seqCluster: Function for assigning clusters based on sequences similarity

In immunomind/immunarch: Bioinformatics Analysis of T-Cell and B-Cell Immune Repertoires

Function for assigning clusters based on sequences similarity

Description

Graph clustering based on distances between sequences

Usage

seqCluster(.data, .dist, .perc_similarity, .nt_similarity, .fixed_threshold)

Arguments

.data	The data which was used to caluculate .dist object. Can be data.frame, data.table, or a list of these objects. Every object must have columns in the immunarch compatible format immunarch_data_format
.dist	List of distance objects produced with seqDist function.
.perc_similarity	Numeric value between 0 and 1 specifying the maximum acceptable weight of an edge in a graph. This threshold depends on the length of sequences.
.nt_similarity	Numeric between 0-sequence length specifying the threshold of allowing a 1 in n nucleotides mismatch in sequencies.
.fixed_threshold	Numeric specifying the threshold on the maximum weight of an edge in a graph.

Value

Immdata data format object. Same as .data, but with extra 'Cluster' column with clusters assigned.

Examples

data(immdata)
# In this example, we will use only 2 samples with 500 clonotypes in each for time saving
input_data <- lapply(immdata$data[1:2], head, 500)
dist_result <- seqDist(input_data)
cluster_result <- seqCluster(input_data, dist_result, .fixed_threshold = 1)

immunomind/immunarch documentation built on March 20, 2024, 12:01 p.m.