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1_Core-analysis.html
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1_Core-analysis.html
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<body>
<h1>Wastewater treatment plants contain a conserved core community of bacteria</h1>
<pre><code class="r">source("R/functions.R")
</code></pre>
<pre><code>## Loading required package: phyloseq
## Loading required package: ade4
## Loading required package: picante
## Loading required package: ape
## Loading required package: vegan
## Loading required package: permute
## Loading required package: lattice
## This is vegan 2.0-10
##
## Attaching package: 'vegan'
##
## The following object is masked from 'package:ade4':
##
## cca
##
## Loading required package: nlme
## Note: the specification for S3 class "AsIs" in package 'BiocGenerics' seems equivalent to one from package 'RJSONIO': not turning on duplicate class definitions for this class.
## Note: the specification for S3 class "connection" in package 'BiocGenerics' seems equivalent to one from package 'RJSONIO': not turning on duplicate class definitions for this class.
## Note: the specification for S3 class "file" in package 'BiocGenerics' seems equivalent to one from package 'RJSONIO': not turning on duplicate class definitions for this class.
## Note: the specification for S3 class "pipe" in package 'BiocGenerics' seems equivalent to one from package 'RJSONIO': not turning on duplicate class definitions for this class.
## Note: the specification for S3 class "textConnection" in package 'BiocGenerics' seems equivalent to one from package 'RJSONIO': not turning on duplicate class definitions for this class.
## Loading required package: gridExtra
## Loading required package: grid
##
## Attaching package: 'dplyr'
##
## The following object is masked _by_ '.GlobalEnv':
##
## n
##
## The following object is masked from 'package:nlme':
##
## collapse
##
## The following objects are masked from 'package:plyr':
##
## arrange, desc, failwith, id, mutate, summarise
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
</code></pre>
<pre><code class="r">source("R/core_community.R")
sessionInfo()
</code></pre>
<pre><code>## R version 3.0.3 (2014-03-06)
## Platform: x86_64-pc-linux-gnu (64-bit)
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] grid graphics grDevices utils datasets stats methods
## [8] base
##
## other attached packages:
## [1] dplyr_0.1.1 gridExtra_0.9.1 phyloseq_1.7.3 picante_1.6-1
## [5] nlme_3.1-115 vegan_2.0-10 lattice_0.20-27 permute_0.8-3
## [9] ape_3.0-11 ade4_1.6-2 knitr_1.5 plyr_1.8
## [13] reshape2_1.2.2 ggplot2_0.9.3.1
##
## loaded via a namespace (and not attached):
## [1] annotate_1.40.0 AnnotationDbi_1.24.0
## [3] assertthat_0.1 Biobase_2.22.0
## [5] BiocGenerics_0.8.0 biom_0.3.11
## [7] Biostrings_2.30.1 cluster_1.15.1
## [9] codetools_0.2-8 colorspace_1.2-4
## [11] DBI_0.2-7 DESeq2_1.2.10
## [13] dichromat_2.0-0 digest_0.6.3
## [15] evaluate_0.5.1 foreach_1.4.1
## [17] formatR_0.10 genefilter_1.44.0
## [19] GenomicRanges_1.14.4 gtable_0.1.2
## [21] igraph_0.7.0 IRanges_1.20.6
## [23] iterators_1.0.6 labeling_0.2
## [25] locfit_1.5-9.1 MASS_7.3-30
## [27] Matrix_1.1-2-2 multtest_2.18.0
## [29] munsell_0.4.2 parallel_3.0.3
## [31] proto_0.3-10 RColorBrewer_1.0-5
## [33] Rcpp_0.11.0 RcppArmadillo_0.4.000.2
## [35] RJSONIO_1.0-3 RSQLite_0.11.4
## [37] scales_0.2.3 splines_3.0.3
## [39] stats4_3.0.3 stringr_0.6.2
## [41] survival_2.37-7 tools_3.0.3
## [43] XML_3.98-1.1 xtable_1.7-1
## [45] XVector_0.2.0
</code></pre>
<h1>Data</h1>
<p>The source dataset contains all:</p>
<ol>
<li>Q3 samples from 2008 and 2008 from all plants. </li>
<li>time series from AAW</li>
</ol>
<pre><code class="r">identities <- as.character(c(94, 97, 99))
fname_template <- "data/otuXX/seqs_XX_otutable.biom"
filenames <- sapply(identities, function(id) gsub("XX", id, fname_template))
datasets <- lapply(identities, function(identity) LoadData(biompath = filenames[identity],
mapfpath = "data/mapfile.txt"))
names(datasets) <- paste0("otu", identities)
print(datasets_summary <- data.frame(samples = sapply(datasets, function(identity) sum(nsamples(identity))),
reads = sapply(datasets, function(identity) sum(sample_sums(identity))),
OTUs = sapply(datasets, function(identity) sum(ntaxa(identity)))))
</code></pre>
<pre><code>## samples reads OTUs
## otu94 48 2374197 2541
## otu97 48 2374197 4586
## otu99 48 2374197 8276
</code></pre>
<h3>Datasets</h3>
<p>1) <code>coreDatasets</code> Two samples from each plant from the summer 2008 and 2009 were used to calculate the core microbial community in the cross-section of Danish plants.
2) <code>tsDatasets</code> All the samples from Aalborg West from 2006 and 2010 were used to calculate the core community in the time-series.</p>
<pre><code class="r">subsample_depth <- 40000
# List containing phyloseq objects for core dataset at 94, 97 and 99%
# identity
coreDatasets <- lapply(datasets, function(identity) selectCoreDataset(identity,
depth = subsample_depth, seed = 1234))
</code></pre>
<pre><code>## `set.seed(1234)` was used to initialize repeatable random subsampling.
## Please record this for your records so others can reproduce.
## Try `set.seed(1234); .Random.seed` for the full vector
## ...
## 187 OTUs were removed because they are no longer
## present in any sample after random subsampling
## ...
## `set.seed(1234)` was used to initialize repeatable random subsampling.
## Please record this for your records so others can reproduce.
## Try `set.seed(1234); .Random.seed` for the full vector
## ...
## 389 OTUs were removed because they are no longer
## present in any sample after random subsampling
## ...
## `set.seed(1234)` was used to initialize repeatable random subsampling.
## Please record this for your records so others can reproduce.
## Try `set.seed(1234); .Random.seed` for the full vector
## ...
## 863 OTUs were removed because they are no longer
## present in any sample after random subsampling
## ...
</code></pre>
<pre><code class="r">names(coreDatasets) <- paste0("otu", identities)
lapply(coreDatasets, function(identity) printDatasetStats(identity))
</code></pre>
<pre><code>## [1] "number of plants: 13"
##
## 2008 2009
## AAE 1 1
## AAW 1 1
## BJE 1 1
## EGA 1 1
## EJB 1 1
## HJO 1 1
## HLV 1 1
## ONE 1 1
## ONW 1 1
## RAN 1 1
## SKI 1 1
## SOE 1 1
## VIB 1 1
## [1] 1040000
## [1] "total reads: 1040000"
## [1] "number of plants: 13"
##
## 2008 2009
## AAE 1 1
## AAW 1 1
## BJE 1 1
## EGA 1 1
## EJB 1 1
## HJO 1 1
## HLV 1 1
## ONE 1 1
## ONW 1 1
## RAN 1 1
## SKI 1 1
## SOE 1 1
## VIB 1 1
## [1] 1040000
## [1] "total reads: 1040000"
## [1] "number of plants: 13"
##
## 2008 2009
## AAE 1 1
## AAW 1 1
## BJE 1 1
## EGA 1 1
## EJB 1 1
## HJO 1 1
## HLV 1 1
## ONE 1 1
## ONW 1 1
## RAN 1 1
## SKI 1 1
## SOE 1 1
## VIB 1 1
## [1] 1040000
## [1] "total reads: 1040000"
</code></pre>
<pre><code>## $otu94
## [1] "total reads: 1040000"
##
## $otu97
## [1] "total reads: 1040000"
##
## $otu99
## [1] "total reads: 1040000"
</code></pre>
<pre><code class="r">
# List containing phyloseq objects for time-series dataset at 94, 97 and 99%
# id
tseriesDatasets <- lapply(datasets, function(identity) selectAAWDataset(identity,
depth = subsample_depth))
</code></pre>
<pre><code>## [1] "sample seed = 1234"
## [1] "subsampled at 40000 reads"
## `set.seed(1234)` was used to initialize repeatable random subsampling.
## Please record this for your records so others can reproduce.
## Try `set.seed(1234); .Random.seed` for the full vector
## ...
## 1467 OTUs were removed because they are no longer
## present in any sample after random subsampling
## ...
## [1] "sample seed = 1234"
## [1] "subsampled at 40000 reads"
## `set.seed(1234)` was used to initialize repeatable random subsampling.
## Please record this for your records so others can reproduce.
## Try `set.seed(1234); .Random.seed` for the full vector
## ...
## 2965 OTUs were removed because they are no longer
## present in any sample after random subsampling
## ...
## [1] "sample seed = 1234"
## [1] "subsampled at 40000 reads"
## `set.seed(1234)` was used to initialize repeatable random subsampling.
## Please record this for your records so others can reproduce.
## Try `set.seed(1234); .Random.seed` for the full vector
## ...
## 5865 OTUs were removed because they are no longer
## present in any sample after random subsampling
## ...
</code></pre>
<pre><code class="r">names(tseriesDatasets) <- paste0("otu", identities)
lapply(tseriesDatasets, function(identity) printDatasetStats(identity))
</code></pre>
<pre><code>## [1] "number of plants: 1"
##
## 2006 2007 2008 2009 2010 2011
## AAW 2 1 2 2 3 3
## [1] 520000
## [1] "total reads: 520000"
## [1] "number of plants: 1"
##
## 2006 2007 2008 2009 2010 2011
## AAW 2 1 2 2 3 3
## [1] 520000
## [1] "total reads: 520000"
## [1] "number of plants: 1"
##
## 2006 2007 2008 2009 2010 2011
## AAW 2 1 2 2 3 3
## [1] 520000
## [1] "total reads: 520000"
</code></pre>
<pre><code>## $otu94
## [1] "total reads: 520000"
##
## $otu97
## [1] "total reads: 520000"
##
## $otu99
## [1] "total reads: 520000"
</code></pre>
<h2>1. Core community</h2>
<h2>Quantifying the core community</h2>
<pre><code class="r">coredataframe <- calcSummaryData(coreDatasets, identities, core_cutoff = 1)
coredata_by_id <- group_by(coredataframe, identities)
summarise(coredata_by_id, coretotalOTUs = sum(taxsum), coreOTUs = sum(taxsum[corestatus ==
"core"]), percentcorereads = round(readprop[corestatus == "core"] * 100,
1))
</code></pre>
<pre><code>## Source: local data frame [3 x 4]
##
## identities coretotalOTUs coreOTUs percentcorereads
## 1 99 7413 77 36.6
## 2 97 4197 100 54.3
## 3 94 2354 86 68.1
</code></pre>
<pre><code class="r">
coreplots <- plotCore(coredataframe)
otuplot <- coreplots[[1]]
readsplot <- coreplots[[2]]
print(Figure1 <- grid.arrange(otuplot, readsplot, nrow = 2))
</code></pre>
<p><img 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" alt="plot of chunk Figure 1: Core OTU conservation plot"/> </p>
<pre><code>## NULL
</code></pre>
<pre><code class="r">
pdf(file = "figs/Figure_1_core_otu_conservation40k.pdf")
Figure1
</code></pre>
<pre><code>## NULL
</code></pre>
<pre><code class="r">dev.off()
</code></pre>
<pre><code>## pdf
## 2
</code></pre>
<h3>Fraction of Transient OTUs (observed no more than 5 times)</h3>
<pre><code class="r">filter(coredataframe, numsamples < 6) %.% group_by(identities) %.% summarise(total_OTUs = sum(taxsum),
percent_reads = round(sum(readprop) * 100, 1))
</code></pre>
<pre><code>## Source: local data frame [3 x 3]
##
## identities total_OTUs percent_reads
## 1 99 5796 8.9
## 2 97 3003 5.8
## 3 94 1546 2.3
</code></pre>
<h3>Fraction of Frequently occuring OTUs (observed 20 + times)</h3>
<pre><code class="r">filter(coredataframe, numsamples >= 20) %.% group_by(identities) %.% summarise(total_OTUs = sum(taxsum),
percent_reads = round(sum(readprop) * 100, 1))
</code></pre>
<pre><code>## Source: local data frame [3 x 3]
##
## identities total_OTUs percent_reads
## 1 99 371 69.1
## 2 97 321 79.3
## 3 94 242 86.2
</code></pre>
<h2>Cumulative abundance distribution</h2>
<p>The distribution of abundances in bacterial communities is uneven - some
organisms are abundant some are rare.</p>
<p>Plotting percent cumulative abundance vs. rank ordered OTUs describes this
distribution.</p>
<pre><code class="r">cum_abun_plot <- plot_CumulRankAbundance(dataset = coreDatasets[["otu94"]],
fname = "figs/figure2_cum_abun_plot_94.pdf", dominant_fraction = 0.8)
</code></pre>
<pre><code>## [1] "percent for count of 1: 0.0025"
## [1] "80% cutoff: 65"
## [1] "quantiles for #otus for 80% of reads"
## 0% 25% 50% 75% 100%
## 19.00 48.50 59.50 72.25 134.00
## [1] "abundance of otu at 80th percentile: 0.15 (+- 0.08, n=26)"
## [1] "wrote plot to: figs/figure2_cum_abun_plot_94.pdf"
</code></pre>
<pre><code class="r">print(cum_abun_plot)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk Figure 2: Cumulative abundance curve"/> </p>
<h3>Distribution of abundances per OTU</h3>
<p>There is a lot of variation in the abundances but many of the abundant organisms are consitently abundant. These are the organisms that are core. The size of the core will increase as a function of depth for these consistent organisms.</p>
<pre><code class="r">plotTopN(coreDatasets[["otu94"]], topN = 50, plot_filename = "figs/Figure_3_top50_id94_boxplot.pdf",
core_cutoff = 1)
</code></pre>
<pre><code>## sampled top 50 OTUs
## 62.9% of the total reads
</code></pre>
<p><img 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" alt="plot of chunk Figure 3: boxplot the top OTUs"/> </p>
<h3>Temporal stability of a single plant through time</h3>
<p>Time series in AAW</p>
<p>What proportion of the core OTUs are core in: </p>
<ul>
<li>the two AAW samples used for the core</li>
<li>all the AAW samples</li>
</ul>
<pre><code class="r">tsdataframe <- calcSummaryData(tseriesDatasets, identities, core_cutoff = 1)
group_by(tsdataframe, identities) %.% summarise(coretotalOTUs = sum(taxsum),
coreOTUs = sum(taxsum[corestatus == "core"]), percentcorereads = round(readprop[corestatus ==
"core"] * 100, 1))
</code></pre>
<pre><code>## Source: local data frame [3 x 4]
##
## identities coretotalOTUs coreOTUs percentcorereads
## 1 99 2411 329 87.3
## 2 97 1621 254 90.6
## 3 94 1074 190 93.4
</code></pre>
<pre><code class="r">
tscoreplot <- plotCore(tsdataframe)
tsotuplot <- tscoreplot[[1]]
tsreadsplot <- tscoreplot[[2]]
print(tscoreplot <- grid.arrange(tsotuplot, tsreadsplot, nrow = 2))
</code></pre>
<p><img 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" alt="plot of chunk calc AAW timeseries core"/> </p>
<pre><code>## NULL
</code></pre>
<pre><code class="r">
pdf(file = "figs/Figure_S2_aawtimeseries_core_conservation_40k.png")
tscoreplot
</code></pre>
<pre><code>## NULL
</code></pre>
<pre><code class="r">dev.off()
</code></pre>
<pre><code>## pdf
## 2
</code></pre>
<h3>Binning of OTUs by observation frequency and frequency of high-abundance</h3>
<pre><code class="r">dataset <- coreDatasets[["otu94"]]
core_prop <- 1
dominant <- sapply(sample_names(dataset), function(samplename) fill_ha_data(dataset,
samplename))
row.names(dominant) <- taxa_names(dataset)
observed <- as.data.frame(otu_table(transform_sample_counts(dataset, function(x) ifelse(x >
0, 1, 0))))
summary.df <- data.frame(OTU = taxa_names(dataset), nHA = apply(dominant, 1,
sum), nObs = apply(observed, 1, sum), median = apply(otu_table(dataset),
1, median), geomean = apply(otu_table(dataset), 1, function(x) round(exp(mean(log(x))),
1)), max = apply(otu_table(dataset), 1, max), min = apply(otu_table(dataset),
1, min), n1per = apply(otu_table(dataset), 1, function(x) sum(x > 400)))
# how does median relate to nHA and nObs?
ggplot(summary.df, aes(nObs, median)) + geom_point(alpha = 0.2) + scale_y_log10()
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk Dominant OTUs"/> </p>
<pre><code class="r">ggplot(summary.df, aes(nHA, median)) + geom_point() + scale_y_log10()
</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfgAAAH4CAMAAACR9g9NAAACBFBMVEUAAAACAgIGBgYHBwcICAgJCQkKCgoLCwsNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEjIyMmJiYnJycqKiosLCwuLi4wMDAyMjIzMzM1NTU2NjY5OTk6Ojo7Ozs8PDw+Pj5AQEBCQkJDQ0NERERFRUVGRkZKSkpOTk5SUlJTU1NXV1daWlpfX19gYGBmZmZnZ2dpaWlsbGxubm5vb29xcXFycnJ0dHR2dnZ3d3d4eHh7e3t8fHx/f3+BgYGCgoKDg4OHh4eIiIiLi4uNjY2Pj4+RkZGTk5OUlJSXl5eYmJiZmZmbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqaqqqqrq6uvr6+xsbGzs7O0tLS1tbW2tra3t7e4uLi5ubm7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PFxcXGxsbHx8fLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHT09PU1NTV1dXW1tbX19fY2Nja2trb29vd3d3e3t7f39/h4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7////2GMvmAAAACXBIWXMAAAsSAAALEgHS3X78AAASiElEQVR4nO3djZ8dV1nA8RWQQMCKVVuNUhSI8qKAb1TxDam8pEpRKVgaq4Q2YojSpCbVWFtq3UZtiyTZkCwbtZuUUHbnn/Tu5tM82WfunvM8zzlzZ+7M7/f5dD+9uXfn3JNvZu7cmbm7Kw1NspW+nwD1E/ATDfiJBvxEA36iAT/RgJ9owE804Cca8BMN+IkG/EQDfqIF4G9stLp2s/1n4a5dq7iwzc2KC/ufisvauFlzmpZlFcP/38VWa9vtP4u3XnFZr855tuGuVlzWxeZSxYVZ/sqADwe8Dnh/wIdmYQ14GRD4aMDrgPcHfGgW1oCXAYGPBrwOeH/Ah2ZhDXgZEPhowOuA9wd8aBbWgJcBgY8GvA54f8CHZmENeBkQ+GjA64D3B3xoFtYmCb+ysjJvQOCjLRH8HHngwwGvA94fm/rMLMqaJPw+AwIfDXgd8P6AD83C2iTheY2fLjx79cDfHhD4aEsEP29A4KMtC/w+AwIfDXgd8P6AD83CGvAyIPDRgNcB7w/40CysAS8DAh8NeB3w/oAPzcIa8DIg8NGA1wHvD/jQLKwBLwMCHw14HfD+gA/NwhrwMiDw0YDXAe8P+NAsrAEvAwIfDXgd8P6AD83CGvAyIPDRgNcB7w/40CysAS8DAh8NeB3w/oAPzcIa8DIg8NGA1wHvD/jQLKwBLwMCHw14HfD+gA/NwhrwMiDw0YDXAe8P+NAsrAEvAwIfDXgd8P6AD83CGvAyIPDRgNcB7w/40CysAS8DAh8NeB3w/oAPzcIa8DIg8NGA1wHvD/jQLKwBLwMCH20a8GdXm+0Tjx3blC/AV1zWUOG3jj642rz8RPPck/IF+IrLGir89tap1ebMuebKUfmyg76xsXH1cqsr2+0/i3et4rKub1Zc2EbFZV1uvltvWSsr+cesWTf1p1ebky82Nx+WL7M/PPbQQw+d3WrXzPmzcNs1l1V1YRWXVfOvbOfXjWUftLmXNwn/1Gw9f1S+sKmvuKyam/r5v2euNaAd/qXjzfNPyhfgKy5r0PDbJ489fl2+AF9xWUPduUsEfJX4TZOZWZQ1VXh+tyzwtwcEPtoSwc8bEPhoywK/z4DARwNeB7w/4EOzsAa8DAh8NOB1wPsDPjQLa8DLgMBHA14HvD/gQ7OwBrwMCHw04HXA+wM+NAtrwMuAwEcDXge8P+BDs7AGvAwIfLThwOsrLYDXjRO+dW0V8LrhwGsr4FuNEr5lBXwr4N0BrxsMfNVNfSvgdcOB1wEfmoU14GVA4KP1B5/7OCzwuvHAJ+WB1wEvAwIfjU29Dnh/7NyFZmENeBkQ+GjA64D3B3xoFtaAlwGBjwa8Dnh/wIdmYW248CvAR2ZhbbDwph9GaQ543VDhV2w/htQa8Lr+4DOyrPGxWVgbLDyv8bFZWBsuPHv1oVlY6/E1PrMtBz40C2tD3bkDPjgLa8DLgMBHA14HvD/gQ7OwBrwMCHw04HXA+wM+NAtrwMuAwEcDXge8P+BDs7A2WHjOzsVmYW2o8JyPD87C2lDh9cm7wn8HwOuGDK9uAg+8O+B1Q4VvvcaXveIDrxsqfG6v3rkBAF43WPj0NXfeLT/wuuHCJw/gAF/aYOHZ1MdmYQ342wEfbnFH7tjUlzZU+MwqDXxpg4XPnJ0DvrBeP0mTvJ/TsqFZWOMjVDIg8NGA1wHfzgdfeHYeeN1g4Tk7F5uFtaHC6/fxwBtnYW2B8Jl1uH2vgi95ZsDrFgffWmdz/w642DIyC2s9wmfuZq8+NAtri4X3HH0HPjQLa/29xucCPjQLa53CF70hAz40C2sdwjvfkLFzV2UW1hYH7zzRCnxsFtaGCs9HqIKzsOaCz2Go13gfJWt8lVlY6xC+cOPNzl1oFtYWCH/RtwoDH5qFNd9rvO/K17KtNfChWViruXNX94Qa8KFZWANeBgTeXHLTDvxY4efstrPG72288BWlVcCHZmGtaFNfd9uuAj40C2uV1/ji5yMBH5qFtZqv8fyAw1bjhGdTD/wbN4Hf2zjhM2/nCgM+NAtrVV/jp75zd6XVetP+s3gbFZd1/XrBN8/g99yu+cSuNFcrLszwzNbK4ddafXe7/WfxvldxWZuvlnz3yt6b6yXL0jWXKy7M8Fd2qRy+vRVZmk2978Qqb+fGAu/bXWtdTAW8biTwuWPzwOuWBT7zzjx3FR3wuqWBVznfqAOvW1b49s5e8tHA65YV3nksDnjdYOG9H1pPB7wOeH/Ah2Yh5Q7BAP/GgKOCL5MFfrrwjrGAb7U08GUnVoHX9fganz3NUvECaeB1y7JXDzzwkYDX9bqpT51XAf6OARPwpz7wnp0WC9/CKNm5y55v8z67Oxsv/M9++oXzsxYOv5ejQ/iyxgv/UzfS5ksHv7S/jMhZKfwXHvphL/D+WdzxzcCXw3/owNvvW/hrfOuFtxB+z/3pK3CcjRf+/K0WvsY7NvUZ6fQaz179PvC3+ptlgs/cDbwMmFrjf/f+++//zbuBn9944X/pEw/8xld/+cRi4Z2z8H0Ggtd4GTAB/+b1zY81lz88ZHjnSstevQyYgD/0dHN448aPAT+/8cL/5Zu+/bn3/8qvLxN85p8B8DJgAr7579de/9oX/3dQ8GXHZIGXAVPwlhYP7/l8mwp4GXBf+MPfOHyrfuHbsoltO/C7lcE/s/7MrXqFn7NKF1wvCbwMuC+8scXCF55oBV4G3Bf+4K3ecc+g4J3bdhXwMuC+8Neu/dlHzq2d+9jRYcGn72ZTv1Pppv7enXsv3LtgeO8PplEPBr4c/p6nZ1/O/sxi4ategaMDXgZMwP/FOz/zyGfe+cVSeN/LcBE8m/rdivfqn/7k/X/0z2l3G7xDvgw+E/AyYAq++eGl7Yy7H965Pwa8v1L4C7/61nc88+GXK8Crm8C3GhT8r/3Ba4def+CjpfAq57E4PYuiC+OBlwET8D96rTnUXH1rt/DZHzKWPDvnDHgZMAH/3r+bwZ/4xY7hc3ev63sL5IGXARPwZ+76+Nt+613fdMMX2eTXeE7S5Creq1//qz/9ivrB5gb4spWyaI3P7TgCLwOm4K/8104dw5e9xmffMfARqn0GTMD/4cqhd8/ywpcdqmt9c/Kmc08ReBkwAX/gX9Lm+8G7jtU7N97uu/fcBl4GTMAf/vYC4DPbhzJ4HfAyYAL+3E/83pFZteFd513L9hR1wMuACfgPHv70Z2dVhs9RFl1ikwl4GTABf9dG2rwT+LKL6jIBLwMm4D/1eB349I64qr2fbpiFNeBlwAT8B3/knho/9Sq3DufeoBlmYQ14GTABX+knYvjOx7OpL28g19WnKVvv49m5K24g8GnKsgM4zoCXARcAn8x7IUZRwMuAi1jjk8+AS692GiN8flue3I0H3t1SwDtPy5YFvAzYN7xe44Evb6Dw6ZM0wJc3TPjMkTte48tbDvj0xVTABxoIvCpzAIdj9eUtBbz3WD2fpMm3FPCZNT73wuALeBmwA3jvCXfndRl8kiZXT/Bz1mF998X9724vTi/cMKv9Al4G7AY+cdu7DvNpWXf9berVzZJXbU7L+hvKzl3Jpp7z8YGGAq9yUQIfaJh79b6ADzTMvXrW+HmNBT5x20vJsXp3wOuAlwG7gXcchK0xC2vAy4D14Z2nXXL/DoB3txSbeu+Ru6KAlwG7hy/6QAXwgYbzGq/vtj8a+EADWeN9r/GtDQAnadwtB3zu4/P8SFN3S7Gpn/PdiVlwIYalJVnjXbNgjTc0nDW+5LpZ12t85t8F8DLg4uE7mIV1KOBlwG7gU7c7mIV1KOBlwMWv8bmLK0vexwO/0zDX+PxePR+aLGyYazzw8xoLfOI28PMaC3zy6Hvr4cWzsAa8DLjwNX7Ow4tnYQ14GXAha3yCtnUQlrNzxS3lGs9rfHkDXeOzL/nAF9YN/NnVZvvEY8c2PWt86u7MdwPvrwv4raMPrjYvP9E896R5jZ9z994/KJ+FNeBlQCf89tap1ebMuebK0X3huco20ODhm+b0anPyxebmw7P/ffTIkSOnWw/YXePV7f0Xpx9NC+n63ptG+Kdma/yjs//9/o0bNzbWdDuSqdvthydurq19L/G93jZfrbiw9YrLWmsuV1yY4a/sUgj+pePN8+nX+NTt9qOT7/rZ1LvrbFO/ffLY429sK/zw7XuBXwZ4VQeXV/tnYQ14GbAbeD406WwM8M63czVmYQ14GbCbNX7Bs7AGvAwIfDTgdUXH6kOzsAa8DNjNGs/OnbOxwCduAz+vMcDX3dRX3WEAXgbsZo2vtqmvu6cIvAzYDXziNvDzGgN87iCs73x81beGwMuAfa/xmet12LkLBLwOeBmwPnzdTT3w/oa5xncxC2vAy4DdwPf0EzFyAS8D1ocvPTgfmIU14GXAhcA77uYKnPKG+Rqf2avnmrvygNcBLwPWh2dTHwj4zMLKAl4GrA9f9cgdJ2kCAa8DXgasD8+mPtBI4NO368/CGvAyYH14jtUHGgs8x+qdjQGeY/WBRgGf+TXiHczCGvAyYPdrPDt3hsYAz85doLHAs3PnbAzw3ovqKszCGvAyYAfw7Nz5Gwe8bw3nJ1teHAm86yQNl1fvNgb4zM4dH6iY11jgE7dZ4+c1BvjWa3zmeA6v8RfHAs9evTvgQ7OwBrwM2AG87zW+/c0VD/upgJcB68P79urnfbf90c6AlwG7gU/cBn5eY4DPnZbNSFY9p6sCXgbsAJ6dO3/Ah2ZhDXgZEPhowOs4H+9vDPBcgRNoLPCp2x3MwhrwMmB9eK6yDTQKeHbu/AEfmoU14GVA4KMBrwPe3yjgy37yAT/8qLjleDuXezTw7pbiAE720cC7W8pNfevRwLtj504HvAwIfDTgdcD7Az40C2vAy4DARwNeB7y/UcD7LqPVn53j7VxxAzlyl7mbI3c7jQXe/vOoOXK32xjgM4fqWOPnNQp4fgaOv3HAs1fvDvjQLKwBLwMCHw14HfD+gA/NwhrwMiDw0YDXAe8P+NAsrAEvA3YBX/OnlwDvbzkO2daYhTXgZcD68L6TNFVmYQ14GbAb+MSIwM9rDPC513jnx+WBd8devQ54GRD4aMDrgPcHfGgW1oCXAYGPBrwOeH/Ah2ZhDXgZEPhowOuA9wd8aBbWgJcBgY8GvA54f8CHZmENeBkQ+GjA6/gNFf6WEP6SbudKi9RtZ+sF36vbfLXiwmo+sUvNWsWFWZ5ZOfwV3e6lV+p260H2Ngq+V3f9esWF1XxiV5qrFRdmeGaXy+HbWxE29f6WcFPfXiY7d/6AD83CGvAyIPDRgNcB7w/40CysAS8DAh8NeB3w/oAPzcIa8DIg8NGA1wHvD/jQLKwBLwMCHw14HfD+gA/NwhrwMiDw0YDXAe8P+NAsrAEvAwIfDXgd8P6AD83CGvAyIPDRgNcB7w/40CysAS8DAh8NeB3w/oAPzcIa8DIg8NGA1wHvbxTwmQ9N8vPq5zQG+Dm/Ljr5e8IrzMIa8DIg8NGA17Gp9zcKeHbu/AEfmoU14GVA4KMBrwPeH/ChWVgDXgYEPhrwOuD9AR+ahTXgZUDgowGvA94f8KFZWANeBgQ+GvA64P0BH5qFNeBlQOCjAa8D3t844NWFFvpm+joMfbeaRdEvPQBeBuwAvujSq9bd6/puw6z2C3gZsD78yps+kpJ2wl+6rO8tkH/22fj3trqcf4i5Cye+U3FplmdWDD+nA5/bs5wdq/1v6vJ3Fzyzb54u+OYu2zry/V7HrwO/+SdVFtNBwO8T8D01DvjXvlplMR30r9/q+xns0/Zf/6DX8evA09IF/EQDfqJVgd8+8dixzRoLqt3Z1WE+t9f/9rEvX+j3mVWBf/mJ5rknayyobltHH1wd5nP7j280Fx/p95lVgT9zrrlytMaC6ra9dWp1mM/t0tXm2lf6fWZV4E++2Nx8uMaCand6dajP7cKX/7PfZ1YF/qnZv91HayyodjP4QT637VNH13p+ZlXgXzrePD+419GdZvCDfG7/fnyr77+1Onv1J489fr3Ggmo3gx/kc/v6n3/pS1/r95nxPn6iAT/RgJ9owN9u5TuzL498dPZl+767+j11toCAv53AP/uT7/6Hvp9N1wHfnP/QF376507fCf/AA5/9RN/PquuAb84f+Pz1P/7AHfBb9/7bCwf7vT6m+4Bvzr/99eaF98z+Kt528ODBt8zgn35f0/zC1/t+Wh0HfHP+vtl/O/D/9Morr3x+Bv/7b7n77jf/dt9Pq+OA30XfhX+l2d3U/+DHz6yt/eOBwR3uqxvwLfi///nt2Tu6Q8f7fl7dBnwL/nc+tfOnn/x4z0+r44CfaMBPNOAnGvATDfiJBvxEA36iAT/RgJ9owE804Cfa/wN1xEc5hjvuMgAAAABJRU5ErkJggg==" alt="plot of chunk Dominant OTUs"/> </p>
<p>These plots are the justification for having nHA > 10 as the cutoff for significance. </p>
<h2>Figure 4</h2>
<p>Sets up the empty Figure 4 plot. The data was filled manually using Inkscape.</p>
<pre><code class="r">summary.df <- mutate(summary.df, group = factor(cut(nHA, breaks = c(-1, 0, 9,
25, 26), labels = c("4", "3", "2", "1")), levels = 1:4), Obsclass = cut(nObs,
breaks = c(0, 19, 25, 26), labels = c("ob1", "ob20", "ob26")))
summary.df <- cbind(summary.df, as.data.frame(otu_table(dataset)))
summary.df <- arrange(summary.df, desc(nHA), desc(nObs), desc(median))
# Empty plot
print(Figure4 <- plotFigure4())
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk Figure 4: Dominant vs. Frequency"/> </p>
<pre><code class="r">ggsave(filename = "figs/Figure4.pdf", plot = Figure4, width = 8, height = 8,
units = "cm")
</code></pre>
<h2>Data for plotting onto Figure 4</h2>
<p>How many OTUs/reads are in each category</p>
<pre><code class="r">r <- melt(select(summary.df, OTU, group, Obsclass, AMPA057:AMPA724), id.vars = c("OTU",
"group", "Obsclass"), variable.name = "sample", value.name = "count") %.%
group_by(Obsclass, group) %.% summarise(readpercent = round((sum(count)/(26 *
40000)) * 100, 1), nOTUs = n_distinct(OTU))
acast(r, group ~ Obsclass, value.var = "nOTUs", fun.aggregate = sum, margins = TRUE)
</code></pre>
<pre><code>## ob1 ob20 ob26 (all)
## 1 0 0 3 3
## 2 0 10 51 61
## 3 130 94 28 252
## 4 1982 52 4 2038
## (all) 2112 156 86 2354
</code></pre>
<pre><code class="r">acast(r, group ~ Obsclass, value.var = "readpercent", fun.aggregate = sum, margins = TRUE)
</code></pre>
<pre><code>## ob1 ob20 ob26 (all)
## 1 0.0 0.0 25.7 25.7
## 2 0.0 6.1 36.9 43.0
## 3 7.6 10.4 5.4 23.4
## 4 6.2 1.7 0.2 8.1
## (all) 13.8 18.2 68.2 100.2
</code></pre>
<pre><code class="r"># NB rounding error# 3. transiently highly-abundant
group3otus <- as.vector(with(summary.df, summary.df[group == 3, "OTU"]))
tHA_percent <- round(as.data.frame(otu_table(dataset)[group3otus, ]/40000 *
100), 1)
df <- data.frame(rank = 1:26, percentHA = sort(colSums(tHA_percent), decreasing = TRUE))
plant <- samData(dataset)[row.names(df), "plant"]
df$plant <- as.character(plant$plant) # crazy phyloseq object!!
# TODO fix the label on this? why does the sample_data object persist after
# as.character() ggplot(df, aes(x=rank, y= percentHA)) + geom_point(stat =
# 'identity') + scale_x_discrete(labels= plant) + xlab('samples') +
# ylab('transiently HA read percent')
# num trans.abun OTUs per sample
apply(tHA_percent, 2, function(sample) sum(sample > 1))
</code></pre>
<pre><code>## AMPA057 AMPA578 AMPA562 AMPA110 AMPA615 AMPA047 AMPA032 AMPA034 AMPA632
## 0 1 4 0 2 0 1 1 2
## AMPA566 AMPA568 AMPA581 AMPA563 AMPA564 AMPA726 AMPA580 AMPA570 AMPA056
## 5 6 5 8 4 8 0 2 0
## AMPA053 AMPA055 AMPA561 AMPA725 AMPA054 AMPA102 AMPA727 AMPA724
## 3 1 0 2 1 0 2 1
</code></pre>
<pre><code class="r">samData(dataset)[names(sort(colSums(tHA_percent), decreasing = TRUE)), "plant"]
</code></pre>
<pre><code>## Sample Data: [26 samples by 1 sample variables]:
## plant
## AMPA563 HLV
## AMPA726 VIB
## AMPA725 ONE
## AMPA724 ONW
## AMPA566 ONW
## AMPA564 VIB
## AMPA568 SOE
## AMPA615 SOE
## AMPA562 SKI
## AMPA581 ONE
## AMPA580 HLV
## AMPA727 EJB
## AMPA632 BJE
## AMPA570 RAN
## AMPA561 EGA
## AMPA056 SKI
## AMPA047 AAE
## AMPA578 HJO
## AMPA034 AAW
## AMPA110 AAE
## AMPA032 AAW
## AMPA054 EGA
## AMPA053 BJE
## AMPA055 HJO
## AMPA102 EJB
## AMPA057 RAN
</code></pre>
<pre><code class="r">
HA.tran <- subset(summary.df, nObs <= 20 & nHA > 0 & (n1per > 0))
tax_table(dataset)[as.vector(HA.tran$OTU), 1:6]
</code></pre>
<pre><code>## Taxonomy Table: [22 taxa by 6 taxonomic ranks]:
## Kingdom Phylum Class
## 1401 "Bacteria" "Actinobacteria" "Actinobacteria"
## 1628 "Bacteria" "Actinobacteria" "Actinobacteria"
## 2389 "Bacteria" "Nitrospirae" "Nitrospira"
## 792 "Bacteria" "Proteobacteria" "Deltaproteobacteria"
## 511 "Bacteria" NA NA
## 1720 "Bacteria" "Proteobacteria" "Betaproteobacteria"
## 2095 "Bacteria" "Chloroflexi" "Chloroflexi"
## 2504 "Bacteria" "Cyanobacteria" "4C0d-2"
## 1988 "Bacteria" "Gemmatimonadetes" "Gemmatimonadetes"
## 785 "Bacteria" "Actinobacteria" "Actinobacteria"
## 637 "Bacteria" NA NA
## 1178 "Bacteria" "Chloroflexi" "Anaerolineae"
## 2169 "Bacteria" "Chloroflexi" NA
## 1658 "Bacteria" "Proteobacteria" "Alphaproteobacteria"
## 863 "Bacteria" "Proteobacteria" "Alphaproteobacteria"
## 1892 "Bacteria" "Proteobacteria" "Gammaproteobacteria"
## 365 "Bacteria" "Chloroflexi" "Anaerolineae"
## 2207 "Bacteria" "Actinobacteria" "Actinobacteria"
## 703 "Bacteria" "Planctomycetes" "Planctomycea"
## 1419 "Bacteria" "Proteobacteria" "Gammaproteobacteria"
## 1127 "Bacteria" "Proteobacteria" "Deltaproteobacteria"
## 1748 "Bacteria" "Chloroflexi" "Chloroflexi"
## Order Family Genus
## 1401 "Acidimicrobiales" "Microthrixaceae" "Microthrix"
## 1628 "Actinomycetales" "Intrasporangiaceae" "Tetrasphaera_etal"
## 2389 "Nitrospirales" "Nitrospiraceae" "Nitrospira"
## 792 "Myxococcales" "OM27" NA
## 511 NA NA NA
## 1720 "Methylophilales" "Methylophilaceae" "Methylotenera"
## 2095 "Roseiflexales" "Kouleothrixaceae" "Kouleothrix"
## 2504 "mle1-12" NA NA
## 1988 "Gemmatimonadales" NA NA
## 785 "Acidimicrobiales" NA NA
## 637 NA NA NA
## 1178 "Anaerolineales" "Anaerolinaceae" "Anaerolinea"
## 2169 NA NA NA
## 1658 "Rhodospirillales" "Rhodospirillaceae" "Defluviicoccus"
## 863 "Rhodobacterales" "Rhodobacteraceae" "Amaricoccus"
## 1892 "Thiotrichales" "Thiotrichaceae" "Thiothrix"
## 365 "WCHB1-50" NA NA
## 2207 "Actinomycetales" "Intrasporangiaceae" "Tetrasphaera_etal"
## 703 "Gemmatales" "Isosphaeraceae" "Nostocoida"
## 1419 "Pseudomonadales" "Moraxellaceae" "Psychrobacter"
## 1127 "Myxococcales" "Haliangiaceae" NA
## 1748 "Roseiflexales" NA NA
</code></pre>
<pre><code class="r">nrow(tax_table(dataset)[as.vector(HA.tran$OTU), 1:6])
</code></pre>
<pre><code>## [1] 22
</code></pre>
<h2>Single sequence resolution on the abundant OTUs</h2>
<h1>export the OTU data to a table for the Table S2</h1>
<pre><code class="r"># list of tax names
eco.core.taxa <- as.vector(with(summary.df, summary.df[HAclass %in% c("HA10",
"HA26"), "OTU"]))
</code></pre>
<pre><code>## Error: object 'HAclass' not found
</code></pre>
<pre><code class="r">trans.abun.noncore.taxa <- as.vector(with(summary.df, summary.df[HAclass ==
"HA1" & n1per > 0, "OTU"]))
</code></pre>
<pre><code>## Error: object 'HAclass' not found
</code></pre>
<pre><code class="r">
length(trans.abun.noncore.taxa)
</code></pre>
<pre><code>## Error: object 'trans.abun.noncore.taxa' not found
</code></pre>
<pre><code class="r">
# lists of taxnames ecocore and abun.noncore
taxa.to.export <- c(ecocore.taxa, trans.abun.noncore.taxa)
</code></pre>
<pre><code>## Error: object 'ecocore.taxa' not found
</code></pre>
<pre><code class="r">outstats <- select(summary.df, 1:12) %.% filter(OTU %in% taxa.to.export)
</code></pre>
<pre><code>## Error: binding not found: 'taxa.to.export'
</code></pre>
<pre><code class="r">outtaxtable <- as.data.frame(tax_table(coreDatasets[["data.94"]])[taxa.to.export,
1:6])
</code></pre>
<pre><code>## Error: tax_table slot is empty.
</code></pre>
<pre><code class="r">outdata <- cbind(outstats, outtaxtable) %.% arrange(desc(median))
</code></pre>
<pre><code>## Error: object 'outstats' not found
</code></pre>
<pre><code class="r">
table(outdata$group)
</code></pre>
<pre><code>## Error: object 'outdata' not found
</code></pre>
<pre><code class="r">
write.table(outdata, file = "figs/Table_S2_newcore_otutable.txt", sep = "\t",
quote = FALSE, row.names = FALSE, col.names = TRUE)
</code></pre>
<pre><code>## Error: object 'outdata' not found
</code></pre>
<h3>Nitrotoga and Nitrospira</h3>
<pre><code class="r"># Compare replicate data
data.97 <- datasets[["otu97"]]
amplibs <- sample_data(data.97)$SampleID[sample_data(datasets[["otu97"]])$sample_id ==
"293"]
data.97 <- prune_samples(x = data.97, samples = as.character(amplibs))
table(sam_data(data.97)$sample_id, sam_data(data.97)$dna_id)
</code></pre>
<pre><code>##
## 148 149 150 151 152
## 293 1 6 1 1 1
</code></pre>
<pre><code class="r">data.97 <- rarefy_even_depth(data.97, rngseed = 1234, sample.size = 14000, trimOTUs = TRUE)
</code></pre>
<pre><code>## `set.seed(1234)` was used to initialize repeatable random subsampling.
## Please record this for your records so others can reproduce.
## Try `set.seed(1234); .Random.seed` for the full vector
## ...
## 2 samples removed because they contained fewer reads than `sample.size`.
## Up to first five removed samples are:
## AMPA232 AMPA161
## ...
## 3742 OTUs were removed because they are no longer
## present in any sample after random subsampling
## ...
</code></pre>
<pre><code class="r">
data.97 <- transform_sample_counts(physeq = data.97, function(x) x/sum(x) *
100)
NOB <- subset_taxa(data.97, (Family == "Gallionellaceae") | (Genus == "Nitrospira"))
plot_bar(NOB, "Genus", facet_grid = . ~ SampleID) + geom_bar(aes(fill = Genus),
stat = "identity", position = "stack")
</code></pre>
<p><img 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" alt="plot of chunk NOB"/> </p>
<pre><code class="r">
plot_bar(NOB, "Genus", facet_grid = dna_id ~ SampleID) + geom_bar(aes(fill = Genus),
stat = "identity", position = "stack")
</code></pre>
<p><img 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" alt="plot of chunk NOB"/> </p>
<pre><code class="r">
# compare core samples
data.97 <- coreDatasets[["otu97"]]
data.97 <- transform_sample_counts(physeq = data.97, function(x) x/sum(x) *
100)
NOB <- subset_taxa(data.97, (Genus == "Nitrotoga_etal") | (Genus == "Nitrospira"))
p <- plot_bar(NOB, "Genus", facet_grid = year ~ plant) + geom_bar(aes(fill = Genus),
stat = "identity", position = "stack")
levels(p$data$Genus) <- c("Nitrospira", "Nitrotoga")
p$data$plant_name <- factor(gsub(x = p$data$plant_name, pattern = "oe", replacement = "ø"))
p$data$plant_name <- factor(gsub(x = p$data$plant_name, pattern = "aa", replacement = "å"))
totals <- as.data.frame(select(p$data, OTU, Sample, Abundance, year, Genus,
plant_name) %.% filter(year == 2009, Genus == "Nitrotoga") %.% acast(plant_name ~
Genus, value.var = "Abundance", sum))
totals$plant_name <- row.names(totals)
plant_names_by <- with(totals, totals[order(Nitrotoga, decreasing = TRUE), "plant_name"])
p$data$plant_name <- factor(p$data$plant_name, levels = plant_names_by)
# formatted in greyscale for publication
p2 <- ggplot(p$data, aes(x = plant_name, y = Abundance, fill = Genus)) + geom_bar(position = position_dodge(),
stat = "identity") + facet_grid(year ~ .) + theme_bw() + ylab(label = "Read abundance (%)") +
xlab(label = "Plant") + scale_fill_manual(values = c("grey50", "black")) +
annotate("text", x = "Aalborg East", y = 2.5, label = "2008", size = 2) +
theme(axis.title.x = element_text(size = 8), axis.title.y = element_text(size = 8,
vjust = 0.2), axis.text.x = element_text(size = 6, hjust = 1, vjust = 1,
angle = 45), axis.text.y = element_text(size = 6), axis.ticks.x = element_line(size = 0.3),
axis.ticks.y = element_line(size = 0.3), panel.grid.minor = element_blank(),
strip.background = element_rect(linetype = "blank", fill = "white"),