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Type:

Package

Title:

Inference Tool for Antibody Haplotype

Version:

0.4.0

Description:

Infers V-D-J (Variable-Diversity-Joining) haplotypes and gene deletions from AIRR-seq (Adaptive Immune Receptor Repertoire sequencing) data for Ig (Immunoglobulin) and TR (T cell Receptor) chains, based on J (Joining), D (Diversity), or V (Variable) genes as anchor, by adapting a Bayesian framework. It also calculates a Bayes factor, a number that indicates the certainty level of the inference, for each haplotyped gene. Citation: Gidoni, et al (2019) <doi:10.1038/s41467-019-08489-3>. Peres and Gidoni, et al (2019) <doi:10.1093/bioinformatics/btz481>.

License:

CC BY-SA 4.0

URL:

https://github.com/ayeletperes/rabhit, https://ayeletperes.github.io/rabhit/

BugReports:

https://github.com/ayeletperes/rabhit/issues

LazyData:

true

BuildVignettes:

true

VignetteBuilder:

knitr

Encoding:

UTF-8

Depends:

R (≥ 3.5.0)

Imports:

ggplot2 (≥ 3.2.0), dplyr (≥ 1.0.0), tidyr (≥ 1.0.0), cowplot (≥ 0.9.1), gtable (≥ 0.3.0), grid, stats, utils, graphics, grDevices

Suggests:

knitr, rmarkdown, plotly (≥ 4.7.1), htmlwidgets (≥ 1.3.0), ggdendro (≥ 0.1.20), piglet, testthat (≥ 3.0.0)

RoxygenNote:

7.3.3

NeedsCompilation:

Config/testthat/edition:

Collate:

'Data.R' 'rabhit.R' 'asc_support.R' 'internal_functions.R' 'functions.R' 'graphic_functions.R' 'zzz.R'

Packaged:

2026-07-16 19:11:48 UTC; ayelet

Author:

Ayelet Peres [aut, cre], Moriah Gidoni [aut], Gur Yaari [aut, cph]

Maintainer:

Ayelet Peres <ayelet.peres@yale.edu>

Repository:

CRAN

Date/Publication:

2026-07-24 09:30:02 UTC

.onAttach start message

Description

.onAttach start message

Usage

.onAttach(libname, pkgname)

Arguments

libname

defunct

pkgname

defunct

Value

invisible()

Human Gene order on the chromosome

Description

A list of the chains genes order by their location on the chromosomes

Usage

GENE.loc

Format

A nested list with three enteries, each a vector of the IG chains (IGH, IGL, and IGK) genes ordered by location.

Human germlines

Description

A list of the germline genes from the human immunoglobulin loci

Usage

GERM

Format

Values correspond to IMGT-gaped nuceltoide sequences (with nucleotides capitalized and gaps represented by '.').

Human IGHD germlines

Description

A character vector of all 37 human IGHD germline gene segment alleles in IMGT Gene-db release 2018-12-4.

Usage

HDGERM

Format

Values correspond to IMGT nuceltoide sequences.

References

Xochelli et al. (2014) Immunoglobulin heavy variable (IGHV) genes and alleles: new entities, new names and implications for research and prognostication in chronic lymphocytic leukaemia. Immunogenetics. 67(1):61-6.

Human IGHJ germlines

Description

A character vector of all 13 human IGHJ germline gene segment alleles in IMGT Gene-db release 2018-12-4.

Usage

HJGERM

Format

Values correspond to IMGT nuceltoide sequences.

References

Human IGHV germlines

Description

A character vector of all 342 human IGHV germline gene segment alleles in IMGT Gene-db release 2018-12-4.

Usage

HVGERM

Format

Values correspond to IMGT-gaped nuceltoide sequences (with nucleotides capitalized and gaps represented by '.').

References

Human IGKJ germlines

Description

A character vector of all 342 human IGKJ germline gene segment alleles in IMGT Gene-db release 2019-11-18.

Usage

KJGERM

Format

Values correspond to IMGT-gaped nuceltoide sequences (with nucleotides capitalized and gaps represented by '.').

Human IGKV germlines

Description

A character vector of all 342 human IGKV germline gene segment alleles in IMGT Gene-db release 2019-11-18.

A character vector of all 342 human IGLV germline gene segment alleles in IMGT Gene-db release 2019-11-18.

Usage

KVGERM

LVGERM

Format

Values correspond to IMGT-gaped nuceltoide sequences (with nucleotides capitalized and gaps represented by '.').

Human IGLJ germlines

Description

A character vector of all 342 human IGLJ germline gene segment alleles in IMGT Gene-db release 2019-11-18.

Usage

LJGERM

Format

Values correspond to IMGT-gaped nuceltoide sequences (with nucleotides capitalized and gaps represented by '.').

Prepare AIRR-seq data and germline for haplotype inference with allele similarity clusters (ASC)

Description

convertToASC converts IMGT-named V alleles to PIgLET Allele Similarity Cluster (ASC) names and returns the pieces needed to run createFullHaplotype on the clustered data. ASCs collapse near-identical and duplicated IGHV alleles (for example IGHV1-69 / IGHV1-69D) that are otherwise difficult to assign unambiguously, and provide a naming scheme compatible with OGRDB reference sets. It is a thin wrapper around the piglet package.

Usage

convertToASC(
  clip_db,
  germline,
  chain = c("IGH", "IGK", "IGL", "TRB"),
  allele_cluster_table = NULL,
  v_call = "v_call",
  ...
)

Arguments

clip_db

AIRR-seq repertoire data.frame.

germline

named vector of IMGT-gapped germline sequences for the V genes (e.g. HVGERM).

chain

the chain. One of "IGH", "IGK", "IGL" or "TRB".

allele_cluster_table

optional precomputed ASC table (a data.frame with iuis_allele and new_allele columns, as produced by piglet::inferAlleleClusters() or downloaded from OGRDB with piglet::recentAlleleClusters() / piglet::extractASCTable()). If NULL it is inferred from germline.

v_call

the column holding the V allele calls to convert. Default "v_call".

...

further arguments passed to piglet::inferAlleleClusters() when allele_cluster_table is inferred.

Details

The returned objects feed directly into the standard workflow:

asc <- convertToASC(clip_db, HVGERM, chain = "IGH")
hap <- createFullHaplotype(asc$clip_db, toHap_col = "v_call",
                           hapBy_col = "j_call", hapBy = "IGHJ6",
                           toHap_GERM = asc$germline, chain = "IGH")
plotHaplotype(hap, genes_order = asc$genes_order)

Allele similarity clustering is applied to the V genes only; D and J calls are left unchanged.

Value

a list with:

clip_db - the input data with v_call converted to ASC names.
germline - the ASC-named germline vector to pass as toHap_GERM.
genes_order - an ASC gene order (derived from the chromosomal order in GENE.loc) to pass to the plotting functions.
allele_cluster_table - the ASC table that was used.

Examples


# requires the Suggested 'piglet' package
if (requireNamespace("piglet", quietly = TRUE)) {
  data(samples_db, HVGERM)
  clip_db <- samples_db[samples_db$subject == "I5", ]
  asc <- convertToASC(clip_db, HVGERM, chain = "IGH")
  hap <- createFullHaplotype(asc$clip_db, toHap_col = "v_call",
                             hapBy_col = "j_call", hapBy = "IGHJ6",
                             toHap_GERM = asc$germline, chain = "IGH")
  plotHaplotype(hap, genes_order = asc$genes_order)
}

Anchor gene haplotype inference

Description

The createFullHaplotype functions infers haplotype based on an anchor gene.

Usage

createFullHaplotype(
  clip_db,
  toHap_col = c("v_call", "d_call"),
  hapBy_col = "j_call",
  hapBy = "IGHJ6",
  toHap_GERM = NULL,
  relative_freq_priors = TRUE,
  kThreshDel = 3,
  rmPseudo = TRUE,
  deleted_genes = c(),
  nonReliable_Vgenes = c(),
  min_minor_fraction = 0.3,
  single_gene = TRUE,
  chain = c("IGH", "IGK", "IGL", "TRB")
)

Arguments

clip_db

a data.frame in AIRR format. See details.

toHap_col

a vector of column names for which a haplotype should be inferred. Default is v_call and d_call

hapBy_col

column name of the anchor gene. Default is j_call

hapBy

a string of the anchor gene name. Default is IGHJ6.

toHap_GERM

a vector of named nucleotide germline sequences matching the allele calls in toHap_col columns in clip_db.

relative_freq_priors

if TRUE, the priors for Bayesian inference are estimated from the relative frequencies in clip_db. Else, priors are set to c(0.5,0.5). Default is TRUE

kThreshDel

the minimum lK (log10 of the Bayes factor) to call a deletion. Default is 3.

rmPseudo

if TRUE non-functional and pseudo genes are removed. Default is TRUE.

deleted_genes

double chromosome deletion summary table. A data.frame created by deletionsByBinom.

nonReliable_Vgenes

a list of known non reliable gene assignments. A list created by nonReliableVGenes.

min_minor_fraction

the minimum minor allele fraction to be used as an anchor gene. Default is 0.3

single_gene

if to only consider genes from single assignment. If true then calls where genes appear with others are discarded. If false then the calls are seperated an counted for all genes that appeared. Default is True.

chain

the IG/TR chain: IGH,IGK,IGL,TRB. Default is IGH.

Details

Function accepts a data.frame in AIRR format (https://changeo.readthedocs.io/en/stable/standard.html) containing the following columns:

'subject': The subject name
'v_call': V allele call(s) (in an IMGT format)
'd_call': D allele call(s) (in an IMGT format, only for heavy chains)
'j_call': J allele call(s) (in an IMGT format)

Value

A data.frame, in which each row is the haplotype inference summary of a gene from the column selected in toHap_col.

The output containes the following columns:

subject: the subject name.
gene: the gene name.
Anchor gene allele 1: the haplotype inference for chromosome one. The column name is the anchor gene with the first allele.
Anchor gene allele 2: the haplotype inference for chromosome two. The column name is the anchor gene with the second allele.
alleles: allele calls for the gene.
proirs_row: priors based on relative allele usage of the anchor gene.
proirs_col: priors based on relative allele usage of the inferred gene.
counts1: the appereance count on each chromosome of the first allele from alleles, the counts are seperated by a comma.
k1: the Bayesian factor value for the first allele (from alleles) inference.
counts2: the appereance count on each chromosome of the second allele from alleles, the counts are seperated by a comma.
k2: the Bayesian factor value for the second allele (from alleles) inference.
counts3: the appereance count on each chromosome of the third allele from alleles, the counts are seperated by a comma.
k3: the Bayesian factor value for the third allele (from alleles) inference.
counts4: the appereance count on each chromosome of the fourth allele from alleles, the counts are seperated by a comma.
k4: the Bayesian factor value for the fourth allele (from alleles) inference.

Examples

# Load example data and germlines
data(samples_db, HVGERM, HDGERM)

# Selecting a single individual
clip_db = samples_db[samples_db$subject=='I5', ]

# Infering haplotype
haplo_db = createFullHaplotype(clip_db,toHap_col=c('v_call','d_call'),
hapBy_col='j_call',hapBy='IGHJ6',toHap_GERM=c(HVGERM,HDGERM))

Create haplotype table

Description

createHaplotypeTable Haplotype of a specific gene

Usage

createHaplotypeTable(
  df,
  HapByPriors = c(0.5, 0.5),
  toHapByCol = TRUE,
  toHapPriors = c(0.5, 0.5)
)

Arguments

df

table of counts

HapByPriors

vector of frequencies of each of the anchor gene alleles

toHapByCol

logical, haplotype each chromosome separetly to imrove the aligner assignmnet

toHapPriors

vector of frequencies of the haplotyped gene alleles

Value

data frame with chromosomal associasions of alleles of a specific gene

Graphical output of single chromosome deletions

Description

The deletionHeatmap function generates a graphical output of the single chromosome deletions in multiple samples.

Usage

deletionHeatmap(
  hap_table,
  chain = c("IGH", "IGK", "IGL", "TRB", "TRA"),
  kThreshDel = 3,
  genes_order = NULL,
  html_output = FALSE
)

Arguments

hap_table

haplotype summary table. See details.

chain

the IG chain: IGH,IGK,IGL. Default is IGH.

kThreshDel

the minimum lK (log10 of the Bayes factor) used in createFullHaplotype to call a deletion. Indicates the color for strong deletion. Default is 3.

genes_order

A vector of the genes by the desired order. Default is by GENE.loc

html_output

If TRUE, a html5 interactive graph is outputed insteaed of the normal plot. Default is FALSE

Details

A data.frame created by createFullHaplotype.

Value

A single chromosome deletion visualization.

Examples

# Plotting single choromosme deletion from haplotype inference
deletionHeatmap(samplesHaplotype)

Double chromosome deletion by relative gene usage

Description

The deletionsByBinom function inferes double chromosome deletion events by relative gene usage.

Usage

deletionsByBinom(
  clip_db,
  chain = c("IGH", "IGK", "IGL"),
  nonReliable_Vgenes = c(),
  genes_order = NULL
)

Arguments

clip_db

a data.frame in AIRR format. See details.

chain

the IG/TR chain: IGH,IGK,IGL,TRB. Default is IGH.

nonReliable_Vgenes

a list of known non reliable gene assignments. A list created by nonReliableVGenes.

genes_order

A vector of the genes by the desired order. Default is by GENE.loc

Details

The function accepts a data.frame in AIRR format (https://changeo.readthedocs.io/en/stable/standard.html) containing the following columns:

'subject': The subject name
'v_call': V allele call(s) (in an IMGT format)
'd_call': D allele call(s) (in an IMGT format, only for heavy chains)
'j_call': J allele call(s) (in an IMGT format)

Value

A data.frame, in which each row is the double chomosome deletion inference of a gene.

The output containes the following columns:

subject: the subject name.
gene: the gene call
frac: the relative gene usage of the gene
cutoff: the the cutoff of for the binomial test
pval: the p-value of the binomial test
deletion: if a double chromosome deletion event of a gene occured.

Examples

# Load example data and germlines
data(samples_db)

# Selecting a single individual
clip_db = samples_db[samples_db$subject=='I5', ]
# Infering haplotype
del_binom_df = deletionsByBinom(clip_db)
head(del_binom_df)

Single chromosomal D or J gene deletions inferred by the V pooled method

Description

The deletionsByVpooled function inferes single chromosomal deletion for D and J gene .

Usage

deletionsByVpooled(
  clip_db,
  chain = c("IGH", "IGK", "IGL"),
  deletion_col = c("d_call", "j_call"),
  count_thresh = 50,
  deleted_genes = "",
  min_minor_fraction = 0.3,
  kThreshDel = 3,
  nonReliable_Vgenes = c()
)

Arguments

clip_db

a data.frame in AIRR format. See details.

chain

the IG chain: IGH,IGK,IGL. Default is IGH.

deletion_col

a vector of column names for which single chromosome deletions should be inferred. Default is j_call and d_call.

count_thresh

integer, the minimun number of sequences mapped to a specific V gene to be included in the V pooled inference.

deleted_genes

double chromosome deletion summary table. A data.frame created by deletionsByBinom.

min_minor_fraction

the minimum minor allele fraction to be used as an anchor gene. Default is 0.3

kThreshDel

the minimum lK (log10 of the Bayes factor) to call a deletion. Default is 3.

nonReliable_Vgenes

a list of known non reliable gene assignments. A list created by nonReliableVGenes.

Details

The function accepts a data.frame in AIRR format (https://changeo.readthedocs.io/en/stable/standard.html) containing the following columns:

'subject': The subject name
'v_call': V allele call(s) (in an IMGT format)
'd_call': D allele call(s) (in an IMGT format, only for heavy chains)
'j_call': J allele call(s) (in an IMGT format)

Value

A data.frame, in which each row is the single chomosome deletion inference of a gene.

The output containes the following columns:

subject: the subject name.
gene: the gene call
deletion: chromosome deletions inferred. Encoded 1 for deletion and 0 for no deletion.
k: the Bayesian factor value for the deletion inference.
counts: the appereance count of the gene on each chromosome, the counts are seperated by a comma.

Examples


data(samples_db)

# Infering V pooled deletions
del_db <- deletionsByVpooled(samples_db)
head(del_db)

Double chromosome deletion by relative gene usage

Description

The geneUsage function calculates the relative gene usage.

Usage

geneUsage(
  clip_db,
  chain = c("IGH", "IGK", "IGL", "TRB"),
  genes_order = NULL,
  rmPseudo = TRUE
)

Arguments

clip_db

a data.frame in AIRR format. See details.

chain

the IG/TR chain: IGH,IGK,IGL,TRB. Default is IGH.

genes_order

A vector of the genes by the desired order. Default is by GENE.loc

rmPseudo

if TRUE non-functional and pseudo genes are removed. Default is TRUE.

Details

The function accepts a data.frame in AIRR format (https://changeo.readthedocs.io/en/stable/standard.html) containing the following columns:

'subject': The subject name
'v_call': V allele call(s) (in an IMGT format)
'd_call': D allele call(s) (in an IMGT format, only for heavy chains)
'j_call': J allele call(s) (in an IMGT format)

Value

A data.frame, in which each row is the relative gene usage value per individual.

The output containes the following columns:

subject: the subject name.
gene: the gene call
frac: the relative gene usage of the gene

Hierarchical clustering of haplotypes graphical output

Description

The hapDendo function generates a graphical output of an hierarchical clustering based on the Jaccard distance between multiple samples' haplotypes.

Usage

hapDendo(
  hap_table,
  chain = c("IGH", "IGK", "IGL", "TRB", "TRA"),
  genes_order = NULL,
  removeIGH = TRUE,
  mark_low_lk = TRUE,
  lk_cutoff = 1
)

Arguments

hap_table

haplotype summary table. See details.

chain

the IG/TR chain: IGH,IGK,IGL,TRB. Default is IGH.

genes_order

A vector of the genes by the desired order. Default is by GENE.loc

removeIGH

if TRUE, 'IGH'\'IGK'\'IGL' prefix is removed from gene names. Default is TRUE.

mark_low_lk

if TRUE, a texture is add for low lK values. Default is TRUE.

lk_cutoff

the lK cutoff value to be considerd low for texture layer. Default is lK<1.

Details

A data.frame created by createFullHaplotype.

Value

A multitple samples visualization of the distances between haplotypes.

Examples

# Plotting haplotype hierarchical clustering based on the Jaccard distance
hapDendo(samplesHaplotype)

Graphical output of alleles division by chromosome

Description

The hapHeatmap function generates a graphical output of the alleles per gene in multiple samples.

Usage

hapHeatmap(
  hap_table,
  chain = c("IGH", "IGK", "IGL", "TRB", "TRA"),
  genes_order = NULL,
  removeIGH = TRUE,
  lk_cutoff = 1,
  mark_low_lk = TRUE,
  size_annot = 1.5,
  color_y = NULL,
  order_subject = NULL,
  file = NULL,
  size_text = NULL,
  ylabel_size = 1
)

Arguments

hap_table

haplotype summary table. See details.

chain

the IG chain: IGH,IGK,IGL. Default is IGH.

genes_order

A vector of the genes by the desired order. Default is by GENE.loc

removeIGH

if TRUE, 'IGH'\'IGK'\'IGL'\'TRB' prefix is removed from gene names.

lk_cutoff

the lK cutoff value to be considered low for texture layer. Default is lK<1.

mark_low_lk

if TRUE, a texture is add for low lK values. Default is TRUE.

size_annot

size of bottom annotation text. Default is 1.5 .

color_y

named list of the colors for y axis labels.

order_subject

order subject by a vector.

file

file path for rendering the plot to pdf. If non is supplied than the plot is returned as object. Default is NULL.

size_text

text size for annotations.

ylabel_size

text size for y axis labels.

Details

A data.frame created by createFullHaplotype.

Value

A list with the following:

'p': heat-map visualization of the haplotype inference for multiple samples.
'width': Optimal width value for rendering plot.
'height': Optimal width value for rendering plot.

When a file is supplied the graph is also rendered to pdf.

Examples

# Plotting haplotpe heatmap
p <- hapHeatmap(samplesHaplotype)
p$p

Detect non reliable gene assignment

Description

nonReliableVGenes Takes a data.frame in AIRR format and detect non reliable IGHV genes. A non reliable gene is when the ratio of the multiple assignments with a gene is below the threshold.

Usage

nonReliableVGenes(clip_db, thresh = 0.9, appearance = 0.01)

Arguments

clip_db

a data.frame in AIRR format. See details.

thresh

the threshold to consider non reliable gene. Default is 0.9

appearance

the minimum fraction of gene appearance to be considered for reliability check. Default is 0.01.

Details

The function accepts a data.frame in AIRR format (https://changeo.readthedocs.io/en/stable/standard.html) containing the following columns:

'subject': subject names
'v_call': V allele call(s) (in an IMGT format)

Value

a nested list of non reliable genes for all subject.

Examples

# Example IGHV call data frame
clip_db <- data.frame(subject=rep('S1',6),
v_call=c('IGHV1-69*01','IGHV1-69*01','IGHV1-69*01,IGHV1-69*02',
'IGHV4-59*01,IGHV4-61*01','IGHV4-59*01,IGHV4-31*02','IGHV4-59*01'))
# Detect non reliable genes
nonReliableVGenes(clip_db)

Graphical output of double chromosome deletions

Description

The plotDeletionsByBinom function generates a graphical output of the double chromosome deletions in multiple samples.

Usage

plotDeletionsByBinom(
  GENE.usage.df,
  chain = c("IGH", "IGK", "IGL", "TRB", "TRA"),
  genes.low.cer = c("IGHV3-43", "IGHV3-20"),
  genes.dup = c("IGHD4-11", "IGHD5-18"),
  genes_order = NULL
)

Arguments

GENE.usage.df

double chromosome deletion summary table. See details.

chain

the IG chain: IGH,IGK,IGL. Default is IGH.

genes.low.cer

a vector of IGH genes known to be with low certantiny in the binomial test. Default is IGHV3-43 and IGHV3-20

genes.dup

a vector of IGH genes known to have a duplicated gene. Default is IGHD4-11 that his duplicate is IGHD4-4 and IGHD5-18 that his duplicate is IGHD5-5

genes_order

A vector of the genes by the desired order. Default is by GENE.loc

Details

A data.frame created by binom_test_deletion.

Value

A double chromosome deletion visualization.

Examples


# Load example data and germlines
data(samples_db)

# Infering haplotype
deletions_db = deletionsByBinom(samples_db);
plotDeletionsByBinom(deletions_db)

Graphical output for single chromosome D or J gene deletions according to V pooled method

Description

The plotDeletionsByVpooled function generates a graphical output for single chromosome D or J gene deletions (for heavy chain only).

Usage

plotDeletionsByVpooled(
  del.df,
  chain = c("IGH", "IGK", "IGL", "TRB", "TRA"),
  K_ranges = c(3, 7)
)

Arguments

del.df

a data.frame created by deletionsByVpooled

chain

the IG chain: IGH,IGK,IGL. Default is IGH..

K_ranges

vector of one or two integers for log(K) certainty level thresholds

Details

A data.frame created by deletionsByVpooled.

Value

A single chromosome deletion visualization.

Examples


# Load example data and germlines
data(samples_db)
del_db <- deletionsByVpooled(samples_db)
plotDeletionsByVpooled(del_db)

Graphical output of an inferred haplotype

Description

The plotHaplotype functions visualizes an inferred haplotype.

Usage

plotHaplotype(
  hap_table,
  html_output = FALSE,
  genes_order = NULL,
  text_size = 14,
  removeIGH = TRUE,
  plotYaxis = TRUE,
  chain = c("IGH", "IGK", "IGL", "TRB"),
  dir,
  width = 15,
  height = 20
)

Arguments

hap_table

haplotype summary table. See details.

html_output

if TRUE, a html5 interactive graph is outputed. Default is FALSE.

genes_order

A vector of the genes by the desired order. Default is by GENE.loc

text_size

the size of graph labels. Default is 14 (pts).

removeIGH

if TRUE, 'IGH'\'IGK'\'IGL'\'TRB' prefix is removed from gene names.

plotYaxis

if TRUE, Y axis labels (gene names) are plotted on the middle and right plots. Default is TRUE.

chain

the Ig/TR chain: IGH,IGK,IGL,TRB. Default is IGH.

dir

The output folder for saving the haplotype map for multiple individuals.

width

width (in inches) of the multi-subject pdf output. Default is 15.

height

height (in inches) of the multi-subject pdf output. Default is 20.

Details

A data.frame in a haplotype format created by createFullHaplotype function.

Value

A haplotype map visualization. If more than one subject is visualized, a pdf is created. If html_output is TRUE, a folder named html_output is created with individual graphs.

Examples


# Selecting a single individual from the haplotype samples data
haplo_db = samplesHaplotype[samplesHaplotype$subject=='I5', ]

# plot haplotype
plotHaplotype(haplo_db)

The RAbHIT package

Description

The rabhit package provides a robust novel method for determining antibody heavy and light chain haplotypes by adapting a Bayesian framework. The key functions in rabhit, broken down by topic, are described below.

Haplotype and deletions inference

rabhit provides tools to infer haplotypes based on given anchor genes, deletion detection based on relative gene usage, pooling v genes, and a single anchor gene.

createFullHaplotype: Haplotypes inference and single chromosome deletions based on an anchor gene.
deletionsByVpooled: Single chromosomal deletion detection by pooling V genes.
deletionsByBinom: Double chromosomal deletion detection by relative gene usage.
geneUsage: Relative gene usage.
nonReliableVGenes: Non reliable gene assignment detection.

Haplotype and deletions visualization

Functions for visualization of the inferred haplotypes and deletions

plotHaplotype: Haplotype inference map.
deletionHeatmap: Single chromosome deletions heatmap.
hapHeatmap: Chromosome comparison of multiple samples.
hapDendo: Hierarchical clustering of multiple haplotypes based on Jaccard distance.
plotDeletionsByVpooled: V pooled based single chromosome deletions heatmap.
plotDeletionsByBinom: Double chromosome deletions heatmap.

References

Gidoni, M., Snir, O., Peres, A., Polak, P., Lindeman, I., Mikocziova, I., . . . Yaari, G. (2019). Mosaic deletion patterns of the human antibody heavy chain gene locus shown by Bayesian haplotyping. Nature Communications, 10(1). doi:10.1038/s41467-019-08489-3

Read a Change-O tab-delimited database file

Description

readHaplotypeDb reads a tab-delimited haplotype file created by a createFullHaplotype into a data.frame. Based on readChangeoDb function from alakazam.

Usage

readHaplotypeDb(file)

Arguments

file

tab-delimited database file output by a Change-O tool.

Value

A data.frame of the haplotype file. Columns will be imported as is, except for the following columns which will be explicitly converted into character values:

alleles
subject

Example haplotype inference results

Description

A data.frame of example haplotype infrence results from createFullHaplotype after double chromosome deletion inference via deletionsByBinom and non reliable V genes detection via nonReliableVGenes. Source data is a colletion of IGH human naive b-cell repertiore data from five individuals (see references). Overall, the data set includes 6 samples. A single individual has two samples (Individual I5), one is short read sequences from BIOMED-2 protocol primers for framework 2 region (The sample is annotated I5_FR2).

Usage

samplesHaplotype

Format

A data.frame, in which each row is the haplotype inference summary of a gene of an individual, from the column selected to prefrom the haplptype infrence on.

References

Gidoni, Moriah, et al. Mosaic deletion patterns of the human antibody heavy chain gene locus shown by Bayesian haplotyping. Nature Communications. 10.1 (2019): 628.

Example IGH human naive b-cell repertiore

Description

A data.frame of example IGH human naive b-cell repertiore data from five individuals (see references). Overall, the data set includes 6 samples. A single individual has two samples (Individual I5), one is short read sequences from BIOMED-2 protocol primers for framework 2 region (The sample is annotated I5_FR2).

Usage

samples_db

Format

A data.frame in Change-O format (https://changeo.readthedocs.io/en/stable/standard.html) containing the following columns:

'SUBJECT': subject names
'V_CALL': V allele call(s) (in an IMGT format)
'D_CALL': D allele call(s) (in an IMGT format, only for heavy chains)
'J_CALL': J allele call(s) (in an IMGT format)

References

Gidoni, Moriah, et al. Mosaic deletion patterns of the human antibody heavy chain gene locus shown by Bayesian haplotyping. Nature Communications. 10.1 (2019): 628.

Package {rabhit}

.onAttach start message

Description

Usage

Arguments

Value

Human Gene order on the chromosome

Description

Usage

Format

Human germlines

Description

Usage

Format

Human IGHD germlines

Description

Usage

Format

References

Human IGHJ germlines

Description

Usage

Format

References

Human IGHV germlines

Description

Usage

Format

References

Human IGKJ germlines

Description

Usage

Format

Human IGKV germlines

Description

Usage

Format

Human IGLJ germlines

Description

Usage

Format

Prepare AIRR-seq data and germline for haplotype inference with allele similarity clusters (ASC)

Description

Usage

Arguments

Details

Value

See Also

Examples

Anchor gene haplotype inference

Description

Usage

Arguments

Details

Value

Examples

Create haplotype table

Description

Usage

Arguments

Value

Graphical output of single chromosome deletions

Description

Usage

Arguments

Details

Value

Examples

Double chromosome deletion by relative gene usage

Description

Usage

Arguments

Details

Value

Examples

Single chromosomal D or J gene deletions inferred by the V pooled method

Description

Usage

Arguments

Details