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Given Adaptive Immune Receptor Repertoire Sequencing (AIRR-Seq) data, builds the network graph for the immune repertoire based on sequence similarity, computes specified network properties and generates customized visualizations.

buildNet() is identical to buildRepSeqNetwork(), existing as an alias for convenience.

Usage

buildRepSeqNetwork(

  ## Input ##
  data,
  seq_col,
  count_col = NULL,
  subset_cols = NULL,
  min_seq_length = 3,
  drop_matches = NULL,

  ## Network ##
  dist_type = "hamming",
  dist_cutoff = 1,
  drop_isolated_nodes = TRUE,
  node_stats = FALSE,
  stats_to_include = chooseNodeStats(),
  cluster_stats = FALSE,
  cluster_fun = "fast_greedy",
  cluster_id_name = "cluster_id",

  ## Visualization ##
  plots = TRUE,
  print_plots = FALSE,
  plot_title = "auto",
  plot_subtitle = "auto",
  color_nodes_by = "auto",
  ...,

  ## Output ##
  output_dir = NULL,
  output_type = "rds",
  output_name = "MyRepSeqNetwork",
  pdf_width = 12,
  pdf_height = 10,
  verbose = FALSE

)

# Alias for buildRepSeqNetwork()
buildNet(
  data,
  seq_col,
  count_col = NULL,
  subset_cols = NULL,
  min_seq_length = 3,
  drop_matches = NULL,
  dist_type = "hamming",
  dist_cutoff = 1,
  drop_isolated_nodes = TRUE,
  node_stats = FALSE,
  stats_to_include = chooseNodeStats(),
  cluster_stats = FALSE,
  cluster_fun = "fast_greedy",
  cluster_id_name = "cluster_id",
  plots = TRUE,
  print_plots = FALSE,
  plot_title = "auto",
  plot_subtitle = "auto",
  color_nodes_by = "auto",
  ...,
  output_dir = NULL,
  output_type = "rds",
  output_name = "MyRepSeqNetwork",
  pdf_width = 12,
  pdf_height = 10,
  verbose = FALSE

)

Arguments

data

A data frame containing the AIRR-Seq data, with variables indexed by column and observations (e.g., clones or cells) indexed by row.

seq_col

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).

count_col

Optional. Specifies the column of data containing a measure of abundance, e.g., clone count or unique molecular identifier (UMI) count. Accepts either the column name or column index. Passed to addClusterStats(); only relevant if cluster_stats = TRUE.

subset_cols

Specifies which columns of the AIRR-Seq data are included in the output. Accepts a vector of column names or a vector of column indices. The default NULL includes all columns. The receptor sequence column is always included regardless of this argument's value. Passed to filterInputData().

min_seq_length

A numeric scalar, or NULL. Observations whose receptor sequences have fewer than min_seq_length characters are removed prior to network analysis.

drop_matches

Optional. Passed to filterInputData(). Accepts a character string containing a regular expression (see regex). Checks receptor sequences for a pattern match using grep(). Those returning a match are removed prior to network analysis.

dist_type

Specifies the function used to quantify the similarity between sequences. The similarity between two sequences determines the pairwise distance between their respective nodes in the network graph, with greater similarity corresponding to shorter distance. Valid options are "hamming" (the default), which uses hamDistBounded(), and "levenshtein", which uses levDistBounded().

dist_cutoff

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.

drop_isolated_nodes

A logical scalar. When TRUE, removes each node that is not joined by an edge connection to any other node in the network graph.

node_stats

A logical scalar. Specifies whether node-level network properties are computed.

stats_to_include

A named logical vector returned by chooseNodeStats() or exclusiveNodeStats(). Specifies the node-level network properties to compute. Also accepts the value "all". Only relevant if node_stats = TRUE.

cluster_stats

A logical scalar. Specifies whether to compute cluster-level network properties.

cluster_fun

Passed to addClusterMembership(). Specifies the clustering algorithm used when cluster analysis is performed. Cluster analysis is performed when cluster_stats = TRUE or when node_stats = TRUE with the cluster_id property enabled via the stats_to_include argument.

cluster_id_name

Passed to addClusterMembership(). Specifies the name of the cluster membership variable added to the node metadata when cluster analysis is performed (see cluster_fun).

plots

A logical scalar. Specifies whether to generate plots of the network graph.

print_plots

A logical scalar. If plots = TRUE, specifies whether the plots should be printed to the R plotting window.

plot_title

A character string or NULL. If plots = TRUE, this is the title used for each plot. The default value "auto" generates the title based on the value of the output_name argument.

plot_subtitle

A character string or NULL. If plots = TRUE, this is the subtitle used for each plot. The default value "auto" generates a subtitle based on the values of the dist_type and dist_cutoff arguments.

color_nodes_by

Optional. Specifies a variable to be used as metadata for coloring the nodes in the network graph plot. Accepts a character string. This can be a column name of data or (if node_stats = TRUE) the name of a computed node-level network property (based on stats_to_include). Also accepts a character vector specifying multiple variables, in which case one plot will be generated for each variable. The default value "auto" attempts to use one of several potential variables to color the nodes, depending on what is available. A value of NULL leaves the nodes uncolored.

...

Other named arguments to addPlots().

output_dir

A file path specifying the directory for saving the output. The directory will be created if it does not exist. If NULL, output will be returned but not saved.

output_type

A character string specifying the file format to use when saving the output. The default value "individual" saves each element of the returned list as an individual uncompressed file, with data frames saved in csv format. For better compression, the values "rda" and "rds" save the returned list as a single file using the rda and rds format, respectively (in the former case, the list will be named net within the rda file). Regardless of the argument value, any plots generated will saved to a pdf file containing one plot per page.

output_name

A character string. All files saved will have file names beginning with this value.

pdf_width

Sets the width of each plot when writing to pdf. Passed to saveNetwork().

pdf_height

Sets the height of each plot when writing to pdf. Passed to saveNetwork().

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().

Details

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 in the graph are joined by an edge if the distance between them is sufficiently small, i.e., if their receptor sequences are sufficiently similar.

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").

Value

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:

details

A list containing information about the network and the settings used during its construction.

igraph

An object of class igraph containing the list of nodes and edges for the network graph.

adjacency_matrix

The network graph adjacency matrix, stored as a sparse matrix of class dgCMatrix from the Matrix package. See dgCMatrix-class.

node_data

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.

cluster_data

A data frame containing network properties for the clusters, where each row corresponds to a cluster in the network graph. Only included if cluster_stats = TRUE.

plots

A list containing one element for each plot generated as well as an additional element for the matrix that specifies the graph layout. Each plot is an object of class ggraph. Only included if plots = TRUE.

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

buildRepSeqNetwork vignette

Author

Brian Neal (Brian.Neal@ucsf.edu)

Examples

set.seed(42)
toy_data <- simulateToyData()

# Simple call
network = buildNet(
  toy_data,
  seq_col = "CloneSeq",
  print_plots = TRUE
)


# Customized:
network <- buildNet(
  toy_data, "CloneSeq",
  dist_type = "levenshtein",
  node_stats = TRUE,
  cluster_stats = TRUE,
  cluster_fun = "louvain",
  cluster_id_name = "cluster_membership",
  count_col = "CloneCount",
  color_nodes_by = c("SampleID", "cluster_membership", "coreness"),
  color_scheme = c("default", "Viridis", "plasma-1"),
  size_nodes_by = "degree",
  node_size_limits = c(0.1, 1.5),
  plot_title = NULL,
  plot_subtitle = NULL,
  print_plots = TRUE,
  verbose = TRUE
)
#> Input data contains 200 rows.
#> Removing sequences with length fewer than 3 characters...
#>  Done. 200 rows remaining.
#> Computing network edges based on a max levenshtein distance of 1...
#>  Done.
#> Network contains 124 nodes (after removing isolated nodes).
#> Network objects and node metadata stored in a list
#> Obtaining cluster properties...
#> Partitioning the network graph into clusters...
#>  Done.
#> Cluster membership variable ‘cluster_membership’ added to node metadata.
#> Computing statistics for the 21 clusters in the network...
#>  Done.
#> Data frame ‘cluster_data’ added to list of network objects.
#> Generating graph plot with nodes colored by SampleID...
#>  Done.
#> Generating graph plot with nodes colored by cluster_membership...

#>  Done.
#> Generating graph plot with nodes colored by coreness...

#>  Done.
#> New plots added to ‘net$plots’
#> ‘output_dir’ is “NULL”. Output will not be saved.


typeof(network)
#> [1] "list"

names(network)
#> [1] "details"          "igraph"           "adjacency_matrix" "node_data"       
#> [5] "cluster_data"     "plots"           

network$details
#> $seq_col
#> [1] "CloneSeq"
#> 
#> $dist_type
#> [1] "levenshtein"
#> 
#> $dist_cutoff
#> [1] 1
#> 
#> $drop_isolated_nodes
#> [1] TRUE
#> 
#> $nodes_in_network
#> [1] 124
#> 
#> $clusters_in_network
#> louvain 
#>      21 
#> 
#> $cluster_id_variable
#>              louvain 
#> "cluster_membership" 
#> 
#> $cluster_data_goes_with
#> [1] "cluster_membership"
#> 
#> $count_col_for_cluster_data
#> [1] "CloneCount"
#> 
#> $min_seq_length
#> [1] 3
#> 
#> $drop_matches
#> [1] "NULL"
#> 

head(network$node_data)
#>         CloneSeq CloneFrequency CloneCount SampleID degree transitivity
#> 2  GGAGATGAATCGG    0.007777102       3057  Sample1      1          NaN
#> 5  GAAAGAGAATTCG    0.009336593       3670  Sample1      3    0.3333333
#> 8  GGGGAGAAATTGG    0.006220155       2445  Sample1      2    1.0000000
#> 11 GGGGGAGAATTGC    0.012969469       5098  Sample1      4    0.6666667
#> 12 GGGGGGGAATTGC    0.009079646       3569  Sample1     10    0.3555556
#> 13 AGGGGGAAATTGG    0.014941093       5873  Sample1      5    0.1000000
#>    eigen_centrality centrality_by_eigen betweenness centrality_by_betweenness
#> 2        0.00000000          0.00000000    0.000000                  0.000000
#> 5        0.00000000          0.00000000   48.619048                 48.619048
#> 8        0.04558284          0.04558284    0.000000                  0.000000
#> 11       0.15054597          0.15054597    2.156593                  2.156593
#> 12       0.52695160          0.52695160  111.034076                111.034076
#> 13       0.14681104          0.14681104   77.439560                 77.439560
#>    authority_score coreness   page_rank cluster_membership
#> 2     5.280244e-19        1 0.008064516                  1
#> 5     6.266760e-18        2 0.009136977                  2
#> 8     4.558284e-02        2 0.003869458                  3
#> 11    1.505460e-01        4 0.004979346                  3
#> 12    5.269516e-01        6 0.011562784                  3
#> 13    1.468110e-01        3 0.008554352                  4

head(network$cluster_data)
#>   cluster_id node_count mean_seq_length mean_degree max_degree seq_w_max_degree
#> 1          1          2           13.00        1.00          1    GGAGATGAATCGG
#> 2          2          7           12.86        2.29          3    GAAAGAGAATTCG
#> 3          3         26           13.00        8.96         18    GGGGGGGAATTGG
#> 4          4          3           13.00        2.33          5    AGGGGGAAATTGG
#> 5          5          2           13.00        1.00          1    GGTTAGGAATTCG
#> 6          6          5           13.00        3.60          7    AGGGGAGAATTGG
#>   agg_count max_count seq_w_max_count diameter_length global_transitivity
#> 1      8299      5242   GGAGCTGAATCGG               2                 NaN
#> 2     23255      4422   GAAATAGAATTCG               6           0.3333333
#> 3    104676      6526   GGGGGGGAATTGG               6           0.6223539
#> 4     12596      5873   AGGGGGAAATTGG               3           0.0000000
#> 5      7630      4553   GGTTAGGAATTCG               2                 NaN
#> 6     17971      4422   AGCGGAGAATTGG               3           0.8000000
#>   assortativity edge_density degree_centrality_index closeness_centrality_index
#> 1           NaN    1.0000000               0.0000000                        NaN
#> 2   -0.16666667    0.3333333               0.1666667                  0.3650918
#> 3   -0.09412785    0.3353846               0.3446154                  0.4808997
#> 4   -1.00000000    0.6666667               0.3333333                  1.0000000
#> 5           NaN    1.0000000               0.0000000                        NaN
#> 6   -0.55555556    0.7000000               0.3000000                  0.6000000
#>   eigen_centrality_index eigen_centrality_eigenvalue
#> 1                    NaN                    1.000000
#> 2              0.5678500                    2.342923
#> 3              0.5216029                   11.819166
#> 4              0.5857864                    1.414214
#> 5                    NaN                    1.000000
#> 6              0.3046233                    3.086130