added link to Giannarelli lab (CyTOF data) and HIMC in homepage

docs/_build_html/_sources/case_studies.rst.txt

@@ -45,7 +45,7 @@ CyTOF Data: Single Cell Immune Response to PMA Treatment
 
   Screenshot from the `Plasma_vs_PMA_Phosphrylation.ipynb`_ Jupyter notebook showing downsampled single cell CyTOF data (K-means downsampled from 220,000 single cells to 2,000 cell-clusters). Cell-clusters are shown as rows with cell-type categories (e.g. Natural Killer cells) and phosphorylations are shown as columns. See the interactive Jupyter notebook `Plasma_vs_PMA_Phosphrylation.ipynb`_ for more information.
 
-White blood cells are a key component of the immune system and kinase signaling is known to play an important role in immune cell function (see `Isakov and Altman 2013`_). Our collaborators at the `Icahn School of Medicine Human Immune Monitoring Core`_ used Mass Cytometry, CyTOF (Fluidigm), to investigate the phosphorylation response of peripheral blood mononuclear cells (PBMC) immune cells exposed to PMA (phorbol 12-myristate 13-acetate), a tumor promoter and activator of protein kinase C (PKC). A total of 28 markers (18 surface markers and 10 phosphorylation markers) were measured in over 200,000 single cells. In the Jupyter notebook `Plasma_vs_PMA_Phosphrylation.ipynb`_ we semi-automatically identify cell types using surface markers and cluster cells based on phosphorylation to identify cell-type specific behavior at the phosphorylation level. See the `Plasma_vs_PMA_Phosphrylation.ipynb`_ Jupyter notebook for more information.
+White blood cells are a key component of the immune system and kinase signaling is known to play an important role in immune cell function (see `Isakov and Altman 2013`_). Our collaborators in the `Giannarelli Lab`_ at the `Icahn School of Medicine Human Immune Monitoring Core`_ used Mass Cytometry, CyTOF (Fluidigm), to investigate the phosphorylation response of peripheral blood mononuclear cells (PBMC) immune cells exposed to PMA (phorbol 12-myristate 13-acetate), a tumor promoter and activator of protein kinase C (PKC). A total of 28 markers (18 surface markers and 10 phosphorylation markers) were measured in over 200,000 single cells. In the Jupyter notebook `Plasma_vs_PMA_Phosphrylation.ipynb`_ we semi-automatically identify cell types using surface markers and cluster cells based on phosphorylation to identify cell-type specific behavior at the phosphorylation level. See the `Plasma_vs_PMA_Phosphrylation.ipynb`_ Jupyter notebook for more information.
 
 Large Network: Kinase Substrate Similarity Network
 ==================================================
@@ -88,6 +88,7 @@ Clustergrammer was used to visualize published single-cell gene expression data:
 
 .. _`Kinase Substrate Similarity Network`: https://maayanlab.github.io/kinase_substrate_similarity_network/
 .. _`MNIST Data`: http://yann.lecun.com/exdb/mnist/
+.. _`Giannarelli Lab`: http://labs.icahn.mssm.edu/giannarellilab/
 .. _`Icahn School of Medicine Human Immune Monitoring Core`: http://icahn.mssm.edu/research/portal/resources/deans-cores/human-immune-monitoring-core
 .. _`Plasma_vs_PMA_Phosphrylation.ipynb`: http://nbviewer.jupyter.org/github/MaayanLab/Cytof_Plasma_PMA/blob/master/notebooks/Plasma_vs_PMA_Phosphorylation.ipynb
 .. _`Isakov and Altman 2013`: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3831523/

docs/_build_html/_sources/index.rst.txt

@@ -2,12 +2,14 @@
 
 Welcome to Clustergrammer's Documentation!
 ------------------------------------------
-Clustergrammer is a web-based tool for visualizing and analyzing high-dimensional data as interactive and shareable hierarchically clustered heatmaps (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:
+Clustergrammer is a web-based tool for visualizing and analyzing high-dimensional data as interactive and shareable hierarchically clustered heatmaps (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`_.
 
 .. raw:: html
 
          <iframe id='iframe_preview' src="https://amp.pharm.mssm.edu/clustergrammer/demo/" frameBorder="0" style='height: 495px; width:730px; margin-bottom:20px;'></iframe>
 
+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.
+
 JupyterCon 2018
 ===============
 
@@ -18,7 +20,7 @@ JupyterCon 2018
          <iframe width="560" height="315" src="https://www.youtube.com/embed/82epZkmfkrE" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
          </div>
 
-Clustergrammer-Widget was recently presented at JupyterCon 2018.
+The Clustergrammer-Widget was recently presented at JupyterCon 2018.
 
 Using Clustergrammer
 ====================
@@ -60,7 +62,7 @@ Fernandez, N. F. et al. Clustergrammer, a web-based heatmap visualization and an
 
 Funding
 =======
-Clustergrammer is being developed by the `Ma'ayan Lab`_ at the `Icahn School of Medicine at Mount Sinai`_ for the `BD2K-LINCS DCIC`_ and the `KMC-IDG`_.
+Clustergrammer is being developed by the `Ma'ayan Lab`_ and the `Human Immune Monitoring Center`_ at the `Icahn School of Medicine at Mount Sinai`_ for the `BD2K-LINCS DCIC`_ and the `KMC-IDG`_.
 
 Contents:
 =========
@@ -110,6 +112,7 @@ Contents:
 .. _`USDA Nutrient Dataset`: http://nbviewer.jupyter.org/github/MaayanLab/USDA_Nutrients_Viz/blob/master/USDA_Nutrients.ipynb
 
 .. _`Ma'ayan Lab`: http://labs.icahn.mssm.edu/maayanlab/
+.. _`Human Immune Monitoring Center`: https://icahn.mssm.edu/research/human-immune-monitoring-center
 .. _`Icahn School of Medicine at Mount Sinai`: http://icahn.mssm.edu/
 .. _`BD2K-LINCS DCIC`: http://lincs-dcic.org/
 .. _`KMC-IDG`: http://commonfund.nih.gov/idg/overview

docs/_build_html/case_studies.html

@@ -199,7 +199,7 @@ <h2>CyTOF Data: Single Cell Immune Response to PMA Treatment<a class="headerlink
 <a class="reference external image-reference" href="http://nbviewer.jupyter.org/github/MaayanLab/Cytof_Plasma_PMA/blob/master/notebooks/Plasma_vs_PMA_Phosphorylation.ipynb"><img alt="CyTOF Screenshot" src="_images/CyTOF_screenshot.png" style="width: 450px;" /></a>
 <p class="caption"><span class="caption-text">Screenshot from the <a class="reference external" href="http://nbviewer.jupyter.org/github/MaayanLab/Cytof_Plasma_PMA/blob/master/notebooks/Plasma_vs_PMA_Phosphorylation.ipynb">Plasma_vs_PMA_Phosphrylation.ipynb</a> Jupyter notebook showing downsampled single cell CyTOF data (K-means downsampled from 220,000 single cells to 2,000 cell-clusters). Cell-clusters are shown as rows with cell-type categories (e.g. Natural Killer cells) and phosphorylations are shown as columns. See the interactive Jupyter notebook <a class="reference external" href="http://nbviewer.jupyter.org/github/MaayanLab/Cytof_Plasma_PMA/blob/master/notebooks/Plasma_vs_PMA_Phosphorylation.ipynb">Plasma_vs_PMA_Phosphrylation.ipynb</a> for more information.</span></p>
 </div>
-<p>White blood cells are a key component of the immune system and kinase signaling is known to play an important role in immune cell function (see <a class="reference external" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3831523/">Isakov and Altman 2013</a>). Our collaborators at the <a class="reference external" href="http://icahn.mssm.edu/research/portal/resources/deans-cores/human-immune-monitoring-core">Icahn School of Medicine Human Immune Monitoring Core</a> used Mass Cytometry, CyTOF (Fluidigm), to investigate the phosphorylation response of peripheral blood mononuclear cells (PBMC) immune cells exposed to PMA (phorbol 12-myristate 13-acetate), a tumor promoter and activator of protein kinase C (PKC). A total of 28 markers (18 surface markers and 10 phosphorylation markers) were measured in over 200,000 single cells. In the Jupyter notebook <a class="reference external" href="http://nbviewer.jupyter.org/github/MaayanLab/Cytof_Plasma_PMA/blob/master/notebooks/Plasma_vs_PMA_Phosphorylation.ipynb">Plasma_vs_PMA_Phosphrylation.ipynb</a> we semi-automatically identify cell types using surface markers and cluster cells based on phosphorylation to identify cell-type specific behavior at the phosphorylation level. See the <a class="reference external" href="http://nbviewer.jupyter.org/github/MaayanLab/Cytof_Plasma_PMA/blob/master/notebooks/Plasma_vs_PMA_Phosphorylation.ipynb">Plasma_vs_PMA_Phosphrylation.ipynb</a> Jupyter notebook for more information.</p>
+<p>White blood cells are a key component of the immune system and kinase signaling is known to play an important role in immune cell function (see <a class="reference external" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3831523/">Isakov and Altman 2013</a>). Our collaborators in the <a class="reference external" href="http://labs.icahn.mssm.edu/giannarellilab/">Giannarelli Lab</a> at the <a class="reference external" href="http://icahn.mssm.edu/research/portal/resources/deans-cores/human-immune-monitoring-core">Icahn School of Medicine Human Immune Monitoring Core</a> used Mass Cytometry, CyTOF (Fluidigm), to investigate the phosphorylation response of peripheral blood mononuclear cells (PBMC) immune cells exposed to PMA (phorbol 12-myristate 13-acetate), a tumor promoter and activator of protein kinase C (PKC). A total of 28 markers (18 surface markers and 10 phosphorylation markers) were measured in over 200,000 single cells. In the Jupyter notebook <a class="reference external" href="http://nbviewer.jupyter.org/github/MaayanLab/Cytof_Plasma_PMA/blob/master/notebooks/Plasma_vs_PMA_Phosphorylation.ipynb">Plasma_vs_PMA_Phosphrylation.ipynb</a> we semi-automatically identify cell types using surface markers and cluster cells based on phosphorylation to identify cell-type specific behavior at the phosphorylation level. See the <a class="reference external" href="http://nbviewer.jupyter.org/github/MaayanLab/Cytof_Plasma_PMA/blob/master/notebooks/Plasma_vs_PMA_Phosphorylation.ipynb">Plasma_vs_PMA_Phosphrylation.ipynb</a> Jupyter notebook for more information.</p>
 </div>
 <div class="section" id="large-network-kinase-substrate-similarity-network">
 <h2>Large Network: Kinase Substrate Similarity Network<a class="headerlink" href="#large-network-kinase-substrate-similarity-network" title="Permalink to this headline">¶</a></h2>

docs/_build_html/index.html

@@ -161,13 +161,14 @@
 
   <div class="section" id="welcome-to-clustergrammer-s-documentation">
 <h1>Welcome to Clustergrammer’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 as interactive and shareable hierarchically clustered heatmaps (see <a class="reference internal" href="interacting_with_viz.html#intro-heatmap-clustergram"><span class="std std-ref">Introduction to Clustergrams</span></a>). Clustergrammer’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’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="jupytercon-2018">
+<p>Clustergrammer is a web-based tool for visualizing and analyzing high-dimensional data as interactive and shareable hierarchically clustered heatmaps (see <a class="reference internal" href="interacting_with_viz.html#intro-heatmap-clustergram"><span class="std std-ref">Introduction to Clustergrams</span></a>). Clustergrammer’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>.</p>
+<iframe id='iframe_preview' src="https://amp.pharm.mssm.edu/clustergrammer/demo/" frameBorder="0" style='height: 495px; width:730px; margin-bottom:20px;'></iframe><p>Press play or interact with the gene-expression demo below to see some of Clustergrammer’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>
+<div class="section" id="jupytercon-2018">
 <h2>JupyterCon 2018<a class="headerlink" href="#jupytercon-2018" title="Permalink to this headline">¶</a></h2>
 <div style="position: relative; padding-bottom: 10px; height: 0; overflow: hidden; max-width: 100%; height: auto;">
 
 <iframe width="560" height="315" src="https://www.youtube.com/embed/82epZkmfkrE" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
-</div><p>Clustergrammer-Widget was recently presented at JupyterCon 2018.</p>
+</div><p>The Clustergrammer-Widget was recently presented at JupyterCon 2018.</p>
 </div>
 <div class="section" id="using-clustergrammer">
 <h2>Using Clustergrammer<a class="headerlink" href="#using-clustergrammer" title="Permalink to this headline">¶</a></h2>
@@ -204,7 +205,7 @@ <h2>Citing Clustergrammer<a class="headerlink" href="#citing-clustergrammer" tit
 </div>
 <div class="section" id="funding">
 <h2>Funding<a class="headerlink" href="#funding" title="Permalink to this headline">¶</a></h2>
-<p>Clustergrammer is being developed by the <a class="reference external" href="http://labs.icahn.mssm.edu/maayanlab/">Ma’ayan Lab</a> at the <a class="reference external" href="http://icahn.mssm.edu/">Icahn School of Medicine at Mount Sinai</a> for the <a class="reference external" href="http://lincs-dcic.org/">BD2K-LINCS DCIC</a> and the <a class="reference external" href="http://commonfund.nih.gov/idg/overview">KMC-IDG</a>.</p>
+<p>Clustergrammer is being developed by the <a class="reference external" href="http://labs.icahn.mssm.edu/maayanlab/">Ma’ayan Lab</a> and the <a class="reference external" href="https://icahn.mssm.edu/research/human-immune-monitoring-center">Human Immune Monitoring Center</a> at the <a class="reference external" href="http://icahn.mssm.edu/">Icahn School of Medicine at Mount Sinai</a> for the <a class="reference external" href="http://lincs-dcic.org/">BD2K-LINCS DCIC</a> and the <a class="reference external" href="http://commonfund.nih.gov/idg/overview">KMC-IDG</a>.</p>
 </div>
 <div class="section" id="contents">
 <h2>Contents:<a class="headerlink" href="#contents" title="Permalink to this headline">¶</a></h2>