improving intro paragraph

docs/_build_html/_sources/index.txt

@@ -2,7 +2,7 @@
 
 Welcome to Clustergrammer's Documentation!
 ------------------------------------------
-Clustergrammer is a web-based tool for visualizing and analyzing high-dimensional data (e.g. a matrix) as an interactive and shareable hierarchically clustered heatmap (see :ref:`intro_heatmap_clustergram` for more information). Clustergrammer's front end (:ref:`clustergrammer_js`) is built using `D3.js`_ and its back end (:ref:`clustergrammer_py`) is built using `Python`_. Clustergrammer produces highly interactive visualizations that enable intuitive exploration of high-dimensional data and has several optional biology-specific features (e.g. enrichment analysis; see :ref:`biology_specific_features`) to facilitate the exploration of gene-level biological data. The project is free and open-source and can be found on `GitHub`_. Press play or interact with the gene-expression demo below to see some of Clustergrammer's interactive features and refer to :ref:`interacting_with_viz` for more information:
+Clustergrammer is a web-based tool for visualizing and analyzing high-dimensional data as an interactive and shareable hierarchically clustered heatmap (see :ref:`intro_heatmap_clustergram`). Clustergrammer's front end (:ref:`clustergrammer_js`) is built using `D3.js`_ and its back end (:ref:`clustergrammer_py`) is built using `Python`_. Clustergrammer produces highly interactive visualizations that enable intuitive exploration of high-dimensional data and has several optional biology-specific features (e.g. enrichment analysis; see :ref:`biology_specific_features`) to facilitate the exploration of gene-level biological data. The project is free and open-source and can be found on `GitHub`_. Press play or interact with the gene-expression demo below to see some of Clustergrammer's interactive features and refer to :ref:`interacting_with_viz` for more information:
 
 .. raw:: html
 

docs/_build_html/_sources/interacting_with_viz.txt

@@ -6,8 +6,8 @@ Data visualization benefits enormously from user interaction -- particularly int
 
 .. _intro_heatmap_clustergram:
 
-Introduction to Clustergrams/Heatmaps
-=====================================
+Introduction to Clustergrams
+============================
 Clustergrammer visualizes high-dimensional data as a hierarchically clustered matrix with colored tiles (red for positive numbers and blue for negative numbers) and row/column labels. This type of visualization is commonly referred to as a heatmap or clustergram and this documentation uses these terms interchangeably; refer to `Eisen et al., 1998`_ for an early example using biological data. Clustergrams also typically use `dendrogram trees`_ to depict the hierarchy of row and column clusters produced by `hierarchical clustering`_.
 
 Heatmaps are powerful visualization tools that enable 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 data-points (e.g. measured entities) and rows can depict data-dimensions (e.g. measured variables). In this way, heatmaps can visualize thousands of data-points in thousands of dimensions (e.g. data in thousand(s)-dimensional space). However, static heatmaps are of limited use for visualizing large datasets because visualization elements and labels become too small to read. Furthermore, static heatmaps prevent users from interactively exploring their data, e.g. reordering rows/columns. We built Clustergrammer to address these problems and to extend the capabilities of heatmap visualizations.

docs/_build_html/index.html

@@ -142,7 +142,7 @@
 
   <div class="section" id="welcome-to-clustergrammer-s-documentation">
 <h1>Welcome to Clustergrammer&#8217;s Documentation!<a class="headerlink" href="#welcome-to-clustergrammer-s-documentation" title="Permalink to this headline">¶</a></h1>
-<p>Clustergrammer is a web-based tool for visualizing and analyzing high-dimensional data (e.g. a matrix) as an interactive and shareable hierarchically clustered heatmap (see <a class="reference internal" href="interacting_with_viz.html#intro-heatmap-clustergram"><span class="std std-ref">Introduction to Clustergrams/Heatmaps</span></a> for more information). Clustergrammer&#8217;s front end (<a class="reference internal" href="clustergrammer_js.html#clustergrammer-js"><span class="std std-ref">Clustergrammer-JS</span></a>) is built using <a class="reference external" href="https://d3js.org/">D3.js</a> and its back end (<a class="reference internal" href="clustergrammer_py.html#clustergrammer-py"><span class="std std-ref">Clustergrammer-PY</span></a>) is built using <a class="reference external" href="https://www.python.org/">Python</a>. Clustergrammer produces highly interactive visualizations that enable intuitive exploration of high-dimensional data and has several optional biology-specific features (e.g. enrichment analysis; see <a class="reference internal" href="biology_specific_features.html#biology-specific-features"><span class="std std-ref">Biology-Specific Features</span></a>) to facilitate the exploration of gene-level biological data. The project is free and open-source and can be found on <a class="reference external" href="https://github.com/MaayanLab/clustergrammer">GitHub</a>. Press play or interact with the gene-expression demo below to see some of Clustergrammer&#8217;s interactive features and refer to <a class="reference internal" href="interacting_with_viz.html#interacting-with-viz"><span class="std std-ref">Interacting with the Visualization</span></a> for more information:</p>
+<p>Clustergrammer is a web-based tool for visualizing and analyzing high-dimensional data as an interactive and shareable hierarchically clustered heatmap (see <a class="reference internal" href="interacting_with_viz.html#intro-heatmap-clustergram"><span class="std std-ref">Introduction to Clustergrams</span></a>). Clustergrammer&#8217;s front end (<a class="reference internal" href="clustergrammer_js.html#clustergrammer-js"><span class="std std-ref">Clustergrammer-JS</span></a>) is built using <a class="reference external" href="https://d3js.org/">D3.js</a> and its back end (<a class="reference internal" href="clustergrammer_py.html#clustergrammer-py"><span class="std std-ref">Clustergrammer-PY</span></a>) is built using <a class="reference external" href="https://www.python.org/">Python</a>. Clustergrammer produces highly interactive visualizations that enable intuitive exploration of high-dimensional data and has several optional biology-specific features (e.g. enrichment analysis; see <a class="reference internal" href="biology_specific_features.html#biology-specific-features"><span class="std std-ref">Biology-Specific Features</span></a>) to facilitate the exploration of gene-level biological data. The project is free and open-source and can be found on <a class="reference external" href="https://github.com/MaayanLab/clustergrammer">GitHub</a>. Press play or interact with the gene-expression demo below to see some of Clustergrammer&#8217;s interactive features and refer to <a class="reference internal" href="interacting_with_viz.html#interacting-with-viz"><span class="std std-ref">Interacting with the Visualization</span></a> for more information:</p>
 <iframe id='iframe_preview' src="https://amp.pharm.mssm.edu/clustergrammer/demo/" frameBorder="0" style='height: 495px; width:730px; margin-bottom:20px;'></iframe><div class="section" id="using-clustergrammer">
 <h2>Using Clustergrammer<a class="headerlink" href="#using-clustergrammer" title="Permalink to this headline">¶</a></h2>
 <p>The easiest ways to use Clustergrammer to produce an interactive visualization of your data are to:</p>
@@ -201,7 +201,7 @@ <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#introduction-to-clustergrams-heatmaps">Introduction to Clustergrams/Heatmaps</a></li>
+<li class="toctree-l2"><a class="reference internal" href="interacting_with_viz.html#introduction-to-clustergrams">Introduction to Clustergrams</a></li>
 <li class="toctree-l2"><a class="reference internal" href="interacting_with_viz.html#interactive-demo">Interactive Demo</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>

docs/_build_html/interacting_with_viz.html

@@ -88,7 +88,7 @@
 <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="#introduction-to-clustergrams-heatmaps">Introduction to Clustergrams/Heatmaps</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#introduction-to-clustergrams">Introduction to Clustergrams</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#interactive-demo">Interactive Demo</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>
@@ -163,8 +163,8 @@
   <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 visualization benefits enormously from user interaction &#8211; particularly interactions that allow users to explore their data and interactively generate new views. Clustergrammer produces highly interactive heatmaps that enable users to intuitively explore their data and perform complex data transformations. Clustergrammer visualizations are built using the <a class="reference internal" href="clustergrammer_js.html#clustergrammer-js"><span class="std std-ref">Clustergrammer-JS</span></a> library and are consistent across the <a class="reference internal" href="clustergrammer_web.html#clustergrammer-web"><span class="std std-ref">Clustergrammer Web App</span></a> and the <a class="reference internal" href="clustergrammer_widget.html#clustergrammer-widget"><span class="std std-ref">Clustergrammer Jupyter Widget</span></a>. This section will overview heatmaps as a visualization tool and cover Clustergrammer&#8217;s interactive features.</p>
-<div class="section" id="introduction-to-clustergrams-heatmaps">
-<span id="intro-heatmap-clustergram"></span><h2>Introduction to Clustergrams/Heatmaps<a class="headerlink" href="#introduction-to-clustergrams-heatmaps" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="introduction-to-clustergrams">
+<span id="intro-heatmap-clustergram"></span><h2>Introduction to Clustergrams<a class="headerlink" href="#introduction-to-clustergrams" 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/column labels. This type of visualization is commonly referred to as a heatmap or clustergram and this documentation uses these terms interchangeably; refer to <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 use <a class="reference external" href="https://en.wikipedia.org/wiki/Dendrogram">dendrogram trees</a> to depict the hierarchy of row and column clusters produced by <a class="reference external" href="https://en.wikipedia.org/wiki/Hierarchical_clustering">hierarchical clustering</a>.</p>
 <p>Heatmaps are powerful visualization tools that enable 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 data-points (e.g. measured entities) and rows can depict data-dimensions (e.g. measured variables). In this way, heatmaps can visualize thousands of data-points in thousands of dimensions (e.g. data in thousand(s)-dimensional space). However, static heatmaps are of limited use for visualizing large datasets because visualization elements and labels become too small to read. Furthermore, static heatmaps prevent users from interactively exploring their data, e.g. reordering rows/columns. We built Clustergrammer to address these problems and to extend the capabilities of heatmap visualizations.</p>
 </div>

docs/index.rst

@@ -2,7 +2,7 @@
 
 Welcome to Clustergrammer's Documentation!
 ------------------------------------------
-Clustergrammer is a web-based tool for visualizing and analyzing high-dimensional data (e.g. a matrix) as an interactive and shareable hierarchically clustered heatmap (see :ref:`intro_heatmap_clustergram` for more information). Clustergrammer's front end (:ref:`clustergrammer_js`) is built using `D3.js`_ and its back end (:ref:`clustergrammer_py`) is built using `Python`_. Clustergrammer produces highly interactive visualizations that enable intuitive exploration of high-dimensional data and has several optional biology-specific features (e.g. enrichment analysis; see :ref:`biology_specific_features`) to facilitate the exploration of gene-level biological data. The project is free and open-source and can be found on `GitHub`_. Press play or interact with the gene-expression demo below to see some of Clustergrammer's interactive features and refer to :ref:`interacting_with_viz` for more information:
+Clustergrammer is a web-based tool for visualizing and analyzing high-dimensional data as an interactive and shareable hierarchically clustered heatmap (see :ref:`intro_heatmap_clustergram`). Clustergrammer's front end (:ref:`clustergrammer_js`) is built using `D3.js`_ and its back end (:ref:`clustergrammer_py`) is built using `Python`_. Clustergrammer produces highly interactive visualizations that enable intuitive exploration of high-dimensional data and has several optional biology-specific features (e.g. enrichment analysis; see :ref:`biology_specific_features`) to facilitate the exploration of gene-level biological data. The project is free and open-source and can be found on `GitHub`_. Press play or interact with the gene-expression demo below to see some of Clustergrammer's interactive features and refer to :ref:`interacting_with_viz` for more information:
 
 .. raw:: html
 

docs/interacting_with_viz.rst

@@ -6,8 +6,8 @@ Data visualization benefits enormously from user interaction -- particularly int
 
 .. _intro_heatmap_clustergram:
 
-Introduction to Clustergrams/Heatmaps
-=====================================
+Introduction to Clustergrams
+============================
 Clustergrammer visualizes high-dimensional data as a hierarchically clustered matrix with colored tiles (red for positive numbers and blue for negative numbers) and row/column labels. This type of visualization is commonly referred to as a heatmap or clustergram and this documentation uses these terms interchangeably; refer to `Eisen et al., 1998`_ for an early example using biological data. Clustergrams also typically use `dendrogram trees`_ to depict the hierarchy of row and column clusters produced by `hierarchical clustering`_.
 
 Heatmaps are powerful visualization tools that enable 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 data-points (e.g. measured entities) and rows can depict data-dimensions (e.g. measured variables). In this way, heatmaps can visualize thousands of data-points in thousands of dimensions (e.g. data in thousand(s)-dimensional space). However, static heatmaps are of limited use for visualizing large datasets because visualization elements and labels become too small to read. Furthermore, static heatmaps prevent users from interactively exploring their data, e.g. reordering rows/columns. We built Clustergrammer to address these problems and to extend the capabilities of heatmap visualizations.