lcminove: Minimum fold-enrichment per motif length (default c(1000, 100, 10) for 2-mers, 3-mers, 4-mers)
kmer_mindepth: Minimum motif observations in the sample (default 3)

res_strict <- runGLIPH(
  cdr3_sequences = gliph_input_data[seq_len(200), ],
  method         = "gliph2",
  refdb_beta     = ref_df,
  lcminp         = 0.001,            # Stricter p-value
  lcminove       = c(10000, 1000, 100), # Higher fold-change
  sim_depth      = 100,
  n_cores        = 1
)

Choosing a Reference Database

immGLIPH ships with reference repertoires for human and mouse from the original GLIPH publications. Each is available as CD4, CD8, or combined (CD48) subsets:

Name	Species	Version	Subset	Source
`"human_v1.0_CD4"`	Human	v1.0	CD4	Glanville et al. (2017)
`"human_v1.0_CD8"`	Human	v1.0	CD8	Glanville et al. (2017)
`"human_v1.0_CD48"`	Human	v1.0	CD4+CD8	Glanville et al. (2017)
`"human_v2.0_CD4"`	Human	v2.0	CD4	Huang et al. (2020)
`"human_v2.0_CD8"`	Human	v2.0	CD8	Huang et al. (2020)
`"human_v2.0_CD48"`	Human	v2.0	CD4+CD8	Huang et al. (2020)
`"mouse_v1.0_CD4"`	Mouse	v1.0	CD4	Huang et al. (2020)
`"mouse_v1.0_CD8"`	Mouse	v1.0	CD8	Huang et al. (2020)
`"mouse_v1.0_CD48"`	Mouse	v1.0	CD4+CD8	Huang et al. (2020)
`"gliph_reference"`	Human	v1.0	CD4+CD8	Legacy alias for `human_v1.0_CD48`

Each reference includes pre-computed V-gene usage and CDR3 length frequency distributions, which are automatically used for cluster scoring.

To analyse mouse data, supply mouse CDR3$\beta$ sequences as cdr3_sequences and set refdb_beta = "mouse_v1.0_CD48" (or one of the CD4/CD8 subsets in the table). The reference is fetched and cached the first time it is requested.

Using a Custom Reference Database

You can also supply your own reference as a data frame. A minimal reference is a two-column table of CDR3$\beta$ amino-acid sequences and their corresponding V-gene names. Here we build a small one from the bundled gliph_input_data for illustration:

custom_ref <- gliph_input_data[, c("CDR3b", "TRBV")]
head(custom_ref, 3)

##               CDR3b     TRBV
## 1     CAISEQGKGELFF TRBV10-3
## 2 CASSVRRERANTGELFF    TRBV9
## 3 CASSVRRERANTGELFF    TRBV9

res <- runGLIPH(
  cdr3_sequences = gliph_input_data[seq_len(100), ],
  refdb_beta     = custom_ref,
  method         = "gliph2",
  sim_depth      = 100,
  n_cores        = 1
)

Motif Discovery with `findMotifs()`

The findMotifs() function searches for continuous and discontinuous k-mer motifs in a set of CDR3 sequences. It is used internally by runGLIPH() but can also be called independently.

Key Arguments

Argument	Default	Description
`seqs`	–	Character vector of CDR3$\beta$ sequences
`q`	`2:4`	Motif lengths to search
`kmer_mindepth`	`NULL`	Minimum motif count to return
`discontinuous`	`FALSE`	Include discontinuous motifs (with one variable position)

Example

data("gliph_input_data")
sample_seqs <- as.character(gliph_input_data$CDR3b[seq_len(100)])

# Find all 3-mers appearing at least 5 times
motifs <- findMotifs(seqs = sample_seqs, 
                     q = 3, 
                     kmer_mindepth = 5)
head(motifs[order(motifs$V1, decreasing = TRUE), ])

##    motif V1
## 49   CAS 85
## 46   ASS 77
## 45   QYF 34
## 30   SSL 27
## 19   LFF 26
## 21   GEL 22

Including discontinuous motifs (e.g., C.S where . is any amino acid):

disc_motifs <- findMotifs(seqs          = sample_seqs,
                          q             = 2,
                          kmer_mindepth = 5,
                          discontinuous = TRUE
)
# Show discontinuous motifs (those containing a dot)
disc_only <- disc_motifs[grep("\\.", disc_motifs$motif), ]
head(disc_only[order(disc_only$V1, decreasing = TRUE), ])

##    motif  V1
## 52    .S 233
## 71    S. 233
## 58    .A 180
## 64    A. 178
## 41    .F 163
## 24    G. 162

Cluster Scoring with `clusterScoring()`

The clusterScoring() function evaluates convergence groups using up to five metrics. This is called automatically by runGLIPH(), but can be re-run with different parameters on existing results.

Key Arguments

Argument	Default	Description
`cluster_list`	–	Named list of cluster data frames (from `runGLIPH()$cluster_list`)
`cdr3_sequences`	–	Original input data frame
`refdb_beta`	`"human_v2.0_CD48"`	Reference database
`gliph_version`	1	Scoring formula: 1 (GLIPH) or 2 (GLIPH2)
`sim_depth`	1000	Resampling depth for score estimation

Scoring Components

The total score is derived from up to five components (depending on available data):

network.size.score: Probability of observing a cluster of this size by chance
cdr3.length.score: Enrichment of CDR3 length distribution within the cluster
vgene.score: Enrichment of V-gene usage (requires TRBV column)
clonal.expansion.score: Enrichment of expanded clones (requires counts column)
hla.score: Enrichment of shared HLA alleles among donors (requires patient + HLA columns)

Example

# Re-score with GLIPH2 formula
if (length(res_gliph1$cluster_list) > 0) {
  rescored <- clusterScoring(
    cluster_list   = res_gliph1$cluster_list,
    cdr3_sequences = gliph_input_data[seq_len(200), ],
    refdb_beta     = ref_df,
    gliph_version  = 2,
    sim_depth      = 100,
    n_cores        = 1)
  head(rescored)
}

De Novo TCR Generation with `deNovoTCRs()`

The deNovoTCRs() function generates artificial CDR3$\beta$ sequences that resemble the positional amino acid composition of a given convergence group. This can be used to predict novel TCR sequences with similar binding characteristics.

Key Arguments

Argument	Default	Description
`convergence_group_tag`	–	Tag identifying the cluster (from `cluster_properties$tag`)
`clustering_output`	`NULL`	Output list from `runGLIPH()`
`result_folder`	`""`	Alternative: load from files
`sims`	100,000	Number of de novo sequences to generate
`num_tops`	1,000	Return top N highest-scoring sequences
`normalization`	`FALSE`	Normalize scores against the reference database
`make_figure`	`FALSE`	Plot score vs. rank

Example

# Generate de novo TCRs for the first convergence group (if any found)
if (length(res_gliph1$cluster_list) > 0) {
  de_novo <- deNovoTCRs(
    convergence_group_tag = names(res_gliph1$cluster_list)[1],
    clustering_output     = res_gliph1,
    refdb_beta            = ref_df,
    sims                  = 10000,
    num_tops              = 100,
    make_figure           = FALSE,
    n_cores               = 1
  )

  # Top predicted sequences
  head(de_novo$de_novo_sequences)

  # Positional weight matrix used for generation
  head(de_novo$PWM_Scoring)
}

Network Visualization with `plotNetwork()`

The plotNetwork() function creates an interactive network visualization of the convergence groups using the visNetwork package.

Key Arguments

Argument	Default	Description
`clustering_output`	`NULL`	Output list from `runGLIPH()`
`color_info`	`"total.score"`	Column name for node coloring
`color_palette`	`viridis::viridis`	Color palette function
`local_edge_color`	`"orange"`	Color for local similarity edges
`global_edge_color`	`"#68bceb"`	Color for global similarity edges
`size_info`	`NULL`	Column name for node sizing
`cluster_min_size`	3	Minimum cluster size to display

Example

if (!is.null(res_gliph1$cluster_properties) &&
    nrow(res_gliph1$cluster_properties) > 0) {
  plotNetwork(
    clustering_output = res_gliph1,
    color_info        = "total.score",
    cluster_min_size  = 2,
    n_cores           = 1
  )
}

Loading Saved Results with `loadGLIPH()`

If you save results to disk using result_folder, you can reload them later. We use tempdir() here so the example does not write to your working directory:

out_dir <- file.path(tempdir(), "gliph_results")

res_saved <- runGLIPH(
  cdr3_sequences = gliph_input_data[seq_len(200), ],
  method         = "gliph2",
  refdb_beta     = ref_df,
  result_folder  = out_dir,
  sim_depth      = 100,
  n_cores        = 1
)

reloaded <- loadGLIPH(result_folder = out_dir)
names(reloaded)

## [1] "motif_enrichment"   "global_enrichment"  "connections"       
## [4] "cluster_properties" "cluster_list"       "parameters"

Saving Results to Disk

When result_folder is specified, runGLIPH() writes several output files:

File	Description
`local_similarities.txt`	Enriched motifs
`all_motifs.txt`	All tested motifs with statistics
`clone_network.txt`	Network edge list
`convergence_groups.txt`	Cluster properties and scores
`cluster_member_details.txt`	Full member information per cluster
`parameters.txt`	All parameters used

Performance

Accelerated Computation with immApex

When immApex is installed, immGLIPH automatically uses its C++-accelerated backends for two computationally intensive steps:

Motif enumeration (findMotifs()): Uses immApex::calculateMotif() with OpenMP multithreading instead of the pure-R stringdist::qgrams() approach.
Global Hamming distance network (GLIPH1 method): Uses immApex::buildNetwork() to compute pairwise distances in a single C++ call, replacing the parallel loop over stringdist::stringdist().

If immApex is not installed, immGLIPH falls back to the original pure-R implementations transparently, no code changes are needed.

# Install immApex for performance acceleration
BiocManager::install("BorchLab/immApex")

Tips and Best Practices

Start with GLIPH2: The Fisher-based approach is generally more statistically rigorous than repeated random sampling.
Sample size matters: GLIPH works best with >200 unique CDR3$\beta$ sequences. Very small samples may yield few or no convergence groups.
Include V-gene information: When available, TRBV data improves both global similarity detection and scoring accuracy.
Adjust sim_depth: For publication-quality results, use sim_depth >= 1000. For exploratory analysis, sim_depth = 100 is faster.
Parallelization: For large datasets (>5,000 sequences), set n_cores > 1 to use parallel processing via BiocParallel.
Install immApex: For best performance, install immApex to enable C++-accelerated motif enumeration and network construction (see Performance section above).
Choose the right reference: For mouse data, use refdb_beta = "mouse_v1.0_CD48". For specialized repertoires, provide a custom data frame via refdb_beta.

Session Info

sessionInfo()

## R version 4.6.0 (2026-04-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.4 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] SingleCellExperiment_1.35.1 SummarizedExperiment_1.43.0
##  [3] Biobase_2.73.1              GenomicRanges_1.65.0       
##  [5] Seqinfo_1.3.0               IRanges_2.47.2             
##  [7] S4Vectors_0.51.3            BiocGenerics_0.59.6        
##  [9] generics_0.1.4              MatrixGenerics_1.25.0      
## [11] matrixStats_1.5.0           scRepertoire_2.9.0         
## [13] ggplot2_4.0.3               immGLIPH_0.99.5            
## [15] BiocStyle_2.41.0           
## 
## loaded via a namespace (and not attached):
##  [1] gridExtra_2.3       rlang_1.2.0         magrittr_2.0.5     
##  [4] otel_0.2.0          compiler_4.6.0      vctrs_0.7.3        
##  [7] reshape2_1.4.5      ggalluvial_0.12.6   gsl_2.1-9          
## [10] quantreg_6.1        stringr_1.6.0       pkgconfig_2.0.3    
## [13] fastmap_1.2.0       XVector_0.53.0      ggraph_2.2.2       
## [16] rmarkdown_2.31      MatrixModels_0.5-4  purrr_1.2.2        
## [19] xfun_0.58           cachem_1.1.0        jsonlite_2.0.0     
## [22] DelayedArray_0.39.3 BiocParallel_1.47.0 tweenr_2.0.3       
## [25] parallel_4.6.0      R6_2.6.1            bslib_0.11.0       
## [28] stringi_1.8.7       RColorBrewer_1.1-3  parallelly_1.47.0  
## [31] jquerylib_0.1.4     Rcpp_1.1.1-1.1      knitr_1.51         
## [34] future.apply_1.20.2 iNEXT_3.0.2         Matrix_1.7-5       
## [37] splines_4.6.0       igraph_2.3.2        tidyselect_1.2.1   
## [40] stringdist_0.9.17   abind_1.4-8         yaml_2.3.12        
## [43] viridis_0.6.5       codetools_0.2-20    listenv_0.10.1     
## [46] lattice_0.22-9      tibble_3.3.1        plyr_1.8.9         
## [49] withr_3.0.2         S7_0.2.2            evaluate_1.0.5     
## [52] survival_3.8-6      future_1.70.0       polyclip_1.10-7    
## [55] pillar_1.11.1       BiocManager_1.30.27 sp_2.2-1           
## [58] scales_1.4.0        globals_0.19.1      immApex_1.7.0      
## [61] glue_1.8.1          maketools_1.3.2     tools_4.6.0        
## [64] sys_3.4.3           SparseM_1.84-2      buildtools_1.0.0   
## [67] graphlayouts_1.2.3  dotCall64_1.2       tidygraph_1.3.1    
## [70] grid_4.6.0          tidyr_1.3.2         evmix_2.12         
## [73] ggforce_0.5.0       cli_3.6.6           spam_2.11-4        
## [76] S4Arrays_1.13.0     viridisLite_0.4.3   ggdendro_0.2.0     
## [79] dplyr_1.2.1         gtable_0.3.6        sass_0.4.10        
## [82] digest_0.6.39       progressr_0.19.0    SparseArray_1.13.2 
## [85] ggrepel_0.9.8       rjson_0.2.23        SeuratObject_5.4.0 
## [88] farver_2.1.2        memoise_2.0.1       htmltools_0.5.9    
## [91] lifecycle_1.0.5     MASS_7.3-65

Column	Required	Description
CDR3b	Yes	CDR3\(\beta\) amino acid sequences
TRBV	No	V-gene usage (e.g., “TRBV5-1”)
patient	No	Donor/sample identifier
HLA	No	HLA alleles, comma-separated
counts	No	Clone frequency

Argument	Default	Description
`seqs`	–	Character vector of CDR3\(\beta\) sequences
`q`	`2:4`	Motif lengths to search
`kmer_mindepth`	`NULL`	Minimum motif count to return
`discontinuous`	`FALSE`	Include discontinuous motifs (with one variable position)

Getting Started with immGLIPH

Introduction

Installation

Loading Package

Integration with the scRepertoire Ecosystem

Quick Start

Loading the Example Data

Input Data Format

Working with Single-Cell Data

The runGLIPH() Function

Key Arguments

Method Presets

Running GLIPH2 (Default)

Running GLIPH1

Understanding the Output

Cluster Properties

Cluster Membership

Motif Enrichment

Network Edges

Customizing the Analysis

Using method = "custom"

Adjusting Significance Thresholds

Choosing a Reference Database

Using a Custom Reference Database

Motif Discovery with findMotifs()

Key Arguments

Example

Cluster Scoring with clusterScoring()

Key Arguments

Scoring Components

Example

De Novo TCR Generation with deNovoTCRs()

Key Arguments

Example

Network Visualization with plotNetwork()

Key Arguments

Example

Loading Saved Results with loadGLIPH()

Saving Results to Disk

Performance

Accelerated Computation with immApex

Tips and Best Practices

Session Info

The `runGLIPH()` Function

Using `method = "custom"`

Motif Discovery with `findMotifs()`

Cluster Scoring with `clusterScoring()`

De Novo TCR Generation with `deNovoTCRs()`

Network Visualization with `plotNetwork()`

Loading Saved Results with `loadGLIPH()`