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Guillaume Perréal
easy16S
Commits
b0f10e89
Commit
b0f10e89
authored
Sep 24, 2018
by
Midoux Cedric
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add perf.R
parent
5f0c2aad
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b0f10e89
library
(
microbenchmark
)
mb
<-
microbenchmark
(
"clear"
=
{
rm
(
list
=
ls
())
lapply
(
paste
(
'package:'
,
names
(
sessionInfo
()
$
otherPkgs
),
sep
=
""
),
detach
,
character.only
=
TRUE
,
unload
=
TRUE
)
},
"load packages"
=
{
library
(
shinydashboard
)
library
(
glue
)
source
(
"https://raw.githubusercontent.com/mahendra-mariadassou/phyloseq-extended/master/R/load-extra-functions.R"
)
source
(
"internals.R"
)
},
"load demo Chailloux"
=
{
load
(
"demo/demo.RData"
)
data
<-
get
(
"food"
)
},
"barplot"
=
{
p
<-
plot_bar
(
physeq
=
data
,
fill
=
"Phylum"
,
x
=
"Description"
,
title
=
"OTU abundance barplot"
)
p
<-
p
+
facet_grid
(
". ~ EnvType"
,
scales
=
"free_x"
)
# plot(p)
},
"filtered plot"
=
{
p
<-
plot_composition
(
physeq
=
data
,
taxaRank1
=
"Kingdom"
,
taxaSet1
=
"Bacteria"
,
taxaRank2
=
"Phylum"
,
numberOfTaxa
=
10
,
fill
=
"Phylum"
,
x
=
"Description"
)
p
<-
p
+
facet_grid
(
". ~ EnvType"
,
scales
=
"free_x"
)
# plot(p)
},
"heatmap"
=
{
p
<-
plot_heatmap
(
prune_taxa
(
names
(
sort
(
taxa_sums
(
data
),
decreasing
=
TRUE
)[
1
:
250
]),
data
),
distance
=
"bray"
,
method
=
"NMDS"
,
low
=
"yellow"
,
high
=
"red"
,
na.value
=
"white"
,
sample.order
=
"Description"
,
title
=
"Taxa heatmap by samples"
)
p
<-
p
+
facet_grid
(
". ~ EnvType"
,
scales
=
"free_x"
)
# plot(p)
},
"alpha"
=
{
p
<-
plot_richness
(
physeq
=
data
,
measures
=
c
(
"Observed"
,
"Chao1"
,
"ACE"
,
"Shannon"
,
"Simpson"
,
"InvSimpson"
,
"Fisher"
),
x
=
"EnvType"
,
color
=
"EnvType"
,
shape
=
"FoodType"
,
title
=
"Alpha diversity graphics"
)
p
<-
p
+
geom_boxplot
()
p
<-
p
+
geom_point
()
# plot(p)
},
"beta"
=
{
beta
<-
melt
(
as
(
distance
(
data
,
method
=
"bray"
),
"matrix"
))
colnames
(
beta
)
<-
c
(
"x"
,
"y"
,
"distance"
)
new_factor
=
as.factor
(
get_variable
(
data
,
"EnvType"
))
variable_sort
<-
as.factor
(
get_variable
(
data
,
"EnvType"
)[
order
(
new_factor
)])
L
=
levels
(
reorder
(
sample_names
(
data
),
as.numeric
(
new_factor
)))
beta
$
x
<-
factor
(
beta
$
x
,
levels
=
L
)
beta
$
y
<-
factor
(
beta
$
y
,
levels
=
L
)
palette
<-
hue_pal
()(
length
(
levels
(
new_factor
)))
tipColor
<-
col_factor
(
palette
,
levels
=
levels
(
new_factor
))(
variable_sort
)
p1
<-
ggplot
(
beta
,
aes
(
x
=
x
,
y
=
y
,
fill
=
distance
))
p1
<-
p1
+
geom_tile
()
p1
<-
p1
+
ggtitle
(
"Beta diversity heatmap"
)
p1
<-
p1
+
theme
(
axis.text.x
=
element_text
(
angle
=
90
,
hjust
=
1
,
color
=
tipColor
),
axis.text.y
=
element_text
(
color
=
tipColor
),
axis.title.x
=
element_blank
(),
axis.title.y
=
element_blank
())
# plot(p1 + scale_fill_gradient2())
},
"rarefaction"
=
{
p
<-
ggrare
(
physeq
=
data
,
step
=
100
,
se
=
FALSE
,
color
=
"EnvType"
,
label
=
"Description"
)
p
<-
p
+
facet_grid
(
". ~ FoodType"
)
p
<-
p
+
geom_vline
(
xintercept
=
min
(
sample_sums
(
data
)),
color
=
"gray60"
)
p
<-
p
+
ggtitle
(
"Rarefaction curves"
)
# plot(p)
},
"acp"
=
{
p
<-
plot_samples
(
physeq
=
data
,
ordination
=
ordinate
(
data
,
method
=
"MDS"
,
distance
=
"unifrac"
),
axes
=
c
(
1
,
2
),
color
=
"EnvType"
,
shape
=
"FoodType"
,
replicate
=
"EnvType"
,
label
=
"Description"
,
title
=
"Samples ordination graphic"
)
p
<-
p
+
stat_ellipse
(
aes_string
(
group
=
"EnvType"
))
# plot(p + theme_bw())
},
"tree"
=
{
p
<-
plot_tree
(
physeq
=
prune_taxa
(
names
(
sort
(
taxa_sums
(
data
),
decreasing
=
TRUE
)[
1
:
20
]),
data
),
method
=
"sampledodge"
,
color
=
"EnvType"
,
size
=
"abundance"
,
label.tips
=
"taxa_names"
,
sizebase
=
5
,
ladderize
=
"left"
,
plot.margin
=
0
,
title
=
"Phylogenetic tree"
)
# plot(p)
},
"clustering"
=
{
p
<-
plot_clust
(
physeq
=
data
,
dist
=
"unifrac"
,
method
=
"ward.D2"
,
color
=
"EnvType"
)
# plot(p)
},
times
=
100
,
unit
=
"s"
,
control
=
list
(
order
=
"inorder"
))
mb
autoplot
(
mb
)
save
(
mb
,
file
=
"benchmark.RData"
)
ggsave
(
"benchmark.png"
)
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