cleaning cytof example

docs/_build_html/_sources/case_studies.rst.txt

@@ -60,8 +60,8 @@ Lung cancer is a complex disease that is known to be regulated at the post-trans
 - Identify co-regulated clusters of PTMs/genes in distinct lung cancer cell line subtypes
 - Perform enrichment analysis to understand the biological processes involved in PTM/expression clusters
 
-CyTOF Data: Single Cell Immune Response to PMA Treatment
-========================================================
+Single Cell CyTOF Dataset
+==========================
 
 .. figure:: _static/CyTOF_screenshot.png
   :width: 450px
@@ -71,7 +71,7 @@ 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 in the `Giannarelli Lab`_ and 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.
+Our collaborators in the `Giannarelli Lab`_ and 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
 ==================================================
@@ -109,7 +109,6 @@ These examples demonstrate the generality of heatmap visualizations and enable u
 .. _`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/
 .. _`CST_Data_Viz.ipynb`: http://nbviewer.jupyter.org/github/MaayanLab/CST_Lung_Cancer_Viz/blob/master/notebooks/CST_Data_Viz.ipynb?flush_cache=true
 .. _`Cell Signaling Technology Inc`: https://www.cellsignal.com/
 .. _`CCLE Explorer`: http://amp.pharm.mssm.edu/clustergrammer/CCLE/

docs/_build_html/case_studies.html

@@ -93,7 +93,7 @@
 </li>
 <li class="toctree-l2"><a class="reference internal" href="#cancer-cell-line-encyclopedia-gene-expression-data">Cancer Cell Line Encyclopedia Gene Expression Data</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#lung-cancer-post-translational-modification-and-gene-expression-regulation">Lung Cancer Post-Translational Modification and Gene Expression Regulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#cytof-data-single-cell-immune-response-to-pma-treatment">CyTOF Data: Single Cell Immune Response to PMA Treatment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#single-cell-cytof-dataset">Single Cell CyTOF Dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#large-network-kinase-substrate-similarity-network">Large Network: Kinase Substrate Similarity Network</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#machine-learning-and-miscellaneous-datasets">Machine Learning and Miscellaneous Datasets</a></li>
 </ul>
@@ -222,13 +222,13 @@ <h2>Lung Cancer Post-Translational Modification and Gene Expression Regulation<a
 <li>Perform enrichment analysis to understand the biological processes involved in PTM/expression clusters</li>
 </ul>
 </div>
-<div class="section" id="cytof-data-single-cell-immune-response-to-pma-treatment">
-<h2>CyTOF Data: Single Cell Immune Response to PMA Treatment<a class="headerlink" href="#cytof-data-single-cell-immune-response-to-pma-treatment" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="single-cell-cytof-dataset">
+<h2>Single Cell CyTOF Dataset<a class="headerlink" href="#single-cell-cytof-dataset" title="Permalink to this headline">¶</a></h2>
 <div class="figure align-left" id="id3">
 <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 in the <a class="reference external" href="http://labs.icahn.mssm.edu/giannarellilab/">Giannarelli Lab</a> and 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>Our collaborators in the <a class="reference external" href="http://labs.icahn.mssm.edu/giannarellilab/">Giannarelli Lab</a> and 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

@@ -243,7 +243,7 @@ <h2>Contents:<a class="headerlink" href="#contents" title="Permalink to this hea
 <li class="toctree-l2"><a class="reference internal" href="case_studies.html#single-cell-gene-expression-2-700-pbmc">Single Cell Gene Expression 2,700 PBMC</a></li>
 <li class="toctree-l2"><a class="reference internal" href="case_studies.html#cancer-cell-line-encyclopedia-gene-expression-data">Cancer Cell Line Encyclopedia Gene Expression Data</a></li>
 <li class="toctree-l2"><a class="reference internal" href="case_studies.html#lung-cancer-post-translational-modification-and-gene-expression-regulation">Lung Cancer Post-Translational Modification and Gene Expression Regulation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="case_studies.html#cytof-data-single-cell-immune-response-to-pma-treatment">CyTOF Data: Single Cell Immune Response to PMA Treatment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="case_studies.html#single-cell-cytof-dataset">Single Cell CyTOF Dataset</a></li>
 <li class="toctree-l2"><a class="reference internal" href="case_studies.html#large-network-kinase-substrate-similarity-network">Large Network: Kinase Substrate Similarity Network</a></li>
 <li class="toctree-l2"><a class="reference internal" href="case_studies.html#machine-learning-and-miscellaneous-datasets">Machine Learning and Miscellaneous Datasets</a></li>
 </ul>

docs/case_studies.rst

@@ -60,8 +60,8 @@ Lung cancer is a complex disease that is known to be regulated at the post-trans
 - Identify co-regulated clusters of PTMs/genes in distinct lung cancer cell line subtypes
 - Perform enrichment analysis to understand the biological processes involved in PTM/expression clusters
 
-CyTOF Data: Single Cell Immune Response to PMA Treatment
-========================================================
+Single Cell CyTOF Dataset
+==========================
 
 .. figure:: _static/CyTOF_screenshot.png
   :width: 450px
@@ -71,7 +71,7 @@ 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 in the `Giannarelli Lab`_ and 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.
+Our collaborators in the `Giannarelli Lab`_ and 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
 ==================================================
@@ -109,7 +109,6 @@ These examples demonstrate the generality of heatmap visualizations and enable u
 .. _`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/
 .. _`CST_Data_Viz.ipynb`: http://nbviewer.jupyter.org/github/MaayanLab/CST_Lung_Cancer_Viz/blob/master/notebooks/CST_Data_Viz.ipynb?flush_cache=true
 .. _`Cell Signaling Technology Inc`: https://www.cellsignal.com/
 .. _`CCLE Explorer`: http://amp.pharm.mssm.edu/clustergrammer/CCLE/