This package is considered a duplicate. The official version of this package is found at:https://mlizhangx.r-universe.dev/NAIR

Package: NAIR 1.0.4

Brian Neal

NAIR: Network Analysis of Immune Repertoire

Pipelines for studying the adaptive immune repertoire of T cells and B cells via network analysis based on receptor sequence similarity. Relate clinical outcomes to immune repertoires based on their network properties, or to particular clusters and clones within a repertoire. Yang et al. (2023) <doi:10.3389/fimmu.2023.1181825>.

Authors:Brian Neal [aut, cre], Hai Yang [aut], Daniil Matveev [aut], Phi Long Le [aut], Li Zhang [cph, aut]

NAIR_1.0.4.tar.gz
NAIR_1.0.4.tar.gz(r-4.5-noble)NAIR_1.0.4.tar.gz(r-4.4-noble)
NAIR_1.0.4.tgz(r-4.4-emscripten)NAIR_1.0.4.tgz(r-4.3-emscripten)
NAIR.pdf |NAIR.html✨
NAIR/json (API)
NEWS

# Install 'NAIR' in R:

install.packages('NAIR', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mlizhangx/network-analysis-for-repertoire-sequencing-/issues1 issues

Pkgdown site:https://mlizhangx.github.io

Uses libs:

c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library

On CRAN:

3.48 score 184 downloads 40 exports 54 dependencies

Last updated 1 years agofrom:64e5f129c5. Checks:3 OK. Indexed: no.

Target	Result	Latest binary
Doc / Vignettes	OK	Mar 28 2025
R-4.5-linux-x86_64	OK	Mar 28 2025
R-4.4-linux-x86_64	OK	Mar 28 2025

Exports:addClusterLabels addClusterMembership addClusterStats addGraphLabels addNodeNetworkStats addNodeStats addPlots aggregateIdenticalClones buildAssociatedClusterNetwork buildNet buildPublicClusterNetwork buildPublicClusterNetworkByRepresentative buildRepSeqNetwork chooseNodeStats combineSamples exclusiveNodeStats extractLayout filterInputData findAssociatedClones findAssociatedSeqs findAssociatedSeqs2 findPublicClusters generateAdjacencyMatrix generateNetworkFromAdjacencyMat generateNetworkGraph generateNetworkGraphPlots generateNetworkObjects getClusterStats getNeighborhood hamDistBounded labelClusters labelNodes levDistBounded loadDataFromFileList node_stat_settings plotNetworkGraph saveNetwork saveNetworkPlots simulateToyData sparseAdjacencyMatFromSeqs

Dependencies:cachem cli colorspace cpp11 dplyr fansi farver fastmap generics ggforce ggplot2 ggraph ggrepel glue graphlayouts gridExtra gtable igraph isoband jsonlite labeling lattice lifecycle magrittr MASS Matrix memoise mgcv munsell nlme pillar pkgconfig polyclip purrr R6 RColorBrewer Rcpp RcppArmadillo RcppEigen rlang scales stringi stringr systemfonts tibble tidygraph tidyr tidyselect tweenr utf8 vctrs viridis viridisLite withr

buildRepSeqNetwork()/buildNet()

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Cluster Analysis

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Dual-Chain Network Analysis

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Introduction to the NAIR package

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Node-Level Network Properties

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Supplementary Functions

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Citation

To cite package ‘NAIR’ in publications use:

Neal B, Yang H, Matveev D, Le P, Zhang L (2024). NAIR: Network Analysis of Immune Repertoire. R package version 1.0.4, https://CRAN.R-project.org/package=NAIR.

Corresponding BibTeX entry:

  @Manual{,
    title = {NAIR: Network Analysis of Immune Repertoire},
    author = {Brian Neal and Hai Yang and Daniil Matveev and Phi Long
      Le and Li Zhang},
    year = {2024},
    note = {R package version 1.0.4},
    url = {https://CRAN.R-project.org/package=NAIR},
  }

Readme and manuals

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:

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. https://doi.org/10.3389/fimmu.2023.1181825

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`

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 the immune repertoire as a network?`NAIR`

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.)

Installation

To install the latest release version of NAIR, use the following command:

install.packages("NAIR")

To install the latest development version of NAIR from source (which requires compilation), use the following command:

devtools::install_github(
  "mlizhangx/Network-Analysis-for-Repertoire-Sequencing-",
  dependencies = TRUE, 
  build_vignettes = TRUE
)

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 igraph and 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

See this vignette 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

This vignette provides an introduction to computing node-level network properties with NAIR.

This vignette explains how to perform cluster analysis with NAIR.

Supplementary Functions

This vignette provides an overview of NAIR utility functions that supplement the main function buildNet().

Help Manual

Help page	Topics
NAIR: Network Analysis of Immune Repertoire	NAIR-package NAIR
Partition a Network Graph Into Clusters	addClusterMembership
Compute Cluster-Level Network Properties	addClusterStats
Compute Node-Level Network Properties	addNodeNetworkStats
Compute Node-Level Network Properties	addNodeStats
Generate Plots of a Network Graph	addPlots generateNetworkGraphPlots
Aggregate Counts/Frequencies for Clones With Identical Receptor Sequences	aggregateIdenticalClones
Build Global Network of Associated TCR/BCR Clusters	buildAssociatedClusterNetwork
Build Global Network of Public TCR/BCR Clusters	buildPublicClusterNetwork
Build Global Network of Public TCR/BCR Clusters Using Representative Clones	buildPublicClusterNetworkByRepresentative
Network Analysis of Immune Repertoire	buildNet buildRepSeqNetwork
Specify Node-level Network Properties to Compute	chooseNodeStats exclusiveNodeStats node_stat_settings
Load and Combine Data From Multiple Samples	combineSamples loadDataFromFileList
Get Coordinate Layout From Graph Plot	extractLayout
Filter Data Rows and Subset Data Columns	filterInputData
Identify TCR/BCR Clones in a Neighborhood Around Each Associated Sequence	findAssociatedClones
Identify TCR/BCR Sequences Associated With a Binary Variable	findAssociatedSeqs findAssociatedSeqs2
Find Public Clusters Among RepSeq Samples	findPublicClusters
Compute Graph Adjacency Matrix for Immune Repertoire Network	generateAdjacencyMatrix sparseAdjacencyMatFromSeqs
Generate the 'igraph' for a Network Adjacency Matrix	generateNetworkFromAdjacencyMat generateNetworkGraph
Generate Basic Output for an Immune Repertoire Network	generateNetworkObjects
Compute Cluster-Level Network Properties	getClusterStats
Identify Cells or Clones in a Neighborhood Around a Target Sequence	getNeighborhood
Bounded Computation of Hamming Distance	hamDistBounded
Label Clusters in a Network Graph Plot	addClusterLabels labelClusters
Label Nodes in a Network Graph Plot	addGraphLabels labelNodes
Bounded Computation of Levenshtein Distance	levDistBounded
Plot the Graph of an Immune Repertoire Network	plotNetworkGraph
Save List of Network Objects	saveNetwork
Write Plots to a PDF	saveNetworkPlots
Generate Toy AIRR-Seq Data	simulateToyData

Usage by other packages (reverse dependencies)