NAIR: Network Analysis of Immune Repertoire
NAIR is an R package for analyzing the adaptive immune
repertoire using network analysis based on similarities among receptor
sequences. It implements methods from the following paper:
NAIR allows users to perform network analysis on
Adaptive Immune Receptor Repertoire Sequencing (AIRR-Seq) data,
including computing local and global network properties of nodes and
clusters, which can provide insights into the structural organization of
the immune repertoire network.
NAIR also enables users to search across multiple
AIRR-Seq samples for clones/clusters associated with subject
characteristics, disease conditions or clinical outcomes, as well as
identify public clones/clusters. This can help researchers identify
potentially important TCR/BCR clones.
To aid in interpretation of the immune repertoire network,
NAIR includes convenient functionality for generating
customized network visualizations.
What data does NAIR support?
NAIR supports bulk and single-cell immune repertoire
sequence data for T-cell or B-cell receptors (TCR or BCR).
- Single-cell data: Each row is a single cell
- Bulk data: Each row is a distinct TCR/BCR clone (unique combination of V-D-J genes and nucleotide sequence) and typically includes a corresponding measurement of clonal abundance (e.g., clone count and clone frequency/fraction)
How does NAIR model and analyze the adaptive immune
receptor repertoire as a network?
- Each cell (single-cell data) or clone (bulk data) is modeled as a node (vertex) in the network
- For each node, we consider the corresponding receptor sequence (nucleotide or amino acid)
- For each pair of nodes, we measure the similarity in their receptor sequences (using the Hamming or Levenshtein distance)
- An edge is drawn between two nodes if the distance is below a
specified threshold
- For single-cell data, sequences from two chains (e.g., alpha chain and beta chain) can be jointly used to determine similarity between cells, considering cells as similar when the sequences for both chains are similar (i.e., when the distance for each chain is below the threshold)
- Clustering analysis is used to partition the network graph into
clusters (densely-connected subgraphs)
- Many clustering algorithms are available, with each seeking to identify the “best” configuration of clusters according to different graph criteria
- Network statistics characterize the repertoire in terms of the local and global structural properties of its graph
- Customized visual plots of the network graph are generated, with nodes colored according to desired metadata (e.g., disease status, sample, cluster, clonal abundance, etc.)
Getting Started
Main Function
General network analysis on AIRR-Seq data is performed using
buildRepSeqNetwork() or its convenient alias
buildNet(). This function does the following:
- Filters the AIRR-Seq data according to user specifications
- Builds the network graph for the immune repertoire
- Performs additional network analysis, which can include:
- Cluster analysis
- Network properties
- Customizable visual plots of the network graph
- Returns (and optionally saves) the following output:
- The network graph (as
igraphand adjacency matrix) - Metadata for the network
- Metadata for the nodes in the network
- Metadata for the clusters in the network
- Plots of the network graph
- The network graph (as
See vignette("buildRepSeqNetwork")
for a tutorial.
Searching for Associated Clusters
Given multiple samples of bulk AIRR-Seq data, NAIR can
be used to search for TCR/BCR clusters associated with a binary variable
of interest, such as a disease condition, treatment or clinical outcome.
See this
article for a tutorial.
Searching for Public Clusters
The NAIR package includes a set of functions that
facilitate searching for public TCR/BCR clusters across multiple samples
of bulk AIRR-seq data. In this context, a public cluster consists of
similar TCR/BCR clones (e.g., those whose CDR3 amino acid sequences
differ by at most one amino acid) that are shared across samples (e.g.,
across individuals or across time points for a single individual). See
this
article for a tutorial.
Additional Resources
Visualization
This
article provides an introduction to the creation and customization
of network visualizations using NAIR.
Network Properties and Cluster Analysis
vignette("node_properties")
provides an introduction to computing node-level network properties with
NAIR.
vignette("cluster_analysis")
explains how to perform cluster analysis with NAIR.
Supplementary Functions
vignette("supplementary")
provides an overview of NAIR utility functions that
supplement the main function buildNet().