writing introduction to clustergrams/heatmaps

cornhundred · Feb 15, 2017 · 5e1c250 · 5e1c250
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diff --git a/docs/_build_html/.doctrees/environment.pickle b/docs/_build_html/.doctrees/environment.pickle
diff --git a/docs/_build_html/.doctrees/interacting_with_viz.doctree b/docs/_build_html/.doctrees/interacting_with_viz.doctree
diff --git a/docs/_build_html/_sources/interacting_with_viz.txt b/docs/_build_html/_sources/interacting_with_viz.txt
@@ -4,9 +4,11 @@ Interacting with the Visualization
 ----------------------------------
 Data visualizations benefit enormously from user interactions that allow users to explore and generate new views of their data. Clustergrammer produces highly interactive heatmaps that enable users to intuitively explore and perform complex transformations on their data. This section will cover the types of interactions available to the user.
 
-Heatmap/Clustergram Introduction
-================================
-Clustergrammer visualizes high-dimensional data as a heirarchically clustered matrix with colored tiles (red for positive numbers and blue for negative numbers), commonly referred to as a heatmap or clustergram (this documentation uses heatmap and clustergram interchangably, see `Eisen et al., 1998`_ for an example using biological data).
+Introduction to Clustergrams/Heatmaps
+=====================================
+Clustergrammer visualizes high-dimensional data as a hierarchically clustered matrix with colored tiles (red for positive numbers and blue for negative numbers) and row and column labels, commonly referred to as a heatmap or clustergram (this documentation uses heatmap and clustergram interchangeably, see `Eisen et al., 1998`_ for an early example using biological data). Clustergrams also typically have `dendrogram trees`_ to depict the hierarchy of row and column clusters formed from hierarchical clustering.
+
+Heatmaps are a powerful visualization tool that enables users to directly visualize high-dimensional data without the loss of information and interpretability associated with dimensionality reduction techniques (e.g. `t-SNE`_). For instance, columns can depict measurements (e.g. data-points) and rows can depict dimensions (e.g. measured variables). In this way heatmaps can visualize thousands of data-points in thousands of dimensions. However, static heatmaps are of limited use for visualizing large datasets (where matrix cells and labels become too small to read). Furthermore, static heatmaps prevent users from interactively exploring their data, e.g. reordering rows/columns.
 
 Zooming and Panning
 ===================
@@ -50,4 +52,6 @@ Expanding
 Biology Specific Interactions
 =============================
 
-.. _`Eisen et al., 1998`: http://www.pnas.org/content/95/25/14863.full
+.. _`Eisen et al., 1998`: http://www.pnas.org/content/95/25/14863.full
+.. _`dendrogram trees`: https://en.wikipedia.org/wiki/Dendrogram
+.. _`t-SNE`: https://lvdmaaten.github.io/tsne/
diff --git a/docs/_build_html/index.html b/docs/_build_html/index.html
@@ -191,7 +191,7 @@ <h2>Contents:<a class="headerlink" href="#contents" title="Permalink to this hea
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="interacting_with_viz.html">Interacting with the Visualization</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="interacting_with_viz.html#heatmap-clustergram-introduction">Heatmap/Clustergram Introduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="interacting_with_viz.html#introduction-to-clustergrams-heatmaps">Introduction to Clustergrams/Heatmaps</a></li>
 <li class="toctree-l2"><a class="reference internal" href="interacting_with_viz.html#zooming-and-panning">Zooming and Panning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="interacting_with_viz.html#mouseover-interactions">Mouseover Interactions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="interacting_with_viz.html#row-and-column-reordering">Row and Column Reordering</a></li>

diff --git a/docs/_build_html/interacting_with_viz.html b/docs/_build_html/interacting_with_viz.html
@@ -86,7 +86,7 @@
 <li class="toctree-l1"><a class="reference internal" href="clustergrammer_web.html">Clustergrammer Web App</a></li>
 <li class="toctree-l1"><a class="reference internal" href="clustergrammer_widget.html">Clustergrammer Jupyter Widget</a></li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Interacting with the Visualization</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="#heatmap-clustergram-introduction">Heatmap/Clustergram Introduction</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#introduction-to-clustergrams-heatmaps">Introduction to Clustergrams/Heatmaps</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#zooming-and-panning">Zooming and Panning</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#mouseover-interactions">Mouseover Interactions</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#row-and-column-reordering">Row and Column Reordering</a></li>
@@ -159,9 +159,10 @@
   <div class="section" id="interacting-with-the-visualization">
 <span id="interacting-with-viz"></span><h1>Interacting with the Visualization<a class="headerlink" href="#interacting-with-the-visualization" title="Permalink to this headline">¶</a></h1>
 <p>Data visualizations benefit enormously from user interactions that allow users to explore and generate new views of their data. Clustergrammer produces highly interactive heatmaps that enable users to intuitively explore and perform complex transformations on their data. This section will cover the types of interactions available to the user.</p>
-<div class="section" id="heatmap-clustergram-introduction">
-<h2>Heatmap/Clustergram Introduction<a class="headerlink" href="#heatmap-clustergram-introduction" title="Permalink to this headline">¶</a></h2>
-<p>Clustergrammer visualizes high-dimensional data as a heirarchically clustered matrix with colored tiles (red for positive numbers and blue for negative numbers), commonly referred to as a heatmap or clustergram (this documentation uses heatmap and clustergram interchangably, see <a class="reference external" href="http://www.pnas.org/content/95/25/14863.full">Eisen et al., 1998</a> for an example using biological data).</p>
+<div class="section" id="introduction-to-clustergrams-heatmaps">
+<h2>Introduction to Clustergrams/Heatmaps<a class="headerlink" href="#introduction-to-clustergrams-heatmaps" title="Permalink to this headline">¶</a></h2>
+<p>Clustergrammer visualizes high-dimensional data as a hierarchically clustered matrix with colored tiles (red for positive numbers and blue for negative numbers) and row and column labels, commonly referred to as a heatmap or clustergram (this documentation uses heatmap and clustergram interchangeably, see <a class="reference external" href="http://www.pnas.org/content/95/25/14863.full">Eisen et al., 1998</a> for an early example using biological data). Clustergrams also typically have <a class="reference external" href="https://en.wikipedia.org/wiki/Dendrogram">dendrogram trees</a> to depict the hierarchy of row and column clusters formed from hierarchical clustering.</p>
+<p>Heatmaps are a powerful visualization tool that enables users to directly visualize high-dimensional data without the loss of information and interpretability associated with dimensionality reduction techniques (e.g. <a class="reference external" href="https://lvdmaaten.github.io/tsne/">t-SNE</a>). For instance, columns can depict measurements (e.g. data-points) and rows can depict dimensions (e.g. measured variables). In this way heatmaps can visualize thousands of data-points in thousands of dimensions. However, static heatmaps are of limited use for visualizing large datasets (where matrix cells and labels become too small to read). Furthermore, static heatmaps prevent users from interactively exploring their data, e.g. reordering rows/columns.</p>
 </div>
 <div class="section" id="zooming-and-panning">
 <h2>Zooming and Panning<a class="headerlink" href="#zooming-and-panning" title="Permalink to this headline">¶</a></h2>

diff --git a/docs/_build_html/searchindex.js b/docs/_build_html/searchindex.js
diff --git a/docs/build/doctrees/environment.pickle b/docs/build/doctrees/environment.pickle
diff --git a/docs/build/doctrees/interacting_with_viz.doctree b/docs/build/doctrees/interacting_with_viz.doctree
diff --git a/docs/interacting_with_viz.rst b/docs/interacting_with_viz.rst
@@ -4,9 +4,11 @@ Interacting with the Visualization
 ----------------------------------
 Data visualizations benefit enormously from user interactions that allow users to explore and generate new views of their data. Clustergrammer produces highly interactive heatmaps that enable users to intuitively explore and perform complex transformations on their data. This section will cover the types of interactions available to the user.
 
-Heatmap/Clustergram Introduction
-================================
-Clustergrammer visualizes high-dimensional data as a hierarchically clustered matrix with colored tiles (red for positive numbers and blue for negative numbers), commonly referred to as a heatmap or clustergram (this documentation uses heatmap and clustergram interchangeably, see `Eisen et al., 1998`_ for an example using biological data).
+Introduction to Clustergrams/Heatmaps
+=====================================
+Clustergrammer visualizes high-dimensional data as a hierarchically clustered matrix with colored tiles (red for positive numbers and blue for negative numbers) and row and column labels, commonly referred to as a heatmap or clustergram (this documentation uses heatmap and clustergram interchangeably, see `Eisen et al., 1998`_ for an early example using biological data). Clustergrams also typically have `dendrogram trees`_ to depict the hierarchy of row and column clusters formed from hierarchical clustering.
+
+Heatmaps are a powerful visualization tool that enables users to directly visualize high-dimensional data without the loss of information and interpretability associated with dimensionality reduction techniques (e.g. `t-SNE`_). For instance, columns can depict measurements (e.g. data-points) and rows can depict dimensions (e.g. measured variables). In this way heatmaps can visualize thousands of data-points in thousands of dimensions. However, static heatmaps are of limited use for visualizing large datasets (where matrix cells and labels become too small to read). Furthermore, static heatmaps prevent users from interactively exploring their data, e.g. reordering rows/columns.
 
 Zooming and Panning
 ===================
@@ -50,4 +52,6 @@ Expanding
 Biology Specific Interactions
 =============================
 
-.. _`Eisen et al., 1998`: http://www.pnas.org/content/95/25/14863.full
+.. _`Eisen et al., 1998`: http://www.pnas.org/content/95/25/14863.full
+.. _`dendrogram trees`: https://en.wikipedia.org/wiki/Dendrogram
+.. _`t-SNE`: https://lvdmaaten.github.io/tsne/
diff --git a/docs/spelling_wordlist.txt b/docs/spelling_wordlist.txt
@@ -34,4 +34,7 @@ screenshot
 snippits
 numpy
 Heatmap
-heatmap
+heatmap
+Heatmaps
+heatmaps
+interpretability