Commit 7a750efa by Eric Coissac

Patch the extract.obiclean function

parent c09e222c
...@@ -35,4 +35,4 @@ Collate: ...@@ -35,4 +35,4 @@ Collate:
'taxoDBtree.R' 'taxoDBtree.R'
'taxonomic.resolution.R' 'taxonomic.resolution.R'
'taxonomy_classic_table.R' 'taxonomy_classic_table.R'
RoxygenNote: 6.0.1 RoxygenNote: 6.1.1
...@@ -56,9 +56,8 @@ setMethod("extracts.obiclean", "metabarcoding.data", function(obj) { ...@@ -56,9 +56,8 @@ setMethod("extracts.obiclean", "metabarcoding.data", function(obj) {
cols = colnames(obj@motus) cols = colnames(obj@motus)
cleancols = grep(pat,cols) cleancols = grep(pat,cols)
clean.names=cols[cleancols] clean.names=cols[cleancols]
p = grep(pat,cols) d = t(as.factor.or.matrix(obj@motus[,cleancols]))
d = t(as.factor.or.matrix(obj@motus[,p])) n = sapply(strsplit(cols[cleancols],':'),function(y) y[[2]])
n = sapply(strsplit(cols[p],':'),function(y) y[[2]])
rownames(d)=n rownames(d)=n
d = d[rownames(obj@reads),] d = d[rownames(obj@reads),]
obj[["obiclean_status"]]=d obj[["obiclean_status"]]=d
...@@ -68,28 +67,31 @@ setMethod("extracts.obiclean", "metabarcoding.data", function(obj) { ...@@ -68,28 +67,31 @@ setMethod("extracts.obiclean", "metabarcoding.data", function(obj) {
pat = "^obiclean_count:.*$" pat = "^obiclean_count:.*$"
cols = colnames(newmotus) cols = colnames(newmotus)
cleancols = grep(pat,cols) cleancols = grep(pat,cols)
clean.names=cols[cleancols] if (length(cleancols) > 0) {
p = grep(pat,cols) clean.names=cols[cleancols]
d = t(as.factor.or.matrix(newmotus[,p])) d = t(as.factor.or.matrix(newmotus[,cleancols]))
n = sapply(strsplit(cols[p],':'),function(y) y[[2]]) n = sapply(strsplit(cols[cleancols],':'),function(y) y[[2]])
rownames(d)=n rownames(d)=n
d = d[rownames(obj@reads),] d = d[rownames(obj@reads),]
obj[["obiclean_count"]]=d obj[["obiclean_count"]]=d
newmotus = newmotus[-cleancols] newmotus = newmotus[-cleancols]
}
pat = "^obiclean_cluster:.*$" pat = "^obiclean_cluster:.*$"
cols = colnames(newmotus) cols = colnames(newmotus)
cleancols = grep(pat,cols) cleancols = grep(pat,cols)
clean.names=cols[cleancols]
p = grep(pat,cols)
d = t(as.factor.or.matrix(newmotus[,p]))
n = sapply(strsplit(cols[p],':'),function(y) y[[2]])
rownames(d)=n
d = d[rownames(obj@reads),]
obj[["obiclean_cluster"]]=d
newmotus = newmotus[-cleancols] if (length(cleancols) > 0) {
clean.names=cols[cleancols]
d = t(as.factor.or.matrix(newmotus[,cleancols]))
n = sapply(strsplit(cols[cleancols],':'),function(y) y[[2]])
rownames(d)=n
d = d[rownames(obj@reads),]
obj[["obiclean_cluster"]]=d
newmotus = newmotus[-cleancols]
}
newdata = copy.metabarcoding.data(obj,motus=newmotus) newdata = copy.metabarcoding.data(obj,motus=newmotus)
......
...@@ -4,8 +4,8 @@ ...@@ -4,8 +4,8 @@
\alias{extrapol.freq} \alias{extrapol.freq}
\title{Read frequencies krigging} \title{Read frequencies krigging}
\usage{ \usage{
extrapol.freq(x, min.coord, max.coord, grid.grain = 100, coords, otus.table, extrapol.freq(x, min.coord, max.coord, grid.grain = 100, coords,
cutoff = 0.001, return.metabarcoding.data = FALSE) otus.table, cutoff = 0.001, return.metabarcoding.data = FALSE)
} }
\arguments{ \arguments{
\item{x}{a vector or matrix from a row-normalized read table \item{x}{a vector or matrix from a row-normalized read table
......
...@@ -7,7 +7,8 @@ ...@@ -7,7 +7,8 @@
\title{metabarcoding.data constructor} \title{metabarcoding.data constructor}
\usage{ \usage{
\S4method{initialize}{metabarcoding.data}(.Object, reads, samples, motus, \S4method{initialize}{metabarcoding.data}(.Object, reads, samples, motus,
taxonomy = NULL, taxid = NULL, sample.margin = NA, layers = list()) taxonomy = NULL, taxid = NULL, sample.margin = NA,
layers = list())
} }
\description{ \description{
metabarcoding.data constructor metabarcoding.data constructor
......
...@@ -4,7 +4,8 @@ ...@@ -4,7 +4,8 @@
\alias{m.univariate.test} \alias{m.univariate.test}
\title{Test the significance of the M statistics by Monte-Carlo} \title{Test the significance of the M statistics by Monte-Carlo}
\usage{ \usage{
m.univariate.test(w, groups, resampling = 100, alternative = "two.sided") m.univariate.test(w, groups, resampling = 100,
alternative = "two.sided")
} }
\arguments{ \arguments{
\item{w}{the weigth matrix indicating the presence probability of each motu \item{w}{the weigth matrix indicating the presence probability of each motu
......
...@@ -4,8 +4,8 @@ ...@@ -4,8 +4,8 @@
\alias{map.extrapol.freq} \alias{map.extrapol.freq}
\title{Maps of krigged log10-transformed frequencies} \title{Maps of krigged log10-transformed frequencies}
\usage{ \usage{
map.extrapol.freq(x, path = NULL, col.name = NULL, index, cutoff = 0.001, map.extrapol.freq(x, path = NULL, col.name = NULL, index,
add.points = NULL, adj = 4) cutoff = 0.001, add.points = NULL, adj = 4)
} }
\arguments{ \arguments{
\item{x}{an extrapol.freq output} \item{x}{an extrapol.freq output}
......
...@@ -4,8 +4,8 @@ ...@@ -4,8 +4,8 @@
\alias{plot.PCRplate} \alias{plot.PCRplate}
\title{Plot PCR plates} \title{Plot PCR plates}
\usage{ \usage{
\method{plot}{PCRplate}(x, samples = NULL, col = "cyan2", different = T, \method{plot}{PCRplate}(x, samples = NULL, col = "cyan2",
...) different = T, ...)
} }
\arguments{ \arguments{
\item{x}{a \code{\link{metabarcoding.data}} object} \item{x}{a \code{\link{metabarcoding.data}} object}
......
...@@ -6,8 +6,8 @@ ...@@ -6,8 +6,8 @@
\alias{threshold.mask-methods,metabarcoding.data} \alias{threshold.mask-methods,metabarcoding.data}
\title{Computes a cumulatif thresold mask for filtering read aboundancies.} \title{Computes a cumulatif thresold mask for filtering read aboundancies.}
\usage{ \usage{
\S4method{threshold.mask}{metabarcoding.data}(data, MARGIN, threshold = 0.97, \S4method{threshold.mask}{metabarcoding.data}(data, MARGIN,
operator = "<") threshold = 0.97, operator = "<")
} }
\arguments{ \arguments{
\item{data}{The \code{\linkS4class{metabarcoding.data}} instance \item{data}{The \code{\linkS4class{metabarcoding.data}} instance
......
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