Commit 47aabbd5 authored by Andreas Tille's avatar Andreas Tille

New upstream version 1.22.3

parent 2cdbfe71
# Adapted from sample .travis.yml for R projects
language: r
bioc_required: true
warnings_are_errors: true
sudo: required
env:
global:
- CRAN: http://cran.rstudio.com
notifications:
email:
on_success: change
on_failure: change
Package: phyloseq
Version: 1.20.0
Date: 2016-12-29
Version: 1.22.3
Date: 2017-11-04
Title: Handling and analysis of high-throughput microbiome census data
Description: phyloseq provides a set of classes and tools
to facilitate the import, storage, analysis, and
......@@ -10,16 +10,16 @@ Author: Paul J. McMurdie <joey711@gmail.com>,
Susan Holmes <susan@stat.stanford.edu>, with
contributions from Gregory Jordan and Scott Chamberlain
License: AGPL-3
Imports: BiocGenerics (>= 0.18.0), ade4 (>= 1.7.4), ape (>= 3.4),
biomformat (>= 1.0.0), Biostrings (>= 2.40.0), cluster (>=
2.0.4), data.table (>= 1.9.6), foreach (>= 1.4.3), ggplot2 (>=
2.1.0), igraph (>= 1.0.1), methods (>= 3.3.0), multtest (>=
2.28.0), plyr (>= 1.8.3), reshape2 (>= 1.4.1), scales (>=
0.4.0), vegan (>= 2.3.5), Biobase
Depends: R (>= 3.3.0)
Suggests: BiocStyle (>= 2.0.0), DESeq2 (>= 1.12.0), genefilter (>=
1.54), testthat (>= 1.0.2), knitr (>= 1.13), metagenomeSeq (>=
1.14), rmarkdown (>= 0.9.6)
Imports: ade4 (>= 1.7.4), ape (>= 5.0), Biobase (>= 2.36.2),
BiocGenerics (>= 0.22.0), biomformat (>= 1.0.0), Biostrings (>=
2.40.0), cluster (>= 2.0.4), data.table (>= 1.10.4), foreach
(>= 1.4.3), ggplot2 (>= 2.1.0), igraph (>= 1.0.1), methods (>=
3.3.0), multtest (>= 2.28.0), plyr (>= 1.8.3), reshape2 (>=
1.4.1), scales (>= 0.4.0), vegan (>= 2.4)
Depends: R (>= 3.4.0)
Suggests: BiocStyle (>= 2.4), DESeq2 (>= 1.16.1), genefilter (>= 1.58),
knitr (>= 1.16), metagenomeSeq (>= 1.14), rmarkdown (>= 1.6),
testthat (>= 1.0.2)
VignetteBuilder: knitr
Enhances: doParallel (>= 1.0.10)
biocViews: Sequencing, Microbiome, Metagenomics, Clustering,
......@@ -36,6 +36,6 @@ Collate: 'allClasses.R' 'allPackage.R' 'allData.R' 'as-methods.R'
'network-methods.R' 'distance-methods.R'
'deprecated_functions.R' 'extend_DESeq2.R' 'phylo-class.R'
'extend_metagenomeSeq.R'
RoxygenNote: 5.0.1
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2017-04-24 23:14:31 UTC; biocbuild
Packaged: 2017-11-06 00:01:50 UTC; biocbuild
......@@ -177,6 +177,8 @@ importFrom(ape,drop.tip)
importFrom(ape,is.rooted)
importFrom(ape,ladderize)
importFrom(ape,node.depth)
importFrom(ape,node.depth.edgelength)
importFrom(ape,node.height)
importFrom(ape,pcoa)
importFrom(ape,prop.part)
importFrom(ape,read.nexus)
......
......@@ -559,6 +559,7 @@ setMethod("UniFrac", "phyloseq", function(physeq, weighted=FALSE, normalized=TRU
#' @importFrom ape prop.part
#' @importFrom ape reorder.phylo
#' @importFrom ape node.depth
#' @importFrom ape node.depth.edgelength
#' @keywords internal
#' @import foreach
fastUniFrac <- function(physeq, weighted=FALSE, normalized=TRUE, parallel=FALSE){
......@@ -631,11 +632,7 @@ fastUniFrac <- function(physeq, weighted=FALSE, normalized=TRUE, parallel=FALSE)
z = reorder.phylo(tree, order="postorder")
# Call phyloseq-internal function that in-turn calls ape's internal
# horizontal position function, in C, using the re-ordered phylo object, `z`
tipAges = ape_node_depth_edge_length(Ntip = length(tree$tip.label),
Nnode = tree$Nnode,
edge = z$edge,
Nedge = nrow(tree$edge)[1],
edge.length = z$edge.length)
tipAges = node.depth.edgelength(tree)
# Keep only the tips, and add the tip labels in case `z` order differs from `tree`
tipAges <- tipAges[1:length(tree$tip.label)]
names(tipAges) <- z$tip.label
......
......@@ -15,12 +15,3 @@ setMethod("fix_phylo", "phylo", function(tree){
return(tree)
})
################################################################################
# Define horizontal position / node-ages by depth to root
# For instance, `xx` in `plot_tree` and `tipAges` in `fastUniFrac`
#' @keywords internal
ape_node_depth_edge_length <- function(Ntip, Nnode, edge, Nedge, edge.length){
.C(ape:::node_depth_edgelength, PACKAGE="ape", as.integer(Ntip),
as.integer(Nnode), as.integer(edge[, 1]),
as.integer(edge[, 2]), as.integer(Nedge),
as.double(edge.length), double(Ntip + Nnode))[[7]]
}
\ No newline at end of file
......@@ -1130,7 +1130,7 @@ plot_ordination = function(physeq, ordination, type="samples", axes=1:2,
}
# Add text labels to points
if( !is.null(label) ){
label_map <- aes_string(x=x, y=y, label=label, na.rm=TRUE)
label_map <- aes_string(x=x, y=y, label=label)
p = p + geom_text(label_map, data=rm.na.phyloseq(DF, label),
size=2, vjust=1.5, na.rm=TRUE)
}
......@@ -1695,6 +1695,8 @@ plot_bar = function(physeq, x="Sample", y="Abundance", fill=NULL,
#'
#' @importFrom ape ladderize
#' @importFrom ape reorder.phylo
#' @importFrom ape node.depth.edgelength
#' @importFrom ape node.height
#'
#' @importFrom data.table data.table
#' @importFrom data.table setkey
......@@ -1740,29 +1742,13 @@ tree_layout = function(phy, ladderize=FALSE){
# Descending order of left-hand side of edge (the ancestor to the node)
z = reorder.phylo(phy, order="postorder")
# Initialize some characteristics of the tree.
Nedge = nrow(phy$edge)[1]
Nnode = phy$Nnode
Ntip = length(phy$tip.label)
ROOT = Ntip + 1
TIPS = phy$edge[(phy$edge[, 2] <= Ntip), 2]
NODES = (ROOT):(Ntip + Nnode)
nodelabels = phy$node.label
# Call phyloseq-internal function that in-turn calls ape's internal
# horizontal position function, in C, using the re-ordered phylo object.
xx = ape_node_depth_edge_length(Ntip, Nnode, z$edge, Nedge, z$edge.length)
# Initialize `yy`, before passing to ape internal function in C.
yy <- numeric(Ntip + Nnode)
yy[TIPS] <- 1:Ntip
# Define the ape_node_height wrapping function
ape_node_height <- function(Ntip, Nnode, edge, Nedge, yy){
.C(ape:::node_height, PACKAGE="ape",
as.integer(Ntip), as.integer(Nnode),
as.integer(edge[, 1]), as.integer(edge[, 2]),
as.integer(Nedge), as.double(yy))[[6]]
}
# The call in ape
#yy <- .nodeHeight(Ntip, Nnode, z$edge, Nedge, yy)
yy <- ape_node_height(Ntip, Nnode, z$edge, Nedge, yy)
# Horizontal positions
xx = node.depth.edgelength(phy)
# vertical positions
yy = node.height(phy = phy, clado.style = FALSE)
# Initialize an edge data.table
# Don't set key, order matters
edgeDT = data.table(phy$edge, edge.length=phy$edge.length, OTU=NA_character_)
......
No preview for this file type
## ---- warning=FALSE, message=FALSE---------------------------------------
## ---- warning=FALSE, message=FALSE-----------------------------------------
library("phyloseq"); packageVersion("phyloseq")
library("ggplot2"); packageVersion("ggplot2")
theme_set(theme_bw())
## ------------------------------------------------------------------------
## --------------------------------------------------------------------------
data(esophagus)
plot_tree(esophagus)
## ------------------------------------------------------------------------
## --------------------------------------------------------------------------
p1 = plot_tree(esophagus, color = "Sample")
p1
p1 +
......@@ -16,7 +16,7 @@ p1 +
color = "orange",
label = "my annotation")
## ------------------------------------------------------------------------
## --------------------------------------------------------------------------
data("esophagus")
mdf = psmelt(esophagus)
# Simple bar plot. See plot_bar() for more.
......
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## ---- eval=FALSE---------------------------------------------------------
## ---- eval=FALSE-----------------------------------------------------------
# vignette("phyloseq_analysis")
## ----load-packages, message=FALSE, warning=FALSE-------------------------
## ----load-packages, message=FALSE, warning=FALSE---------------------------
library("phyloseq")
## ---- eval=FALSE---------------------------------------------------------
## ---- eval=FALSE-----------------------------------------------------------
# myOTU1 <- import_RDP_cluster("path/to/my/filename.clust")
## ---- eval=FALSE---------------------------------------------------------
## ---- eval=FALSE-----------------------------------------------------------
# data(GlobalPatterns)
# data(esophagus)
# data(enterotype)
# data(soilrep)
## ------------------------------------------------------------------------
## --------------------------------------------------------------------------
data(GlobalPatterns)
GlobalPatterns
## ---- eval=FALSE---------------------------------------------------------
## ---- eval=FALSE-----------------------------------------------------------
# otu1 <- otu_table(raw_abundance_matrix, taxa_are_rows=FALSE)
# sam1 <- sample_data(raw_sample_data.frame)
# tax1 <- tax_table(raw_taxonomy_matrix)
# tre1 <- read_tree(my_tree_file)
## ---- eval=FALSE---------------------------------------------------------
## ---- eval=FALSE-----------------------------------------------------------
# ex1b <- phyloseq(my_otu_table, my_sample_data, my_taxonomyTable, my_tree)
## ---- eval=FALSE---------------------------------------------------------
## ---- eval=FALSE-----------------------------------------------------------
# ex1c <- phyloseq(my_otu_table, my_sample_data)
## ----echo=FALSE----------------------------------------------------------
## ----echo=FALSE------------------------------------------------------------
topN <- 20
## ------------------------------------------------------------------------
## --------------------------------------------------------------------------
data(GlobalPatterns)
most_abundant_taxa <- sort(taxa_sums(GlobalPatterns), TRUE)[1:topN]
ex2 <- prune_taxa(names(most_abundant_taxa), GlobalPatterns)
## ------------------------------------------------------------------------
## --------------------------------------------------------------------------
topFamilies <- tax_table(ex2)[, "Family"]
as(topFamilies, "vector")
## ---- eval=FALSE---------------------------------------------------------
## ---- eval=FALSE-----------------------------------------------------------
# testOTU <- otu_table(matrix(sample(1:50, 25, replace=TRUE), 5, 5), taxa_are_rows=FALSE)
# f1<- filterfun_sample(topk(2))
# wh1 <- genefilter_sample(testOTU, f1, A=2)
......@@ -49,24 +49,24 @@ as(topFamilies, "vector")
# prune_taxa(wh1, testOTU)
# prune_taxa(wh2, testOTU)
## ------------------------------------------------------------------------
## --------------------------------------------------------------------------
data(GlobalPatterns)
f1<- filterfun_sample(topp(0.1))
wh1 <- genefilter_sample(GlobalPatterns, f1, A=(1/2*nsamples(GlobalPatterns)))
sum(wh1)
ex2 <- prune_taxa(wh1, GlobalPatterns)
## ------------------------------------------------------------------------
## --------------------------------------------------------------------------
print(ex2)
## ---- eval=FALSE---------------------------------------------------------
## ---- eval=FALSE-----------------------------------------------------------
# data(GlobalPatterns)
# f1<- filterfun_sample(topf(0.9))
# wh1 <- genefilter_sample(GlobalPatterns, f1, A=(1/3*nsamples(GlobalPatterns)))
# sum(wh1)
# prune_taxa(wh1, GlobalPatterns)
## ------------------------------------------------------------------------
## --------------------------------------------------------------------------
data("enterotype")
library("genefilter")
flist<- filterfun(kOverA(5, 2e-05))
......@@ -76,35 +76,35 @@ identical(ent.trim, prune_taxa(ent.logi, enterotype))
identical(sum(ent.logi), ntaxa(ent.trim))
filter_taxa(enterotype, flist, TRUE)
## ------------------------------------------------------------------------
## --------------------------------------------------------------------------
ex3 <- subset_samples(GlobalPatterns, SampleType%in%c("Freshwater", "Ocean", "Freshwater (creek)"))
ex3
## ------------------------------------------------------------------------
## --------------------------------------------------------------------------
subset(sample_data(GlobalPatterns), SampleType%in%c("Freshwater", "Ocean", "Freshwater (creek)"))
## ------------------------------------------------------------------------
## --------------------------------------------------------------------------
ex4 <- subset_taxa(GlobalPatterns, Phylum=="Firmicutes")
ex4
## ------------------------------------------------------------------------
## --------------------------------------------------------------------------
randomSpecies100 <- sample(taxa_names(GlobalPatterns), 100, replace=FALSE)
ex5 <- prune_taxa(randomSpecies100, GlobalPatterns)
## ---- eval=FALSE---------------------------------------------------------
## ---- eval=FALSE-----------------------------------------------------------
# data(GlobalPatterns)
# ex2 <- transform_sample_counts(GlobalPatterns, I)
## ------------------------------------------------------------------------
## --------------------------------------------------------------------------
ex4<- transform_sample_counts(GlobalPatterns, threshrankfun(500))
## ---- eval=FALSE---------------------------------------------------------
## ---- eval=FALSE-----------------------------------------------------------
# ex6 <- tax_glom(GlobalPatterns, taxlevel="Genus")
## ---- eval=FALSE---------------------------------------------------------
## ---- eval=FALSE-----------------------------------------------------------
# ex7 <- tip_glom(GlobalPatterns, speciationMinLength = 0.05)
## ---- eval=FALSE---------------------------------------------------------
## ---- eval=FALSE-----------------------------------------------------------
# install.packages("doParallel")
# install.packages("doMC")
# install.packages("doSNOW")
......
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## ----load-phyloseq, message=FALSE, warning=FALSE-------------------------
## ----load-phyloseq, message=FALSE, warning=FALSE---------------------------
library("phyloseq"); packageVersion("phyloseq")
## ----filepath------------------------------------------------------------
## ----filepath--------------------------------------------------------------
filepath = system.file("extdata", "study_1457_split_library_seqs_and_mapping.zip", package="phyloseq")
kostic = microbio_me_qiime(filepath)
## ----example-path-local, eval=FALSE--------------------------------------
## ----example-path-local, eval=FALSE----------------------------------------
# filepath = "~/Downloads/study_1457_split_library_seqs_and_mapping.zip"
# kostic = microbio_me_qiime(filepath)
## ----example-path-remote, eval=FALSE-------------------------------------
## ----example-path-remote, eval=FALSE---------------------------------------
# kostic = microbio_me_qiime(1457)
## ----show-variables------------------------------------------------------
## ----show-variables--------------------------------------------------------
kostic
head(sample_data(kostic)$DIAGNOSIS, 10)
## ----deseq2, message=FALSE, warning=FALSE--------------------------------
## ----deseq2, message=FALSE, warning=FALSE----------------------------------
library("DESeq2"); packageVersion("DESeq2")
## ----rm-bad-samples------------------------------------------------------
## ----rm-bad-samples--------------------------------------------------------
kostic <- subset_samples(kostic, DIAGNOSIS != "None")
kostic <- prune_samples(sample_sums(kostic) > 500, kostic)
kostic
## ----run-deseq2----------------------------------------------------------
## ----run-deseq2------------------------------------------------------------
diagdds = phyloseq_to_deseq2(kostic, ~ DIAGNOSIS)
# calculate geometric means prior to estimate size factors
gm_mean = function(x, na.rm=TRUE){
......@@ -34,7 +34,7 @@ geoMeans = apply(counts(diagdds), 1, gm_mean)
diagdds = estimateSizeFactors(diagdds, geoMeans = geoMeans)
diagdds = DESeq(diagdds, fitType="local")
## ----grab-results-process-table------------------------------------------
## ----grab-results-process-table--------------------------------------------
res = results(diagdds)
res = res[order(res$padj, na.last=NA), ]
alpha = 0.01
......@@ -42,11 +42,11 @@ sigtab = res[(res$padj < alpha), ]
sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(kostic)[rownames(sigtab), ], "matrix"))
head(sigtab)
## ----table-prelim--------------------------------------------------------
## ----table-prelim----------------------------------------------------------
posigtab = sigtab[sigtab[, "log2FoldChange"] > 0, ]
posigtab = posigtab[, c("baseMean", "log2FoldChange", "lfcSE", "padj", "Phylum", "Class", "Family", "Genus")]
## ----make-markdown-table, echo=FALSE, results='asis'---------------------
## ----make-markdown-table, echo=FALSE, results='asis'-----------------------
# Make a markdown table
posigtab = data.frame(OTU=rownames(posigtab), posigtab)
cat(paste(colnames(posigtab), collapse=" | "), fill=TRUE)
......@@ -55,7 +55,7 @@ dummy = apply(posigtab, 1, function(x){
cat(paste(x, collapse=" | "), fill=TRUE)
})
## ----bar-plot------------------------------------------------------------
## ----bar-plot--------------------------------------------------------------
library("ggplot2")
theme_set(theme_bw())
sigtabgen = subset(sigtab, !is.na(Genus))
......
This diff is collapsed.
......@@ -19,14 +19,14 @@ should return an object without error.}
and return a new \code{dist}ance object that is Euclidean.
If testing a distance object, try \code{\link[ade4]{is.euclid}}.
In most real-life, real-data applications, the phylogenetic tree
will not provide a Euclidean distance matrix, and so a correction
will be needed.
Two recommended correction methods are
Although the distance matrix should always be Euclidean, for numerical
reasons it will sometimes appear non-Euclidean and a correction method must
be applied. Two recommended correction methods are
\code{\link[ade4]{cailliez}} and \code{\link[ade4]{lingoes}}.
The default is \code{cailliez},
but not for any particularly special reason. If the patristic
distance matrix turns out to be Euclidian, no correction will be
but not for any particularly special reason. If the
distance matrix is Euclidian, no correction will be
performed, regardless of the value of the \code{correction} argument.}
\item{scannf}{(Optional). Logical. Default is \code{FALSE}. This
......@@ -48,13 +48,13 @@ phylogenetic (\code{\link[ape]{phylo}}) components of a
to perform a
Double Principle Coordinate Analysis (DPCoA), relying heavily on
the underlying (and more general) function, \code{\link[ade4]{dpcoa}}.
The distance object ultimately provided as the cophenetic/patristic
(\code{\link[ape]{cophenetic.phylo}}) distance between the species.
}
\details{
In most real-life, real-data applications, the phylogenetic tree
will not provide a Euclidean distance matrix, and so a correction
will be performed, if needed. See \code{correction} argument.
The distance object ultimately provided is the square root of the
cophenetic/patristic (\code{\link[ape]{cophenetic.phylo}}) distance
between the species, which is always Euclidean.
Although this distance is Euclidean, for numerical reasons it
will sometimes look non-Euclidean, and a correction will be performed.
See \code{correction} argument.
}
\examples{
# # # # # # Esophagus
......@@ -75,10 +75,6 @@ GP <- prune_taxa(keepTaxa, GlobalPatterns)
GP.dpcoa <- DPCoA(GP)
plot_ordination(GP, GP.dpcoa, color="SampleType")
}
\author{
Julia Fukuyama \email{julia.fukuyama@gmail.com}.
Adapted for phyloseq by Paul J. McMurdie.
}
\references{
Pavoine, S., Dufour, A.B. and Chessel, D. (2004)
From dissimilarities among species to dissimilarities among communities:
......@@ -88,4 +84,7 @@ Journal of Theoretical Biology, 228, 523-537.
\seealso{
\code{\link[ade4]{dpcoa}}
}
\author{
Julia Fukuyama \email{julia.fukuyama@gmail.com}.
Adapted for phyloseq by Paul J. McMurdie.
}
......@@ -39,10 +39,6 @@ the analysis of the \code{\link{enterotype}} dataset.
# p <- plot_ordination(enterotype, ent.PCoA, color="Enterotype", shape="SeqTech")
# (p <- p + geom_point(size=5, alpha=0.5))
}
\author{
Susan Holmes \email{susan@stat.stanford.edu}.
Adapted for phyloseq by Paul J. McMurdie.
}
\references{
Jensen-Shannon Divergence and Hilbert space embedding.
Bent Fuglede and Flemming Topsoe University of Copenhagen,
......@@ -56,5 +52,8 @@ Department of Mathematics
\url{http://en.wikipedia.org/wiki/Jensen-Shannon_divergence}
}
\author{
Susan Holmes \email{susan@stat.stanford.edu}.
Adapted for phyloseq by Paul J. McMurdie.
}
\keyword{internal}
......@@ -171,4 +171,3 @@ communities.'' Appl Environ Microbiol. 2005 71 (12):8228-35.
\code{unifrac} in the picante package.
}
......@@ -46,4 +46,3 @@ be set to \code{TRUE} if an error is desired.
\seealso{
\code{\link{getslots.phyloseq}}, \code{\link{merge_phyloseq}}
}
......@@ -2,10 +2,10 @@
% Please edit documentation in R/assignment-methods.R
\docType{methods}
\name{otu_table<-}
\alias{assign-otu_table}
\alias{otu_table<-}
\alias{otu_table<-,otu_table,otu_table-method}
\alias{assign-otu_table}
\alias{otu_table<-,phyloseq,otu_table-method}
\alias{otu_table<-,otu_table,otu_table-method}
\alias{otu_table<-,phyloseq,phyloseq-method}
\title{Assign a new OTU Table to \code{x}}
\usage{
......@@ -43,4 +43,3 @@ Assign a new OTU Table to \code{x}
# otu_table(ex2c) <- ex2b
# identical(ex2a, ex2c)
}
......@@ -2,8 +2,8 @@
% Please edit documentation in R/assignment-methods.R
\docType{methods}
\name{phy_tree<-}
\alias{assign-phy_tree}
\alias{phy_tree<-}
\alias{assign-phy_tree}
\alias{phy_tree<-,phyloseq,phylo-method}
\alias{phy_tree<-,phyloseq,phyloseq-method}
\title{Assign a (new) phylogenetic tree to \code{x}}
......@@ -32,4 +32,3 @@ ex2b <- ex2a
phy_tree(ex2b) <- phy_tree(esophagus)
identical(ex2a, ex2b)
}
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/assignment-methods.R
\name{sample_data<-}
\alias{assign-sample_data}
\alias{sample_data<-}
\alias{assign-sample_data}
\title{Assign (new) sample_data to \code{x}}
\usage{
sample_data(x) <- value
......@@ -45,4 +45,3 @@ are included in any components. This has the added benefit of re-checking
sample_data(soilrep)$Time <- as.integer(substr(sample_data(soilrep)$Sample, 1, 1))
head(sample_data(soilrep))
}
......@@ -2,13 +2,13 @@
% Please edit documentation in R/assignment-methods.R
\docType{methods}
\name{sample_names<-}
\alias{assign-sample_names}
\alias{sample_names<-}
\alias{assign-sample_names}
\alias{sample_names<-,ANY,ANY-method}
\alias{sample_names<-,ANY,character-method}
\alias{sample_names<-,otu_table,character-method}
\alias{sample_names<-,phyloseq,character-method}
\alias{sample_names<-,sample_data,character-method}
\alias{sample_names<-,phyloseq,character-method}
\title{Replace OTU identifier names}
\usage{
sample_names(x) <- value
......@@ -48,4 +48,3 @@ sample_names(esophagus)
# sample_names(esophagus) <- sample(c(TRUE, FALSE), nsamples(esophagus), TRUE)
# sample_names(esophagus) <- sample(sample_names(esophagus), nsamples(esophagus)-1, FALSE)
}
......@@ -2,12 +2,12 @@
% Please edit documentation in R/assignment-methods.R
\docType{methods}
\name{tax_table<-}
\alias{assign-tax_table}
\alias{tax_table<-}
\alias{tax_table<-,phyloseq,ANY-method}
\alias{assign-tax_table}
\alias{tax_table<-,phyloseq,taxonomyTable-method}
\alias{tax_table<-,taxonomyTable,ANY-method}
\alias{tax_table<-,phyloseq,ANY-method}
\alias{tax_table<-,taxonomyTable,taxonomyTable-method}
\alias{tax_table<-,taxonomyTable,ANY-method}
\title{Assign a (new) Taxonomy Table to \code{x}}
\usage{
tax_table(x) <- value
......@@ -50,4 +50,3 @@ Assign a (new) Taxonomy Table to \code{x}
# tax_table(ex2c) <- as(tax_table(ex2b), "matrix")
# identical(ex2a, ex2c)
}
......@@ -2,8 +2,8 @@
% Please edit documentation in R/assignment-methods.R
\docType{methods}
\name{taxa_are_rows<-}
\alias{assign-taxa_are_rows}
\alias{taxa_are_rows<-}
\alias{assign-taxa_are_rows}
\alias{taxa_are_rows<-,otu_table,logical-method}
\alias{taxa_are_rows<-,phyloseq,logical-method}
\title{Manually change taxa_are_rows through assignment.}
......@@ -30,4 +30,3 @@ abundance table contained in object \code{x}.
taxa_are_rows(esophagus)
taxa_are_rows(otu_table(esophagus))
}
......@@ -2,15 +2,15 @@
% Please edit documentation in R/assignment-methods.R
\docType{methods}
\name{taxa_names<-}
\alias{assign-taxa_names}
\alias{taxa_names<-}
\alias{assign-taxa_names}
\alias{taxa_names<-,ANY,ANY-method}
\alias{taxa_names<-,ANY,character-method}
\alias{taxa_names<-,XStringSet,character-method}
\alias{taxa_names<-,otu_table,character-method}
\alias{taxa_names<-,taxonomyTable,character-method}
\alias{taxa_names<-,phylo,character-method}
\alias{taxa_names<-,XStringSet,character-method}
\alias{taxa_names<-,phyloseq,character-method}
\alias{taxa_names<-,taxonomyTable,character-method}
\title{Replace OTU identifier names}
\usage{
taxa_names(x) <- value
......@@ -54,4 +54,3 @@ taxa_names(esophagus)
# taxa_names(esophagus) <- sample(c(TRUE, FALSE), ntaxa(esophagus), TRUE)
# taxa_names(esophagus) <- sample(taxa_names(esophagus), ntaxa(esophagus)-5, FALSE)
}
......@@ -40,4 +40,3 @@ Build a \code{\link{tax_table}} from a named possibly-jagged list
\code{\link{import_biom}}
\code{\link{import_qiime}}
}
......@@ -3,8 +3,8 @@
\docType{methods}
\name{capscale.phyloseq}
\alias{capscale.phyloseq}
\alias{capscale.phyloseq,phyloseq,formula,character-method}
\alias{capscale.phyloseq,phyloseq,formula,dist-method}
\alias{capscale.phyloseq,phyloseq,formula,character-method}
\title{Constrained Analysis of Principal Coordinates, \code{\link[vegan]{capscale}}.}
\usage{
capscale.phyloseq(physeq, formula, distance, ...)
......@@ -67,4 +67,3 @@ plot_ordination(GP, ordcap, "samples", color="SampleType")
\code{\link[vegan]{capscale}}
}
\keyword{internal}
......@@ -3,11 +3,11 @@
\docType{methods}
\name{cca.phyloseq}
\alias{cca.phyloseq}
\alias{rda.phyloseq}
\alias{cca.phyloseq,phyloseq,formula-method}
\alias{cca.phyloseq,otu_table,ANY-method}
\alias{cca.phyloseq,otu_table-method}
\alias{cca.phyloseq,phyloseq,NULL-method}
\alias{cca.phyloseq,phyloseq,formula-method}
\alias{rda.phyloseq}
\title{Constrained Correspondence Analysis and Redundancy Analysis.}
\usage{
cca.phyloseq(physeq, formula = NULL, method = "CCA", ...)
......@@ -57,4 +57,3 @@ to these methods is optional, and results in an unconstrained ordination.
\code{\link[vegan]{rda}}, \code{\link[vegan]{cca}}
}
\keyword{internal}
......@@ -32,4 +32,3 @@ Different than relevel.
# chunkReOrder(LETTERS, "g")
}
\keyword{internal}
......@@ -2,8 +2,8 @@
% Please edit documentation in R/allData.R
\docType{data}
\name{data-GlobalPatterns}
\alias{GlobalPatterns}
\alias{data-GlobalPatterns}
\alias{GlobalPatterns}
\title{(Data) Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample (2011)}
\description{
Published in PNAS in early 2011. This work compared the microbial
......@@ -28,9 +28,6 @@ for inclusion in this package.
data(GlobalPatterns)
plot_richness(GlobalPatterns, x="SampleType", measures=c("Observed", "Chao1", "Shannon"))
}
\author{
Caporaso, J. G., et al.
}
\references{
Caporaso, J. G., et al. (2011).
Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample.
......@@ -46,5 +43,7 @@ The examples on the phyloseq wiki page for \code{\link{plot_ordination}} show
\url{https://github.com/joey711/phyloseq/wiki/plot_ordination}
}
\author{
Caporaso, J. G., et al.
}
\keyword{data}
......@@ -29,9 +29,6 @@ data(enterotype)
ig <- make_network(enterotype, "samples", max.dist=0.3)
plot_network(ig, enterotype, color="SeqTech", shape="Enterotype", line_weight=0.3, label=NULL)
}
\author{
Arumugam, M., Raes, J., et al.
}
\references{
Arumugam, M., et al. (2011). Enterotypes of the human gut microbiome.
......@@ -44,5 +41,7 @@ OTU-clustered data was downloaded from the publicly-accessible: