In mlizhangx/Network-Analysis-for-Repertoire-Sequencing-: Network Analysis of Immune Repertoire

knitr::opts_chunk$set(
  echo = TRUE, warning = FALSE, message = FALSE, collapse = TRUE, comment = "#>", 
  fig.align = "center", fig.width = 10, fig.height = 10, out.width = "100%")

For single-cell data, cell-level network analysis can be performed based on joint similarity in alpha chain sequence and beta chain sequence.

We simulate some toy data to demonstrate the usage.

set.seed(42)
library(NAIR)

dat <- simulateToyData(chains = 2)
head(dat)

The input data is assumed to have the following format:

• Each row corresponds to a unique cell
• The data contains separate columns for alpha chain sequence and beta chain sequence

Dual-chain network analysis can be performed using buildRepSeqNetwork() (or generateNetworkObjects()) by supplying a length-2 vector to the seq_col parameter:

• First entry should reference the column for alpha chain sequence
• Second entry should reference the column for beta chain sequence

# Build network based on joint dual-chain similarity
network <- buildNet(dat, 
                    seq_col = c("AlphaSeq", "BetaSeq"),
                    count_col = "UMIs",
                    node_stats = TRUE, 
                    stats_to_include = "all",
                    cluster_stats = TRUE, 
                    color_nodes_by = "SampleID",
                    size_nodes_by = "UMIs",
                    node_size_limits = c(0.5, 3)
)

We print the network graph plot with labels added for the largest two clusters:

addClusterLabels(network$plots$SampleID, network, top_n_clusters = 2, size = 8)

The list returned buildRepSeqNetwork() the following items:

names(network)

Notice that the list contains three adjacency matrices: adjacency_matrix corresponds to the network based on joint similarity in both chain sequences, while adj_mat_a corresponds to the network based only on similarity in the alpha-chain sequence (and similarly for adj_mat_b).

The cluster-level data contains sequence-based cluster statistics for each of the alpha and beta chain sequences:

head(network$cluster_data)

The remainder of the output and customization follows the general case for buildRepSeqNetwork().

mlizhangx/Network-Analysis-for-Repertoire-Sequencing- documentation built on April 7, 2024, 12:02 p.m.