bcRep (version 1.3.6)

bcRep-package: Advanced Analysis of B Cell Receptor Repertoire Data

Description

This package helps to analyze IMGT/HighV-QUEST output data, in more detail. It functions well with B cells, but can also be used for T cell data, in some cases. Using this package IMGT/HighV-QUEST output files can be readed and sequences and clones studied. In special their functionality, junction frames, gene usage and mutations. Functions to analyze clones, but also to compare sequences and clones, are provided. Further distances based on sequence or gene usage data can be calculated and multidimensional scaling can be performed.

Details

Package:

bcRep

Type:

Package

Version:

1.0

Date:

2015-09-09

For many of the functions output data from IMGT/HighV-QUEST is required. In this case the particularly input file is given in the corresponding help file and readIMGT() can be used to read these files. IMGT files from different folders can be combined using combineIMGT(). For sequence analysis functions like sequences.functionality() or sequences.junctionFrame() exist, which give an overview about functionality or junction frame usage of sequences. Further gene usage (geneUsage()) and gene/gene combinations (sequences.geneComb()) can be analyzed. The function sequences.mutation() returns an overview of all mutations, replacement and silent mutations, as well as the R/S ratio in several regions like V, FR1-3, CDR1-2. IMGT/HighV-QUEST output can also be used to analyze clones (clones()). Therefore criteria can be changed (CDR3 identity, V and J gene usage) and results of different samples be compared (clones.shared()). Further more amino acid distributions, as well as Gini index, richness and diversity of sequences, can be studied (AADistribution(), trueDiversity(), clones.GiniIndex()). Principal coordinate analysis on sequences, as well as on gene usage data can be perfomed, using different distances (sequences.distance(), geneUsage.distance(), dist.PCoA). Special plot function for most of the methods are provided.

References

Alamyar, E. et al., IMGT/HighV-QUEST: A High-Throughput System and Web Portal for the Analysis of Rearranged Nucleotide Sequences of Antigen Receptors, JOBIM 2010, Paper 63 (2010). Website: http://www.imgt.org/

Brochet, X. et al., Nucl. Acids Res. 36, W503-508 (2008). PMID: 18503082

IMGT/V-QUEST Documentation: http://www.imgt.org/IMGT_vquest/share/textes/imgtvquest.html#output3

IMGT Repertoire (IG and TR): http://www.imgt.org/IMGTrepertoire/LocusGenes/

Lou Jost: Entropy and diversity; OIKOS 113:2 (2006)

Examples

## Not run: 
# data(summarytab)
# data(aaseqtab)
# data(aaseqtab2)
# data(mutationtab)
# data(clones.ind)
# data(clones.allind)
# data(vgenes) 
# 
# ## Combine IMGT/HighV-QUEST folders and read data
# combineIMGT(folders = c("pathTo/IMGT1a", "pathTo/IMGT1b", "pathTo/IMGT1c"), 
# name = "NewProject)
# tab<-readIMGT("PathTo/file.txt",filterNoResults=TRUE)
# 
# 
# ## Get information about functionality and filter for functional sequences
# functionality<-sequences.functionality(data = summarytab$Functionality)
# ProductiveSequences<-sequences.getProductives(summarytab)
# 
# ## Gene usage
# Vsubgroup.usage<-geneUsage(genes = clones.ind$V_gene, 
#      functionality = clones.ind$Functionality_all_sequences, level = "subgroup", 
#      abundance="relative")
# 
# Vgenes.comp<-compare.geneUsage(gene.list = list(aaseqtab$V_GENE_and_allele, 
#      aaseqtab2$V_GENE_and_allele), level = "subgroup", abundance = "relative", 
#      names = c("IndA", "IndB"), nrCores = 1)
# plotCompareGeneUsage(comp.tab = Vgenes.comp, color = c("gray97", "darkblue"), 
#      PDF = "Example")
# 
# 
# ## Gene/gene combinations
# VDcomb.tab<-sequences.geneComb(family1 = summarytab$V_GENE_and_allele, 
#      family2 = summarytab$D_GENE_and_allele, level = "subgroup", 
#      abundance = "relative")
# plotGeneComb(geneComb.tab=VDcomb.tab, color="red", withNA=FALSE,PDF="test")
# 
# 
# ## Mutation analysis
# mutation.V<-sequences.mutation(mutationtab = mutationtab, summarytab = summarytab, 
#      sequence = "V")
# mutation.CDR1<-sequences.mutation(mutationtab = mutationtab, sequence = "CDR1", 
#      functionality = TRUE, junctionFr = TRUE)
# 
# ## Defining, Filtering and Plotting Clone features
# clones.tab<-clones(aaseqtab=aaseqtab,summarytab=summarytab, identity=0.85, useJ=TRUE, 
#      dispCDR3aa=TRUE, dispFunctionality.ratio=TRUE, dispFunctionality.list=TRUE)
# plotClonesCDR3Length(CDR3Length = clones.ind$CDR3_length_AA, 
#      functionality = clones.ind$Functionality_all_sequences,  
#      color="gray",abundance="relative", PDF="test")
# clones.func<-clones.filterFunctionality(clones.tab = clones.ind, 
#      filter = "productive")   
# 
# ## Find shared clones between individuals
# sharedclones<-clones.shared(clones.tab = clones.allind, identity = 0.85, useJ = TRUE, 
#      dispD = TRUE, dispCDR3aa = TRUE)
# sharedclones.summary<-clones.shared.summary(shared.tab = sharedclones)
# 
# ## True diversity
# trueDiv<-trueDiversity(sequences = aaseqtab$CDR3_IMGT, order = 1)
# plotTrueDiversity(trueDiversity.tab=trueDiv,color="red",PDF="test")
# 
# trueDiv.comp<-compare.trueDversity(sequence.list = list(aaseqtab$CDR3_IMGT, 
#      aaseqtab2$CDR3_IMGT), names = c("IndA", "IndB"), order = 1, nrCores = 1)
# plotCompareTrueDiversity(comp.tab = trueDiv.comp, PDF = "Example")
# 
# 
# ## Gini index
# gini<-gini<-clones.giniIndex(clone.size=clones.ind$total_number_of_sequences)
# 
# ## Dissmilarity/distance indices of gene usage and sequence data
# 
# distGeneUsage<-geneUsage.distance(geneUsage.tab = Vgenes, method = "bc")
# distSequence<-sequences.distance(sequences = clones.ind$unique_CDR3_sequences_AA, 
#      method = "levenshtein", divLength=TRUE)
# 
# ## Principal coordinate analysis of distance matrices + visualization
# distpcoa<-dist.PCoA(dist.tab = distGeneUsage, correction = "none")
#      # 'groups' data.frame for plot function: in the case, there are no groups:
# groups.vec<-unlist(apply(data.frame(clones.ind$unique_CDR3_sequences_AA),1,
#           function(x){strsplit(x,split=", ")[[1]]}))
# groups.vec<-cbind(groups.vec, 1)
# plotDistPCoA(pcoa.tab = distpcoa, groups=groups.vec, axes = c(1,2), 
#      plotCorrection = FALSE, title = NULL, plotLegend=TRUE, PDF = "TEST")    
# 
# ## End(Not run)