distToNearest - Distance to nearest neighbor
Get non-zero distance of every heavy chain (
IGH) sequence (as defined by
sequenceColumn) to its nearest sequence in a partition of heavy chains sharing the same
V gene, J gene, and junction length (VJL), or in a partition of single cells with heavy chains
sharing the same heavy chain VJL combination, or of single cells with heavy and light chains
sharing the same heavy chain VJL and light chain VJL combinations.
distToNearest( db, sequenceColumn = "JUNCTION", vCallColumn = "V_CALL", jCallColumn = "J_CALL", model = c("ham", "aa", "hh_s1f", "hh_s5f", "mk_rs1nf", "mk_rs5nf", "m1n_compat", "hs1f_compat"), normalize = c("len", "none"), symmetry = c("avg", "min"), first = TRUE, VJthenLen = TRUE, nproc = 1, fields = NULL, cross = NULL, mst = FALSE, subsample = NULL, progress = FALSE, cellIdColumn = NULL, locusColumn = NULL, groupUsingOnlyIGH = TRUE, keepVJLgroup = TRUE )
- data.frame containing sequence data.
- name of the column containing the junction for grouping and for calculating nearest neighbot distances. Note that while both heavy and light chain junctions may be used for VJL grouping, only the heavy chain junction is used to calculate distances.
- name of the column containing the V-segment allele calls.
- name of the column containing the J-segment allele calls.
- underlying SHM model, which must be one of
c("ham", "aa", "hh_s1f", "hh_s5f", "mk_rs1nf", "hs1f_compat", "m1n_compat"). See Details for further information.
- method of normalization. The default is
"len", which divides the distance by the length of the sequence group. If
"none"then no normalization if performed.
- if model is hs5f, distance between seq1 and seq2 is either the average (avg) of seq1->seq2 and seq2->seq1 or the minimum (min).
TRUEonly the first call of the gene assignments is used. if
FALSEthe union of ambiguous gene assignments is used to group all sequences with any overlapping gene calls.
- a Boolean value specifying whether to perform partitioning as a 2-stage
TRUE, partitions are made first based on V and J annotations, and then further split based on junction lengths corresponding to
FALSE, perform partition as a 1-stage process during which V annotation, J annotation, and junction length are used to create partitions simultaneously. Defaults to
- number of cores to distribute the function over.
- additional fields to use for grouping.
- character vector of column names to use for grouping to calculate distances across groups. Meaning the columns that define self versus others.
TRUE, return comma-separated branch lengths from minimum spanning tree.
- number of sequences to subsample for speeding up pairwise-distance-matrix calculation.
Subsampling is performed without replacement in each VJL group of heavy chain sequences.
subsampleis larger than the unique number of heavy chain sequences in each VJL group, then the subsampling process is ignored for that group. For each heavy chain sequence in
db, the reported
DIST_NEARESTis the distance to the closest heavy chain sequence in the subsampled set for the VJL group. If
NULLno subsampling is performed.
TRUEprint a progress bar.
- name of the column containing cell IDs. Only applicable and required for single-cell mode.
- name of the column containing locus information. Only applicable and required for single-cell mode.
- use only heavy chain (
IGH) sequences for VJL grouping, disregarding light chains. Only applicable and required for single-cell mode. Default is
TRUE. Also see groupGenes.
- a Boolean value specifying whether to keep in the output the the column
column indicating grouping based on VJL combinations. Only applicable for
1-stage partitioning (i.e.
VJthenLen=FALSE). Also see groupGenes.
Returns a modified
db data.frame with nearest neighbor distances between heavy chain
sequences in the
DIST_NEAREST column if
specified, distances will be added as the
Note that distances between light chain sequences are not calculated, even if light chains
were used for VJL grouping via
groupUsingOnlyIGH=FALSE. Light chain sequences, if any,
NA in the
To invoke single-cell mode, both
locusColumn must be supplied.
Otherwise, the function will run under non-single-cell mode.
Under single-cell mode, only heavy chain sequences will be used for calculating nearest neighbor
distances. Under non-single-cell mode, all input sequences will be used for calculating nearest
neighbor distances, regardless of the values in the
locusColumn field (if present).
For single-cell mode, the input format is the same as that for groupGenes.
Namely, each row represents a sequence/chain. Sequences/chains from the same cell are linked
by a cell ID in the
cellIdColumn field. Under this mode, there is a choice of whether
grouping should be done using only heavy chain (
IGH) sequences only, or using both
heavy chain (
IGH) and light chain (
IGL) sequences. This is governed
If used, values in the
locusColumn column must be one of
Note that for
distToNearest, a cell with multiple heavy chains is not allowed.
The distance to nearest (heavy chain) neighbor can be used to estimate a threshold for assigning Ig sequences to clonal groups. A histogram of the resulting vector is often bimodal, with the ideal threshold being a value that separates the two modes.
The following distance measures are accepted by the
"ham": Single nucleotide Hamming distance matrix from getDNAMatrix with gaps assigned zero distance.
"aa": Single amino acid Hamming distance matrix from getAAMatrix.
"hh_s1f": Human single nucleotide distance matrix derived from HH_S1F with calcTargetingDistance.
"hh_s5f": Human 5-mer nucleotide context distance matix derived from HH_S5F with calcTargetingDistance.
"mk_rs1nf": Mouse single nucleotide distance matrix derived from MK_RS1NF with calcTargetingDistance.
"mk_rs5nf": Mouse 5-mer nucleotide context distance matrix derived from MK_RS1NF with calcTargetingDistance.
"hs1f_compat": Backwards compatible human single nucleotide distance matrix used in SHazaM v0.1.4 and Change-O v0.3.3.
"m1n_compat": Backwards compatibley mouse single nucleotide distance matrix used in SHazaM v0.1.4 and Change-O v0.3.3.
NAs: if, for a given combination of V gene, J gene, and sequence length,
there is only 1 heavy chain sequence (as defined by
returned instead of a distance (since it has no heavy chain neighbor). If for a given combination
there are multiple heavy chain sequences but only 1 unique one, (in which case every heavy cahin
sequence in this group is the de facto nearest neighbor to each other, thus giving rise to distances
NAs are returned instead of zero-distances.
subsample: Subsampling is performed independently in each VJL group for heavy chain
subsample is larger than number of heavy chain sequences in the group, it is
ignored. In other words, subsampling is performed only on groups in which the number of heavy chain
sequences is equal to or greater than
DIST_NEAREST has values calculated
using all heavy chain sequences in the group for groups with fewer than
subsample heavy chain
sequences, and values calculated using a subset of heavy chain sequences for the larger groups.
To select a value of
subsample, it can be useful to explore the group sizes in
(and the number of heavy chain sequences in those groups).
- Smith DS, et al. Di- and trinucleotide target preferences of somatic mutagenesis in normal and autoreactive B cells. J Immunol. 1996 156:2642-52.
- Glanville J, Kuo TC, von Budingen H-C, et al. Naive antibody gene-segment frequencies are heritable and unaltered by chronic lymphocyte ablation. Proc Natl Acad Sci USA. 2011 108(50):20066-71.
- Yaari G, et al. Models of somatic hypermutation targeting and substitution based on synonymous mutations from high-throughput immunoglobulin sequencing data. Front Immunol. 2013 4:358.
# Subset example data to one sample as a demo data(ExampleDb, package="alakazam") db <- subset(ExampleDb, SAMPLE == "-1h") # Use genotyped V assignments, Hamming distance, and normalize by junction length # First partition based on V and J assignments, then by junction length # Take into consideration ambiguous V and J annotations dist <- distToNearest(db, vCallColumn="V_CALL_GENOTYPED", model="ham", first=FALSE, VJthenLen=TRUE, normalize="len") # Plot histogram of non-NA distances p1 <- ggplot(data=subset(dist, !is.na(DIST_NEAREST))) + theme_bw() + ggtitle("Distance to nearest: Hamming") + xlab("distance") + geom_histogram(aes(x=DIST_NEAREST), binwidth=0.025, fill="steelblue", color="white") plot(p1)