1. LymphoSeq
  2. Commit fe276997a88644f231d4a3fc5896bcbc718b7b99
Browse code

Added functions alignSeq, phyloSeq, exportFasta, differentialAbundance

davidcoffey authored on 10/05/2016 23:14:17
Showing 16 changed files
.Rbuildignore
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+^.*\.Rproj$
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+^\.Rproj\.user$
.gitignore
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+.Rproj.user
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+.Rhistory
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+.RData
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+.Ruserdata
DESCRIPTION
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@@ -10,7 +10,8 @@ Description: This R package analyzes high-throughput sequencing of T
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         exported from the ImmunoSEQ analyzer.
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 Depends: R (>= 3.2), LymphoSeqDB
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 Imports: data.table, plyr, dplyr, reshape, VennDiagram, ggplot2, ineq,
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-        RColorBrewer, circlize, grid, utils, stats
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+        RColorBrewer, circlize, grid, utils, stats, ggtree, msa, Biostrings,
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+        phangorn, doMC
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 License: Artistic-2.0
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 LazyData: true
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 RoxygenNote: 5.0.1
LymphoSeq.Rproj
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+Version: 1.0
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+
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+RestoreWorkspace: Default
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+SaveWorkspace: Default
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+AlwaysSaveHistory: Default
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+
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+EnableCodeIndexing: Yes
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+UseSpacesForTab: Yes
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+NumSpacesForTab: 4
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+Encoding: UTF-8
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+
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+RnwWeave: knitr
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+LaTeX: pdfLaTeX
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+
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+BuildType: Package
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+PackageUseDevtools: Yes
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+PackageInstallArgs: --no-multiarch --with-keep.source
NAMESPACE
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 # Generated by roxygen2: do not edit by hand
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+export(alignSeq)
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 export(bhattacharyyaCoefficient)
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 export(bhattacharyyaMatrix)
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 export(chordDiagramVDJ)
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@@ -8,10 +9,13 @@ export(cloneTrack)
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 export(commonSeqs)
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 export(commonSeqsPlot)
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 export(commonSeqsVenn)
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+export(differentialAbundance)
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+export(exportFasta)
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 export(geneFreq)
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 export(lorenzCurve)
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 export(mergeFiles)
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 export(pairwisePlot)
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+export(phyloTree)
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 export(productive)
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 export(productiveSeq)
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 export(readImmunoSeq)
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@@ -27,21 +31,32 @@ export(topSeqsPlot)
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 export(uniqueSeqs)
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 import(LymphoSeqDB)
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 import(ggplot2)
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+import(ggtree)
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+import(msa)
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+importFrom(Biostrings,AAStringSet)
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+importFrom(Biostrings,DNAStringSet)
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+importFrom(Biostrings,writeXStringSet)
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 importFrom(RColorBrewer,brewer.pal)
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 importFrom(VennDiagram,draw.pairwise.venn)
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 importFrom(VennDiagram,draw.triple.venn)
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 importFrom(circlize,chordDiagram)
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 importFrom(circlize,colorRamp2)
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 importFrom(data.table,fread)
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+importFrom(doMC,registerDoMC)
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 importFrom(dplyr,group_by)
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 importFrom(dplyr,summarise)
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 importFrom(grid,grid.draw)
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 importFrom(grid,grid.newpage)
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 importFrom(ineq,Gini)
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 importFrom(ineq,Lc)
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+importFrom(phangorn,NJ)
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+importFrom(phangorn,as.phyDat)
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+importFrom(phangorn,dist.ml)
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 importFrom(plyr,ldply)
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 importFrom(plyr,llply)
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 importFrom(reshape,melt.data.frame)
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 importFrom(stats,aggregate)
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+importFrom(stats,fisher.test)
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 importFrom(stats,na.omit)
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+importFrom(stats,p.adjust)
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 importFrom(utils,adist)
R/alignSeq.R
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+#' Align mutliple sequences
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+#'
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+#' Perform multiple sequence alignment using one of three methods and output results as a pdf file.
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+#'
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+#' @param list A list of data frames consisting of antigen receptor sequences
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+#' imported by the LymphoSeq function readImmunoSeq.
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+#' @param sample A character vector indicating the name of the sample in the productive
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+#' sequence list.
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+#' @param type A character vector indicating whether "aminoAcid" or "nucleotide" sequences
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+#' should be aligned.  If "aminoAcid" is specified, then run productiveSeqs first.
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+#' @param method A character vector indicating the multiple sequence alignment method to
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+#' be used.  Refer to the Bioconductor msa package for more details.  Options incude
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+#' "ClustalW", "ClustalOmega", and "Muscle".
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+#' @return Saves a pdf file of the multiple sequence alignemnt to the working directory.
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+#' @examples
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+#' file.path <- system.file("extdata", "TCRB_sequencing", package = "LymphoSeq")
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+#'
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+#' file.list <- readImmunoSeq(path = file.path)
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+#'
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+#' productive.nt <- productiveSeq(file.list = file.list, aggregate = "nucleotide")
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+#'
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+#' alignSeq(list = productive.nt, sample = "TCRB_Day1320_CD8_CMV", type = "nucleotide",
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+#'          method = "ClustalW")
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+#' @export
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+#' @importFrom Biostrings DNAStringSet
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+#' @importFrom Biostrings AAStringSet
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+#' @import msa
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+alignSeq = function(list, sample, type = "nucleotide", method = "ClustalOmega") {
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+    file = list[[sample]]
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+    if(nrow(file) > 150){
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+        file = file[1:150,]
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+        message("Only the first 150 sequences will be aligned")
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+    }
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+    file[file == ""] <- "unresolved"
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+    if(type == "nucleotide"){
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+        file = file[nchar(file$nucleotide) > 9, ]
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+        string = Biostrings::DNAStringSet(file$nucleotide)
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+    }
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+    if(type == "aminoAcid"){
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+        file = file[nchar(file$aminoAcid) > 3, ]
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+        string = Biostrings::AAStringSet(file$aminoAcid)
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+    }
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+        names(string) = paste(file$vFamilyName, file$dFamilyName, file$jFamilyName, file$count)
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+        names(string) = gsub(names(string), pattern = "IGH|IGL|IGK|TCRB|TCRA", replacement = "")
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+        names(string) = gsub(names(string), pattern = "unresolved", replacement = "UNR")
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+        alignment = msa::msa(string, method = method)
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+        msa::msaPrettyPrint(alignment, output = "pdf", file = paste(names(list[sample]), ".pdf", sep = ""),
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+                       showNumbering = "right", showNames = "left", showConsensus = "top",
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+                       shadingMode = "similar", shadingColors = "blues",
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+                       paperWidth = ncol(alignment)*0.1 + 5, paperHeight = nrow(alignment)*0.1 + 5,
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+                       showLogo = "bottom", showLogoScale = "right", logoColors = "rasmol",
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+                       psFonts = TRUE, showLegend = TRUE, askForOverwrite = FALSE,
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+                       furtherCode=c("\\defconsensus{.}{lower}{upper}","\\showruler{1}{top}"))
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+        message(paste("Aligment files saved to", getwd()))
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+}
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R/differentialAbundance.R
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+#' Differential abundance analysis
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+#'
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+#' Use a Fisher exact test to calculate differential abdunance of each sequence in
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+#' two samples and report the log2 transformed fold change, P value and adjusted P value.
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+#'
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+#' @param list A list of data frames consisting of antigen receptor sequences
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+#' imported by the LymphoSeq function readImmunoSeq.
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+#' @param sample1 A character vector indicating the name of the first sample in the list
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+#' to be compared.
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+#' @param sample2 A character vector indicating the name of the second sample in the list
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+#' to be compared.
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+#' @param type A character vector indicating whether "aminoAcid" or "nucleotide" sequences
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+#' should be used.  If "aminoAcid" is specified, then run productiveSeqs first.
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+#' @param q A numeric value between 0.0 and 1.0 indicating the threshold Holms adjusted
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+#' P value (also knowns as the false discovery rate or q value) to subset the results with.
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+#' Any sequences with a q value greater than this value will not be shown.
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+#' @param zero A numeric value to set all zero values to when calculating the log2
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+#' transformed fold change between samples 1 and 2.  This does not apply to the
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+#' p and q value calculations.
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+#' @param abundance The input value for the Fisher exact test.  "estimatedNumberGenomes"
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+#' is recommend but "count" may also be used.
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+#' @param parallel A boolean indicating wheter parallel processing should be used or not.
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+#' @param cores An integer specifying the number of processor cores if parallel processing
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+#' is used.
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+#' @return Returns a data frame with columns corresponding to the frequency of the abudance
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+#' measure in samples 1 and 2, the P value, Q value (Holms adjusted P value, also knowns as
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+#' the false discovery rate), and log2 transformed fold change.
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+#' @examples
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+#' file.path <- system.file("extdata", "TCRB_sequencing", package = "LymphoSeq")
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+#'
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+#' file.list <- readImmunoSeq(path = file.path)
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+#'
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+#' productive.aa <- productiveSeq(file.list = file.list, aggregate = "aminoAcid")
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+#'
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+#' differentialAbundance(list = productive.aa, sample1 = "TCRB_Day949_Unsorted", sample2 = "TCRB_Day1320_Unsorted",
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+#'                      type = "aminoAcid", q = 0.01, zero = 0.001, parallel = FALSE)
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+#' @export
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+#' @importFrom stats p.adjust
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+#' @importFrom stats fisher.test
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+#' @importFrom doMC registerDoMC
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+differentialAbundance <- function(sample1, sample2, list, abundance = "estimatedNumberGenomes",
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+                                  type = "aminoAcid", q = 1, zero = 0.001, parallel = TRUE, cores = 4) {
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+    x <- list[[sample1]]
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+    y <- list[[sample2]]
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+    sequences <- union(x[, type], y[, type])
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+    fisherFunction <- function(sequences) {
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+        p.value <- as.numeric()
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+        sample1.freq <- as.numeric()
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+        sample2.freq <- as.numeric()
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+        if (any(x[, type] == sequences)) {
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+            in.x <- x[x[, type] == sequences, abundance]
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+            x.freq <- x[x[, type] == sequences, abundance]/sum(x[, abundance]) *
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+                100
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+        } else {
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+            in.x <- 0
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+            x.freq <- 0
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+        }
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+        if (any(y[, type] == sequences)) {
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+            in.y <- y[y[, type] == sequences, abundance]
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+            y.freq <- y[y[, type] == sequences, abundance]/sum(y[, abundance]) *
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+                100
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+        } else {
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+            in.y <- 0
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+            y.freq <- 0
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+        }
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+        not.x <- sum(x[, abundance]) - in.x
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+        not.y <- sum(y[, abundance]) - in.y
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+        matrix <- matrix(c(in.x, in.y, not.x, not.y), nrow = 2)
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+        fisher <- stats::fisher.test(matrix)
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+        p.value <- c(p.value, fisher$p.value)
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+        sample1.freq <- c(sample1.freq, x.freq)
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+        sample2.freq <- c(sample2.freq, y.freq)
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+        results <- data.frame(aminoAcid = sequences,
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+                              x = sample1.freq,
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+                              y = sample2.freq,
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+                              p = p.value)
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+        return(results)
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+    }
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+    doMC::registerDoMC(cores = cores)
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+    results <- plyr::ldply(sequences, fisherFunction, .parallel = parallel)
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+    results$q <- stats::p.adjust(results$p, method = "holm")
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+    x.not.zero <- results[, "x"]
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+    x.not.zero[x.not.zero == 0] <- zero
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+    y.not.zero <- results[, "y"]
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+    y.not.zero[y.not.zero == 0] <- zero
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+    results$l2fc <- log2(x.not.zero/y.not.zero)
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+    names(results)[2] <- sample1
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+    names(results)[3] <- sample2
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+    results <- results[order(results$q, decreasing = FALSE), ]
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+    results <- results[results$q <= q, ]
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+    rownames(results) <- NULL
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+    return(results)
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+}
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R/exportFasta.R
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+#' Export sequences in fasta format
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+#'
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+#' Export nucleotide or amino acid sequences in fasta format.
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+#'
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+#' @param list A list of data frames consisting of antigen receptor sequences
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+#' imported by the LymphoSeq function readImmunoSeq.
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+#' @param type A character vector indicating whether "aminoAcid" or "nucleotide" sequences
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+#' should be exported.  If "aminoAcid" is specified, then run productiveSeqs first.
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+#' @param names A character vector of one or more column names to name the sequences.
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+#' If "rank" is specified, then the rank order of the sequences by frequency is used.
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+#' @return Exports fasta files to the working directory.
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+#' @examples
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+#' file.path <- system.file("extdata", "TCRB_sequencing", package = "LymphoSeq")
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+#'
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+#' file.list <- readImmunoSeq(path = file.path)
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+#'
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+#' exportFasta(list = file.list, type = "nucleotide", names = c("rank", "aminoAcid", "count"))
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+#'
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+#' productive.aa <- productiveSeq(file.list = file.list, aggregate = "aminoAcid")
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+#'
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+#' exportFasta(list = productive.aa, type = "aminoAcid", names = "frequencyCount")
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+#' @export
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+#' @importFrom Biostrings DNAStringSet
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+#' @importFrom Biostrings AAStringSet
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+#' @importFrom Biostrings writeXStringSet
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+exportFasta <- function(list, type = "nucleotide", names = c("rank", "aminoAcid", "count")) {
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+    if (type == "nucleotide") {
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+        i <- 1
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+        for (i in 1:length(list)) {
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+            file <- list[[i]]
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+            sequences <- file$nucleotide
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+            fasta <- Biostrings::DNAStringSet(sequences)
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+            if (any(names %in% "rank")) {
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+                file$rank <- rownames(file)
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+            }
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+            names(fasta) <- do.call(paste, c(file[names], sep = "_"))
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+            Biostrings::writeXStringSet(fasta, paste(names(list[i]), ".fasta", sep = ""))
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+        }
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+    }
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+    if (type == "aminoAcid") {
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+        i <- 1
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+        for (i in 1:length(list)) {
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+            file <- list[[i]]
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+            sequences <- file$aminoAcid
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+            if (any(sequences == "") | any(grepl("\\*", sequences))) {
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+                stop("Your list contains unproductive sequences.  Remove unproductive sequences first using the function productiveSeq.", call. = FALSE)
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+            }
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+            fasta <- Biostrings::AAStringSet(sequences)
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+            if (any(names %in% "rank")) {
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+                file$rank <- rownames(file)
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+            }
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+            names(fasta) <- do.call(paste, c(file[names], sep = "_"))
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+            Biostrings::writeXStringSet(fasta, paste(names(list[i]), ".fasta", sep = ""))
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+        }
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+    }
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+    message(paste("Fasta files saved to", getwd()))
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+}
R/phyloTree.R
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+#' Create phylogenetic tree
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+#'
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+#' Create a phylogenetic tree from amino acid or nucleotide CDR3 sequences in a list of
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+#' data frames.
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+#'
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+#' @param list A list data frames of unproductive nucleotide sequences or productive
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+#' nucleotide or amino acid sequenes generated by the LymphoSeq function productiveSeq
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+#' where the parameter aggregate is set to "nucleotide".  vFamilyName, dFamilyName, and
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+#' jFamilyName are required columns.
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+#' @param sample A character vector indicating the name of the sample in the productive
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+#' sequence list.
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+#' @param type A character vector indicating whether "aminoAcid" or "nucleotide" sequences
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+#' should be aligned.  Available options are "nucleotide" and "aminoAcid".
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+#' @param method A character vector indicating the multiple sequence alignment method to
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+#' be used.  Refer to the Bioconductor msa package for more details.  Options incude
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+#' "ClustalW", "ClustalOmega", and "Muscle".
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+#' @param aa.model A character vector indicating the pairwise distance model to be used
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+#' to compare amino acid sequences.  Refer to the phangorn package for more details.
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+#' Available options incude "WAG", "JTT", "LG", "Dayhoff", "cpREV", "mtmam", "mtArt", "MtZoa",
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+#' "mtREV24", "VT","RtREV", "HIVw", "HIVb", "FLU", "Blossum62", "Dayhoff_DCMut" and "JTT_DCMut".
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+#' The default options is "JTT".
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+#' @param nt.model A character vector indicating the pairwise distance model to be used
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+#' to compare nucleotide sequences.  Refer to the phangorn package for more details.
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+#' Available options incude "JC69" or "F81".  The default options is "F81".
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+#' @param layout A character vector indicating the tree layout.  Options include
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+#' "rectangular", "slanted", "fan", "circular", "radial" and "unrooted".
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+#' @return Returns a phylogenetic tree where each leaf represents a sequence color coded by the
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+#' V, D, and J gene usage.  The number next to each leaf refers to the sequence count.  A triangle
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+#' shaped leaf indicates the dominant sequence.  Refer to the ggtree Bioconductor package
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+#' documentation for details on how to manipulate the tree.
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+#' @examples
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+#' file.path <- system.file("extdata", "TCRB_sequencing", package = "LymphoSeq")
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+#'
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+#' file.list <- readImmunoSeq(path = file.path)
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+#'
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+#' productive.nt <- productiveSeq(file.list = file.list, aggregate = "nucleotide")
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+#'
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+#' phyloTree(list = productive.nt, sample = "TCRB_Day1320_CD8_CMV", type = "nucleotide",
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+#'          layout = "rectangular", method = "ClustalW", nt.model = "F81")
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+#'
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+#' phyloTree(list = productive.nt, sample = "TCRB_Day1320_CD8_CMV", type = "aminoAcid",
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+#'          layout = "circular", method = "ClustalOmega", nt.model = "JTT")
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+#' @export
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+#' @importFrom Biostrings DNAStringSet
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+#' @importFrom Biostrings AAStringSet
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+#' @importFrom phangorn as.phyDat
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+#' @importFrom phangorn dist.ml
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+#' @importFrom phangorn NJ
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+#' @import msa
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+#' @import ggtree
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+phyloTree <- function(list, sample, type = "nucleotide", method = "ClustalOmega",
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+                      layout = "rectangular", aa.model = "JTT", nt.model = "F81") {
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+    file <- list[[sample]]
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+    if(length(grep(pattern = "vFamilyName|dFamilyName|jFamilyName", names(file))) != 3){
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+        stop("vFamilyName, dFamilyName, and jFamilyName are required columns.  Try aggregating the sequences by 'nucleotide' usng the function productiveSeq.", call. = FALSE)
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+    }
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+    if(nrow(file) < 3){
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+        stop("Cannot draw phlogenetic tree with less than 3 sequences.", call. = FALSE)
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+    }
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+    file[file == ""] <- "unresolved"
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+    if (type == "nucleotide") {
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+        file <- file[nchar(file$nucleotide) > 3, ]
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+        string <- Biostrings::DNAStringSet(file$nucleotide)
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+    }
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+    if (type == "aminoAcid") {
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+        file <- file[nchar(file$aminoAcid) > 3, ]
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+        string <- Biostrings::AAStringSet(file$aminoAcid)
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+    }
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+    names(string) <- file$count
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+    alignment <- msa::msa(string, method = method)
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+    if (type == "nucleotide") {
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+        alignment.phyDat <- phangorn::as.phyDat(x = alignment, type = "DNA", format = clustal)
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+        alignment.dist <- phangorn::dist.ml(alignment.phyDat, model = nt.model)
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+    }
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+    if (type == "aminoAcid") {
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+        alignment.phyDat <- phangorn::as.phyDat(x = alignment, type = "AA", format = clustal)
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+        alignment.dist <- phangorn::dist.ml(alignment.phyDat, model = aa.model)
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+    }
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+    tree <- phangorn::NJ(alignment.dist)
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+    geneFamilies <- paste(file$vFamilyName, file$dFamilyName, file$jFamilyName)
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+    geneFamilies <- gsub(geneFamilies, pattern = "IGH|IGL|IGK|TCRB|TCRA", replacement = "")
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+    geneFamilies <- gsub(geneFamilies, pattern = "unresolved", replacement = "UNR")
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+    tree.annotation <- data.frame(Names = file$count, Genomes = file$estimatedNumberGenomes,
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+                                  Count = file$count, Genes = geneFamilies,
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+                                  Frequency = round(file$frequencyCount, digits = 2),
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+                                  Dominant = c("Yes", rep("No", length(string) - 1)))
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+    getPalette <- grDevices::colorRampPalette(RColorBrewer::brewer.pal(9, "Set1"))
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+    plot <- ggtree::ggtree(tree, layout = layout) %<+% tree.annotation +
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+        ggtree::geom_tippoint(aes(color = Genes, shape = Dominant), size = 3) +
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+        ggplot2::scale_color_manual(values = getPalette(length(unique(geneFamilies)))) +
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+        ggplot2::guides(shape = FALSE) +
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+        ggtree::geom_tiplab(size = 2, hjust = - 0.3) +
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+        ggplot2::theme(legend.position = "bottom", legend.key = element_rect(colour = "white")) +
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+        ggplot2::labs(color = "")
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+    return(plot)
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+}
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+
README.md
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+# LymphoSeq
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+This R package analyzes high-throughput sequencing of T and B cell receptor
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+complementarity determining region 3 (CDR3) sequences generated by [Adaptive
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+Biotechnologies' ImmunoSEQ assay](http://www.adaptivebiotech.com/immunoseq).
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+
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+### Installation instructions
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+
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+#### Install release version 1.0.0
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+###### Install from [Bioconductor](https://www.bioconductor.org/packages/LymphoSeq)
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+```
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+source("https://bioconductor.org/biocLite.R")
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+biocLite("LymphoSeq")
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+```
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+
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+#### Install developer version 1.1.0
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+###### Option 1:  Install from [Bioconductor developer branch](https://www.bioconductor.org/developers/how-to/useDevel/)
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+```
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+# Switch to Bioconductor developer branch (requires latest version of R)
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+library(BiocInstaller)
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+useDevel()
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+
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+# Download developer release
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+source("https://bioconductor.org/biocLite.R")
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+biocLite("LymphoSeq")
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+
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+# Switch back to Bioconductor release branch
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+library(BiocInstaller)
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+useDevel()
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+```
30 
+###### Option 2:  Install from GitHub
31 
+```
32 
+# Install the latest version of Bioconductor
33 
+source("https://bioconductor.org/biocLite.R")
34 
+biocLite()
35 
+
36 
+# Download developer tools
37 
+install.packages("devtools")
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+
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+# Download package from GitHub
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+install_github("davidcoffey/LymphoSeqDB")
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+install_github("davidcoffey/LymphoSeq")
42 
+```
43 
+
44 
+### Documentation
45 
+* [LymphoSeq vignette](https://bioconductor.org/packages/release/bioc/vignettes/LymphoSeq/inst/doc/LymphoSeq.pdf)
46 
+* [LymphoSeq manual](https://bioconductor.org/packages/release/bioc/manuals/LymphoSeq/man/LymphoSeq.pdf)
47 
+* [LymphoSeq news](https://bioconductor.org/packages/release/bioc/news/LymphoSeq/NEWS)
48 
+
49 
+### Citation
50 
+Coffey D (2016). LymphoSeq: Analyze high-throughput sequencing of T and B cell receptors. R package version 1.0.0.
inst/NEWS
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@@ -1 +1,7 @@
1 1 
 LymphoSeq News
2 
+
3 
+Version 1.1.0 [May 10, 2016]
4 
+ * Added function alignSeq
5 
+ * Added funciton phyloSeq
6 
+ * Added function exportFasta
7 
+ * Added function differentialAbundance
man/alignSeq.Rd
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1 
+% Generated by roxygen2: do not edit by hand
2 
+% Please edit documentation in R/alignSeq.R
3 
+\name{alignSeq}
4 
+\alias{alignSeq}
5 
+\title{Align mutliple sequences}
6 
+\usage{
7 
+alignSeq(list, sample, type = "nucleotide", method = "ClustalOmega")
8 
+}
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+\arguments{
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+\item{list}{A list of data frames consisting of antigen receptor sequences
11 
+imported by the LymphoSeq function readImmunoSeq.}
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+
13 
+\item{sample}{A character vector indicating the name of the sample in the productive
14 
+sequence list.}
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+
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+\item{type}{A character vector indicating whether "aminoAcid" or "nucleotide" sequences
17 
+should be aligned.  If "aminoAcid" is specified, then run productiveSeqs first.}
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+
19 
+\item{method}{A character vector indicating the multiple sequence alignment method to
20 
+be used.  Refer to the Bioconductor msa package for more details.  Options incude
21 
+"ClustalW", "ClustalOmega", and "Muscle".}
22 
+}
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+\value{
24 
+Saves a pdf file of the multiple sequence alignemnt to the working directory.
25 
+}
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+\description{
27 
+Perform multiple sequence alignment using one of three methods and output results as a pdf file.
28 
+}
29 
+\examples{
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+file.path <- system.file("extdata", "TCRB_sequencing", package = "LymphoSeq")
31 
+
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+file.list <- readImmunoSeq(path = file.path)
33 
+
34 
+productive.nt <- productiveSeq(file.list = file.list, aggregate = "nucleotide")
35 
+
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+alignSeq(list = productive.nt, sample = "TCRB_Day1320_CD8_CMV", type = "nucleotide",
37 
+         method = "ClustalW")
38 
+}
39 
+
man/differentialAbundance.Rd
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1 
+% Generated by roxygen2: do not edit by hand
2 
+% Please edit documentation in R/differentialAbundance.R
3 
+\name{differentialAbundance}
4 
+\alias{differentialAbundance}
5 
+\title{Differential abundance analysis}
6 
+\usage{
7 
+differentialAbundance(sample1, sample2, list,
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+  abundance = "estimatedNumberGenomes", type = "aminoAcid", q = 1,
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+  zero = 0.001, parallel = TRUE, cores = 4)
10 
+}
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+\arguments{
12 
+\item{sample1}{A character vector indicating the name of the first sample in the list
13 
+to be compared.}
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+
15 
+\item{sample2}{A character vector indicating the name of the second sample in the list
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+to be compared.}
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+
18 
+\item{list}{A list of data frames consisting of antigen receptor sequences
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+imported by the LymphoSeq function readImmunoSeq.}
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+
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+\item{abundance}{The input value for the Fisher exact test.  "estimatedNumberGenomes"
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+is recommend but "count" may also be used.}
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+
24 
+\item{type}{A character vector indicating whether "aminoAcid" or "nucleotide" sequences
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+should be used.  If "aminoAcid" is specified, then run productiveSeqs first.}
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+
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+\item{q}{A numeric value between 0.0 and 1.0 indicating the threshold Holms adjusted
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+P value (also knowns as the false discovery rate or q value) to subset the results with.
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+Any sequences with a q value greater than this value will not be shown.}
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+
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+\item{zero}{A numeric value to set all zero values to when calculating the log2
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+transformed fold change between samples 1 and 2.  This does not apply to the
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+p and q value calculations.}
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+
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+\item{parallel}{A boolean indicating wheter parallel processing should be used or not.}
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+
37 
+\item{cores}{An integer specifying the number of processor cores if parallel processing
38 
+is used.}
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+}
40 
+\value{
41 
+Returns a data frame with columns corresponding to the frequency of the abudance
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+measure in samples 1 and 2, the P value, Q value (Holms adjusted P value, also knowns as
43 
+the false discovery rate), and log2 transformed fold change.
44 
+}
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+\description{
46 
+Use a Fisher exact test to calculate differential abdunance of each sequence in
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+two samples and report the log2 transformed fold change, P value and adjusted P value.
48 
+}
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+\examples{
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+file.path <- system.file("extdata", "TCRB_sequencing", package = "LymphoSeq")
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+
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+file.list <- readImmunoSeq(path = file.path)
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+
54 
+productive.aa <- productiveSeq(file.list = file.list, aggregate = "aminoAcid")
55 
+
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+differentialAbundance(list = productive.aa, sample1 = "TCRB_Day949_Unsorted", sample2 = "TCRB_Day1320_Unsorted",
57 
+                     type = "aminoAcid", q = 0.01, zero = 0.001, parallel = FALSE)
58 
+}
59 
+
man/exportFasta.Rd
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1 
+% Generated by roxygen2: do not edit by hand
2 
+% Please edit documentation in R/exportFasta.R
3 
+\name{exportFasta}
4 
+\alias{exportFasta}
5 
+\title{Export sequences in fasta format}
6 
+\usage{
7 
+exportFasta(list, type = "nucleotide", names = c("rank", "aminoAcid",
8 
+  "count"))
9 
+}
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+\arguments{
11 
+\item{list}{A list of data frames consisting of antigen receptor sequences
12 
+imported by the LymphoSeq function readImmunoSeq.}
13 
+
14 
+\item{type}{A character vector indicating whether "aminoAcid" or "nucleotide" sequences
15 
+should be exported.  If "aminoAcid" is specified, then run productiveSeqs first.}
16 
+
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+\item{names}{A character vector of one or more column names to name the sequences.
18 
+If "rank" is specified, then the rank order of the sequences by frequency is used.}
19 
+}
20 
+\value{
21 
+Exports fasta files to the working directory.
22 
+}
23 
+\description{
24 
+Export nucleotide or amino acid sequences in fasta format.
25 
+}
26 
+\examples{
27 
+file.path <- system.file("extdata", "TCRB_sequencing", package = "LymphoSeq")
28 
+
29 
+file.list <- readImmunoSeq(path = file.path)
30 
+
31 
+exportFasta(list = file.list, type = "nucleotide", names = c("rank", "aminoAcid", "count"))
32 
+
33 
+productive.aa <- productiveSeq(file.list = file.list, aggregate = "aminoAcid")
34 
+
35 
+exportFasta(list = productive.aa, type = "aminoAcid", names = "frequencyCount")
36 
+}
37 
+
man/phyloTree.Rd
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1 
+% Generated by roxygen2: do not edit by hand
2 
+% Please edit documentation in R/phyloTree.R
3 
+\name{phyloTree}
4 
+\alias{phyloTree}
5 
+\title{Create phylogenetic tree}
6 
+\usage{
7 
+phyloTree(list, sample, type = "nucleotide", method = "ClustalOmega",
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+  layout = "rectangular", aa.model = "JTT", nt.model = "F81")
9 
+}
10 
+\arguments{
11 
+\item{list}{A list data frames of unproductive nucleotide sequences or productive
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+nucleotide or amino acid sequenes generated by the LymphoSeq function productiveSeq
13 
+where the parameter aggregate is set to "nucleotide".  vFamilyName, dFamilyName, and
14 
+jFamilyName are required columns.}
15 
+
16 
+\item{sample}{A character vector indicating the name of the sample in the productive
17 
+sequence list.}
18 
+
19 
+\item{type}{A character vector indicating whether "aminoAcid" or "nucleotide" sequences
20 
+should be aligned.  Available options are "nucleotide" and "aminoAcid".}
21 
+
22 
+\item{method}{A character vector indicating the multiple sequence alignment method to
23 
+be used.  Refer to the Bioconductor msa package for more details.  Options incude
24 
+"ClustalW", "ClustalOmega", and "Muscle".}
25 
+
26 
+\item{layout}{A character vector indicating the tree layout.  Options include
27 
+"rectangular", "slanted", "fan", "circular", "radial" and "unrooted".}
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+
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+\item{aa.model}{A character vector indicating the pairwise distance model to be used
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+to compare amino acid sequences.  Refer to the phangorn package for more details.
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+Available options incude "WAG", "JTT", "LG", "Dayhoff", "cpREV", "mtmam", "mtArt", "MtZoa",
32 
+"mtREV24", "VT","RtREV", "HIVw", "HIVb", "FLU", "Blossum62", "Dayhoff_DCMut" and "JTT_DCMut".
33 
+The default options is "JTT".}
34 
+
35 
+\item{nt.model}{A character vector indicating the pairwise distance model to be used
36 
+to compare nucleotide sequences.  Refer to the phangorn package for more details.
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+Available options incude "JC69" or "F81".  The default options is "F81".}
38 
+}
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+\value{
40 
+Returns a phylogenetic tree where each leaf represents a sequence color coded by the
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+V, D, and J gene usage.  The number next to each leaf refers to the sequence count.  A triangle
42 
+shaped leaf indicates the dominant sequence.  Refer to the ggtree Bioconductor package
43 
+documentation for details on how to manipulate the tree.
44 
+}
45 
+\description{
46 
+Create a phylogenetic tree from amino acid or nucleotide CDR3 sequences in a list of
47 
+data frames.
48 
+}
49 
+\examples{
50 
+file.path <- system.file("extdata", "TCRB_sequencing", package = "LymphoSeq")
51 
+
52 
+file.list <- readImmunoSeq(path = file.path)
53 
+
54 
+productive.nt <- productiveSeq(file.list = file.list, aggregate = "nucleotide")
55 
+
56 
+phyloTree(list = productive.nt, sample = "TCRB_Day1320_CD8_CMV", type = "nucleotide",
57 
+         layout = "rectangular", method = "ClustalW", nt.model = "F81")
58 
+
59 
+phyloTree(list = productive.nt, sample = "TCRB_Day1320_CD8_CMV", type = "aminoAcid",
60 
+         layout = "circular", method = "ClustalOmega", nt.model = "JTT")
61 
+}
62 
+
vignettes/LymphoSeq.Rmd
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1 1 
 ---
2 2 
 title: "Analysis of high-throughput sequencing of T and B cell receptors with LymphoSeq"
3 3 
 author: "David G. Coffey, MD"
4 
-date: "`r Sys.Date()`"
4 
+date: '`r Sys.Date()`'
5 5 
 output: pdf_document
6 6 
 geometry: margin=0.5in
7 7 
 fontsize: 10pt
8 
-vignette: >
9 
-  %\VignetteIndexEntry{Analyze high throughput sequencing of T and B cell receptors with LymphoSeq}
10 
-  %\VignetteEncoding{UTF-8}
11 
-  %\VignetteEngine{knitr::rmarkdown}
8 
+toc: yes
9 
+vignette: |
10 
+  %\VignetteIndexEntry{Analyze high throughput sequencing of T and B cell receptors with LymphoSeq} %\VignetteEncoding{UTF-8} %\VignetteEngine{knitr::rmarkdown}
12 11 
 ---
13 12
14 13 
 Application of high-throughput sequencing of T and B lymphocyte antigen receptors has great potential for improving the monitoring of lymphoid malignancies, assessing immune reconstitution after hematopoietic stem cell transplantation, and characterizing the composition of lymphocyte repertoires.^[Warren, E. H. *et al.* *Blood* 2013;122:19–22.]   LymhoSeq is an R package designed to import, analyze, and visualize antigen receptor sequencing from Adaptive Biotechnologies' ImmunoSEQ assay.^[http://www.adaptivebiotech.com/immunoseq]  The package is also adaptable to the analysis of T and B cell receptor sequencing processed using other platforms such as MiXCR^[http://mixcr.milaboratory.com] or IMGT/HighV-QUEST.^[http://www.imgt.org/HighV-QUEST]  This vignette has been written to highlight some of the features of LymphoSeq and guide the user through a typical workflow.