/usr/lib/R/site-library/phyloseq/doc/phyloseq-analysis.R is in r-bioc-phyloseq 1.22.3-1.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 | ## ----dontrun-basics-vignette, eval=FALSE-----------------------------------
# vignette("phyloseq-basics")
## ----load-packages, message=FALSE, warning=FALSE---------------------------
library("phyloseq")
library("ggplot2")
## ----ggplot2-themes--------------------------------------------------------
theme_set(theme_bw())
## --------------------------------------------------------------------------
data(GlobalPatterns)
## --------------------------------------------------------------------------
# prune OTUs that are not present in at least one sample
GP <- prune_taxa(taxa_sums(GlobalPatterns) > 0, GlobalPatterns)
# Define a human-associated versus non-human categorical variable:
human <- get_variable(GP, "SampleType") %in% c("Feces", "Mock", "Skin", "Tongue")
# Add new human variable to sample data:
sample_data(GP)$human <- factor(human)
## ----richness_estimates0, fig.width=13, fig.height=7-----------------------
alpha_meas = c("Observed", "Chao1", "ACE", "Shannon", "Simpson", "InvSimpson")
(p <- plot_richness(GP, "human", "SampleType", measures=alpha_meas))
## ----richness_estimates, fig.width=13,height=7-----------------------------
p + geom_boxplot(data=p$data, aes(x=human, y=value, color=NULL), alpha=0.1)
## --------------------------------------------------------------------------
GP.chl <- subset_taxa(GP, Phylum=="Chlamydiae")
## ----GP-chl-tree, fig.width=15, fig.height=7, message=FALSE, warning=FALSE----
plot_tree(GP.chl, color="SampleType", shape="Family", label.tips="Genus", size="Abundance")
## --------------------------------------------------------------------------
data(enterotype)
## ----EntAbundPlot, fig.height=6, fig.width=8-------------------------------
par(mar = c(10, 4, 4, 2) + 0.1) # make more room on bottom margin
N <- 30
barplot(sort(taxa_sums(enterotype), TRUE)[1:N]/nsamples(enterotype), las=2)
## --------------------------------------------------------------------------
rank_names(enterotype)
## --------------------------------------------------------------------------
TopNOTUs <- names(sort(taxa_sums(enterotype), TRUE)[1:10])
ent10 <- prune_taxa(TopNOTUs, enterotype)
print(ent10)
## --------------------------------------------------------------------------
sample_variables(ent10)
## ----entbarplot0, fig.height=6, fig.width=10-------------------------------
plot_bar(ent10, "SeqTech", fill="Enterotype", facet_grid=~Genus)
## ----GPheatmap-------------------------------------------------------------
data("GlobalPatterns")
gpac <- subset_taxa(GlobalPatterns, Phylum=="Crenarchaeota")
(p <- plot_heatmap(gpac, "NMDS", "bray", "SampleType", "Family"))
## ----GPheatmap-rename-axes-------------------------------------------------
p$scales$scales[[1]]$name <- "My X-Axis"
p$scales$scales[[2]]$name <- "My Y-Axis"
print(p)
## ----plot_sample_network, fig.width=11, fig.height=7, message=FALSE, warning=FALSE----
data(enterotype)
plot_net(enterotype, maxdist=0.4, color="SeqTech", shape="Enterotype")
## ----eval=FALSE------------------------------------------------------------
# my.physeq <- import("Biom", BIOMfilename="myBiomFile.biom")
# my.ord <- ordinate(my.physeq)
# plot_ordination(my.physeq, my.ord, color="myFavoriteVarible")
## ----help-import, eval=FALSE-----------------------------------------------
# help(import)
# help(ordinate)
# help(distance)
# help(plot_ordination)
## ----GP-data-load----------------------------------------------------------
data(GlobalPatterns)
## ---- eval=FALSE-----------------------------------------------------------
# GPUF <- UniFrac(GlobalPatterns)
## ----load-precomputed-UF---------------------------------------------------
load(system.file("doc", "Unweighted_UniFrac.RData", package="phyloseq"))
## --------------------------------------------------------------------------
GloPa.pcoa = ordinate(GlobalPatterns, method="PCoA", distance=GPUF)
## ----PCoAScree, fig.width=6, fig.height=4----------------------------------
plot_scree(GloPa.pcoa, "Scree plot for Global Patterns, UniFrac/PCoA")
## ----GPfig5ax1213----------------------------------------------------------
(p12 <- plot_ordination(GlobalPatterns, GloPa.pcoa, "samples", color="SampleType") +
geom_point(size=5) + geom_path() + scale_colour_hue(guide = FALSE) )
(p13 <- plot_ordination(GlobalPatterns, GloPa.pcoa, "samples", axes=c(1, 3),
color="SampleType") + geom_line() + geom_point(size=5) )
## ----GP_UF_NMDS0-----------------------------------------------------------
# (Re)load UniFrac distance matrix and GlobalPatterns data
data(GlobalPatterns)
load(system.file("doc", "Unweighted_UniFrac.RData", package="phyloseq"))
# perform NMDS, set to 2 axes
GP.NMDS <- ordinate(GlobalPatterns, "NMDS", GPUF)
(p <- plot_ordination(GlobalPatterns, GP.NMDS, "samples", color="SampleType") +
geom_line() + geom_point(size=5) )
## ----GPCAscree0, fig=FALSE-------------------------------------------------
data(GlobalPatterns)
# Take a subset of the GP dataset, top 200 species
topsp <- names(sort(taxa_sums(GlobalPatterns), TRUE)[1:200])
GP <- prune_taxa(topsp, GlobalPatterns)
# Subset further to top 5 phyla, among the top 200 OTUs.
top5ph <- sort(tapply(taxa_sums(GP), tax_table(GP)[, "Phylum"], sum), decreasing=TRUE)[1:5]
GP <- subset_taxa(GP, Phylum %in% names(top5ph))
# Re-add human variable to sample data:
sample_data(GP)$human <- factor(human)
## ----GPCAscree, fig.width=8, fig.height=5----------------------------------
# Now perform a unconstrained correspondence analysis
gpca <- ordinate(GP, "CCA")
# Scree plot
plot_scree(gpca, "Scree Plot for Global Patterns Correspondence Analysis")
## ----GPCA1234--------------------------------------------------------------
(p12 <- plot_ordination(GP, gpca, "samples", color="SampleType") +
geom_line() + geom_point(size=5) )
(p34 <- plot_ordination(GP, gpca, "samples", axes=c(3, 4), color="SampleType") +
geom_line() + geom_point(size=5) )
## ----GPCAspecplot0---------------------------------------------------------
p1 <- plot_ordination(GP, gpca, "species", color="Phylum")
(p1 <- ggplot(p1$data, p1$mapping) + geom_point(size=5, alpha=0.5) +
facet_wrap(~Phylum) + scale_colour_hue(guide = FALSE) )
## ----GPCAspecplotTopo0-----------------------------------------------------
(p3 <- ggplot(p1$data, p1$mapping) + geom_density2d() +
facet_wrap(~Phylum) + scale_colour_hue(guide = FALSE) )
## ----GPCAjitter0-----------------------------------------------------------
library("reshape2")
# Melt the species-data.frame, DF, to facet each CA axis separately
mdf <- melt(p1$data[, c("CA1", "CA2", "Phylum", "Family", "Genus")],
id=c("Phylum", "Family", "Genus") )
# Select some special outliers for labelling
LF <- subset(mdf, variable=="CA2" & value < -1.0)
# build plot: boxplot summaries of each CA-axis, with labels
p <- ggplot(mdf, aes(Phylum, value, color=Phylum)) +
geom_boxplot() +
facet_wrap(~variable, 2) +
scale_colour_hue(guide = FALSE) +
theme_bw() +
theme( axis.text.x = element_text(angle = -90, vjust = 0.5) )
# Add the text label layer, and render ggplot graphic
(p <- p + geom_text(data=subset(LF, !is.na(Family)),
mapping = aes(Phylum, value+0.1, color=Phylum, label=Family),
vjust=0,
size=2))
## ----GPtaxaplot0-----------------------------------------------------------
plot_bar(GP, x="human", fill="SampleType", facet_grid= ~ Phylum)
## ----GPdpcoa01-------------------------------------------------------------
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)]
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)
## ----GPdpcoa02-------------------------------------------------------------
# Show just Samples or just Taxa
plot_ordination(GP, GP.dpcoa, type="taxa", shape="Phylum")
plot_ordination(GP, GP.dpcoa, type="samples", color="SampleType")
# Split
plot_ordination(GP, GP.dpcoa, type="split",
color="SampleType", shape="Phylum") +
ggplot2::scale_colour_discrete()
## ----distancefun-----------------------------------------------------------
data(esophagus)
distance(esophagus, "bray")
distance(esophagus, "wunifrac") # weighted UniFrac
distance(esophagus, "jaccard") # vegdist jaccard
distance(esophagus, "g") # betadiver method option "g"
## ----eval=FALSE, echo=TRUE-------------------------------------------------
# data(esophagus)
# distance(esophagus, "wUniFrac")
# distance(esophagus, "uUniFrac")
## --------------------------------------------------------------------------
# (Re)load UniFrac distance matrix and GlobalPatterns data
data(GlobalPatterns)
load(system.file("doc", "Unweighted_UniFrac.RData", package="phyloseq"))
# Manually define color-shading vector based on sample type.
colorScale <- rainbow(length(levels(get_variable(GlobalPatterns, "SampleType"))))
cols <- colorScale[get_variable(GlobalPatterns, "SampleType")]
GP.tip.labels <- as(get_variable(GlobalPatterns, "SampleType"), "character")
# This is the actual hierarchical clustering call, specifying average-link clustering
GP.hclust <- hclust(GPUF, method="average")
plot(GP.hclust, col=cols)
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