small rephrasing

UCLouvain-CBIO · Nov 17, 2024 · b1225ff · b1225ff
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diff --git a/64-dimred.Rmd b/64-dimred.Rmd
@@ -61,9 +61,9 @@ data(xy)
 ```
 
 We are going to use the following dataset to illustrate some important
-concepts that are scale and central to PCA. The small dataset show
-below represents the measurement of genes *x* and *y* in 20
-samples. We will be using the scaled and centred version of this data.
+concepts related to PCA. The small dataset show below represents the
+measurement of genes *x* and *y* in 20 samples. We will be using the
+scaled and centred version of this dataset.
 
 ```{r xyplot, echo = FALSE, fig.cap = "Raw (left) and scale/centred (right) expression data for genes *x* and *y* in 20 samples", fig.height = 5, fig.width = 11}
 library("ggplot2")
@@ -195,8 +195,9 @@ pca + pca2
 var <- .pca$sdev^2
 pve <- var/sum(var)
 ```
+
 In the example above the variance, the variance along the PCs are
-`r round(var[1], 2)` and `r round(var[2], 2)`. The first one explains
+`r round(var[1], 2)` and `r round(var[2], 2)` respectively. The first one explains
 `r round(pve[1] * 100, 1)`% or that variance, and the second one merely
 `r round(pve[2] * 100, 1)`%. This is also reflected in the different
 scales along the x and y axis.
@@ -220,15 +221,14 @@ pca_final
 Principal components are linear combinations of the variables that
 were originally measured, they provide a new coordinate system. The
 PC in the previous example is a linear combination of *gene_x* and
-*gene_y*, more specifically
+*gene_y*:
 
 $$ PC = c_{1} ~ gene_{x} + c_{2} ~ gene_{y} $$
 
 It has coefficients $(c_1, c_2)$, also called loading.
 
-PCA in general will find linear combinations of the original
-variables. These new linear combinations will maximise the variance
-of the data.
+PCA will find linear combinations of the original variables. These new
+linear combinations will maximise the variance of the data.
 
 ## Summary and application
 
@@ -430,7 +430,7 @@ expressed than others.
 `r msmbstyle::solution_end()`
 
 Real datasets often come with *missing values*. In R, these should be
-encoded using `NA`. Unfortunately, PCA cannot deal with missing
+encoded using `NA`. Unfortunately, `prcomp()` cannot deal with missing
 values, and observations containing `NA` values will be dropped
 automatically. This is a viable solution only when the proportion of
 missing values is low. Otherwise, it is possible to impute missing

diff --git a/docs/404.html b/docs/404.html
@@ -13,7 +13,7 @@
 
 <meta name="author" content="Laurent Gatto" />
 
-<meta name="date" content="2024-10-28" />
+<meta name="date" content="2024-11-17" />
 
 <script type="text/x-mathjax-config">
 MathJax.Hub.Config({
@@ -205,7 +205,7 @@ <h1>Page not found</h1>
 <p style="text-align: center;">
 </p>
 <p class="build-date">Page built: 
-2024-10-28
+2024-11-17
  using 
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 </p>

diff --git a/docs/WSBIM1322_files/figure-html/exdists-1.png b/docs/WSBIM1322_files/figure-html/exdists-1.png
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diff --git a/docs/index.html b/docs/index.html
@@ -13,7 +13,7 @@
 
 <meta name="author" content="Laurent Gatto" />
 
-<meta name="date" content="2024-10-28" />
+<meta name="date" content="2024-11-17" />
 
 <script type="text/x-mathjax-config">
 MathJax.Hub.Config({
@@ -349,7 +349,7 @@ <h2>Setup<div class="tooltip"><button class="internal-link-btn" onclick="copy_li
 <a href="sec-refresher.html"><button class="btn btn-default">Next</button></a>
 </p>
 <p class="build-date">Page built: 
-2024-10-28
+2024-11-17
  using 
 R version 4.4.1 (2024-06-14)
 </p>

diff --git a/docs/sec-biostrings.html b/docs/sec-biostrings.html
@@ -13,7 +13,7 @@
 
 <meta name="author" content="Laurent Gatto" />
 
-<meta name="date" content="2024-10-28" />
+<meta name="date" content="2024-11-17" />
 
 <script type="text/x-mathjax-config">
 MathJax.Hub.Config({
@@ -252,7 +252,7 @@ <h2>
 <span class="header-section-number">4.2</span> Working with sequences - using <code>Biostrings</code>
 <div class="tooltip"><button class="internal-link-btn" onclick="copy_link('working-with-sequences---using-biostrings')" onmouseout="reset_tooltip('working-with-sequences---using-biostrings-tooltip')"><span class="tooltiptext" id="working-with-sequences---using-biostrings-tooltip">Copy link</span><i class="fa fa-link"></i></button></div>
 </h2>
-<p>The Bioconductor <em><a href="https://bioconductor.org/packages/3.19/Biostrings">Biostrings</a></em>
+<p>The Bioconductor <em><a href="https://bioconductor.org/packages/3.20/Biostrings">Biostrings</a></em>
 package can do all that base R can do, in addition to knowing about
 the semantics of the sequences is handles. Let’s start by loading the
 package:</p>
@@ -937,7 +937,7 @@ <h2>
 <a href="sec-norm.html"><button class="btn btn-default">Next</button></a>
 </p>
 <p class="build-date">Page built: 
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+2024-11-17
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diff --git a/docs/sec-biovis.html b/docs/sec-biovis.html
@@ -13,7 +13,7 @@
 
 <meta name="author" content="Laurent Gatto" />
 
-<meta name="date" content="2024-10-28" />
+<meta name="date" content="2024-11-17" />
 
 <script type="text/x-mathjax-config">
 MathJax.Hub.Config({
@@ -211,7 +211,7 @@ <h1>
 <li>Visualisation of intersections, in particular <strong>Venn</strong> and <strong>UpSet</strong>
 plots.</li>
 </ul>
-<!-- - The *[Gviz](https://bioconductor.org/packages/3.19/Gviz)* to visualise genomic data. --><div id="heatmaps" class="section level2" number="11.1">
+<!-- - The *[Gviz](https://bioconductor.org/packages/3.20/Gviz)* to visualise genomic data. --><div id="heatmaps" class="section level2" number="11.1">
 <h2>
 <span class="header-section-number">11.1</span> Heatmaps<div class="tooltip"><button class="internal-link-btn" onclick="copy_link('heatmaps')" onmouseout="reset_tooltip('heatmaps-tooltip')"><span class="tooltiptext" id="heatmaps-tooltip">Copy link</span><i class="fa fa-link"></i></button></div>
 </h2>
@@ -275,8 +275,8 @@ <h2>
 <p>Based on the caveats above, it is essential to present and interpret
 heatmaps with great care.</p>
 <p>There exists several packages that allow to produce heatmaps with
-various levels of sophistication, such as <code>heatmap.2</code> from the <code>r CRANpkg("gplots")</code> package, the <em><a href="https://bioconductor.org/packages/3.19/Heatplus">Heatplus</a></em> package, or
-the <em><a href="https://bioconductor.org/packages/3.19/ComplexHeatmap">ComplexHeatmap</a></em> packages (full documentation
+various levels of sophistication, such as <code>heatmap.2</code> from the <code>r CRANpkg("gplots")</code> package, the <em><a href="https://bioconductor.org/packages/3.20/Heatplus">Heatplus</a></em> package, or
+the <em><a href="https://bioconductor.org/packages/3.20/ComplexHeatmap">ComplexHeatmap</a></em> packages (full documentation
 <a href="https://jokergoo.github.io/ComplexHeatmap-reference/book/index.html">here</a>),
 demonstrated below.</p>
 <div class="sourceCode" id="cb427"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb427-1"><a href="sec-biovis.html#cb427-1" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"ComplexHeatmap"</span>)</span>
@@ -487,7 +487,7 @@ <h2>
 <a href="sec-ccl.html"><button class="btn btn-default">Next</button></a>
 </p>
 <p class="build-date">Page built: 
-2024-10-28
+2024-11-17
  using 
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 </p>

diff --git a/docs/sec-ccl.html b/docs/sec-ccl.html
@@ -13,7 +13,7 @@
 
 <meta name="author" content="Laurent Gatto" />
 
-<meta name="date" content="2024-10-28" />
+<meta name="date" content="2024-11-17" />
 
 <script type="text/x-mathjax-config">
 MathJax.Hub.Config({
@@ -511,7 +511,7 @@ <h3>Exercise 3<div class="tooltip"><button class="internal-link-btn" onclick="co
 <a href="sec-si.html"><button class="btn btn-default">Next</button></a>
 </p>
 <p class="build-date">Page built: 
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+2024-11-17
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diff --git a/docs/sec-dimred.html b/docs/sec-dimred.html
@@ -13,7 +13,7 @@
 
 <meta name="author" content="Laurent Gatto" />
 
-<meta name="date" content="2024-10-28" />
+<meta name="date" content="2024-11-17" />
 
 <script type="text/x-mathjax-config">
 MathJax.Hub.Config({
@@ -265,9 +265,9 @@ <h2>
 we will focus on here. We are going to focus on a widerly used method
 called Principal Component Analysis (PCA).</p>
 <p>We are going to use the following dataset to illustrate some important
-concepts that are scale and central to PCA. The small dataset show
-below represents the measurement of genes <em>x</em> and <em>y</em> in 20
-samples. We will be using the scaled and centred version of this data.</p>
+concepts related to PCA. The small dataset show below represents the
+measurement of genes <em>x</em> and <em>y</em> in 20 samples. We will be using the
+scaled and centred version of this dataset.</p>
 <div class="figure">
 <span style="display:block;" id="fig:xyplot"></span>
 <p class="caption marginnote shownote">
@@ -331,7 +331,7 @@ <h2>
 <img src="WSBIM1322_files/figure-html/pxaex2-1.png" alt="The second prinicpal component is orthogonal to the second one." width="1056">
 </div>
 <p>In the example above the variance, the variance along the PCs are
-1.77 and 0.23. The first one explains
+1.77 and 0.23 respectively. The first one explains
 88.6% or that variance, and the second one merely
 11.4%. This is also reflected in the different
 scales along the x and y axis.</p>
@@ -353,12 +353,11 @@ <h2>
 <p>Principal components are linear combinations of the variables that
 were originally measured, they provide a new coordinate system. The
 PC in the previous example is a linear combination of <em>gene_x</em> and
-<em>gene_y</em>, more specifically</p>
+<em>gene_y</em>:</p>
 <p><span class="math display">\[ PC = c_{1} ~ gene_{x} + c_{2} ~ gene_{y} \]</span></p>
 <p>It has coefficients <span class="math inline">\((c_1, c_2)\)</span>, also called loading.</p>
-<p>PCA in general will find linear combinations of the original
-variables. These new linear combinations will maximise the variance
-of the data.</p>
+<p>PCA will find linear combinations of the original variables. These new
+linear combinations will maximise the variance of the data.</p>
 </div>
 <div id="summary-and-application" class="section level2" number="8.4">
 <h2>
@@ -648,7 +647,7 @@ <h2>
 </div>
 </div>
 <p>Real datasets often come with <em>missing values</em>. In R, these should be
-encoded using <code>NA</code>. Unfortunately, PCA cannot deal with missing
+encoded using <code>NA</code>. Unfortunately, <code>prcomp()</code> cannot deal with missing
 values, and observations containing <code>NA</code> values will be dropped
 automatically. This is a viable solution only when the proportion of
 missing values is low. Otherwise, it is possible to impute missing
@@ -1341,7 +1340,7 @@ <h2>
 <a href="sec-ul.html"><button class="btn btn-default">Next</button></a>
 </p>
 <p class="build-date">Page built: 
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diff --git a/docs/sec-mlintro.html b/docs/sec-mlintro.html
@@ -13,7 +13,7 @@
 
 <meta name="author" content="Laurent Gatto" />
 
-<meta name="date" content="2024-10-28" />
+<meta name="date" content="2024-11-17" />
 
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diff --git a/docs/sec-norm.html b/docs/sec-norm.html
@@ -13,7 +13,7 @@
 
 <meta name="author" content="Laurent Gatto" />
 
-<meta name="date" content="2024-10-28" />
+<meta name="date" content="2024-11-17" />
 
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@@ -759,7 +759,7 @@ <h2>
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 </p>
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diff --git a/docs/sec-obj.html b/docs/sec-obj.html
@@ -13,7 +13,7 @@
 
 <meta name="author" content="Laurent Gatto" />
 
-<meta name="date" content="2024-10-28" />
+<meta name="date" content="2024-11-17" />
 
 <script type="text/x-mathjax-config">
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@@ -430,19 +430,19 @@ <h3>
 <span class="header-section-number">3.4.1</span> Common Bioconductor classes<div class="tooltip"><button class="internal-link-btn" onclick="copy_link('common-bioconductor-classes')" onmouseout="reset_tooltip('common-bioconductor-classes-tooltip')"><span class="tooltiptext" id="common-bioconductor-classes-tooltip">Copy link</span><i class="fa fa-link"></i></button></div>
 </h3>
 <ul>
-<li>Rectangular feature x sample data – <em><a href="https://bioconductor.org/packages/3.19/SummarizedExperiment">SummarizedExperiment</a></em><code>::SummarizedExperiment()</code> (RNAseq count matrix, microarray, …)</li>
-<li>Genomic coordinates – <em><a href="https://bioconductor.org/packages/3.19/GenomicRanges">GenomicRanges</a></em><code>::GRanges()</code> (1-based, closed interval)</li>
-<li>DNA / RNA / AA sequences – <em><a href="https://bioconductor.org/packages/3.19/Biostrings">Biostrings</a></em><code>::*StringSet()</code>
+<li>Rectangular feature x sample data – <em><a href="https://bioconductor.org/packages/3.20/SummarizedExperiment">SummarizedExperiment</a></em><code>::SummarizedExperiment()</code> (RNAseq count matrix, microarray, …)</li>
+<li>Genomic coordinates – <em><a href="https://bioconductor.org/packages/3.20/GenomicRanges">GenomicRanges</a></em><code>::GRanges()</code> (1-based, closed interval)</li>
+<li>DNA / RNA / AA sequences – <em><a href="https://bioconductor.org/packages/3.20/Biostrings">Biostrings</a></em><code>::*StringSet()</code>
 </li>
-<li>Gene sets – <em><a href="https://bioconductor.org/packages/3.19/GSEABase">GSEABase</a></em><code>::GeneSet()</code> <em><a href="https://bioconductor.org/packages/3.19/GSEABase">GSEABase</a></em><code>::GeneSetCollection()</code>
+<li>Gene sets – <em><a href="https://bioconductor.org/packages/3.20/GSEABase">GSEABase</a></em><code>::GeneSet()</code> <em><a href="https://bioconductor.org/packages/3.20/GSEABase">GSEABase</a></em><code>::GeneSetCollection()</code>
 </li>
-<li>Multi-omics data – <em><a href="https://bioconductor.org/packages/3.19/MultiAssayExperiment">MultiAssayExperiment</a></em><code>::MultiAssayExperiment()</code>
+<li>Multi-omics data – <em><a href="https://bioconductor.org/packages/3.20/MultiAssayExperiment">MultiAssayExperiment</a></em><code>::MultiAssayExperiment()</code>
 </li>
-<li>Single cell data – <em><a href="https://bioconductor.org/packages/3.19/SingleCellExperiment">SingleCellExperiment</a></em><code>::SingleCellExperiment()</code>
+<li>Single cell data – <em><a href="https://bioconductor.org/packages/3.20/SingleCellExperiment">SingleCellExperiment</a></em><code>::SingleCellExperiment()</code>
 </li>
-<li>Mass spectrometry data – <em><a href="https://bioconductor.org/packages/3.19/Spectra">Spectra</a></em><code>::Spectra()</code>
+<li>Mass spectrometry data – <em><a href="https://bioconductor.org/packages/3.20/Spectra">Spectra</a></em><code>::Spectra()</code>
 </li>
-<li>Quantitative proteomics data – <em><a href="https://bioconductor.org/packages/3.19/QFeatures">QFeatures</a></em><code>::QFeatures()</code>
+<li>Quantitative proteomics data – <em><a href="https://bioconductor.org/packages/3.20/QFeatures">QFeatures</a></em><code>::QFeatures()</code>
 </li>
 </ul>
 </div>
@@ -455,8 +455,8 @@ <h3>
 <code>require</code> will return <code>FALSE</code>, otherwise it returns <code>TRUE</code> and
 loads the packages. Given that we negate the return value of
 <code>require</code> using <code>!</code>, the packages will be installed only when
-missing.</span> the<em><a href="https://bioconductor.org/packages/3.19/SummarizedExperiment">SummarizedExperiment</a></em>
-package, that implements the class, and the <em><a href="https://bioconductor.org/packages/3.19/airway">airway</a></em>
+missing.</span> the<em><a href="https://bioconductor.org/packages/3.20/SummarizedExperiment">SummarizedExperiment</a></em>
+package, that implements the class, and the <em><a href="https://bioconductor.org/packages/3.20/airway">airway</a></em>
 data package, that provides an RNA-Seq example data.</p>
 <div class="sourceCode" id="cb66"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb66-1"><a href="sec-obj.html#cb66-1" tabindex="-1"></a><span class="cf">if</span> (<span class="sc">!</span><span class="fu">require</span>(<span class="st">"SummarizedExperiment"</span>))</span>
 <span id="cb66-2"><a href="sec-obj.html#cb66-2" tabindex="-1"></a>    BiocManager<span class="sc">::</span><span class="fu">install</span>(<span class="st">"SummarizedExperiment"</span>)</span>
@@ -1029,7 +1029,7 @@ <h2>
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 </p>
 <p class="build-date">Page built: 
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diff --git a/docs/sec-refresher.html b/docs/sec-refresher.html
@@ -13,7 +13,7 @@
 
 <meta name="author" content="Laurent Gatto" />
 
-<meta name="date" content="2024-10-28" />
+<meta name="date" content="2024-11-17" />
 
 <script type="text/x-mathjax-config">
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 </p>
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