Commit cacaad79 authored by Andreas Tille's avatar Andreas Tille

New upstream version 1.24.2+dfsg

parent 7e91b664
Package: phyloseq
Version: 1.24.0
Date: 2017-11-04
Version: 1.24.2
Date: 2018-07-15
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
......@@ -15,7 +15,7 @@ Imports: ade4 (>= 1.7.4), ape (>= 5.0), Biobase (>= 2.36.2),
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)
1.4.1), scales (>= 0.4.0), vegan (>= 2.5)
Depends: R (>= 3.3.0)
Suggests: BiocStyle (>= 2.4), DESeq2 (>= 1.16.1), genefilter (>= 1.58),
knitr (>= 1.16), metagenomeSeq (>= 1.14), rmarkdown (>= 1.6),
......@@ -37,5 +37,10 @@ Collate: 'allClasses.R' 'allPackage.R' 'allData.R' 'as-methods.R'
'deprecated_functions.R' 'extend_DESeq2.R' 'phylo-class.R'
'extend_metagenomeSeq.R'
RoxygenNote: 6.0.1
git_url: https://git.bioconductor.org/packages/phyloseq
git_branch: RELEASE_3_7
git_last_commit: 829992f
git_last_commit_date: 2018-07-15
Date/Publication: 2018-07-15
NeedsCompilation: no
Packaged: 2018-04-30 23:30:05 UTC; biocbuild
Packaged: 2018-07-15 23:30:27 UTC; biocbuild
......@@ -317,7 +317,7 @@ tip_glom = function(physeq, h=0.2, hcfun=agnes, ...){
#' Agglomerate taxa of the same type.
#'
#' This method merges species that have the same taxonomy at a certain
#' taxaonomic rank.
#' taxonomic rank.
#' Its approach is analogous to \code{\link{tip_glom}}, but uses categorical data
#' instead of a tree. In principal, other categorical data known for all taxa
#' could also be used in place of taxonomy,
......
No preview for this file type
......@@ -165,13 +165,23 @@ p <- ggplot(mdf, aes(Phylum, value, color=Phylum)) +
plot_bar(GP, x="human", fill="SampleType", facet_grid= ~ Phylum)
## ----GPdpcoa01-------------------------------------------------------------
# Perform ordination
GP.dpcoa <- ordinate(GP, "DPCoA")
pdpcoa <- plot_ordination(GP, GP.dpcoa, type="biplot",
color="SampleType", shape="Phylum")
shape.fac <- pdpcoa$data[, deparse(pdpcoa$mapping$shape)]
# Generate default ordination bi-plot
pdpcoa <-
plot_ordination(
physeq = GP,
ordination = GP.dpcoa,
type="biplot",
color="SampleType",
shape="Phylum")
# Adjust the shape scale manually
# to make taxa hollow and samples filled (advanced)
shape.fac <- pdpcoa$data$Phylum
man.shapes <- c(19, 21:25)
names(man.shapes) <- c("Samples", levels(shape.fac)[levels(shape.fac)!="Samples"])
p2dpcoa <- pdpcoa + scale_shape_manual(values=man.shapes)
p2dpcoa
## ----GPdpcoa02-------------------------------------------------------------
# Show just Samples or just Taxa
......
......@@ -7,6 +7,8 @@ output:
toc: yes
toc_depth: 2
number_sections: true
editor_options:
chunk_output_type: console
---
<!--
%% \VignetteEngine{knitr::rmarkdown}
......@@ -418,13 +420,23 @@ In this figure we've used the `threshold` parameter to omit all but phyla accoun
Here is a quick example illustrating the use of Double Principal Coordinate Analysis (DPCoA~\cite{Pavoine2004523), using the using the `ordinate()` function in phyloseq, as well as the "biplot" option for `plot_ordination(). For a description that includes an applied example using the "enterotype" dataset and comparison with UniFrac/PCoA, see Fukuyama et al~\cite{fukuyama2012com.
```{r GPdpcoa01}
# Perform ordination
GP.dpcoa <- ordinate(GP, "DPCoA")
pdpcoa <- plot_ordination(GP, GP.dpcoa, type="biplot",
color="SampleType", shape="Phylum")
shape.fac <- pdpcoa$data[, deparse(pdpcoa$mapping$shape)]
# Generate default ordination bi-plot
pdpcoa <-
plot_ordination(
physeq = GP,
ordination = GP.dpcoa,
type="biplot",
color="SampleType",
shape="Phylum")
# Adjust the shape scale manually
# to make taxa hollow and samples filled (advanced)
shape.fac <- pdpcoa$data$Phylum
man.shapes <- c(19, 21:25)
names(man.shapes) <- c("Samples", levels(shape.fac)[levels(shape.fac)!="Samples"])
p2dpcoa <- pdpcoa + scale_shape_manual(values=man.shapes)
p2dpcoa
```
A biplot representation of a Double Principal Coordinate Analysis (DPCoA), on a simplified version of the "Global Patterns" dataset with only the most abundant 200 OTUs included.
......
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......@@ -7,6 +7,8 @@ output:
toc: yes
toc_depth: 2
number_sections: true
editor_options:
chunk_output_type: console
---
<!--
%% \VignetteEngine{knitr::rmarkdown}
......@@ -418,13 +420,23 @@ In this figure we've used the `threshold` parameter to omit all but phyla accoun
Here is a quick example illustrating the use of Double Principal Coordinate Analysis (DPCoA~\cite{Pavoine2004523), using the using the `ordinate()` function in phyloseq, as well as the "biplot" option for `plot_ordination(). For a description that includes an applied example using the "enterotype" dataset and comparison with UniFrac/PCoA, see Fukuyama et al~\cite{fukuyama2012com.
```{r GPdpcoa01}
# Perform ordination
GP.dpcoa <- ordinate(GP, "DPCoA")
pdpcoa <- plot_ordination(GP, GP.dpcoa, type="biplot",
color="SampleType", shape="Phylum")
shape.fac <- pdpcoa$data[, deparse(pdpcoa$mapping$shape)]
# Generate default ordination bi-plot
pdpcoa <-
plot_ordination(
physeq = GP,
ordination = GP.dpcoa,
type="biplot",
color="SampleType",
shape="Phylum")
# Adjust the shape scale manually
# to make taxa hollow and samples filled (advanced)
shape.fac <- pdpcoa$data$Phylum
man.shapes <- c(19, 21:25)
names(man.shapes) <- c("Samples", levels(shape.fac)[levels(shape.fac)!="Samples"])
p2dpcoa <- pdpcoa + scale_shape_manual(values=man.shapes)
p2dpcoa
```
A biplot representation of a Double Principal Coordinate Analysis (DPCoA), on a simplified version of the "Global Patterns" dataset with only the most abundant 200 OTUs included.
......
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