cluster_sequences | R Documentation |
Cluster cells based on CDR3 sequences
cluster_sequences(
input,
data_col = "cdr3",
chain = NULL,
method = "louvain",
resolution = 0.5,
k = 10,
dist_method = NULL,
run_umap = TRUE,
chain_col = global$chain_col,
prefix = paste0(data_col, "_"),
return_df = FALSE,
sep = global$sep,
...
)
input |
Single cell object or data.frame containing V(D)J data. If a data.frame is provided, the cell barcodes should be stored as row names. |
data_col |
meta.data column containing sequences to use for calculating Levenshtein distance. |
chain |
Chain to use for clustering sequences. Cells with more than one of the provided chain will be excluded from the analysis. If NULL, sequences for cells with multiple chains will be concatenated. |
method |
Method to use for clustering, possible values include:
|
resolution |
Resolution (coarseness) of clusters |
k |
Number of neighbors for k-nearest neighbors algorithm |
dist_method |
Method to use for computing distance between sequences. If NULL, distance is calculated for amino acid sequences using the BLOSUM62 substitution matrix and levenshtein distance is calculated for nucleotide sequences. Other possible values include:
|
run_umap |
Should the Uniform Manifold Approximation and Projection (UMAP) dimensional reduction method be performed. This will add UMAP coordinates to the meta.data. |
chain_col |
meta.data column containing chains for each cell. |
prefix |
Prefix to add to graph name |
return_df |
Return results as a data.frame. If FALSE, results will be added to the input object. |
sep |
Separator used for storing per cell V(D)J data |
... |
Additional parameters to pass to clustering method |
Single cell object or data.frame with clustering results
plot_motifs()
# Cluster cells based on CDR3 amino acid sequences and plot results
res <- cluster_sequences(
vdj_sce,
data_col = "cdr3",
chain = "IGK",
resolution = c(0.5, 1)
)
plot_scatter(
res,
x = "cdr3_UMAP_1",
y = "cdr3_UMAP_2",
data_col = "cdr3_cluster_0.5"
)
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