Creates a similarity network where clones with similar CDR3s are connected.
VDJ_network(
VDJ,
distance.cutoff,
per.sample,
platypus.version,
known.binders,
hcdr3.only,
is.bulk
)Either (for platypus version "v2") output from VDJ_analyze function. This should be a list of clonotype dataframes, with each list element corresponding to a single VDJ repertoire, OR (for platypus version "v3") the the VDJ matrix output of the VDJ_GEX_matrix() function (VDJ.GEX.matrix.output[[1]])
The threshold Levenshtein distance for which two nodes will be connected on the similarity network.
logical value indicating if a single networks should be produced for each mouse.
Character. Defaults to "v3". Can be "v2" or "v3" dependent on the input format
Either a character vector with cdr3s of known binders or a data frame with cdr3s in the first and the corresponding specificity in the second column. If this parameter is defined, the output will be a network with only edges between known binders and the repertoire nodes and edges between the known binders that have at least one edge to a repertoire node
logical value indicating if the network is based on heavy chain cdr3s (hcdr3.only = T) or pasted heavy and light chain cdr3s (hcdr3.only = F), works for platypus.version 3 only
logical value indicating whether the VDJ input was generated from bulk-sequencing data using the bulk_to_vgm function. If is.bulk = T, the VDJ_network function is compatible for use with bulk data. Defaults to False (F).
returns a list containing networks and network information. If per.sample is set to TRUE then the result will be a network for each repertoire. If per.sample ==F, output[[1]] <- will contain the network, output[[2]] will contain the dataframe with information on each node, such as frequency, mouse origin etc. output[[3]] will contain the connected index - these numbers indicate that the nodes are connected to at least one other node. output[[4]] contains the paired graph - so the graph where only the connected nodes are drawn.
#Platypus v2
#network_out <- VDJ_network(VDJ = VDJ_analyze.out[[1]],per.sample = TRUE,distance.cutoff = 2)
#Platypus v3
network_out <- VDJ_network(VDJ = Platypus::small_vgm[[1]],per.sample = FALSE,distance.cutoff = 2)