buildPublicClusterNetwork: Build Global Network of Public TCR/BCR Clusters

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

Build Global Network of Public TCR/BCR Clusters

Description

Part of the workflow Searching for Public TCR/BCR Clusters. Intended for use following findPublicClusters().

Given node-level metadata for each sample's filtered clusters, combines the data into a global network and performs network analysis and cluster analysis.

Usage

buildPublicClusterNetwork(

  ## Input ##
  file_list,
  input_type = "rds",
  data_symbols = "ndat",
  header = TRUE, sep,
  read.args = list(row.names = 1),
  seq_col,

  ## Network Settings ##
  drop_isolated_nodes = FALSE,
  node_stats = deprecated(),
  stats_to_include = deprecated(),
  cluster_stats = deprecated(),

  ## Visualization ##
  color_nodes_by = "SampleID",
  color_scheme = "turbo",
  plot_title = "Global Network of Public Clusters",

  ## Output ##
  output_dir = NULL,
  output_name = "PublicClusterNetwork",
  verbose = FALSE,

  ...

)

Arguments

file_list	A character vector of file paths, or a list containing connections and file paths. Each element corresponds to a single file containing the data for a single sample. loadDataFromFileList().
input_type	A character string specifying the file format of the input files. Options are "csv", "rds" and "rda". Passed to loadDataFromFileList().
data_symbols	Used when input_type = "rda". Specifies the name of the data frame within each Rdata file. Passed to loadDataFromFileList().
header	For values of input_type other than "rds" and "rda", this argument can be used to specify a non-default value of the header argument to read.table(), read.csv(), etc.
sep	For values of input_type other than "rds" and "rda", this argument can be used to specify a non-default value of the sep argument to read.table(), read.csv(), etc.
read.args	For values of input_type other than "rds" and "rda", this argument can be used to specify non-default values of optional arguments to read.table(), read.csv(), etc. Accepts a named list of argument values. Values of header and sep in this list take precedence over values specified via the header and sep arguments.
seq_col	Specifies the column in the node-level metadata that contains the TCR/BCR sequences. Accepts a character string containing the column name or a numeric scalar containing the column index.
drop_isolated_nodes	Passed to buildRepSeqNetwork() when constructing the global network.
node_stats	All network properties are automatically computed.
stats_to_include	All network properties are automatically computed.
cluster_stats	All network properties are automatically computed.
color_nodes_by	Passed to buildRepSeqNetwork() when constructing the global network. The node-level network properties for the global network (see details) are included among the valid options.
color_scheme	Passed to addPlots() when constructing the global network.
plot_title	Passed to buildRepSeqNetwork() when constructing the global network.
output_dir	Passed to buildRepSeqNetwork() when constructing the global network.
output_name	Passed to buildRepSeqNetwork() when constructing the global network.
verbose	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().
...	Other arguments to buildRepSeqNetwork() (including arguments to addPlots()) when constructing the global network. Does not include node_stats, stats_to_include, cluster_stats or cluster_id_name.

Details

The node-level metadata for the filtered clusters from all samples is combined and the global network is constructed by calling buildNet() with node_stats = TRUE, stats_to_include = "all", cluster_stats = TRUE and cluster_id_name = "ClusterIDPublic".

The computed node-level network properties are renamed to reflect their correspondence to the global network. This is done to distinguish them from the network properties that correspond to the sample-level networks. The names are:

• ClusterIDPublic

• PublicNetworkDegree

• PublicTransitivity

• PublicCloseness

• PublicCentralityByCloseness

• PublicEigenCentrality

• PublicCentralityByEigen

• PublicBetweenness

• PublicCentralityByBetweenness

• PublicAuthorityScore

• PublicCoreness

• PublicPageRank

See the Searching for Public TCR/BCR Clusters article on the package website.

Value

A list of network objects as returned by buildRepSeqNetwork(). The list is returned invisibly. If the input data contains a combined total of fewer than two rows, or if the global network contains no nodes, then the function returns NULL, invisibly, with a warning.

Author(s)

Brian Neal ([e-mail])

References

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

Webpage for the NAIR package

Searching for Public TCR/BCR Clusters article on package website

See Also

findPublicClusters()

buildPublicClusterNetworkByRepresentative()

Examples

set.seed(42)

## Simulate 30 samples with a mix of public/private sequences ##
samples <- 30
sample_size <- 30 # (seqs per sample)
base_seqs <- c(
  "CASSIEGQLSTDTQYF", "CASSEEGQLSTDTQYF", "CASSSVETQYF",
  "CASSPEGQLSTDTQYF", "RASSLAGNTEAFF", "CASSHRGTDTQYF", "CASDAGVFQPQHF",
  "CASSLTSGYNEQFF", "CASSETGYNEQFF", "CASSLTGGNEQFF", "CASSYLTGYNEQFF",
  "CASSLTGNEQFF", "CASSLNGYNEQFF", "CASSFPWDGYGYTF", "CASTLARQGGELFF",
  "CASTLSRQGGELFF", "CSVELLPTGPLETSYNEQFF", "CSVELLPTGPSETSYNEQFF",
  "CVELLPTGPSETSYNEQFF", "CASLAGGRTQETQYF", "CASRLAGGRTQETQYF",
  "CASSLAGGRTETQYF", "CASSLAGGRTQETQYF", "CASSRLAGGRTQETQYF",
  "CASQYGGGNQPQHF", "CASSLGGGNQPQHF", "CASSNGGGNQPQHF", "CASSYGGGGNQPQHF",
  "CASSYGGGQPQHF", "CASSYKGGNQPQHF", "CASSYTGGGNQPQHF",
  "CAWSSQETQYF", "CASSSPETQYF", "CASSGAYEQYF", "CSVDLGKGNNEQFF")
# Relative generation probabilities
pgen <- cbind(
  stats::toeplitz(0.6^(0:(sample_size - 1))),
  matrix(1, nrow = samples, ncol = length(base_seqs) - samples)
)
simulateToyData(
  samples = samples,
  sample_size = sample_size,
  prefix_length = 1,
  prefix_chars = c("", ""),
  prefix_probs = cbind(rep(1, samples), rep(0, samples)),
  affixes = base_seqs,
  affix_probs = pgen,
  num_edits = 0,
  output_dir = tempdir(),
  no_return = TRUE
)


## 1. Find Public Clusters in Each Sample
sample_files <-
  file.path(tempdir(),
            paste0("Sample", 1:samples, ".rds")
  )
findPublicClusters(
  file_list = sample_files,
  input_type = "rds",
  seq_col = "CloneSeq",
  count_col = "CloneCount",
  min_seq_length = NULL,
  drop_matches = NULL,
  top_n_clusters = 3,
  min_node_count = 5,
  min_clone_count = 15000,
  output_dir = tempdir()
)

## 2. Build Global Network of Public Clusters
public_clusters <-
  buildPublicClusterNetwork(
    file_list =
      list.files(
        file.path(tempdir(), "node_meta_data"),
        full.names = TRUE
      ),
    seq_col = "CloneSeq",
    count_col = "CloneCount",
    plot_title = NULL,
    plot_subtitle = NULL,
    print_plots = TRUE
  )

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