diff --git a/Tutorial_HTM_2016.Rmd b/Tutorial_HTM_2016.Rmd index fb9874730c59f503f7de806d705ea07fe8b3b8ad..2c77fcd27dd969df08cc5cb43b241485f08bbda3 100755 --- a/Tutorial_HTM_2016.Rmd +++ b/Tutorial_HTM_2016.Rmd @@ -40,7 +40,7 @@ cache = TRUE) devtools::install_github("CellH5/cellh5-R") ``` -```{r required packages and data, echo = TRUE, message=FALSE} +```{r required_packages_and_data, echo = TRUE, message=FALSE} library(rmarkdown) library(tidyverse) library(openxlsx) @@ -304,23 +304,104 @@ is sensible. # Using ggplot to create a PCA plot for the data -```{r} + +Before we can compute a PCA, we have to make sure that the variables that +we have obtained for every single well are normalized. As our data consists +of the number of cells each phenotypic category a straigforward normalization +consits of transforming the counts into percentages by dividing the data +for each well by its total number of cells. + +## The chaining operator + +For this, we use the chaining (or piping) operator `%>%`. +$x \% \textgreater \% f(y) $ is simply $f(x, y)$, so one can use +it to rewrite multiple operations in such a way that they can be read +from you can read from left--to--right, +top--to--bottom. A simple example will make that clear: We create +two vectors and calculate Euclidian distance between them. Instead of +the usual way: + +```{r chainingSimpleExample_1} +x1 <- 1:5; x2 <- 2:6 +sqrt(sum((x1-x2)^2)) +``` + +We can use the piping operatror + +```{r chainingSimpleExample_2} +(x1-x2)^2 %>% +sum() %>% +sqrt() +``` + +Which makes the set of operations much easier to digest and understand. + +## Grouping, summarizing and data transformation + +Having tidy data frames, we can employ a __split--apply--combine__ +strategy for data analysis. What this means is that wer first group our +individual observations by one or more factors (_split_ operation), then +_apply_ computations to each group and then combine the results into new +data frame that contains one line per group + +In the code chunk below, we use the`group\_by()` function +to splot the dataset into groups according to the well ID. +We then apply the function `sum()` to the counts of each well and +use`summarize()` to obtain a table of counts per well. + + +We can now join the table containing the sums to the original +data and compute compute percentage using the sums. + +As PCA works best on data that is on normal distribution (z--score) scale, +we perform at [logit](https://en.wikipedia.org/wiki/Logit) transformation +to turn the percentages +into z--scores. This is similar in spirit to a log transformation on +intensity values. + + +```{r grouping_and_summarizing, dependson="reshape"} no_cells_per_well <- input_data %>% group_by(well) %>% summarize(no_cells = sum(count)) -data_with_sums <- left_join(input_data, no_cells_per_well) +head(no_cells_per_well) -# size_factors <- no_cells_per_well$no_cells / geometric.mean(no_cells_per_well$no_cells) +data_with_sums <- left_join(input_data, no_cells_per_well) data_for_PCA <- mutate(data_with_sums, perc = count / no_cells, z_score = logit(perc)) +head(data_for_PCA) +``` + +## creating the PCA plot using ggplot2 + +We are now ready to create the PCA plot. For this, we first need to turn +the input data into a wide data frame again by spreading the z--scores across +columns. + +We can then use the function `prcomp()` to compute the actual principal components. +We also create a vector genes, giving us the gene each of our siRNAs is targeting. + +We then create a ggplot object by mapping the first principal component to the +x-- and the second one to the y--axis. We use the gene names as plotting symbols +and color the names according to to the gene name (As we have multiple empty wells +as well as multiple siRNAs targeting the same gene). + +Furthemore, we specify that the aspect ratio of x and y axis should be 1, so that +the units are same on both axes. This is important for a correct interpreation +of the PCA plot. + + +```{r PCA, dependson="grouping_and_summarizing"} data_for_PCA <- data_for_PCA %>% select(class, well, z_score) %>% spread(key = class, value = z_score) + + PCA <- prcomp(data_for_PCA[, -1], center = TRUE, scale. = TRUE) @@ -341,8 +422,12 @@ pl <- (ggplot(dataGG, aes(x = PC1, + ggtitle("Principal component plot") ) +pl ``` +We can see that the control wells cluster together. Note that it is easy to turn +this plot into an interactive version using `ggplotly` from the `r CRANpkg("plotly")`. + # Heatmap of apoptosis proportions diff --git a/Tutorial_HTM_2016.html b/Tutorial_HTM_2016.html index 66ce6224cdde78bb7d6be1beb80930a7a5ac3b00..03ef1fb52087c667c0b923d8c3e6eecc818f9709 100644 --- a/Tutorial_HTM_2016.html +++ b/Tutorial_HTM_2016.html @@ -79,7 +79,7 @@ document.addEventListener("DOMContentLoaded", function() { <div id="header"> <h1 class="title">Visual Exploration of High–Throughput–Microscopy Data</h1> <h4 class="author"><em>Bernd Klaus, Andrzej Oles, Mike Smith</em></h4> -<h4 class="date"><em>10 Oktober 2016</em></h4> +<h4 class="date"><em>13 Oktober 2016</em></h4> </div> <h1>Contents</h1> @@ -94,7 +94,11 @@ document.addEventListener("DOMContentLoaded", function() { <li><a href="#reshaping-the-screen-data-and-joining-the-plate-annotation"><span class="toc-section-number">7</span> Reshaping the screen data and joining the plate annotation</a></li> <li><a href="#plotting-in-r-ggplot2"><span class="toc-section-number">8</span> Plotting in R: ggplot2</a></li> <li><a href="#principal-component-analysis-pca-to-for-data-visualization"><span class="toc-section-number">9</span> Principal component analysis (PCA) to for data visualization</a></li> -<li><a href="#using-ggplot-to-create-a-pca-plot-for-the-data"><span class="toc-section-number">10</span> Using ggplot to create a PCA plot for the data</a></li> +<li><a href="#using-ggplot-to-create-a-pca-plot-for-the-data"><span class="toc-section-number">10</span> Using ggplot to create a PCA plot for the data</a><ul> +<li><a href="#the-chaining-operator"><span class="toc-section-number">10.1</span> The chaining operator</a></li> +<li><a href="#grouping-summarizing-and-data-transformation"><span class="toc-section-number">10.2</span> Grouping, summarizing and data transformation</a></li> +<li><a href="#creating-the-pca-plot-using-ggplot2"><span class="toc-section-number">10.3</span> creating the PCA plot using ggplot2</a></li> +</ul></li> <li><a href="#heatmap-of-apoptosis-proportions"><span class="toc-section-number">11</span> Heatmap of apoptosis proportions</a></li> <li><a href="#session-information"><span class="toc-section-number">12</span> Session information</a></li> <li><a href="#references">References</a></li> @@ -246,21 +250,73 @@ V=2\times \mbox{ Beets }+ 1\times \mbox{Carrots } +\frac{1}{2} \mbox{ Gala}+ \fr </div> <div id="using-ggplot-to-create-a-pca-plot-for-the-data" class="section level1"> <h1><span class="header-section-number">10</span> Using ggplot to create a PCA plot for the data</h1> +<p>Before we can compute a PCA, we have to make sure that the variables that we have obtained for every single well are normalized. As our data consists of the number of cells each phenotypic category a straigforward normalization consits of transforming the counts into percentages by dividing the data for each well by its total number of cells.</p> +<div id="the-chaining-operator" class="section level2"> +<h2><span class="header-section-number">10.1</span> The chaining operator</h2> +<p>For this, we use the chaining (or piping) operator <code>%>%</code>. $x % % f(y) $ is simply <span class="math inline">\(f(x, y)\)</span>, so one can use it to rewrite multiple operations in such a way that they can be read from you can read from left–to–right, top–to–bottom. A simple example will make that clear: We create two vectors and calculate Euclidian distance between them. Instead of the usual way:</p> +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">x1 <-<span class="st"> </span><span class="dv">1</span>:<span class="dv">5</span>; x2 <-<span class="st"> </span><span class="dv">2</span>:<span class="dv">6</span> +<span class="kw">sqrt</span>(<span class="kw">sum</span>((x1-x2)^<span class="dv">2</span>))</code></pre></div> +<pre><code> [1] 2.24</code></pre> +<p>We can use the piping operatror</p> +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">(x1-x2)^<span class="dv">2</span> %>% +<span class="kw">sum</span>() %>% +<span class="kw">sqrt</span>()</code></pre></div> +<pre><code> [1] 2.24</code></pre> +<p>Which makes the set of operations much easier to digest and understand.</p> +</div> +<div id="grouping-summarizing-and-data-transformation" class="section level2"> +<h2><span class="header-section-number">10.2</span> Grouping, summarizing and data transformation</h2> +<p>Having tidy data frames, we can employ a <strong>split–apply–combine</strong> strategy for data analysis. What this means is that wer first group our individual observations by one or more factors (<em>split</em> operation), then <em>apply</em> computations to each group and then combine the results into new data frame that contains one line per group</p> +<p>In the code chunk below, we use the<code>group\_by()</code> function to splot the dataset into groups according to the well ID. We then apply the function <code>sum()</code> to the counts of each well and use<code>summarize()</code> to obtain a table of counts per well.</p> +<p>We can now join the table containing the sums to the original data and compute compute percentage using the sums.</p> +<p>As PCA works best on data that is on normal distribution (z–score) scale, we perform at <a href="https://en.wikipedia.org/wiki/Logit">logit</a> transformation to turn the percentages into z–scores. This is similar in spirit to a log transformation on intensity values.</p> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">no_cells_per_well <-<span class="st"> </span>input_data %>% <span class="st"> </span><span class="kw">group_by</span>(well) %>% <span class="st"> </span><span class="kw">summarize</span>(<span class="dt">no_cells =</span> <span class="kw">sum</span>(count)) -data_with_sums <-<span class="st"> </span><span class="kw">left_join</span>(input_data, no_cells_per_well)</code></pre></div> +<span class="kw">head</span>(no_cells_per_well)</code></pre></div> +<pre><code> # A tibble: 6 × 2 + well no_cells + <chr> <int> + 1 A01_01 13089 + 2 A02_01 22185 + 3 A03_01 25407 + 4 A04_01 20361 + 5 A05_01 18708 + 6 A06_01 24389</code></pre> +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">data_with_sums <-<span class="st"> </span><span class="kw">left_join</span>(input_data, no_cells_per_well)</code></pre></div> <pre><code> Joining, by = "well"</code></pre> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># size_factors <- no_cells_per_well$no_cells / geometric.mean(no_cells_per_well$no_cells) </span> - -data_for_PCA <-<span class="st"> </span><span class="kw">mutate</span>(data_with_sums, <span class="dt">perc =</span> count /<span class="st"> </span>no_cells, +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">data_for_PCA <-<span class="st"> </span><span class="kw">mutate</span>(data_with_sums, <span class="dt">perc =</span> count /<span class="st"> </span>no_cells, <span class="dt">z_score =</span> <span class="kw">logit</span>(perc)) -data_for_PCA <-<span class="st"> </span>data_for_PCA %>%<span class="st"> </span> +<span class="kw">head</span>(data_for_PCA)</code></pre></div> +<pre><code> class well count Well Site Row Column siRNA.ID Gene.Symbol Group + 1 Anaphase A01_01 810 A01 1 A 1 s7424 INCENP target + 2 Apoptosis A01_01 1480 A01 1 A 1 s7424 INCENP target + 3 Elongated A01_01 242 A01 1 A 1 s7424 INCENP target + 4 Interphase A01_01 6529 A01 1 A 1 s7424 INCENP target + 5 MAP A01_01 585 A01 1 A 1 s7424 INCENP target + 6 Metaphase A01_01 193 A01 1 A 1 s7424 INCENP target + no_cells perc z_score + 1 13089 0.0619 -2.71861 + 2 13089 0.1131 -2.05974 + 3 13089 0.0185 -3.97193 + 4 13089 0.4988 -0.00474 + 5 13089 0.0447 -3.06219 + 6 13089 0.0147 -4.20198</code></pre> +</div> +<div id="creating-the-pca-plot-using-ggplot2" class="section level2"> +<h2><span class="header-section-number">10.3</span> creating the PCA plot using ggplot2</h2> +<p>We are now ready to create the PCA plot. For this, we first need to turn the input data into a wide data frame again by spreading the z–scores across columns.</p> +<p>We can then use the function <code>prcomp()</code> to compute the actual principal components. We also create a vector genes, giving us the gene each of our siRNAs is targeting.</p> +<p>We then create a ggplot object by mapping the first principal component to the x– and the second one to the y–axis. We use the gene names as plotting symbols and color the names according to to the gene name (As we have multiple empty wells as well as multiple siRNAs targeting the same gene).</p> +<p>Furthemore, we specify that the aspect ratio of x and y axis should be 1, so that the units are same on both axes. This is important for a correct interpreation of the PCA plot.</p> +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">data_for_PCA <-<span class="st"> </span>data_for_PCA %>%<span class="st"> </span> <span class="st"> </span><span class="kw">select</span>(class, well, z_score) %>% <span class="st"> </span><span class="kw">spread</span>(<span class="dt">key =</span> class, <span class="dt">value =</span> z_score) + + PCA <-<span class="st"> </span><span class="kw">prcomp</span>(data_for_PCA[, -<span class="dv">1</span>], <span class="dt">center =</span> <span class="ot">TRUE</span>, <span class="dt">scale. =</span> <span class="ot">TRUE</span>) @@ -279,7 +335,12 @@ pl <-<span class="st"> </span>(<span class="kw">ggplot</span>(dataGG, <span c +<span class="st"> </span><span class="kw">geom_text</span>(<span class="kw">aes</span>(<span class="dt">label =</span> genes), <span class="dt">size =</span> <span class="kw">I</span>(<span class="dv">4</span>)) +<span class="st"> </span><span class="kw">coord_equal</span>() +<span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"Principal component plot"</span>) - )</code></pre></div> + ) + +pl</code></pre></div> +<p><img 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" /></p> +<p>We can see that the control wells cluster together. Note that it is easy to turn this plot into an interactive version using <code>ggplotly</code> from the <em><a href="http://cran.fhcrc.org/web/packages/plotly/index.html">plotly</a></em>.</p> +</div> </div> <div id="heatmap-of-apoptosis-proportions" class="section level1"> <h1><span class="header-section-number">11</span> Heatmap of apoptosis proportions</h1>