Initial commit

ROBITools.Rproj

+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: ISO-8859-1
+
+RnwWeave: knitr
+LaTeX: pdfLaTeX
+
+BuildType: Package
+PackageUseDevtools: Yes
+PackagePath: ROBITools
+PackageInstallArgs: --no-multiarch --with-keep.source
+PackageRoxygenize: rd,collate,namespace

ROBITools/.gitignore

+/man/
+/loopbenchmark.R
+/Read-and-delete-me

ROBITools/DESCRIPTION

+Package: ROBITools
+Type: Package
+Title: Metabarcoding data biodiversity analysis
+Version: 0.1
+Date: 2012-08-23
+Author: LECA - Laboratoire d'ecologie alpine
+Maintainer: LECA OBITools team <obitools@metabarcoding.org>
+Description: More about what it does (maybe more than one line)
+License: CeCILL v2.0
+LazyLoad: yes
+Roxygen: list(wrap = FALSE)
+Collate:
+    's3objects.R'
+    'ROBITools.R'
+    '02_class_metabarcoding.data.R'
+    'aggregate.R'
+    'choose.taxonomy.R'
+    'contaslayer.R'
+    'distrib.extrapol.R'
+    'experimental.section.R'
+    'export-metabarcoding.R'
+    'read.obitab.R'
+    'import.metabarcoding.R'
+    'import.ngsfilter.R'
+    'layers.metabarcoding.R'
+    'metabarcoding_threshold.R'
+    'mstat.R'
+    'obiclean.R'
+    'pcrslayer.R'
+    'plot.PCRplate.R'
+    'plot.seqinsample.R'
+    'rarefy.R'
+    'read.ngsfilter.R'
+    'read.sumatra.R'
+    'taxoDBtree.R'
+    'taxonomic.resolution.R'
+    'taxonomy_classic_table.R'
+RoxygenNote: 5.0.1

ROBITools/LICENSE-SLRE

+Copyright (c) 2004-2013 Sergey Lyubka <valenok@gmail.com>
+Copyright (c) 2013 Cesanta Software Limited
+All rights reserved
+
+This code is dual-licensed: you can redistribute it and/or modify
+it under the terms of the GNU General Public License version 2 as
+published by the Free Software Foundation. For the terms of this
+license, see <http://www.gnu.org/licenses/>.
+
+You are free to use this code under the terms of the GNU General
+Public License, but WITHOUT ANY WARRANTY; without even the implied
+warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the GNU General Public License for more details.
+
+Alternatively, you can license this code under a commercial
+license, as set out in <http://cesanta.com/>.

ROBITools/NAMESPACE

+# Generated by roxygen2: do not edit by hand
+
+S3method(aggregate,metabarcoding.data)
+S3method(plot,PCRplate)
+S3method(plot,seqinsample)
+S3method(summary,taxores)
+export(addS3Class)
+export(colnames)
+export(const.threshold.mask)
+export(contaslayer)
+export(createS3Class)
+export(dbtree)
+export(dist.center.group)
+export(dist.clique.group)
+export(dist.grid)
+export(dm.univariate)
+export(extracts.obiclean)
+export(extracts.obiclean_cluster)
+export(extrapol.freq)
+export(get.classic.taxonomy)
+export(import.metabarcoding.data)
+export(import.ngsfilter.data)
+export(layer.names)
+export(m.bivariate)
+export(m.univariate)
+export(m.univariate.test)
+export(m.weight)
+export(map.extrapol.freq)
+export(marginalsum)
+export(metabarcoding.data)
+export(motus)
+export(normalize)
+export(rarefy)
+export(read.ngsfilter)
+export(read.obitab)
+export(reads)
+export(rmS3Class)
+export(rownames)
+export(samples)
+export(taxo.decider)
+export(threshold)
+export(threshold.mask)
+export(threshold.set)
+exportClasses(metabarcoding.data)
+exportMethods("$")
+exportMethods("$<-")
+exportMethods("[[")
+exportMethods("[[<-")
+exportMethods(colnames)
+exportMethods(rownames)
+import(ROBITaxonomy)
+import(igraph)

ROBITools/R/ROBITools.R

+#' A package to manipulate DNA metabarcoding data.
+#' 
+#' This package was written as a following of the OBITools. 
+#' 
+#' \tabular{ll}{
+#'  Package: \tab ROBITools\cr
+#'  Type: \tab Package\cr
+#'  Version: \tab 0.1\cr
+#'  Date: \tab 2013-06-27\cr
+#'  License: \tab CeCILL 2.0\cr
+#'  LazyLoad: \tab yes\cr
+#'}
+#' 
+#' @name ROBITools-package
+#' @aliases ROBITools
+#' @docType package
+#' @title  A package to manipulate DNA metabarcoding data.
+#' @author Frederic Boyer
+#' @author Aurelie Bonin
+#' @author Lucie Zinger
+#' @author Eric Coissac
+#' 
+#' @references http://metabarcoding.org/obitools
+#' 
+NA
+
+.onLoad <- function(libname, pkgname) { 
+  
+  packageStartupMessage( "ROBITools package" )
+  #print(getwd())
+  
+}
+

ROBITools/R/aggregate.R

+#' @include 02_class_metabarcoding.data.R
+NULL
+
+# TODO: Add comment
+# 
+# Author: coissac
+###############################################################################
+
+#' @export
+aggregate.metabarcoding.data=function(x, by, FUN,..., 
+		                              MARGIN='sample',
+									  default.layer=NULL,
+									  layers=NULL) {	
+  
+	uniq.value = function(z) {
+		
+		if (is.null(z) | 
+				any(is.na(z)) |
+				length(z)==0)
+			ans = NA
+		else {
+      if (all(z==z[1]))
+			  ans = z[1]
+		  else
+			  ans = NA
+		}
+		if (is.factor(z))
+			ans = factor(ans,levels=levels(z))
+		
+		return(ans)
+	}			
+	
+	
+	#
+	# Deals with the supplementaty aggregate arguments
+	#
+			
+	if (is.null(default.layer))
+		default.layer=uniq.value
+	
+	
+	if (is.null(layers)) {
+		layers = as.list(rep(c(default.layer),length(x@layers)))
+		names(layers)=layer.names(x)
+	}
+	else {
+		for (n in layer.names(x))
+			if (is.null(layers[[n]]))
+				layers[[n]]=default.layers
+	}
+	
+	if (MARGIN == 'sample')
+		MARGIN=1
+	
+	if (MARGIN == 'motu')
+		MARGIN=2
+	
+	reads = x@reads
+	  
+	if (MARGIN==1) {
+		# prepare the aggrevation arguments for the read table
+		# from the function arguments
+	  dotted = list(...)
+	  if (length(dotted) > 0)
+	    aggr.args = list(reads,by=by,FUN=FUN,...=dotted,simplify=FALSE)
+	  else
+	    aggr.args = list(reads,by=by,FUN=FUN,simplify=FALSE)
+	  		
+		# Aggregate the read table
+		ragr = do.call(aggregate,aggr.args)
+		
+		# extrat new ids from the aggregated table
+		ncat = length(by)
+		ids  = as.character(interaction(ragr[,1:ncat,drop=FALSE]))
+		
+		# remove the aggregations modalities to rebuild a correct
+		# reads table
+		ragr = as.matrix(ragr[,-(1:ncat),drop=FALSE])
+		dragr= dim(ragr)
+		cragr= colnames(ragr)
+		ragr = as.numeric(ragr)
+		dim(ragr)=dragr
+		colnames(ragr)=cragr		
+		rownames(ragr)=ids
+		
+		#
+		# Apply the same aggragation to each layer
+		#
+		
+		ln = layer.names(x)
+		
+    la = vector(mode="list",length(ln))
+    names(la)=ln
+    
+		for (n in ln) {
+			f = layers[[n]]
+			if (is.factor(x[[n]])){
+			  isfact = TRUE
+			  lf = levels(x[[n]])
+			  df = dim(x[[n]])
+			  m = matrix(as.character(x[[n]]))
+			  dim(m)=df
+			}
+			else
+			  m = x[[n]]
+
+      aggr.args = list(m,by=by,FUN=f,simplify=FALSE)
+			lagr = do.call(aggregate,aggr.args)
+			lagr = as.factor.or.matrix(lagr[,-(1:ncat),drop=FALSE])
+
+			if (isfact){
+			  df = dim(lagr)
+			  lagr = factor(lagr,levels=lf)
+			  dim(lagr)=df
+			}
+			
+      rownames(lagr)=ids
+			la[[n]]=lagr
+		}
+		
+		# aggragate the sample table according to the same criteria
+		#
+		# TODO: We have to take special care of factors in the samples
+		#       data.frame
+		
+		sagr = aggregate(samples(x),by,uniq.value,simplify=FALSE)
+		
+		# move the first columns of the resulting data frame (the aggregations
+		# modalities to the last columns of the data.frame
+		sagr = sagr[,c((ncat+1):(dim(sagr)[2]),1:ncat),drop=FALSE]
+		larg = c(lapply(sagr,unlist),list(stringsAsFactors=FALSE))	
+		sagr = do.call(data.frame,larg)
+		
+		# set samples ids to the ids computed from modalities
+		sagr$id=ids
+		rownames(sagr)=ids
+		
+		# build the new metabarcoding data instance
+		newdata = copy.metabarcoding.data(x,reads=ragr,samples=sagr)
+		
+	}
+	else {
+		# prepare the aggregation arguments for the read table
+		# from the function arguments
+		# BECARFUL : the reads table is transposed
+		#            standard aggregate runs by row and we want
+		# 			 aggregation by column
+    
+    dotted = list(...)
+    if (length(dotted) > 0)
+		  aggr.args = list(t(reads),by=by,FUN=FUN,...=dotted,simplify=FALSE)
+    else
+      aggr.args = list(t(reads),by=by,FUN=FUN,simplify=FALSE)
+    
+		
+    # Aggregate the read table
+		ragr = do.call(aggregate.data.frame,aggr.args)
+		
+		# extrat new ids from the aggregated table
+		ncat = length(by)
+		ids  = as.character(interaction(ragr[,1:ncat,drop=FALSE]))
+		
+		# remove the aggregations modalities to rebuild a correct
+		# reads table
+    
+		ragr = t(ragr[,-(1:ncat),drop=FALSE])		
+    dragr= dim(ragr)
+    rragr= rownames(ragr)
+    ragr = as.numeric(ragr)
+    dim(ragr)=dragr
+    colnames(ragr)=ids
+    rownames(ragr)=rragr
+		
+		#
+		# Apply the same aggragation to each layer
+		#
+		
+		ln = layer.names(x)
+    
+    la = vector(mode="list",length(ln))
+    names(la)=ln
+    
+    for (n in ln) {
+			f = layers[[n]]
+      
+      if (is.factor(x[[n]])){
+			  isfact = TRUE
+        lf = levels(x[[n]])
+        df = dim(x[[n]])
+        m = matrix(as.character(x[[n]]))
+        dim(m)=df
+      }
+      else
+          m = x[[n]]
+
+      aggr.args = list(t(m),by=by,FUN=f,simplify=FALSE)
+			lagr = do.call(aggregate,aggr.args)
+			lagr = t(as.factor.or.matrix(lagr[,-(1:ncat),drop=FALSE]))
+      
+      if (isfact){
+        df = dim(lagr)
+        lagr = factor(lagr,levels=lf)
+        dim(lagr)=df
+			}
+
+      colnames(lagr)=ids
+			la[[n]]=lagr
+		}
+    
+		# aggragate the motus table according to the same criteria
+		magr = aggregate(motus(x),by,uniq.value,simplify=FALSE)
+		
+		# move the first columns of the resulting data frame (the aggregations
+		# modalities to the last columns of the data.frame
+		magr = magr[,c((ncat+1):(dim(magr)[2]),1:ncat),drop=FALSE]
+		larg = c(lapply(magr,unlist),list(stringsAsFactors=FALSE))	
+		magr = do.call(data.frame,larg)
+		
+		# set motus ids to the ids computed from modalities
+		magr$id=ids
+		rownames(magr)=ids
+		
+		# build the new metabarcoding data instance
+		newdata = copy.metabarcoding.data(x,reads=ragr,motus=magr,layers=la)
+	}
+	
+	return(newdata)			
+}
+

ROBITools/R/choose.taxonomy.R

+#' @import ROBITaxonomy 
+#' @include 02_class_metabarcoding.data.R
+NULL
+
+#' Choose between databases for taxonomic classifications
+#' 
+#' Chooses a sequence taxonomic assignment in order of preference for the different 
+#' reference databases that have been used when the assignment is above a certain threshold
+#' 
+#' 
+#' @param x a \code{\link{metabarcoding.data}} object
+#' @param taxonomy a \code{\linkS4class{taxonomy.obitools}} instance
+#' @param dbrank string or vector indicating reference database names ranked by order of preference
+#' @param thresh a best_identity threshold for applying priority. Default is \code{0.95}
+#' 
+#' @return returns a data.frame with the refined taxonomic assignement and classic taxonomy description.
+#' 
+#' @examples
+#' 
+#' data(termes)
+#' 
+#' taxo=default.taxonomy()
+#' 
+#' #create artificial taxonomic assignments
+#' attr(termes, "motus")["best_identity:DB1"] = sample(seq(0.5,1,0.001),size=nrow(termes$motus), replace=T)
+#' attr(termes, "motus")["best_identity:DB2"] = sample(seq(0.5,1,0.001),size=nrow(termes$motus), replace=T)
+#' attr(termes, "motus")["best_identity:DB3"] = sample(seq(0.5,1,0.001),size=nrow(termes$motus), replace=T)
+#' attr(termes, "motus")["taxid_by_db:DB1"] = termes$motus$taxid
+#' attr(termes, "motus")["taxid_by_db:DB2"] = sample(termes$motus$taxid,size=nrow(termes$motus), replace=F)
+#' attr(termes, "motus")["taxid_by_db:DB3"] = sample(termes$motus$taxid,size=nrow(termes$motus), replace=F)
+#' 
+#' #Run taxo.decider
+#' termes.ok = taxo.decider(termes, taxo, "DB2", 0.95)
+#' head(termes.ok$motus[union(grep("DB",  colnames(termes.ok$motus)), grep("_ok", colnames(termes.ok$motus)))])
+#' 
+#' termes.ok = taxo.decider(termes, taxo, c("DB3", "DB1"), 0.95)
+#' head(termes.ok$motus[union(grep("DB",  colnames(termes.ok$motus)), grep("_ok", colnames(termes.ok$motus)))])
+#' 
+#' #Quick look at the enhancement in taxonomic assignements
+#' par(mfrow=c(1,4))
+#' for(i in grep("best_identity.", colnames(termes.ok$motus))){
+#' hist(termes.ok$motus[,i], breaks=20, ylim=c(1,21), main=colnames(termes.ok$motus)[i], xlab="assignment score")
+#' }
+#'     
+#' @seealso \code{\linkS4class{taxonomy.obitools}}, and methods \code{\link{species}},\code{\link{genus}}, \code{\link{family}},\code{\link{kingdom}},
+#'          \code{\link{superkingdom}},\code{\link{taxonatrank}}, \code{\link{taxonmicank}}
+#'
+#' @author Lucie Zinger
+#' @keywords taxonomy
+#' 
+#' @export
+#' 
+
+taxo.decider = function(x, taxonomy, dbrank, thresh=0.95) {
+  
+  noms = colnames(x$motus)
+  best_ids_names = noms[grep("best_identity.", noms)]
+  best_ids = x$motus[,best_ids_names]
+  taxids = x$motus[, gsub("best_identity", "taxid_by_db", best_ids_names)]
+  dbs = unlist(lapply(strsplit(best_ids_names, "\\:"), "[[", 2))
+  
+  
+  #Set max indices
+  ind = as.vector(t(apply(best_ids,1,function(y) order(rank(-y, ties.method="max"), match(dbrank, dbs))))[,1])
+  
+  #Set default vector: db, bestids, taxids with max score
+  db_ok = dbs[ind]
+  best_identity_ok = best_ids[cbind(1:length(ind), ind)]
+  taxids_by_db_ok = taxids[cbind(1:length(ind), ind)]
+  
+  #Get vector of db index that should be used according to condition > thresh
+  db_choice = taxo.decider.routine(dbrank, best_ids, dbs, thresh)
+  
+  #Replacing by right values according to db_ok
+  for(i in 1:length(dbrank)){
+    db_ok[which(db_choice==i)] = dbrank[i]
+    best_identity_ok[which(db_choice==i)] = best_ids[which(db_choice==i),grep(dbrank[i], colnames(best_ids))]
+    taxids_by_db_ok[which(db_choice==i)] = taxids[which(db_choice==i),grep(dbrank[i], colnames(taxids))]
+  }
+  
+  decision = data.frame(db_ok, best_identity_ok, taxids_by_db_ok)
+  
+  coltaxid = colnames(decision)[grep("taxid", colnames(decision))]
+  
+  attr(x, "motus") = data.frame(x$motus, decision)
+  new.tax = get.classic.taxonomy(x, taxonomy, coltaxid)
+  
+  attr(x, "motus") = data.frame(x$motus, new.tax)
+  
+  return(x)  
+}
+
+
+taxo.decider.routine = function(dbrank, best_ids, dbs, thresh) {
+  #Setting mask 
+  mask = matrix(NA,nrow(best_ids),length(dbrank))
+  colnames(mask)=dbrank
+  #For each DB, see if condition T/F
+  for(i in dbrank){
+    mask[,i] = best_ids[,which(dbs==i)]>thresh
+  }
+  #Get the first occurence of T in the table
+  out = apply(mask, 1, function(x) which(x==T)[1])
+  return(out)
+}
+
+

ROBITools/R/contaslayer.R

+#' @include 02_class_metabarcoding.data.R
+NULL
+
+#' Detects contaminants in metabarcoding data
+#' 
+#' Detects sequences/motus in a \code{\link{metabarcoding.data}} object
+#' for which frequencies over the entire dataset are maximum in negative controls and 
+#' hence, most likely to be contaminants. 
+#' 
+#' 
+#' @param x a \code{\link{metabarcoding.data}} object
+#' @param controls a vector of samples names where conta are suspected to be detected 
+#'                 (typically negative control names).
+#' @param clust a vector for grouping sequences. Default set to \code{NULL}.
+#'
+#' @return a vector containing the names of sequences identified as contaminants
+#'
+#' @examples
+#' 
+#' data(termes)
+#' termes.ok = termes[,colSums(termes$reads)>0]
+#' neg = rownames(termes.ok)[grep("r",rownames(termes.ok))]
+#' 
+#' #finds contaminants based on neg samples
+#' contaslayer(termes.ok, neg)
+#' 
+#' # extanding contamininant detection with grouping factor, 
+#' # typically obiclean/sumatra cluster or taxonomy membership
+#' contaslayer(termes.ok, neg, termes.ok$motus$scientific_name)
+#'   
+#' @seealso \code{\link{threshold}} for further trimming
+#' @author Lucie Zinger
+#' @export
+
+contaslayer = function(x,controls,clust=NULL){
+  
+  x.fcol = normalize(x, MARGIN=2)$reads
+  x.max = rownames(x.fcol[apply(x.fcol, 2, which.max),])
+  conta = colnames(x)[!is.na(match(x.max,controls))]
+  
+  if (length(clust)!=0) {
+    agg = data.frame(conta.id=colnames(x.fcol), clust)
+    conta.ext = agg$conta.id[which(!is.na(match( agg$clust, agg$clust[match(conta,agg$conta.id)])))]
+    return(as.vector(conta.ext))
+  } 
+  else {
+    return(conta)
+  }
+}  

ROBITools/R/distrib.extrapol.R

+#' @include 02_class_metabarcoding.data.R
+NULL
+
+#' Read frequencies krigging
+#' 
+#' Extrapolates read frequencies from a \code{\link{metabarcoding.data}} object in space for a finer resolution 
+#' 
+#' @param x a vector or matrix from a row-normalized read table 
+#'          \code{\link{metabarcoding.data}} object
+#' @param min.coord a vector of length = 2 indicating the minimum values of x and y
+#'                  coordinates to be used for the predicted grid
+#' @param max.coord a vector of length = 2 indicating the maximum values of x and y
+#'                  coordinates to be used for the predicted grid
+#' @param grid.grain an integer indicating the resolution (i.e. nb of subpoints) in x and y
+#'                  coordinates required for the predicted grid
+#' @param coords a dataframe containing the x and y coordinates of the abundances 
+#'               from x to be extrapolated.
+#' @param otus.table a motus data.frame containing motus informations of x
+#' @param cutoff a cutoff below which abundances are set to 0. 
+#'               This threshold also determines the value to be added to 0 values for log10 
+#'               transformation
+#' @param return.metabarcoding.data if \code{TRUE}, returns a \code{\link{metabarcoding.data}} object. Default is \code{FALSE}
+#'
+#' @return either a dataframe or a S3 object with a structure similar to \code{\link{metabarcoding.data}} object. 
+#'         The number of samples corresponds to the predicted points.
+#'         The two last columns (if \code{return.metabarcoding.data==F}) or sample data.frame contains x y coordinates of the predicted grid
+#'         The all but last two columns (if \code{return.metabarcoding.data==F}) or read matrix contains the predicted log10 transformed relative abundances 
+#'         instead of reads counts
+#'         If \code{return.metabarcoding.data==F} the motus data.frame contains the motus informations from x
+#'
+#' @examples
+#' 
+#' data(termes)
+#' #Create dummy spatial coordinates
+#' attr(termes, "samples")[c("x", "y")] = expand.grid(1:7,1:3)
+#' 
+#' #compute frequencies
+#' attr(termes, "layers")[["reads.freq"]] = normalize(termes, MARGIN=1)$reads
+#' 
+#' # Getting extrapolations
+#' termes.pred = extrapol.freq(attr(termes, "layers")[["reads.freq"]], min.coord=c(1,1), max.coord=c(7,3), 
+#'                             grid.grain=100,termes$samples[,c("x", "y")], termes$motus, cutoff=1e-3)
+#'
+#' head(termes.pred$reads)
+#' @seealso \code{\link{map.extrapol.freq}} as well as \code{sp} and \code{gstat} packages
+#' @author Lucie Zinger
+#' @export
+
+extrapol.freq = function(x, min.coord, max.coord, grid.grain=100, coords, otus.table, cutoff=1e-3, return.metabarcoding.data = FALSE) {
+  require(gstat)
+  require(sp)
+  
+  #predicted grid setting
+  new.x = seq(min.coord[1], max.coord[1], length.out = grid.grain)
+  new.y = seq(min.coord[2], max.coord[2], length.out = grid.grain)
+  grid.p=expand.grid(new.x, new.y)
+  colnames(grid.p)=c("x", "y")
+  S=sp::SpatialPoints(grid.p); sp::gridded(S)<-TRUE
+  m=gstat::vgm(50, "Exp", 100)
+  
+  #krigging
+  preds = apply(x, 2, function(otu) {
+    otu[otu<cutoff] = cutoff
+    spj=cbind(coords,otu)
+    colnames(spj)=c("x", "y", "otu")
+    spj.g=gstat::gstat(id="Log10.freq", formula=log10(otu)~1,locations=~x+y,data=spj,model=m)
+    gstat::predict.gstat(spj.g, grid.p, quiet=T)$Log10.freq.pred
+  })
+  
+  #formatting the output
+  colnames(preds) = rownames(otus.table)
+  rownames(preds) = paste("s", 1:nrow(grid.p), sep=".")
+  row.names(grid.p) = rownames(preds)
+  
+  if(return.metabarcoding.data==F) {
+    out = data.frame(preds, grid.p)
+  } else{ 
+    out = metabarcoding.data(preds, grid.p, otus.table)
+  }
+  return(out)
+}
+
+
+#' Maps of krigged log10-transformed frequencies
+#' 
+#' Maps the output of extrapol.freq
+#' 
+#' 
+#' @param x an extrapol.freq output
+#' @param path the path of the folder to export the map. Default is \code{NULL} and map is printed in Rplot/quartz
+#' @param col.names a vector containing the names of the columns to be used for defining the file name. Typically
+#'                  the column names containing the taxonomic information and/or sequence/motus id.
+#' @param index  an integer indicating column number of the motu/sequence to be plotted.
+#' @param cutoff  lower motu frequency accepted to consider motu abundance as different
+#'                from 0. Should be the same than the one used in extrapol.freq
+#' @param add.points a 3-column data.frame containing factor levels and associated x and y coordinates
+#'                   to be added to the map. Typically taxa observed in the field.
+#' @param adj a value used for adjusting text position in the map. Default is \code{4}
+#'
+#' @return a map/png file displaying motus distribution.
+#'
+#' @examples
+#' 
+#' data(termes)
+#' attr(termes, "samples")[c("x", "y")] = expand.grid(1:7,1:3)
+#' 
+#' #compute frequencies
+#' attr(termes, "layers")[["reads.freq"]] = normalize(termes, MARGIN=1)$reads
+#' 
+#' # Getting extrapolations
+#' termes.pred = extrapol.freq(attr(termes, "layers")[["reads.freq"]], 
+#' grid.grain=100,termes$samples[,c("x", "y")], termes$motus, cutoff=1e-3)
+#' 
+#' #mapping the distribution of the 3 most abundant sequences (caution, mfrow does not work for lattice's levelplot)
+#' map.extrapol.freq(termes.pred, path=NULL, col.name=NULL, 1, cutoff=1e-3)
+#' map.extrapol.freq(termes.pred, path=NULL, col.name=NULL, 2, cutoff=1e-3)
+#' map.extrapol.freq(termes.pred, path=NULL, col.name=NULL, 3, cutoff=1e-3)
+#' 
+#' #dummy observationnal data
+#' termes.obs = data.frame(x=c(2,3,5), y=c(2.7,2,2.6), taxa = rep("Isoptera Apicotermitinae", 3))
+#' map.extrapol.freq(termes.pred, path=NULL, col.name=NULL, 3, cutoff=1e-3, add.points=termes.obs)
+#' 
+#' @seealso \code{\link{extrapol.freq}}, and \code{levelplot} from \code{lattice} package
+#' @author Lucie Zinger
+#' @export
+
+map.extrapol.freq = function(x, path=NULL, col.name=NULL, index, cutoff=1e-3, add.points=NULL, adj=4) {
+  
+  require(lattice)
+  
+  if(!is.null(path)) {
+    x.motus = apply(x$motus,2,as.character)
+    name = gsub("\\.", "_", paste(gsub(", ", "_", toString(x.motus[index,col.name])), x.motus[index,"id"], sep="_"))
+    file.out = paste(path, "/", name, ".png", sep="")
+  }
+  
+  z=x$reads[,index]
+  z[abs(z)>abs(log10(cutoff))]=log10(cutoff)
+  z[z>0] = 0
+  spj=as.data.frame(cbind(x$samples,z))
+  colnames(spj)=c("x", "y", "z")
+  
+  map.out=levelplot(z~x+y, spj, col.regions=topo.colors(100),