dowser: B Cell Receptor Phylogenetics Toolkit

Description

Provides a set of functions for inferring, visualizing, and analyzing B cell phylogenetic trees. Provides methods to 1) reconstruct unmutated ancestral sequences, 2) build B cell phylogenetic trees using multiple methods, 3) visualize trees with metadata at the tips, 4) reconstruct intermediate sequences, 5) detect biased ancestor-descendant relationships among metadata types Workflow examples available at documentation site (see URL). Citations: Hoehn et al (2022) doi:10.1371/journal.pcbi.1009885, Hoehn et al (2021) doi:10.1101/2021.01.06.425648.

Author(s)

Maintainer: Kenneth Hoehn kenneth.b.hoehn@dartmouth.edu

Authors:

• Cole Jensen cole.jensen@yale.edu

• Jessie Fielding jessie.jo.fielding@dartmouth.edu

• Hunter Melton hunter.j.melton@dartmouth.edu

• Steven Kleinstein steven.kleinstein@yale.edu [copyright holder]

Other contributors:

• Susanna Marquez susanna.marquez@yale.edu [contributor]

• Jason Vander Heiden jason.vanderheiden@gmail.com [contributor]

See Also

Useful links:

• https://dowser.readthedocs.io

• Report bugs at https://github.com/immcantation/dowser/issues

Example Ig lineage trees with biopsy reconstructions.

Description

Same as ExampleClones but with biopsies predicted at internal nodes

Usage

BiopsyTrees

Format

A tibble of airrClone and phylo objects output by getTrees.

• clone_id: Clonal cluster

• data: List of airrClone objects

• seqs: Number of sequences

• trees: List of phylo objects

See Also

BiopsyTrees

Example AIRR database

Description

A small example database subset from Laserson and Vigneault et al, 2014.

Usage

ExampleAirr

Format

A data.frame with the following AIRR style columns:

• sequence_id: Sequence identifier

• sequence_alignment: IMGT-gapped observed sequence.

• germline_alignment_d_mask: IMGT-gapped germline sequence with N, P and D regions masked.

• v_call: V region allele assignments.

• v_call_genotyped: TIgGER corrected V region allele assignment.

• d_call: D region allele assignments.

• j_call: J region allele assignments.

• junction: Junction region sequence.

• junction_length: Length of the junction region in nucleotides.

• np1_length: Combined length of the N and P regions proximal to the V region.

• np2_length: Combined length of the N and P regions proximal to the J region.

• sample: Sample identifier. Time in relation to vaccination.

• isotype: Isotype assignment.

• duplicate_count: Copy count (number of duplicates) of the sequence.

• clone_id: Change-O assignment clonal group identifier.

References

1. Laserson U and Vigneault F, et al. High-resolution antibody dynamics of vaccine-induced immune responses. Proc Natl Acad Sci USA. 2014 111:4928-33.

See Also

ExampleDbChangeo ExampleClones

Example AIRR database for TyCHE

Description

A small example database from simble.

Usage

ExampleAirrTyCHE

Format

A data.frame with the following AIRR style columns:

• sequence_id: Sequence identifier

• sequence_alignment: IMGT-gapped observed sequence.

• germline_alignment: IMGT-gapped germline sequence.

• v_call: V region allele assignments.

• d_call: D region allele assignments.

• j_call: J region allele assignments.

• junction: Junction region sequence.

• junction_length: Length of the junction region in nucleotides.

• np1_length: Combined length of the N and P regions proximal to the V region.

• np2_length: Combined length of the N and P regions proximal to the J region.

• sample_time: Time point of the sample.

• location: Location of tissue from which the sample was taken.

• clone_id: Clonal group identifier.

References

1. Pre-submission.

Example Ig lineage trees

Description

A tibble of Ig lineage trees generated from the ExampleAirr file

Usage

ExampleClones

Format

A tibble of airrClone and phylo objects output by getTrees.

• clone_id: Clonal cluster

• data: List of airrClone objects

• seqs: Number of sequences

• trees: List of phylo objects

See Also

ExampleClones

Example Change-O database

Description

A small example database subset from Laserson and Vigneault et al, 2014.

Usage

ExampleDbChangeo

Format

A data.frame with the following Change-O style columns:

• SEQUENCE_ID: Sequence identifier

• SEQUENCE_IMGT: IMGT-gapped observed sequence.

• GERMLINE_IMGT_D_MASK: IMGT-gapped germline sequence with N, P and D regions masked.

• V_CALL: V region allele assignments.

• V_CALL_GENOTYPED: TIgGER corrected V region allele assignment.

• D_CALL: D region allele assignments.

• J_CALL: J region allele assignments.

• JUNCTION: Junction region sequence.

• JUNCTION_LENGTH: Length of the junction region in nucleotides.

• NP1_LENGTH: Combined length of the N and P regions proximal to the V region.

• NP2_LENGTH: Combined length of the N and P regions proximal to the J region.

• SAMPLE: Sample identifier. Time in relation to vaccination.

• ISOTYPE: Isotype assignment.

• DUPCOUNT: Copy count (number of duplicates) of the sequence.

• CLONE: Change-O assignment clonal group identifier.

References

1. Laserson U and Vigneault F, et al. High-resolution antibody dynamics of vaccine-induced immune responses. Proc Natl Acad Sci USA. 2014 111:4928-33.

See Also

ExampleAirr ExampleClones

Example Multiple Partition Trees

Description

A small example database subset from Turner, J. S. et al. Human germinal centres engage memory and naive B cells after influenza vaccination. Nature 586, 127–132 (2020).

Usage

ExampleMixedClones

Format

A data.frame with the following Change-O style columns:

• clone_id: Clonal cluster

• data: List of airrClone objects

• locus: Locus identifier.

• seqs: Number of sequences

• igphyml_partitioned_trees: IgPhyML partitioned tree

• raxml_partitioned_trees: RAxML partitioned tree

Example Change-O database

Description

A small example database subset from Turner, J. S. et al. Human germinal centres engage memory and naive B cells after influenza vaccination. Nature 586, 127–132 (2020).

Usage

ExampleMixedDb

Format

A data.frame with the following Change-O style columns:

• sequence_id: Sequence identifier

• sequence: B cell sequence

• productive: A logical indicating if the sequence is productive.

• v_call: V region allele assignments.

• d_call: D region allele assignments.

• j_call: J region allele assignments.

• sequence_alignment: Sequence alignment.

• germline_alignment: Germline alignment without gaps.

• junction: Junction

• juncation_aa: Junction aa

• vj_inframe: A logical to see if the vj genes are in frame

• stop_codon: A indicator if there is a stop codon within the alignment

• locus: Locus identifier.

• v_sequence_start: Where the V gene starts

• v_sequence_end: Where the V gene ends

• v_germline_start: Where the V germline starts

• v_germline_end: Where the V germline ends

• np1_length: Length of np1

• d_sequence_start: Where the D gene starts

• d_sequence_end: Where the D gene ends

• d_germline_start: Where the D germline starts

• d_germline_end: Where the D germline ends

• np2_length: Length of np2

• j_sequence_start: Where the J gene starts

• j_sequence_end: Where the J gene ends

• j_germline_start: Where the J germline starts

• j_germline_end: Where the J germline ends

• junction_length: Length of the junction region in nucleotides.

• v_score: V score

• v_identity: Identity score of V

• v_support: V support

• d_score: D score

• d_identity: D identity

• d_support: D support

• j_score: J score

• j_support: J support

• j_identity: J identity

• cell_id: Cell identifier

• consensus_count: Consensus count

• indels: Logical if indels are present

• sequence_vdj: VDJ sequence

• v_germ_start_vdj: Where the V germline starts on the VDJ

• v_germ_end_vdj: Where the V germline ends on the VDJ

• subject: Subject identifier

• timepoint: Day the sample was taken

• cell_type: Type of cell

• replicate: Replicate number

• clone_id: Change-O assignment clonal group identifier.

• seq_type: Identifier of data type (10x)

• vj_gene: VJ gene

• vj_alt_gene: Alternative VJ gene

• v_germline_length: Length of the V germline segment

• d_germline_length: Length of the D germline segment

• j_germline_lenght: Length of the J germline segment

• germline_alignment_d_mask: Germline alignment with gaps

Example Ig lineage trees with isotype reconstructions.

Description

Same as ExampleClones but with isotypes predicted at internal nodes

Usage

IsotypeTrees

Format

A tibble of airrClone and phylo objects output by getTrees.

• clone_id: Clonal cluster

• data: List of airrClone objects

• seqs: Number of sequences

• trees: List of phylo objects

See Also

IsotypeTrees

Example Ig lineage trees sampled over time.

Description

Same as ExampleClones but with timepoint as a trait value

Usage

TimeTrees

Format

A tibble of airrClone and phylo objects output by getTrees.

• clone_id: Clonal cluster

• data: List of airrClone objects

• seqs: Number of sequences

• trees: List of phylo objects

See Also

TimeTrees

S4 class defining a clone in Dowser

Description

airrClone defines a common data structure for perform lineage reconstruction from AIRR data, based heavily on alakazam::ChangeoClone.

Slots

data

data.frame containing sequences and annotations. Contains the columns sequence_id and sequence, as well as any additional sequence-specific annotation columns

clone

string defining the clone identifier

germline

string containing the heavy chain germline sequence for the clone

lgermline

string containing the light chain germline sequence for the clone

hlgermline

string containing the combined germline sequence for the clone

v_gene

string defining the V segment gene call

j_gene

string defining the J segment gene call

junc_len

numeric junction length (nucleotide count)

locus

index showing which locus represented at each site

region

index showing FWR/CDR region for each site

phylo_seq

sequence column used for phylogenetic tree building

numbers

index (usually IMGT) number of each site in phylo_seq

See Also

See formatClones for use.

Deprecated! Please use findSwitches instead.

Description

bootstrapTrees Phylogenetic bootstrap function.

Usage

bootstrapTrees(
  clones,
  bootstraps,
  nproc = 1,
  trait = NULL,
  dir = NULL,
  id = NULL,
  modelfile = NULL,
  build = "pratchet",
  exec = NULL,
  igphyml = NULL,
  fixtrees = FALSE,
  quiet = 0,
  rm_temp = TRUE,
  palette = NULL,
  resolve = 2,
  rep = NULL,
  keeptrees = TRUE,
  lfile = NULL,
  seq = NULL,
  downsample = FALSE,
  tip_switch = 20,
  boot_part = "locus",
  force_resolve = FALSE,
  ...
)

Arguments

Value

A list of trees and/or switch counts for each bootstrap replicate.

Read in a directory from a BEAST run. Runs treeannotator and loganalyser.

Description

Read in a directory from a BEAST run. Runs treeannotator and loganalyser.

Usage

buildBeast(
  data,
  beast,
  time,
  template,
  dir,
  id,
  mcmc_length = 1e+06,
  resume_clones = NULL,
  trait = NULL,
  asr = FALSE,
  full_posterior = FALSE,
  log_every = "auto",
  include_germline = TRUE,
  nproc = 1,
  quiet = 0,
  burnin = 10,
  low_ram = TRUE,
  germline_range = c(-10000, 10000),
  java = TRUE,
  seed = NULL,
  log_target = 10000,
  trees = NULL,
  tree_states = FALSE,
  start_edge_length = 100,
  start_date = NULL,
  max_start_date = NULL,
  ...
)

Arguments

Value

The input clones tibble with an additional column for the bootstrap replicate trees.

See Also

getTimeTrees

buildClonalGermline Determine consensus clone sequence and create germline for clone

Description

Determine consensus clone sequence and create germline for clone

Usage

buildClonalGermline(
  receptors,
  references,
  chain = "IGH",
  use_regions = FALSE,
  vonly = FALSE,
  seq = "sequence_alignment",
  id = "sequence_id",
  clone = "clone_id",
  v_call = "v_call",
  j_call = "j_call",
  j_germ_length = "j_germline_length",
  j_germ_aa_length = "j_germline_aa_length",
  amino_acid = FALSE,
  ...
)

Arguments

Details

Return object adds/edits following columns:

• seq: Sequences potentially padded same length as germline

• germline_alignment: Full length germline

• germline_alignment_d_mask: Full length, D region masked

• vonly: V gene segment of germline if vonly=TRUE

• regions: String of VDJ segment in position if use_regions=TRUE

Value

Tibble with reconstructed germlines

See Also

createGermlines buildGermline, stitchVDJ

buildGermline reconstruct germline segments from alignment data

Description

Reconstruct germlines from alignment data.

Usage

buildGermline(
  receptor,
  references,
  seq = "sequence_alignment",
  id = "sequence_id",
  clone = "clone_id",
  v_call = "v_call",
  d_call = "d_call",
  j_call = "j_call",
  v_germ_start = "v_germline_start",
  v_germ_end = "v_germline_end",
  v_germ_length = "v_germline_length",
  d_germ_start = "d_germline_start",
  d_germ_end = "d_germline_end",
  d_germ_length = "d_germline_length",
  j_germ_start = "j_germline_start",
  j_germ_end = "j_germline_end",
  j_germ_length = "j_germline_length",
  np1_length = "np1_length",
  np2_length = "np2_length",
  amino_acid = FALSE
)

Arguments

Details

Return object contains multiple IMGT-gapped germlines:

• full: Full length germline

• dmask: Full length germline with D region masked

• vonly: V gene segment of germline

• regions: String showing VDJ segment of each position

Value

List of reconstructed germlines

See Also

buildClonalGermline, stitchVDJ

Wrapper to build IgPhyML trees and infer intermediate nodes

Description

Wrapper to build IgPhyML trees and infer intermediate nodes

Usage

buildIgphyml(
  clone,
  igphyml,
  trees = NULL,
  nproc = 1,
  temp_path = NULL,
  id = NULL,
  rseed = NULL,
  quiet = 0,
  rm_files = TRUE,
  rm_dir = NULL,
  partition = c("single", "cf", "hl", "hlf", "hlc", "hlcf"),
  omega = NULL,
  optimize = "lr",
  motifs = "FCH",
  hotness = "e,e,e,e,e,e",
  rates = NULL,
  asrc = 0.95,
  splitfreqs = FALSE,
  asrp = FALSE,
  make_gyrep = TRUE,
  ...
)

Arguments

Details

Partition options in rate order:

• single: 1 omega for whole sequence

• cf: 2 omegas, 1 for all CDRs and 1 for all FWRs

• hl: 2 omegas, 1 for heavy and 1 for light chain

• hlf: 3 omegas, 1 for heavy FWR, 1 for all CDRs, and 1 for light FWRs

• hlc: 3 omegas, 1 for all FWRs, 1 for heavy CDRs, and 1 for light CDRs

• hlcf: 4 omegas, 1 for each heavy FWR, 1 for heavy CDR, 1 for light FWR, and 1 for light CDR

Value

phylo object created by igphyml with nodes attribute containing reconstructed sequences.

Wrapper for phangorn::optim.pml

Description

Wrapper for phangorn::optim.pml

Usage

buildPML(
  clone,
  seq = "sequence",
  sub_model = "GTR",
  gamma = FALSE,
  asr = "seq",
  asr_thresh = 0.05,
  tree = NULL,
  data_type = "DNA",
  optNni = TRUE,
  optQ = TRUE,
  optEdge = TRUE,
  verbose = FALSE,
  resolve_random = TRUE,
  quiet = 0,
  rep = NULL,
  dir = NULL,
  id = NULL,
  asrp = FALSE
)

Arguments

Value

phylo object created by phangorn::optim.pml with nodes attribute containing reconstructed sequences.

Wrapper for alakazam::buildPhylipLineage

Description

Wrapper for alakazam::buildPhylipLineage

Usage

buildPhylo(
  clone,
  exec,
  temp_path = NULL,
  verbose = 0,
  rm_temp = TRUE,
  seq = "sequence",
  tree = NULL,
  onetree = TRUE
)

Arguments

Value

phylo object created by dnapars or dnaml with nodes attribute containing reconstructed sequences.

Wrapper for phangorn::pratchet

Description

Wrapper for phangorn::pratchet

Usage

buildPratchet(
  clone,
  seq = "sequence",
  asr = "seq",
  asr_thresh = 0.05,
  tree = NULL,
  asr_type = "MPR",
  verbose = 0,
  resolve_random = TRUE,
  data_type = "DNA"
)

Arguments

Value

phylo object created by phangorn::pratchet with nodes attribute containing reconstructed sequences.

Wrapper to build RAxML-ng trees and infer intermediate nodes

Description

Wrapper to build RAxML-ng trees and infer intermediate nodes

Usage

buildRAxML(
  clone,
  exec,
  seq = "sequence",
  sub_model = "GTR",
  partition = NULL,
  rseed = 28,
  name = "run",
  starting_tree = NULL,
  data_type = "DNA",
  from_getTrees = FALSE,
  rm_files = TRUE,
  asr = TRUE,
  rep = 1,
  dir = NULL,
  n_starts = NULL,
  ...
)

Arguments

Value

phylo object created by RAxML-ng with nodes attribute containing reconstructed sequences.

Finds the Robinson-Fould's cluster distance between phylogenies.

Description

calcRF Calculates the RF distance between two phylogenetic trees with the same tips and tip labels.

Usage

calcRF(tree_1, tree_2, nproc = 1)

Arguments

Value

The RF cluster value for the two input trees.

Compare divergence along a tree in terms of mutations (sum of branches) for each tip and reconstructed internal node to its Hamming distance from the germline. Divergence should never be less than Hamming distance. A threshold of -1 is used to represent 1 full mutation difference. The function will throw a warning if any trees cross this threshold

Description

Compare divergence along a tree in terms of mutations (sum of branches) for each tip and reconstructed internal node to its Hamming distance from the germline. Divergence should never be less than Hamming distance. A threshold of -1 is used to represent 1 full mutation difference. The function will throw a warning if any trees cross this threshold

Usage

checkDivergence(
  clones,
  threshold = -1,
  verbose = TRUE,
  germline = "Germline",
  data_type = "DNA"
)

Arguments

Value

tibble showing the clone_id, sequence_id, as well as tree-based divergence, hamming distance, and difference between the two.

Collapse internal nodes with the same predicted sequence

Description

collapseNodes Node collapsing function.

Usage

collapseNodes(trees, tips = FALSE, check = TRUE)

Arguments

Details

Use plotTrees(trees)[[1]] + geom_label(aes(label=node)) + geom_tippoint() to show node labels, and getSeq to return internal node sequences

Value

A tibble with phylo objects that have had internal nodes collapsed.

See Also

getTrees

Get a color palette for a predefined set of trait values

Description

colorTree Gets a color palette for a predefined set of trait values

Usage

colorTrees(trees, palette, ambig = "blend")

Arguments

Details

Trees must have node states represented in a "states" vector. By default, ambiguous states (separated by ",") have their colors blended. If

Value

A list of colored trees

See Also

getPalette, getTrees, plotTrees

Condense a set of equally parsimonious node labels into a single tree

Description

condenseTrees Condenses a set of equally parsimonious node labels into a single tree

Usage

condenseTrees(trees, states, palette = NULL)

Arguments

Value

a phylo object representing all represented internal node states

Run date randomization test for temporal signal on a set of trees.

Description

correlationTest performs root-to-tip regression date randomization test

Usage

correlationTest(
  clones,
  permutations = 1000,
  minlength = 0.001,
  perm_type = c("clustered", "uniform"),
  time = "time",
  sequence = "sequence_id",
  germline = "Germline",
  verbose = FALSE,
  polyresolve = TRUE,
  alternative = c("greater", "two.sided"),
  storeTree = FALSE,
  nproc = 1
)

Arguments

Details

Object returned contains these columns which are added or modified from input:

• data: airrClone object, same as input but with additional columns "cluster" which correspond to permutation cluster, and "divergence."

• slope: Slope of linear regression between divergence and time.

• correlation: Correlation between divergence and time.

• p: p value of correlation compared to permuted correlations.

• random_correlation: Mean correlation of permutation replicates.

• min_p: Minimum p value of data, determined by either the number of distinct clade/timepoint combinations or number of permutations.

• nposs: Number of possible distinct timepoint/clade combinations.

• nclust: Number of clusters used in permutation. If perm_type="uniform" this is the number of tips.

• p_gt/p_lt: P value that permuted correlations are greater or less than observed correlation. Only returned if alternative = "two.sided"

• test_trees: The phylo tree objects used, possibly with resolved polytomies.

Value

A tibble with the same columns as clones, but additional columns corresponding to test statistics for each clone.

See Also

Uses output from getTrees.

createGermlines Determine consensus clone sequence and create germline for clone

Description

createGermlines Determine consensus clone sequence and create germline for clone

Usage

createGermlines(
  data,
  references,
  locus = "locus",
  trim_lengths = FALSE,
  force_trim = FALSE,
  nproc = 1,
  seq = "sequence_alignment",
  v_call = "v_call",
  d_call = "d_call",
  j_call = "j_call",
  amino_acid = FALSE,
  id = "sequence_id",
  clone = "clone_id",
  v_germ_start = "v_germline_start",
  v_germ_end = "v_germline_end",
  v_germ_length = "v_germline_length",
  d_germ_start = "d_germline_start",
  d_germ_end = "d_germline_end",
  d_germ_length = "d_germline_length",
  j_germ_start = "j_germline_start",
  j_germ_end = "j_germline_end",
  j_germ_length = "j_germline_length",
  np1_length = "np1_length",
  np2_length = "np2_length",
  na.rm = TRUE,
  fields = NULL,
  verbose = 0,
  ...
)

Arguments

Details

Return object adds/edits following columns:

• seq: Sequences potentially padded same length as germline

• germline_alignment: Full length germline

• germline_alignment_d_mask: Full length, D region masked

• vonly: V gene segment of germline if vonly=TRUE

• regions: String of VDJ segment in position if use_regions=TRUE

Value

Tibble with reconstructed germlines

See Also

createGermlines buildGermline, stitchVDJ

Examples

vdj_dir <- system.file("extdata", "germlines", "imgt", "human", "vdj", package="dowser")
imgt <- readIMGT(vdj_dir)
db <- createGermlines(ExampleAirr[1,], imgt)

Takes an airr clone object and returns BEAST2 XML for MRCA prior of the germline sequence

Description

Takes an airr clone object and returns BEAST2 XML for MRCA prior of the germline sequence

Usage

create_MRCA_prior_germline(clone, id, germline_range)

Arguments

Value

String of XML setting the MRCA prior of the germline sequence

Takes an airr clone object and returns BEAST2 XML for MRCA prior of the observed sequences

Description

Takes an airr clone object and returns BEAST2 XML for MRCA prior of the observed sequences

Usage

create_MRCA_prior_observed(clone, id)

Arguments

Value

String of XML setting the MRCA prior of the observed sequences

Takes an airr clone object and returns BEAST2 Alignment xml of the sequences

Description

Takes an airr clone object and returns BEAST2 Alignment xml of the sequences

Usage

create_alignment(clone, id, include_germline_as_tip)

Arguments

Value

String of BEAST2 Alignment and TaxonSet xml

Takes an airr clone object and returns BEAST2 XML to set a height prior

Description

Takes an airr clone object and returns BEAST2 XML to set a height prior

Usage

create_height_prior(clone, id, start_date)

Arguments

Value

String of XML setting the height prior

Takes an airr clone object and returns BEAST2 XML to set a maximum height prior

Description

Takes an airr clone object and returns BEAST2 XML to set a maximum height prior

Usage

create_max_height_prior(clone, id, max_start_date)

Arguments

Value

String of XML setting the MRCA prior of the observed sequences

Takes an airr clone object and returns BEAST2 rootfreqs xml of the germline

Description

Takes an airr clone object and returns BEAST2 rootfreqs xml of the germline

Usage

create_root_freqs(clone, id)

Arguments

Value

String of XML setting the root frequencies to the germline sequence

Takes an airr clone object and tree and returns BEAST2 XML for setting the starting tree

Description

Takes an airr clone object and tree and returns BEAST2 XML for setting the starting tree

Usage

create_starting_tree(
  clone,
  id,
  tree,
  include_germline_as_tip,
  tree_states,
  start_edge_length
)

Arguments

Value

String of XML setting the starting tree

Takes an airr clone object and returns BEAST2 XML for a trait/traitSet from a column

Description

Takes an airr clone object and returns BEAST2 XML for a trait/traitSet from a column

Usage

create_traitset(
  clone,
  trait_name,
  column,
  id,
  trait_data_type = NULL,
  isSet = FALSE,
  include_germline_as_tip = FALSE
)

Arguments

Value

String of XML of the trait or traitSet

Write a fasta file of sequences readFasta reads a fasta file

Description

Write a fasta file of sequences readFasta reads a fasta file

Usage

dfToFasta(
  df,
  file,
  id = "sequence_id",
  seq = "sequence",
  imgt_gaps = FALSE,
  columns = NULL
)

Arguments

Value

File of FASTA formatted sequences

downsampleClone Down-sample clone to maximum tip/switch ratio

Description

downsampleClone Down-sample clone to maximum tip/switch ratio

Usage

downsampleClone(clone, trait, tip_switch = 20, tree = NULL)

Arguments

Value

A vector with sequence for each locus at a specified node in tree.

The dowser package

Description

dowser is a phylogenetic analysis package as part of the Immcantation suite of tools. For additional details regarding the use of the dowser package see the vignettes:
browseVignettes("dowser")

References

1. Hoehn KB, Pybus OG, Kleinstein SH (2022) Phylogenetic analysis of migration, differentiation, and class switching in B cells. PLoS Computational Biology. https://doi.org/10.1371/journal.pcbi.1009885

Exports the phylogenetic trees from the airrClone object

Description

exportTrees Exports phylogenetic trees

Usage

exportTrees(clones, filepath, tree_column = "trees", ...)

Arguments

Create a bootstrap distribution for clone sequence alignments, and estimate trees for each bootstrap replicate.

Description

findSwitches Phylogenetic bootstrap function.

Usage

findSwitches(
  clones,
  permutations,
  trait,
  igphyml,
  fixtrees = FALSE,
  downsample = TRUE,
  tip_switch = 20,
  nproc = 1,
  dir = NULL,
  id = NULL,
  modelfile = NULL,
  build = "pratchet",
  exec = NULL,
  quiet = 0,
  rm_temp = TRUE,
  palette = NULL,
  resolve = 2,
  rep = NULL,
  keeptrees = FALSE,
  lfile = NULL,
  seq = NULL,
  boot_part = "locus",
  force_resolve = FALSE,
  ...
)

Arguments

Details

Tree building details are the same as getTrees. If keeptrees=TRUE (default) the returned object will contain a list named "trees" which contains a list of estimated tree objects for each bootstrap replicate. The object is structured like: trees[[<replicate>]][[<tree index>]]. If igphyml is specified (as well as trait), the returned object will contain a tibble named "switches" containing switch count information. This object can be passed to testSP and other functions to perform parsimony based trait value tests.

Trait values cannot contain values N, UCA, or NTIP. These are reserved for use by test statistic functions.

Value

A list of trees and/or switch counts for each bootstrap replicate.

See Also

Uses output from formatClones with similar arguments to getTrees. Output can be visualized with plotTrees, and tested with testPS, testSC, and testSP.

Examples

## Not run: 
data(ExampleAirr)
ExampleAirr$sample_id <- sample(ExampleAirr$sample_id)
clones <- formatClones(ExampleAirr, trait="sample_id")

igphyml <- "~/apps/igphyml/src/igphyml"
btrees <- findSwitches(clones[1:2,], permutations=10, nproc=1,
   igphyml=igphyml, trait="sample_id")
plotTrees(btrees$trees[[4]])[[1]]
testPS(btrees$switches)

## End(Not run)

Generate an ordered list of airrClone objects for lineage construction

Description

formatClones takes a data.frame or tibble with AIRR or Change-O style columns as input and masks gap positions, masks ragged ends, removes duplicates sequences, and merges annotations associated with duplicate sequences. If specified, it will un-merge duplicate sequences with different values specified in the traits option. It returns a list of airrClone objects ordered by number of sequences which serve as input for lineage reconstruction.

Usage

formatClones(
  data,
  seq = "sequence_alignment",
  clone = "clone_id",
  subgroup = "clone_subgroup",
  id = "sequence_id",
  germ = "germline_alignment_d_mask",
  v_call = "v_call",
  j_call = "j_call",
  junc_len = "junction_length",
  mask_char = "N",
  max_mask = 0,
  pad_end = TRUE,
  text_fields = NULL,
  num_fields = NULL,
  seq_fields = NULL,
  add_count = TRUE,
  verbose = FALSE,
  collapse = TRUE,
  cell = "cell_id",
  locus = "locus",
  traits = NULL,
  mod3 = TRUE,
  randomize = TRUE,
  use_regions = TRUE,
  dup_singles = FALSE,
  nproc = 1,
  chain = "H",
  heavy = "IGH",
  filterstop = TRUE,
  minseq = 2,
  split_light = FALSE,
  light_traits = FALSE,
  majoronly = FALSE,
  columns = NULL
)

Arguments

Details

This function is a wrapper for makeAirrClone. Also removes whitespace, ;, :, and = from ids

Value

A tibble of airrClone objects containing modified clones.

See Also

Executes in order makeAirrClone. Returns a tibble of airrClone objects which serve as input to getTrees and findSwitches.

Examples

data(ExampleAirr)
# Select two clones, for demonstration purpose
sel <- c("3170", "3184")
clones <- formatClones(ExampleAirr[ExampleAirr$clone_id %in% sel,],traits="sample_id")

Return all tip and internal node sequences

Description

getNodeSeq Sequence retrieval function.

Usage

getAllSeqs(data, imgt_gaps = TRUE)

Arguments

Details

Column names: clone_id = clone id node_id = name of node, either the sequence name if a tip or Node<number> if internal node node = node number in tree. Tips are nodes 1:<number of tips>. locus = locus of sequence sequence = ungapped sequence, either observed for tips or reconstructed for internal nodes sequence_alignment = sequence with IMGT gaps (optional)

Value

A tibble with sequence information for each tip and internal node of a set of trees.

See Also

getTrees getNodeSeq

Creates a bootstrap distribution for clone sequence alignments, and returns estimated trees for each bootstrap replicate as a nested list as a new input tibble column.

Description

getBootstraps Phylogenetic bootstrap function.

Usage

getBootstraps(
  clones,
  bootstraps,
  nproc = 1,
  bootstrap_nodes = TRUE,
  dir = NULL,
  id = NULL,
  build = "pratchet",
  exec = NULL,
  quiet = 0,
  rm_temp = TRUE,
  rep = NULL,
  seq = NULL,
  boot_part = "locus",
  by_codon = TRUE,
  starting_tree = FALSE,
  switches = FALSE,
  ...
)

Arguments

Value

The input clones tibble with an additional column for the bootstrap replicate trees.

Get divergence from root of tree for each tip

Description

getDivergence get sum of branch lengths leading from the root of the tree. If the germline sequence is included in the tree, this will equal the germline divergence. If germline removed, this will equal the MRCA divergence

Usage

getDivergence(phy, minlength = 0.001)

Arguments

Value

A named vector of each tip's divergence from the tree's root.

getGermline get germline segment from specified receptor and segment

Description

getGermline get germline segment from specified receptor and segment

Usage

getGermline(
  receptor,
  references,
  segment,
  field,
  germ_start,
  germ_end,
  germ_length,
  germ_aa_start,
  germ_aa_length,
  amino_acid = FALSE
)

Arguments

Value

String of germline sequence from specified segment aligned with the sequence in the seq column of receptor.

Return IMGT gapped sequence of specified tree node

Description

getNodeSeq Sequence retrieval function.

Usage

getNodeSeq(data, node, tree = NULL, clone = NULL, gaps = TRUE)

Arguments

Details

Use plotTrees(trees)[[1]] + geom_label(aes(label=node))+geom_tippoint() to show node labels, and getNodeSeq to return internal node sequences

Value

A vector with sequence for each locus at a specified node in tree.

See Also

getTrees

Get a color palette for a predefined set of trait values. 'Germline' defaults to black unless specified.

Description

getPalette Gets a color palette for a predefined set of trait values

Usage

getPalette(states, palette)

Arguments

Value

A named vector with each state corresponding to a color

See Also

getTrees, plotTrees

Deprecated! Use getNodeSeq

Description

getSeq Sequence retrieval function.

Usage

getSeq(data, node, tree = NULL, clone = NULL, gaps = TRUE)

Arguments

Value

A vector with sequence for each locus at a specified node in tree.

See Also

getTrees

Make data frames for Bayesian skyline plots

Description

makeSkylines

Usage

getSkylines(
  clones,
  dir,
  id,
  time,
  burnin = 10,
  bins = 100,
  verbose = 0,
  forward = TRUE,
  nproc = 1,
  max_height = c("min", "median", "mean", "max")
)

Arguments

Details

Burnin set from readBEAST or getTrees

Value

Bayesian Skyline values for given clone

Get the tip labels as part of a clade defined by an internal node

Description

getSubTaxa Gets the tip labels from a clade

Usage

getSubTaxa(node, tree)

Arguments

Value

A vector containing tip labels of the clade

Examples

# Get taxa from all subtrees
data(BiopsyTrees)
tree <- BiopsyTrees$trees[[8]]
all_subtrees <- lapply(1:length(tree$nodes), function(x)getSubTaxa(x, tree))

#' Deprecated! Use resolveLightChains

Description

getSubClones plots a tree or group of trees

Usage

getSubclones(
  heavy,
  light,
  nproc = 1,
  minseq = 1,
  id = "sequence_id",
  seq = "sequence_alignment",
  clone = "clone_id",
  cell = "cell_id",
  v_call = "v_call",
  j_call = "j_call",
  junc_len = "junction_length",
  nolight = "missing"
)

Arguments

Value

a tibble containing

Estimate time trees by running BEAST on each clone Applies XML template to each clone

Description

getTimeTrees Tree building function.

Usage

getTimeTrees(
  clones,
  template,
  beast,
  dir,
  id,
  time,
  mcmc_length = 3e+07,
  log_every = "auto",
  burnin = 10,
  trait = NULL,
  resume_clones = NULL,
  nproc = 1,
  quiet = 0,
  rm_temp = FALSE,
  include_germline = TRUE,
  seq = "sequence",
  germline_range = c(-10000, 10000),
  java = TRUE,
  seed = NULL,
  log_target = 10000,
  tree_states = FALSE,
  trees = NULL,
  ...
)

Arguments

Details

For examples and vignettes, see https://dowser.readthedocs.io

Value

A tibble with a column of phylo objects and parameters column

See Also

getTrees, readBEAST

Iteratively resume getTimeTrees until convergence, as defined by all parameters (except those in ignore vector) having ESS greater than or equal to the specified ess_cutoff

Description

getTimeTreesIterate Iteratively resume getTimeTrees til convergence.

Usage

getTimeTreesIterate(
  clones,
  iterations = 10,
  ess_cutoff = 200,
  ignore = c("traitfrequencies"),
  quiet = 0,
  ...
)

Arguments

Details

For examples and vignettes, see https://dowser.readthedocs.io

Value

A tibble of tidytree and airrClone objects.

Estimate lineage tree topologies, branch lengths, and internal node states if desired

Description

getTrees Tree building function.

Usage

getTrees(
  clones,
  trait = NULL,
  id = NULL,
  dir = NULL,
  modelfile = NULL,
  build = "pratchet",
  exec = NULL,
  igphyml = NULL,
  fixtrees = FALSE,
  nproc = 1,
  quiet = 0,
  rm_temp = TRUE,
  palette = NULL,
  seq = NULL,
  collapse = FALSE,
  check_divergence = TRUE,
  ...
)

Arguments

Details

Estimates phylogenetic tree topologies and branch lengths for a list of airrClone objects. By default, it will use phangorn::pratchet to estimate maximum parsimony tree topologies, and ape::acctran to estimate branch lengths. If igpyhml is specified, internal node trait values will be predicted by maximum parsimony. In this case, dir will need to be specified as a temporary directory to place all the intermediate files (will be created if not available). Further, id will need to specified to serve as a unique identifier for the temporary files. This should be chosen to ensure that multiple getTrees calls using the same dir do not overwrite each others files.

modelfile is written automatically if not specified, but doesn't include any constraints. Intermediate files are deleted by default. This can be toggled using (rm_files).

For examples and vignettes, see https://dowser.readthedocs.io

Value

A list of phylo objects in the same order as data.

See Also

formatClones, findSwitches, buildPhylo, buildPratchet, buildPML, buildIgphyml, buildRAxML

Examples

data(ExampleClones)
trees <- getTrees(ExampleClones[10,])
plotTrees(trees)[[1]]

## Not run: 
data(ExampleClones)

trees <- getTrees(ExampleClones[10,],igphyml="/path/to/igphyml",
         id="temp",dir="temp", trait="sample_id")
plotTrees(trees)[[1]]

## End(Not run)

Generate a airrClone object for lineage construction

Description

makeAirrClone takes a data.frame with AIRR or Change-O style columns as input and masks gap positions, masks ragged ends, removes duplicates sequences, and merges annotations associated with duplicate sequences. It returns a airrClone object which serves as input for lineage reconstruction.

Usage

makeAirrClone(
  data,
  id = "sequence_id",
  seq = "sequence_alignment",
  germ = "germline_alignment_d_mask",
  v_call = "v_call",
  j_call = "j_call",
  junc_len = "junction_length",
  clone = "clone_id",
  subgroup = "clone_subgroup",
  mask_char = "N",
  max_mask = 0,
  pad_end = TRUE,
  text_fields = NULL,
  num_fields = NULL,
  seq_fields = NULL,
  add_count = TRUE,
  verbose = FALSE,
  collapse = TRUE,
  chain = "H",
  heavy = NULL,
  cell = "cell_id",
  locus = "locus",
  traits = NULL,
  mod3 = TRUE,
  randomize = TRUE,
  use_regions = TRUE,
  dup_singles = FALSE,
  light_traits = FALSE
)

Arguments

Details

The input data.frame (data) must columns for each of the required column name arguments: id, seq, germ, v_call, j_call, junc_len, and clone. Additional annotation columns specified in the traits, text_fields, num_fields or seq_fields arguments will be retained in the data slot of the return object, but are not required. These options differ by their behavior among collapsed sequences. Identical sequences that differ by any values specified in the traits option will be kept distinct. Identical sequences that differ only by values in the num_fields option will be collapsed and the values of their num_fields columns will be added together. Similar behavior occurs with text_fields but the unique values will concatenated with a comma.

The default columns are IMGT-gapped sequence columns, but this is not a requirement. However, all sequences (both observed and germline) must be multiple aligned using some scheme for both proper duplicate removal and lineage reconstruction.

The value for the germline sequence, V-segment gene call, J-segment gene call, junction length, and clone identifier are determined from the first entry in the germ, v_call, j_call, junc_len and clone columns, respectively. For any given clone, each value in these columns should be identical.

To allow for cases where heavy and light chains are used, this function returns three sequence columns for heavy chains (sequence), light chain (lsequence, empty if none available), and concatenated heavy+light chain (hlsequence). These contain sequences in alignment with germline, lgermline, and hlgermline slots, respectively. The sequence column used for build trees is specified in the phylo_seq slot. Importantly, this column is also the sequence column that also has uninformative columns removed by cleanAlignment. It is highly likely we will change this system to a single sequence and germline slot in the near future.

The airrClone object also contains vectors locus, region, and numbers, which contain the locus, IMGT region, and IMGT number for each position in the sequence column specified in phylo_seq. If IMGT-gapped sequences are not supplied, this will likely result in an error. Specify use_regions=FALSE if not using IMGT-gapped sequences

Value

A airrClone object containing the modified clone.

See Also

Returns an airrClone. See formatClones to generate an ordered list of airrClone objects.

Examples

data(ExampleAirr)
airr_clone <- makeAirrClone(ExampleAirr[ExampleAirr$clone_id=="3184",])

Make a parsimony model file

Description

makeModelFile Filler

Usage

makeModelFile(file, states, constraints = NULL, exceptions = NULL)

Arguments

Details

Currently the only option for constraints is "irrev", which forbids switches moving from left to right in the states vector.

Value

Name of model file

See Also

readModelFile, getTrees, findSwitches

get values for Bayesian Skyline plot

Description

makeSkyline

Usage

makeSkyline(
  logfile,
  treesfile,
  burnin,
  bins = 100,
  youngest = 0,
  clone_id = NULL,
  max_height = c("min", "median", "mean", "max")
)

Arguments

Value

Bayesian Skyline values for given clone

maskCodons Masks codons split by insertions

Description

maskCodons Masks codons split by insertions

Usage

maskCodons(
  id,
  q,
  s,
  keep_alignment = FALSE,
  gap_opening = 5,
  gap_extension = 1,
  keep_insertions = FALSE,
  mask = TRUE
)

Arguments

Details

Performs global alignment of q and s, masks codons in s that are split by insertions (see example) masking_note notes codon positions in subject_alignment sequence that were masked, if found. subject_alignment contains subject sequence aligned to query (q) sequence query_alignment contains query sequence aligned to subject (q) sequence sequence_masked will be NA if frameshift or alignment error detected/

Value

A list with split codons masked, if found (sequence_masked).

See Also

maskSequences, Biostrings::pairwiseAlignment.

Examples

s = "ATCATCATC..."
q = "ATCTTTATCATC"
print(maskCodons(1,q,s,TRUE))

s <- "ATCATCATC..."
q <- "ATTTTCATCATC"
print(maskCodons("test",q,s,keep_alignment=TRUE,keep_insertions=TRUE))

maskSequences Mask codons split by insertions in V gene

Description

maskSequences Mask codons split by insertions in V gene

Usage

maskSequences(
  data,
  sequence_id = "sequence_id",
  sequence = "sequence",
  sequence_alignment = "sequence_alignment",
  v_sequence_start = "v_sequence_start",
  v_sequence_end = "v_sequence_end",
  v_germline_start = "v_germline_start",
  v_germline_end = "v_germline_end",
  junction_length = "junction_length",
  keep_alignment = FALSE,
  keep_insertions = FALSE,
  mask_codons = TRUE,
  mask_cdr3 = TRUE,
  nproc = 1
)

Arguments

Details

Performs global alignment of sequence and sequence_alignment, masking codons in sequence_alignment that are split by insertions (see examples) masking_note notes codon positions in subject_alignment sequence that were masked, if found. subject_alignment contains subject sequence aligned to query sequence (only if keep_alignment=TRUE) query_alignment contains query sequence aligned to subject sequence (only if keep_alignment=TRUE) sequence_masked will be NA if frameshift or alignment error detected. This will be noted insertions column will be returned if keep_insertions=TRUE, contains a comma-separated list of each <position in query alignment>-<sequence>. See example. in masking_note.

Value

A tibble with masked sequence in sequence_masked column, as well as other columns.

See Also

maskCodons, Biostrings::pairwiseAlignment.

Simple function for plotting Bayesian skyline plots

Description

plotSkylines Simple Bayesian skyline plots

Usage

plotSkylines(clones, file = NULL, width = 8.5, height = 11, ...)

Arguments

Value

if no file specified, a list of ggplot objects. If file specified will plot to specified file

See Also

getSkylines readBEAST getTrees

Plot a tree with colored internal node labels using ggtree

Description

plotTrees plots a tree or group of trees

Usage

plotTrees(
  trees,
  nodes = FALSE,
  tips = NULL,
  tipsize = NULL,
  scale = 0.01,
  palette = "Dark2",
  base = FALSE,
  layout = "rectangular",
  node_nums = FALSE,
  tip_nums = FALSE,
  title = TRUE,
  labelsize = NULL,
  common_scale = FALSE,
  ambig = "grey",
  bootstrap_scores = FALSE,
  tip_palette = NULL,
  node_palette = NULL,
  guide_title = NULL,
  branch_lengths = NULL
)

Arguments

Details

Function uses ggtree functions to plot tree topologies estimated by getTrees, and findSwitches. Object can be further modified with ggtree functions. Please check out https://bioconductor.org/packages/devel/bioc/vignettes/ggtree/inst/doc/ggtree.html and cite ggtree in addition to dowser if you use this function.

Value

a grob containing a tree plotted by ggtree.

See Also

getTrees, findSwitches

Examples

data(ExampleClones)
trees <- getTrees(ExampleClones[10,])
plotTrees(trees)[[1]]

Reads in a BEAST output directory

Description

readBEAST Reads in data from BEAST output directory

Usage

readBEAST(
  clones,
  dir,
  id,
  beast,
  burnin = 10,
  trait = NULL,
  nproc = 1,
  quiet = 0,
  full_posterior = FALSE,
  asr = FALSE,
  low_ram = TRUE
)

Arguments

Value

If data is a tibble, then the input clones tibble with additional columns for trees and parameter estimates given the specified burnin. If input is just a list of airrClone objects, it will return the corresponding list of trees given the burnin

Read a fasta file into a list of sequences readFasta reads a fasta file

Description

Read a fasta file into a list of sequences readFasta reads a fasta file

Usage

readFasta(file)

Arguments

Value

List of sequences

readIMGT read in IMGT database

Description

Loads all reference germlines from an Immcantation-formatted IMGT database.

Usage

readIMGT(dir, quiet = FALSE)

Arguments

Details

Input directory must be formatted to Immcantation standard. See https://changeo.readthedocs.io/en/stable/examples/igblast.html for example of how to download.

Value

List of lists, leading to IMGT-gapped nucleotide sequences. Structure of object is list[[locus]][[segment]] locus refers to locus (e.g. IGH, IGK, TRA) segment refers to gene segment category (V, D, or J)

Examples

# vdj_dir contains a minimal example of reference germlines 
# (IGHV3-11*05, IGHD3-10*01 and IGHJ5*02)
# which are the gene assignments for ExampleDb[1,]
vdj_dir <- system.file("extdata", "germlines", "imgt", "human", "vdj", package="dowser")
imgt <- readIMGT(vdj_dir)

Read in all trees from a lineages file

Description

Read in all trees from a lineages file

Usage

readLineages(
  file,
  states = NULL,
  palette = NULL,
  run_id = "",
  quiet = TRUE,
  append = NULL,
  format = "nexus",
  type = "jointpars"
)

Arguments

Value

A list of phylo objects from file.

Read in a parsimony model file

Description

readModelFile Filler

Usage

readModelFile(file, useambig = FALSE)

Arguments

Value

A named vector containing the states of the model

See Also

makeModelFile, findSwitches, getTrees

Do IgPhyML maximum parsimony reconstruction

Description

reconIgPhyML IgPhyML parsimony reconstruction function

Usage

reconIgPhyML(
  file,
  modelfile,
  id,
  igphyml = "igphyml",
  mode = "switches",
  type = "recon",
  nproc = 1,
  quiet = 0,
  rm_files = FALSE,
  rm_dir = NULL,
  states = NULL,
  palette = NULL,
  resolve = 2,
  rseed = NULL,
  force_resolve = FALSE,
  ...
)

Arguments

Value

Either a tibble of switch counts or a list of trees with internal nodes predicted by parsimony.

Reroot phylogenetic tree to have its germline sequence at a zero-length branch to a node which is the direct ancestor of the tree's UCA. Assigns uca to be the ancestral node to the tree's germline sequence, as germid as the tree's germline sequence ID.

Description

Reroot phylogenetic tree to have its germline sequence at a zero-length branch to a node which is the direct ancestor of the tree's UCA. Assigns uca to be the ancestral node to the tree's germline sequence, as germid as the tree's germline sequence ID.

Usage

rerootTree(tree, germline, min = 0.001, verbose = 1)

Arguments

Value

phylo object rooted at the specified germline

Define subgroups within clones based on light chain rearrangements

Description

resolveLightChains resolve light chain V and J subgroups within a clone

Usage

resolveLightChains(
  data,
  nproc = 1,
  minseq = 1,
  locus = "locus",
  heavy = "IGH",
  id = "sequence_id",
  seq = "sequence_alignment",
  clone = "clone_id",
  cell = "cell_id",
  v_call = "v_call",
  j_call = "j_call",
  junc_len = "junction_length",
  nolight = "missing",
  pad_ends = TRUE
)

Arguments

Details

1. Make temporary array containing light chain clones 2. Enumerate all possible V, J, and junction length combinations 3. Determine which combination is the most frequent 4. Assign sequences with that combination to clone t 5. Copy those sequences to return array 6. Remove all cells with that combination from temp array 7. Repeat 1-6 until temporary array zero. If there is more than rearrangement with the same V/J in the same cell, pick the one with the highest non-ambiguous characters. Cells with missing light chains are grouped with their subgroup with the closest matching heavy chain (Hamming distance) then the largest and lowest index subgroup if ties are present.

Outputs of the function are 1. clone_subgroup which identifies the light chain VJ rearrangement that sequence belongs to within it's clone 2. clone_subgroup_id which combines the clone_id variable and the clone_subgroup variable by a "_". 3. vj_cell which combines the vj_gene and vj_alt_cell columns by a ",".

Value

a tibble containing the same data as inputting, but with the column clone_subgroup added. This column contains subgroups within clones that contain distinct light chain V and J genes, with at most one light chain per cell.

Resolve polytomies to have the minimum number of single timepoint clades

Description

Resolve polytomies to have the minimum number of single timepoint clades

Usage

resolvePolytomies(
  phy,
  clone,
  minlength = 0.001,
  time = "time",
  sequence = "sequence_id",
  germline = "Germline",
  verbose = FALSE
)

Arguments

Details

Iteratively identifies polytomies (clusters of < minlength branches), prunes each descendant branch, combines clades with the same timepoint before grouping them back together. Checks to make sure that the divergence of each tip is the same after resolution.

Value

A phylo tree object in which polytomies are resolved to have the minimum number of single timepoint clades.

See Also

Uses output from getTrees during correlationTest.

Run correlationTest, based on https://doi.org/10.1111/2041-210X.12466

Description

runCorrelationTest performs root-to-tip regression permutation test

Usage

runCorrelationTest(
  phy,
  clone,
  permutations,
  minlength = 0.001,
  polyresolve = TRUE,
  permutation = c("clustered", "uniform"),
  time = "time",
  sequence = "sequence_id",
  germline = "Germline",
  verbose = TRUE,
  alternative = c("greater", "two.sided")
)

Arguments

Details

See correlationTest for details

Value

A list of statistics from running the permutation test.

See Also

correlationTest.

sampleCloneMultiGroup Down-sample clone to specified size with one or multiple groups to sample evenly

Description

sampleCloneMultiGroup Down-sample clone to specified size with one or multiple groups to sample evenly

Usage

sampleCloneMultiGroup(clone, size, weight = NULL, group = NULL)

Arguments

Value

a down-sampled airrClone object

sampleClones Down-sample clones to specified size

Description

sampleClones Down-sample clones to specified size

Usage

sampleClones(clones, size, weight = NULL, group = NULL)

Arguments

Value

The input object with sequences down-sampled

Scale branch lengths to represent either mutations or mutations per site.

Description

scaleBranches Branch length scaling function.

Usage

scaleBranches(clones, edge_type = "mutations")

Arguments

Details

Uses clones$trees[[1]]$edge_type to determine how branches are currently scaled.

Value

A tibble with phylo objects that have had branch lengths rescaled as specified.

See Also

getTrees

stitchRegions Similar to stitchVDJ but with segment IDs instead of nucleotides

Description

stitchRegions Similar to stitchVDJ but with segment IDs instead of nucleotides

Usage

stitchRegions(
  receptor,
  v_seq,
  d_seq,
  j_seq,
  np1_length = "np1_length",
  np2_length = "np1_length",
  n1_length = "n1_length",
  p3v_length = "p3v_length",
  p5d_length = "p5d_length",
  p3d_length = "p3d_length",
  n2_length = "n2_length",
  p5j_length = "p5j_length",
  np1_aa_length = "np1_aa_length",
  np2_aa_length = "np2_aa_length",
  amino_acid = FALSE
)

Arguments

Value

Full length germline VDJ sequence with segment IDs instead of nucleotides.

See Also

stitchVDJ

stitchVDJ combines germline gene segments to a single string

Description

stitchVDJ combines germline gene segments to a single string

Usage

stitchVDJ(
  receptor,
  v_seq,
  d_seq,
  j_seq,
  np1_length = "np1_length",
  np2_length = "np2_length",
  np1_aa_length = "np1_aa_length",
  np2_aa_length = "np2_aa_length",
  amino_acid = FALSE
)

Arguments

Value

Full length germline VDJ sequence aligned with aligned with the sequence in the seq column of receptor.

Check whether sequences have in-frame premature stop codons (PTCs)

Description

Check whether sequences have in-frame premature stop codons (PTCs)

Usage

stopCodonCheck(sequences, nproc = 1, check = TRUE)

Arguments

Value

Boolean vector of whether each sequence has an in-frame PTC

Performs PS (parsimony score) test on switch data

Description

testPS performs a PS test

Usage

testPS(
  switches,
  bylineage = FALSE,
  pseudocount = 0,
  alternative = c("less", "two.sided", "greater")
)

Arguments

Details

Output data table columns: RECON = PS for observed data PERMUTE = PS for permuted data DELTA = RECON - PERMUTE PLT = p value for DELTA < 0 PGT = p value for DELTA < 0

• RECON: PS for observed data.

• PERMUTE: PS for permuted data.

• DELTA: RECON - PERMUTE.

• PLT: p value that DELTA < 0

• PGT: p value that DELTA > 0

• STAT: Statistic used (PS).

• REP: Bootstrap repetition.

• REPS: Total number of bootstrap repetition.

Value

A list containing a tibble with mean PS statistics, and another with PS statistics per repetition.

See Also

Uses output from findSwitches. Related to testSP and testSC.

Examples

## Not run: 
igphyml <- "~/apps/igphyml/src/igphyml"
data(ExampleAirr)
ExampleAirr$sample_id <- sample(ExampleAirr$sample_id)
clones <- formatClones(ExampleAirr, trait="sample_id")
btrees <- findSwitches(clones[1:2], bootstraps=10, nproc=1,
   igphyml=igphyml, trait="sample_id")
testPS(btrees$switches)

## End(Not run)

Performs SC (switch count) test on switch data

Description

testSC performs an SC test

Usage

testSC(
  switches,
  dropzeroes = TRUE,
  bylineage = FALSE,
  pseudocount = 0,
  from = NULL,
  to = NULL,
  permuteAll = FALSE,
  alternative = c("two.sided", "greater", "less")
)

Arguments

Details

Output data table columns: RECON = SC for observed data PERMUTE = SC for permuted data DELTA = RECON - PERMUTE PLT = p value for DELTA < 0 PGT = p value for DELTA < 0

• FROM: State going from.

• TO: State going to.

• RECON: SC for observed data.

• PERMUTE: SC for permuted data.

• DELTA: RECON - PERMUTE.

• PLT: p value that DELTA < 0

• PGT: p value that DELTA > 0

• STAT: Statistic used (SC).

• REP: Bootstrap repetition.

• REPS: Total number of bootstrap repetition.

Value

A list containing a tibble with mean SC statistics, and another with SC statistics per repetition.

See Also

Uses output from findSwitches. Related to testPS and testSP.

Examples

## Not run: 
igphyml <- "~/apps/igphyml/src/igphyml"
data(ExampleAirr)
ExampleAirr$sample_id = sample(ExampleAirr$sample_id)
clones = formatClones(ExampleAirr, trait="sample_id")
btrees = findSwitches(clones[1:2], bootstraps=100, nproc=1,
   igphyml=igphyml, trait="sample_id", id="temp", dir="temp")
testSC(btrees$switches)

## End(Not run)

Performs SP (switch proportion) test on switch data

Description

testSP performs an SP test

Usage

testSP(
  switches,
  permuteAll = FALSE,
  from = NULL,
  to = NULL,
  dropzeroes = TRUE,
  bylineage = FALSE,
  pseudocount = 0,
  alternative = c("greater", "two.sided", "less"),
  tip_switch = 20,
  exclude = FALSE
)

Arguments

Details

Output data table columns: RECON = SP for observed data PERMUTE = SP for permuted data DELTA = RECON - PERMUTE PLT = p value for DELTA < 0 PGT = p value for DELTA < 0

• FROM: State going from.

• TO: State going to.

• RECON: SP for observed data.

• PERMUTE: SP for permuted data.

• DELTA: RECON - PERMUTE.

• PLT: p value that DELTA < 0

• PGT: p value that DELTA > 0

• STAT: Statistic used (SP).

• REP: Bootstrap repetition.

• REPS: Total number of bootstrap repetition.

Value

A list containing a tibble with mean SP statistics, and another with SP statistics per repetition.

See Also

Uses output from findSwitches. Related to testPS and testSC.

Examples

## Not run: 
igphyml <- "~/apps/igphyml/src/igphyml"
data(ExampleAirr)
ExampleAirr$sample_id = sample(ExampleAirr$sample_id)
clones = formatClones(ExampleAirr, trait="sample_id")
btrees = findSwitches(clones[1:2], bootstraps=10, nproc=1,
   igphyml=igphyml, trait="sample_id")
testSP(btrees$switches)

## End(Not run)

Simple function for plotting a lot of trees into a pdf

Description

treesToPDF exports trees to a pdf in an orderly fashion

Usage

treesToPDF(plots, file, nrow = 2, ncol = 2, ...)

Arguments

Value

a PDF of tree plots

See Also

plotTrees

Examples

## Not run: 
data(ExampleClones)
trees <- getTrees(ExampleClones[10,])
plots <- plotTrees(trees)
treesToPDF(plots,"test.pdf",width=5,height=6)

## End(Not run)

Write the sequences used in tree building to a fasta format. If there are more than one tree in airrClone output the sequence id will be followed by "|clone_id".

Description

writeCloneSequences Exports the sequences used in tree building.

Usage

writeCloneSequences(clones, file)

Arguments

Write lineage file for IgPhyML use

Description

Write lineage file for IgPhyML use

Usage

writeLineageFile(
  data,
  trees = NULL,
  dir = ".",
  id = "N",
  rep = NULL,
  trait = NULL,
  empty = TRUE,
  partition = "single",
  heavy = "IGH",
  ...
)

Arguments

Value

Name of created lineage file.

Takes an airr clone object and template and writes a BEAST2 XML file

Description

Takes an airr clone object and template and writes a BEAST2 XML file

Usage

write_clone_to_xml(
  clone,
  file,
  id,
  time = NULL,
  trait = NULL,
  trait_data_type = NULL,
  template = NULL,
  mcmc_length = 1e+06,
  log_every = 1000,
  replacements = NULL,
  include_germline_as_root = FALSE,
  include_germline_as_tip = FALSE,
  germline_range = c(-10000, 10000),
  tree = NULL,
  trait_list = NULL,
  log_every_trait = 10,
  tree_states = FALSE,
  start_edge_length = 100,
  start_date = NULL,
  max_start_date = NULL,
  ...
)

Arguments

Value

File path of the written XML file

Wrapper to write multiple clones to XML files

Description

Wrapper to write multiple clones to XML files

Usage

write_clones_to_xmls(
  data,
  id,
  trees = NULL,
  time = NULL,
  trait = NULL,
  template = NULL,
  outfile = NULL,
  replacements = NULL,
  trait_list = NULL,
  mcmc_length = 1e+06,
  log_every = 1000,
  include_germline_as_root = FALSE,
  include_germline_as_tip = FALSE,
  germline_range = c(-10000, 10000),
  tree_states = FALSE,
  start_edge_length = 100,
  start_date = NULL,
  max_start_date = NULL,
  ...
)

Arguments

Value

File paths of the written XML files