Given Adaptive Immune Receptor Repertoire Sequencing (AIRR-Seq) data, builds the network graph for the immune repertoire based on sequence similarity.
generateNetworkObjects(
data,
seq_col,
dist_type = "hamming",
dist_cutoff = 1,
drop_isolated_nodes = TRUE,
verbose = FALSE
)If the constructed network contains no nodes, the function will return
NULL, invisibly, with a warning. Otherwise, the function invisibly
returns a list containing the following items:
An object of class igraph containing the list of nodes
and edges for the network graph.
The network graph adjacency matrix, stored as a sparse matrix of class
dgCMatrix from the Matrix package. See
dgCMatrix-class.
A data frame containing containing metadata for the network nodes, where each
row corresponds to a node in the network graph. This data frame contains all
variables from data (unless otherwise specified via subset_cols)
in addition to the computed node-level network properties if
node_stats = TRUE. Each row's name is the name of the corresponding
row from data.
A data frame containing the AIRR-Seq data, with variables indexed by column and observations (e.g., clones or cells) indexed by row.
Specifies the column(s) of data containing
the receptor sequences to be used as the basis of similarity between rows.
Accepts a character string containing the column name
or a numeric scalar containing the column index.
Also accepts a vector of length 2 specifying distinct sequence columns
(e.g., alpha chain and beta chain), in which case
similarity between rows depends on similarity in both sequence columns
(see details).
Specifies the function used to measure the similarity between sequences.
The similarity between two sequences determines the pairwise distance between
their respective nodes in the network graph. Valid options are "hamming"
(the default), which
uses hamDistBounded(),
and "levenshtein", which uses
levDistBounded().
A nonnegative scalar. Specifies the maximum pairwise distance (based on
dist_type) for an edge connection to exist between two nodes. Pairs
of nodes whose distance is less than or equal to this value will be joined
by an edge connection in the network graph. Controls the stringency of the
network construction and affects the number and density of edges in the network.
A lower cutoff value requires greater similarity between sequences in order for
their respective nodes to be joined by an edge connection. A value of 0
requires two sequences to be identical in order for their nodes to be joined
by an edge.
A logical scalar. When TRUE, removes each node that is not joined by
an edge connection to any other node in the network graph.
Logical. If TRUE, generates messages about the tasks
performed and their progress, as well as relevant properties of intermediate
outputs. Messages are sent to stderr().
Brian Neal (Brian.Neal@ucsf.edu)
To construct the immune repertoire network, each TCR/BCR clone (bulk data) or cell (single-cell data) is modeled as a node in the network graph, corresponding to a single row of the AIRR-Seq data. For each node, the corresponding receptor sequence is considered. Both nucleotide and amino acid sequences are supported for this purpose. The receptor sequence is used as the basis of similarity and distance between nodes in the network.
Similarity between sequences is measured using either the Hamming distance or Levenshtein (edit) distance. The similarity determines the pairwise distance between nodes in the network graph. The more similar two sequences are, the shorter the distance between their respective nodes. Two nodes are joined by an edge if their receptor sequences are sufficiently similar, i.e., if the distance between the nodes is sufficiently small.
For single-cell data, edge connections between nodes can be based on similarity
in both the alpha chain and beta chain sequences.
This is done by providing a vector of length 2 to seq_cols
specifying the two sequence columns in data.
The distance between two nodes is then the greater of the two distances between
sequences in corresponding chains.
Two nodes will be joined by an edge if their alpha chain sequences are sufficiently
similar and their beta chain sequences are sufficiently similar.
See the
buildRepSeqNetwork
package vignette for more details. The vignette can be accessed offline using
vignette("buildRepSeqNetwork").
Hai Yang, Jason Cham, Brian Neal, Zenghua Fan, Tao He and Li Zhang. (2023). NAIR: Network Analysis of Immune Repertoire. Frontiers in Immunology, vol. 14. doi: 10.3389/fimmu.2023.1181825
set.seed(42)
toy_data <- simulateToyData()
net <-
generateNetworkObjects(
toy_data,
"CloneSeq"
)
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