[med-svn] [r-bioc-phyloseq] 01/04: New upstream version 1.20.0
Andreas Tille
tille at debian.org
Fri Sep 22 21:29:19 UTC 2017
This is an automated email from the git hooks/post-receive script.
tille pushed a commit to branch master
in repository r-bioc-phyloseq.
commit 2cdbfe713e5d2eed4acaeffc6d643fea0082a06f
Author: Andreas Tille <tille at debian.org>
Date: Fri Sep 22 22:54:38 2017 +0200
New upstream version 1.20.0
---
DESCRIPTION | 4 +-
build/vignette.rds | Bin 349 -> 347 bytes
inst/doc/phyloseq-FAQ.html | 6 +--
inst/doc/phyloseq-analysis.html | 64 ++++++++++++-------------
inst/doc/phyloseq-mixture-models.html | 85 +++++++++++++---------------------
5 files changed, 67 insertions(+), 92 deletions(-)
diff --git a/DESCRIPTION b/DESCRIPTION
index f0289b8..f96b137 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,5 +1,5 @@
Package: phyloseq
-Version: 1.19.1
+Version: 1.20.0
Date: 2016-12-29
Title: Handling and analysis of high-throughput microbiome census data
Description: phyloseq provides a set of classes and tools
@@ -38,4 +38,4 @@ Collate: 'allClasses.R' 'allPackage.R' 'allData.R' 'as-methods.R'
'extend_metagenomeSeq.R'
RoxygenNote: 5.0.1
NeedsCompilation: no
-Packaged: 2016-12-30 00:05:28 UTC; biocbuild
+Packaged: 2017-04-24 23:14:31 UTC; biocbuild
diff --git a/build/vignette.rds b/build/vignette.rds
index 2fa1f5b..ba73ddd 100644
Binary files a/build/vignette.rds and b/build/vignette.rds differ
diff --git a/inst/doc/phyloseq-FAQ.html b/inst/doc/phyloseq-FAQ.html
index 5d9895e..00c9d22 100644
--- a/inst/doc/phyloseq-FAQ.html
+++ b/inst/doc/phyloseq-FAQ.html
@@ -50,7 +50,7 @@ document.addEventListener("DOMContentLoaded", function() {
<div id="header">
<h1 class="title">phyloseq Frequently Asked Questions (FAQ)</h1>
<h4 class="author"><em>Paul McMurdie and Susan Holmes</em></h4>
-<h4 class="date"><em>Thu Dec 29 19:03:27 2016</em></h4>
+<h4 class="date"><em>Mon Apr 24 19:12:56 2017</em></h4>
</div>
<h1>Contents</h1>
@@ -94,9 +94,9 @@ document.addEventListener("DOMContentLoaded", function() {
<p>For most issues <a href="https://github.com/joey711/phyloseq/issues">the phyloseq issues tracker</a> should suffice; but occasionally there are questions that are asked repeatedly enough that it becomes appropriate to canonize the answer here in this vignette. This is both (1) to help users find solutions more quickly, and (2) to mitigate redundancy on <a href="https://github.com/joey711/phyloseq/issues">the issues tracker</a>.</p>
<p>All users are encouraged to perform a google search and review other questions/responses to both open and closed issues on <a href="https://github.com/joey711/phyloseq/issues">the phyloseq issues tracker</a> before seeking an active response by posting a new issue.</p>
<pre class="r"><code>library("phyloseq"); packageVersion("phyloseq")</code></pre>
-<pre><code>## [1] '1.19.1'</code></pre>
+<pre><code>## [1] '1.20.0'</code></pre>
<pre class="r"><code>library("ggplot2"); packageVersion("ggplot2")</code></pre>
-<pre><code>## [1] '2.2.0'</code></pre>
+<pre><code>## [1] '2.2.1'</code></pre>
<pre class="r"><code>theme_set(theme_bw())</code></pre>
<div id="i-tried-reading-my-biom-file-using-phyloseq-but-it-didnt-work.-whats-wrong" class="section level1">
<h1><span class="header-section-number">1</span> - I tried reading my biom file using phyloseq, but it didn’t work. What’s wrong?</h1>
diff --git a/inst/doc/phyloseq-analysis.html b/inst/doc/phyloseq-analysis.html
index abb56dc..e67a31a 100644
--- a/inst/doc/phyloseq-analysis.html
+++ b/inst/doc/phyloseq-analysis.html
@@ -192,12 +192,12 @@ print(ent10)</code></pre>
<pre class="r"><code>data("GlobalPatterns")
gpac <- subset_taxa(GlobalPatterns, Phylum=="Crenarchaeota")
(p <- plot_heatmap(gpac, "NMDS", "bray", "SampleType", "Family"))</code></pre>
-<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA2AAAAKgCAIAAAByb4nzAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOzde3wU9b0//s/sNdnsZnfdbAIETIQUjAlNw0WNKEFuEiEgMUdKtWKtKO05dquo7cNLqx5b2mNri1YPP/hWjdVikCYI8YQCCogY1HCxROMNCCQhCWTJJpvNZq/z+2PMNJLIJXnPzu7O6/nwj09mZ995Z8zlzczn/flwPM8zAAAAAIA+KrkTAAAAAIDoggIRAAAAAL4BBSIAAAAAfAMKRAAAAAD4BhSIAAAAAPANKBABAAAA4Bs0cicgv2efffbkyZMXcibP86FQSK1Wcxw3/M8bCoUYY2q1evihaBMLBoMcx5EkFg6Hw+GwRkPzbRYMBlUqlUpF8K8aJHaxyBMj/DnieZ4kMYX8gEdtYvjNc7GQ2KDuu+++t [...]
+<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA2AAAAKgCAIAAAByb4nzAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOzdfXxU5Z3w/+vMZGbyMJOZOHkAAhI1C9LE0vCgpihYBGqUYBOzpVQrvW1R2l03bX3YfWndqtst7e1dW7T25gevVXFVRChRSG+o4CoqBitPLrGpKDoxgRDMkAmTyWQmM3N+f5xmNhLkIbkmJzPn8375x5Uz1/nmm2OEr+dc33MpqqoKAAAAoJ9J7wQAAAAwulAgAgAA4HMoEAEAAPA5FIgAAAD4HApEAAAAfA4FIgAAAD4nTe8E9PfYY48dPXr0XGaqqhqNRs1ms6Iow/++0WhUCGE2m4cfSm5ikUhEURQpicVisVgslpYm59csEomYTCaTScL/1ZDY+ZKemMT/jlRVlZKYQf4DH7WJ8SfP+SKx0/rxj39cU [...]
<p>What if you wanted to change the axis labels?</p>
<pre class="r"><code>p$scales$scales[[1]]$name <- "My X-Axis"
p$scales$scales[[2]]$name <- "My Y-Axis"
print(p)</code></pre>
-<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA2AAAAKgCAIAAAByb4nzAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOzdfVxUdd4//s+ZWxjm1pkBlQxUVjIwLlDbJU3MGxIVDeKbubXaVZnWte7srtW1V9pu7a/W9jJ319paV68s2goxE29ocdVSMsUS7xLDvAUBAWVgYBiGGWbm/P44MUuKd/A5c2bmvJ6P/vhw5sybNydu3p7zeX8+DMuyBAAAAACgm0ToBAAAAAAguKBABAAAAIAfQIEIAAAAAD+AAhEAAAAAfgAFIgAAAAD8AApEAAAAAPgBmdAJCO+NN964ePHizZzJsqzX65VKpQzD9P/zer1eQohUKu1/KLqJeTwehmGoJObz+Xw+n0xG59vM4/FIJBKJhMK/apDYraKeGMWfI5ZlqSQmkh/woE0Mv3luFRLr1a9+9auYm [...]
+<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA2AAAAKgCAIAAAByb4nzAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOzde1xUdf4/8M+ZK8yFGRoGVDRQ+WoG5oJakablhaRAg/hmbqV9K8vabWd3u3y/v6zd2se2tt8uu9bWurpb0bfEC0EKra7aKpViG15aKcpLDQIiysAMwzAXZub8/vgsZ1FQET4zh5nzej7648OZc968OZG+O+fz/nw4nucJAAAAAEAPmdgJAAAAAMDwggIRAAAAAM6BAhEAAAAAzoECEQAAAADOgQIRAAAAAM6BAhEAAAAAzqEQOwHxvfbaa6dOnRrImTzPBwIBuVzOcdzQv28gECCEyOXyoYdim5jf7+c4jkliwWAwGAwqFGx+zfx+v0wmk8kY/F8NErtczBNj+N8Rz/NMEpPIf+DDNjH8yXO5kFi/fvazn [...]
<p>Note that it is possible to order the sample/species indices by any of the ordination methods supported in the <code>ordinate</code> function; and also that the color scheme can be modified with additional arguments.</p>
<p>Heat map representation of the Crenarchaeota phylum abundance pattern across different sample types in the Global Patterns dataset.</p>
</div>
@@ -209,7 +209,7 @@ print(p)</code></pre>
<p>Interestingly, at this level of analysis and parameter-settings the two major sub-graphs appear to be best explained by the sequencing technology and not the subject enterotype, suggesting that the choice of sequencing technology has a major effect on the microbial community one can observe. This seems to differ somewhat with the inferences described in the “enterotype” article (<a href="http://www.nature.com/nature/journal/v473/n7346/full/nature09944.html">Arumugam 2011</a>). However [...]
<pre class="r"><code>data(enterotype)
plot_net(enterotype, maxdist=0.4, color="SeqTech", shape="Enterotype")</code></pre>
-<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABCAAAAKgCAIAAADF7pvQAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOzdeUATZ/4/8GdCIBBOUQS5BUFB8cKLw4qAioIHora2ntVurd0edrvbdtt+7c92u7W3bbdqvVutVltBRRFQQbkURUQhcl9y35CEhIRkfn/MLssiQoiBgfB+/VOYzDP5xCLOe56LommaAAAAAAAAaAKH7QIAAAAAAEB7IGAAAAAAAIDGIGAAAAAAAIDGIGAAAAAAAIDGIGAAAAAAAIDGIGAAAAAAAIDGIGAAAAAAAIDGIGAAAAAAAIDGIGAAAAAAAIDGIGAAAAAAAIDGIGAAAAAAAIDGIGAAAAAAAIDGIGAAAAAAAIDGIGAAAAAAAIDGIGAAAAAAAIDGIGAAAAAAAIDGIGAAAAAAAIDGIGAAAAAAAIDGIGAAA [...]
+<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABCAAAAKgCAIAAADF7pvQAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOzdd3xT9/U//vfV9B7ywNsGgxPALIcCARqzzDDCIeTzYYUykpiUpMk3pB+StM2H9pOOXxPa0maUpg4JI8OQNiEgRjAjTlkh1GZDhGw8ZFvDlvfQuvf3x7u5VWVbSLJkS/br+egfQrq69y1S7Ht03ucchuM4AgAAAAAA4AmCgV4AAAAAAAAMHggwAAAAAADAYxBgAAAAAACAxyDAAAAAAAAAj0GAAQAAAAAAHoMAAwAAAAAAPAYBBgAAAAAAeAwCDAAAAAAA8BgEGAAAAAAA4DEIMAAAAAAAwGMQYAAAAAAAgMcgwAAAAAAAAI9BgAEAAAAAAB6DAAMAAAAAADwGAQYAAAAAAHgMAgwAAAAAAPAYBBgAAAAAA [...]
<p>Network representation of the relationship between microbiome samples in the “Enterotype” dataset (<a href="http://www.nature.com/nature/journal/v473/n7346/full/nature09944.html">Arumugam 2011</a>).</p>
</div>
<div id="ordination-methods" class="section level2">
@@ -257,49 +257,45 @@ load(system.file("doc", "Unweighted_UniFrac.RData", package=
GP.NMDS <- ordinate(GlobalPatterns, "NMDS", GPUF)</code></pre>
<pre><code>## Run 0 stress 0.1432774
## Run 1 stress 0.167021
-## Run 2 stress 0.3869407
+## Run 2 stress 0.3274248
## Run 3 stress 0.1432774
-## ... Procrustes: rmse 4.380552e-05 max resid 0.0001434423
-## ... Similar to previous best
-## Run 4 stress 0.1432625
## ... New best solution
-## ... Procrustes: rmse 0.00349315 max resid 0.01262722
-## Run 5 stress 0.167021
-## Run 6 stress 0.2426281
-## Run 7 stress 0.1856304
-## Run 8 stress 0.2480425
-## Run 9 stress 0.1432774
-## ... Procrustes: rmse 0.00348014 max resid 0.01258164
-## Run 10 stress 0.1432625
-## ... Procrustes: rmse 4.133989e-06 max resid 1.146816e-05
-## ... Similar to previous best
-## Run 11 stress 0.1432774
-## ... Procrustes: rmse 0.003484347 max resid 0.01259783
-## Run 12 stress 0.1432625
-## ... Procrustes: rmse 2.941188e-06 max resid 1.220155e-05
+## ... Procrustes: rmse 4.769305e-06 max resid 1.235353e-05
## ... Similar to previous best
-## Run 13 stress 0.1432774
-## ... Procrustes: rmse 0.003526331 max resid 0.01273739
-## Run 14 stress 0.1432625
+## Run 4 stress 0.2156366
+## Run 5 stress 0.1432625
## ... New best solution
-## ... Procrustes: rmse 1.53826e-06 max resid 4.839912e-06
+## ... Procrustes: rmse 0.0034936 max resid 0.01262965
+## Run 6 stress 0.1809282
+## Run 7 stress 0.1432625
+## ... Procrustes: rmse 2.071226e-06 max resid 7.072485e-06
## ... Similar to previous best
-## Run 15 stress 0.1432625
-## ... Procrustes: rmse 1.985561e-06 max resid 5.926334e-06
+## Run 8 stress 0.2427113
+## Run 9 stress 0.167021
+## Run 10 stress 0.1432629
+## ... Procrustes: rmse 8.407854e-05 max resid 0.0002111493
## ... Similar to previous best
+## Run 11 stress 0.167021
+## Run 12 stress 0.185632
+## Run 13 stress 0.1432774
+## ... Procrustes: rmse 0.003416941 max resid 0.01236303
+## Run 14 stress 0.1432774
+## ... Procrustes: rmse 0.00348 max resid 0.01258196
+## Run 15 stress 0.1432777
+## ... Procrustes: rmse 0.003236672 max resid 0.01174769
## Run 16 stress 0.1432774
-## ... Procrustes: rmse 0.003480274 max resid 0.01258406
+## ... Procrustes: rmse 0.003475139 max resid 0.01256605
## Run 17 stress 0.1432625
-## ... Procrustes: rmse 1.590998e-06 max resid 3.967232e-06
+## ... Procrustes: rmse 4.597367e-06 max resid 1.378576e-05
## ... Similar to previous best
-## Run 18 stress 0.167021
-## Run 19 stress 0.2156369
-## Run 20 stress 0.1432774
-## ... Procrustes: rmse 0.003480002 max resid 0.01258237
+## Run 18 stress 0.1432774
+## ... Procrustes: rmse 0.003455308 max resid 0.01249929
+## Run 19 stress 0.2376474
+## Run 20 stress 0.167021
## *** Solution reached</code></pre>
<pre class="r"><code>(p <- plot_ordination(GlobalPatterns, GP.NMDS, "samples", color="SampleType") +
geom_line() + geom_point(size=5) )</code></pre>
-<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA2AAAAKgCAIAAAByb4nzAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOzdd1wUd/4/8M/s7C5lqVLFQhVFVERIYiFiC0bBaIw9xmhiEtM0luQuXy+/RI0pF3PGaC5qqrFFjZ41BluMFQuIAiIKgmKlLltgy5TfH+txoAvswvZ9Pf/IY5l578wbpLwyM5/Ph+J5ngAAAAAA/JfA2g0AAAAAgG1BQAQAAACARhAQAQAAAKARBEQAAAAAaAQBEQAAAAAaQUAEAAAAgEaE1m6gBV9//fWdO3es3YVxeJ5nWZamaYqirN2L7dJ9iazdhe3iOI7jOKHQ1n9CrYjneY7j8F3UDJZleZ7Hd1EzOI4jhAgEuFbSJIZhKIqiafrVV1+NiIiwdjtgObb+i6OoqGjFihXW7sI4DMNIpVJvb2+RSGTtX [...]
+<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA2AAAAKgCAIAAAByb4nzAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOzdd1wUd/4/8M/s7C5lqVLFQhVFVERIgkrEFoiC0Rh7jNGLSUzTsyR3+Xr5JcaYcjFnjOaiphpbjDGxRrHFWLGAKKCiIChW6rIFtkz5/bGGA11gF7bv6/mHj2XmPTPvpb6cmc9nKJ7nCQAAAADAXwTWbgAAAAAAbAsCIgAAAAA0gYAIAAAAAE0gIAIAAABAEwiIAAAAANAEAiIAAAAANCG0dgOt+OKLL27fvm3tLozD8zzLsjRNUxRl7V5sl+5TZO0ubBfHcRzHCYW2/hNqRTzPcxyH76IWsCzL8zy+i1rAcRwhRCDAuZJmMQxDURRN0y+99FJERIS12wHLsfVfHMXFxcuXL7d2F8ZhGEYqlXp7e4tEImv3Y [...]
<p>The figure nicely shows the relative dissimilarities between microbial communities from different habitats. However, it fails to indicate what was different between the communities. For an ordination method that provides information on the taxa that explain differences between samples (or groups of samples), we use Correspondence Analysis.</p>
</div>
<div id="correspondence-analysis-ca" class="section level3">
diff --git a/inst/doc/phyloseq-mixture-models.html b/inst/doc/phyloseq-mixture-models.html
index 7f6c623..3f03c37 100644
--- a/inst/doc/phyloseq-mixture-models.html
+++ b/inst/doc/phyloseq-mixture-models.html
@@ -97,7 +97,7 @@ document.addEventListener("DOMContentLoaded", function() {
<h1><span class="header-section-number">3</span> Import data with phyloseq, convert to DESeq2</h1>
<p>Start by loading phyloseq.</p>
<pre class="r"><code>library("phyloseq"); packageVersion("phyloseq")</code></pre>
-<pre><code>## [1] '1.19.1'</code></pre>
+<pre><code>## [1] '1.20.0'</code></pre>
<p>Defined file path, and import the published OTU count data into R.</p>
<pre class="r"><code>filepath = system.file("extdata", "study_1457_split_library_seqs_and_mapping.zip", package="phyloseq")
kostic = microbio_me_qiime(filepath)</code></pre>
@@ -131,7 +131,7 @@ kostic = microbio_me_qiime(filepath)</code></pre>
<h1><span class="header-section-number">5</span> DESeq2 conversion and call</h1>
<p>First load DESeq2.</p>
<pre class="r"><code>library("DESeq2"); packageVersion("DESeq2")</code></pre>
-<pre><code>## [1] '1.14.1'</code></pre>
+<pre><code>## [1] '1.16.0'</code></pre>
<p>The following two lines actually do all the complicated DESeq2 work. The function <code>phyloseq_to_deseq2</code> converts your phyloseq-format microbiome data into a <code>DESeqDataSet</code> with dispersions estimated, using the experimental design formula, also shown (the <code>~DIAGNOSIS</code> term). The <code>DESeq</code> function does the rest of the testing, in this case with default testing framework, but you can actually use alternatives.</p>
<p>First remove the 5 samples that had no <code>DIAGNOSIS</code> attribute assigned. These introduce a spurious third design class that is actually a rare artifact in the dataset. Also remove samples with less than <code>500</code> reads (counts). Note that this kind of data cleanup is useful, necessary, and should be well-documented because it can also be dangerous to alter or omit data without clear documentation. In this case I actually explored the data first, and am omitting some of [...]
<pre class="r"><code>kostic <- subset_samples(kostic, DIAGNOSIS != "None")
@@ -161,33 +161,23 @@ sigtab = res[(res$padj < alpha), ]
sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(kostic)[rownames(sigtab), ], "matrix"))
head(sigtab)</code></pre>
<pre><code>## baseMean log2FoldChange lfcSE stat pvalue
-## 64396 123.230731 2.095439 0.4087753 5.126137 2.957468e-07
-## 72853 19.433725 -1.560522 0.3212705 -4.857346 1.189696e-06
-## 180285 114.266166 -1.180600 0.2724565 -4.333171 1.469771e-05
-## 194648 6.191726 -1.237142 0.3010604 -4.109280 3.968938e-05
-## 184729 9.837951 -1.788234 0.4280126 -4.177994 2.940913e-05
-## 374052 42.072484 2.039744 0.4978723 4.096923 4.186780e-05
-## padj Kingdom Phylum Class
-## 64396 0.0001076518 Bacteria Fusobacteria Fusobacteria (class)
-## 72853 0.0002165246 Bacteria Firmicutes Clostridia
-## 180285 0.0017833223 Bacteria Firmicutes Clostridia
-## 194648 0.0025399798 Bacteria Firmicutes Clostridia
-## 184729 0.0025399798 Bacteria Firmicutes Clostridia
-## 374052 0.0025399798 Bacteria Fusobacteria Fusobacteria (class)
-## Order Family Genus
-## 64396 Fusobacteriales Fusobacteriaceae Fusobacterium
-## 72853 Clostridiales Ruminococcaceae Faecalibacterium
-## 180285 Clostridiales Ruminococcaceae Faecalibacterium
-## 194648 Clostridiales <NA> <NA>
-## 184729 Clostridiales Lachnospiraceae Ruminococcus
-## 374052 Fusobacteriales Fusobacteriaceae Fusobacterium
-## Species
-## 64396 <NA>
-## 72853 <NA>
-## 180285 <NA>
-## 194648 <NA>
-## 184729 Ruminococcus gnavus
-## 374052 <NA></code></pre>
+## 64396 181.564648 2.195547 0.4282121 5.127241 2.940184e-07
+## 72853 28.633097 -1.604873 0.3310712 -4.847515 1.250173e-06
+## 374052 75.002469 2.523486 0.5411855 4.662885 3.118072e-06
+## 307981 3.258919 2.153038 0.4832019 4.455772 8.359169e-06
+## 180285 168.356513 -1.204357 0.2782362 -4.328542 1.501000e-05
+## padj Kingdom Phylum Class
+## 64396 0.0007238734 Bacteria Fusobacteria Fusobacteria (class)
+## 72853 0.0015389636 Bacteria Firmicutes Clostridia
+## 374052 0.0025588980 Bacteria Fusobacteria Fusobacteria (class)
+## 307981 0.0051450684 Bacteria Proteobacteria Gammaproteobacteria
+## 180285 0.0073909244 Bacteria Firmicutes Clostridia
+## Order Family Genus Species
+## 64396 Fusobacteriales Fusobacteriaceae Fusobacterium <NA>
+## 72853 Clostridiales Ruminococcaceae Faecalibacterium <NA>
+## 374052 Fusobacteriales Fusobacteriaceae Fusobacterium <NA>
+## 307981 Enterobacteriales Enterobacteriaceae Klebsiella <NA>
+## 180285 Clostridiales Ruminococcaceae Faecalibacterium <NA></code></pre>
<p>Let’s look at just the OTUs that were significantly enriched in the carcinoma tissue. First, cleaning up the table a little for legibility.</p>
<pre class="r"><code>posigtab = sigtab[sigtab[, "log2FoldChange"] > 0, ]
posigtab = posigtab[, c("baseMean", "log2FoldChange", "lfcSE", "padj", "Phylum", "Class", "Family", "Genus")]</code></pre>
@@ -219,10 +209,10 @@ posigtab = posigtab[, c("baseMean", "log2FoldChange", "
<tbody>
<tr class="odd">
<td>64396</td>
-<td>123.230731</td>
-<td>2.095439</td>
-<td>0.4087753</td>
-<td>0.0001076518</td>
+<td>181.564648</td>
+<td>2.195547</td>
+<td>0.4282121</td>
+<td>0.0007238734</td>
<td>Fusobacteria</td>
<td>Fusobacteria (class)</td>
<td>Fusobacteriaceae</td>
@@ -230,10 +220,10 @@ posigtab = posigtab[, c("baseMean", "log2FoldChange", "
</tr>
<tr class="even">
<td>374052</td>
-<td>42.072484</td>
-<td>2.039744</td>
-<td>0.4978723</td>
-<td>0.0025399798</td>
+<td>75.002469</td>
+<td>2.523486</td>
+<td>0.5411855</td>
+<td>0.0025588980</td>
<td>Fusobacteria</td>
<td>Fusobacteria (class)</td>
<td>Fusobacteriaceae</td>
@@ -241,26 +231,15 @@ posigtab = posigtab[, c("baseMean", "log2FoldChange", "
</tr>
<tr class="odd">
<td>307981</td>
-<td>1.910564</td>
-<td>1.773142</td>
-<td>0.4492574</td>
-<td>0.0030523490</td>
+<td>3.258919</td>
+<td>2.153038</td>
+<td>0.4832019</td>
+<td>0.0051450684</td>
<td>Proteobacteria</td>
<td>Gammaproteobacteria</td>
<td>Enterobacteriaceae</td>
<td>Klebsiella</td>
</tr>
-<tr class="even">
-<td>9778</td>
-<td>1.248705</td>
-<td>1.850822</td>
-<td>0.5158122</td>
-<td>0.0071302629</td>
-<td>Proteobacteria</td>
-<td>Gammaproteobacteria</td>
-<td>Enterobacteriaceae</td>
-<td>Salmonella</td>
-</tr>
</tbody>
</table>
<p>As expected from the original study abstract and title, a <em>Fusobacterium</em> OTU was among the most-significantly differentially abundant between the cancerous and healthy samples.</p>
@@ -283,7 +262,7 @@ ggplot(sigtabgen, aes(y=Genus, x=log2FoldChange, color=Phylum)) +
geom_vline(xintercept = 0.0, color = "gray", size = 0.5) +
geom_point(size=6) +
theme(axis.text.x = element_text(angle = -90, hjust = 0, vjust=0.5))</code></pre>
-<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA2AAAAKgCAIAAAByb4nzAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOzdeUATZ94H8CckAUJCAlUQUJFDRQXFi33rwYpFsIq06qp4tdpVt6Ju0Vbd993a1gNrLYqgbqtW2noVj1ZbwVpatSLerFqEolwKKgoKJOFKyDHz/jFdlgEEEiFh4Pv5K06e55lfxjD5Zp6ZCY+maQIAAAAA8B8W5i4AAAAAANoXBEQAAAAAYEFABAAAAAAWBEQAAAAAYEFABAAAAAAWBEQAAAAAYBGYu4DOa/v27Y8fPzZ3FY3T6XQ8Ho/P55u7EGPo9XqOVk7TNFM8j8czdy3G4O6WV6lUGo1GJpOZuxBj0DRN07SFBSe/7WNXYxZm3NX87W9/8/DwMPFKwTgIiGaTl5cXGxtr7ioap1AoBAKBRCIxdyHGq [...]
+<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA2AAAAKgCAIAAAByb4nzAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOzdeUBU9d748e8wiILIpqCioIBL5oIKdF1TUzC365KKW2VPdsu0qG7W8zxZbpiZaG5PZTevNzNF7f7MLS+WBkK5kJhCrqCQC6mgwIAsM3PO74+5cfkCCsM2DL5ffw0zZ/kMA/J2zswZjaqqAgAAAPiDjaUHAAAAQP1CIAIAAEBCIAIAAEBCIAIAAEBCIAIAAEBCIAIAAEBia+kBHl5r1669ceNG3e9XURSNRqPRaOp+1zVFURRFUWxtrfuntwE8EAUFBQUFBS4uLpYepFqMRqNWq7X0FNViNBpVVbX234iG8UAIIRrAvai9u/CXv/zF19e3ljaOmmXd/6BYtZSUlDVr1tT9fvPz87VarZ2dXd3vuqYUFhbqd [...]
</div>
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