Reconstruction of an Ig lineage requires the following steps:
A small example Change-O database,
ExampleDb, is included in the
Lineage reconstruction requires the following fields (columns) to be present
in the Change-O file:
# Load required packages library(alakazam) library(igraph) library(dplyr) # Select clone from example database data(ExampleDb) sub_db <- subset(ExampleDb, CLONE == 3138)
Before a lineage can be constructed the sequences must first be cleaned of gap
(-, .) characters added by IMGT, duplicate sequences must be removed, and
annotations must be combined for each cluster of duplicate sequences.
Optionally, "ragged" ends of sequences, such as may occur from primer template
switching, may also be cleaned by masking mismatched positions and the leading
and trailing ends of each sequence. The function
makeChangeoClone is a wrapper
function which combines these steps and returns a
ChangeoClone object which
may then be passed into the lineage reconstruction function.
Two arguments to
makeChangeoClone control which annotations are retained
following duplicate removal. Unique values appearing within columns given by the
text_fields arguments will be concatenated into a single string delimited by a
"," character. Values appearing within columns given by the
num_fields arguments will be summed.
# This example data set does not have ragged ends # Preprocess clone without ragged end masking (default) clone <- makeChangeoClone(sub_db, text_fields=c("SAMPLE", "ISOTYPE"), num_fields="DUPCOUNT") # Show combined annotations clone@data[, c("SAMPLE", "ISOTYPE", "DUPCOUNT")]
Lineage construction uses the
dnapars (maximum parsimony) application of the
PHYLIP package. The function
buildPhylipLineage performs a number of steps to
dnapars, parse its output, and modify the tree topology to meet the
criteria of an Ig lineage. This function takes as input a
makeChangeoClone and returns an igraph
graph object. The igraph
graph object will contain clone annotations as graph attributes, sequence
annotations as vertex attributes, and mutations along edges as edge attributes.
The system call to
dnapars requires a temporary folder to store input and
output. This is created in the system temporary location (according to
base::tempfile), and is not deleted by default (only because automatically
deleting files is somewhat rude). In most cases, you will want to set
rm_temp=TRUE to delete this folder.
# Run PHYLIP and parse output dnapars_exec <- "~/apps/phylip-3.69/dnapars" graph <- buildPhylipLineage(clone, dnapars_exec, rm_temp=TRUE)
# Load data insted of running phylip # Clone 3138 is at index 23 graph <- ExampleTrees[]
# The graph has shared annotations for the clone data.frame(CLONE=graph$clone, JUNCTION_LENGTH=graph$junc_len, V_GENE=graph$v_gene, J_GENE=graph$j_gene) # The vertices have sequence specific annotations data.frame(SEQUENCE_ID=V(graph)$name, ISOTYPE=V(graph)$ISOTYPE, DUPCOUNT=V(graph)$DUPCOUNT)
Plotting of a lineage tree may be done using the built-in functions of the igraph package. The default edge and vertex labels are edge weights and sequence identifiers, respectively.
# Plot graph with defaults plot(graph)
The default layout and attributes are not very pretty. We can modify the
graphical parameter in the usual igraph ways. A tree layout can be built using
layout_as_tree layout with assignment of the root position to the
germline sequence, which is named "Germline" in the object returned by
# Modify graph and plot attributes V(graph)$color <- "steelblue" V(graph)$color[V(graph)$name == "Germline"] <- "black" V(graph)$color[grepl("Inferred", V(graph)$name)] <- "white" V(graph)$label <- V(graph)$ISOTYPE E(graph)$label <- "" # Remove large default margins par(mar=c(0, 0, 0, 0) + 0.1) # Plot graph plot(graph, layout=layout_as_tree, edge.arrow.mode=0, vertex.frame.color="black", vertex.label.color="black", vertex.size=40) # Add legend legend("topleft", c("Germline", "Inferred", "Sample"), fill=c("black", "white", "steelblue"), cex=0.75)
Which is much better.
Multiple lineage trees may be generated at once, by splitting the Change-O data.frame on the clone column.
# Preprocess clones clones <- ExampleDb %>% group_by(CLONE) %>% do(CHANGEO=makeChangeoClone(., text_fields=c("SAMPLE", "ISOTYPE"), num_fields="DUPCOUNT"))
# Build lineages dnapars_exec <- "~/apps/phylip-3.69/dnapars" graphs <- lapply(clones$CHANGEO, buildPhylipLineage, dnapars_exec=dnapars_exec, rm_temp=TRUE)
# Load data insted of running phylip graphs <- ExampleTrees
# Note, clones with only a single sequence will not be processed. # A warning will be generated and NULL will be returned by buildPhylipLineage # These entries may be removed for clarity graphs[sapply(graphs, is.null)] <- NULL # The set of tree may then be subset by node count for further # analysis, if desired. graphs <- graphs[sapply(graphs, vcount) >= 5]
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