improved case studies

docs/_build_html/.buildinfo

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docs/_build_html/_sources/case_studies.txt

@@ -1,21 +1,31 @@
 .. _case_studies:
 
-Case Studies
-------------
-Clustergrammer was built to visualize biological data but is applicable for visualizing high-dimensional data in general. Below are links to several example use cases for Clustergrammer:
+Case Studies and Examples
+-------------------------
+Clustergrammer was built to visualize biological data but is generally applicable for visualizing high-dimensional data. Below are links to several example case studies and examples using Clustergrammer:
 
 Cancer Cell Line Encyclopedia Gene Expression Data
 ==================================================
-`CCLE Explorer`_
+The Cancer Cell Line Encyclopedia (`CCLE`_) is a publically available project that characterized (e.g. genetic characterization) over one thousand cancer cell lines. We used Clustergrammer to re-analyze and visualize CCLE's gene expression data in the `CCLE Explorer`_. The CCLE Explorer allows users to explore the CCLE by tissue type and visualize the most commonly differentially expressed genes for each tissue type as an interactive heatmap.
 
+Zika Virus RNA-seq Data Visualization
+=====================================
+`Zika Virus RNA-seq Data Visualization`_
 
-- `Zika Virus RNA-seq Data Visualization`_
-- `Single Cell RNA-seq Data Visualization`_
-- `Iris flower dataset`_
-- `MNIST Handwritten Digit Dataset`_
+Single Cell RNA-seq Data Visualization
+======================================
+`Single Cell RNA-seq Data Visualization`_
+
+Machine Learning Datasets
+=========================
+
+`Iris flower dataset`_
+
+`MNIST Handwritten Digit Dataset`_
 
 .. _`CCLE Explorer`: http://amp.pharm.mssm.edu/clustergrammer/CCLE/
 .. _`Zika Virus RNA-seq Data Visualization`: http://nbviewer.jupyter.org/github/maayanlab/Zika-RNAseq-Pipeline/blob/master/Zika.ipynb
 .. _`Iris flower dataset`: http://nbviewer.jupyter.org/github/MaayanLab/iris_clustergrammer_visualization/blob/master/Iris%20Dataset.ipynb
 .. _`MNIST Handwritten Digit Dataset`: https://maayanlab.github.io/MNIST_heatmaps/
-.. _`Single Cell RNA-seq Data Visualization`: http://nbviewer.jupyter.org/github/MaayanLab/single_cell_RNAseq_Visualization/blob/master/Single%20Cell%20RNAseq%20Visualization%20Example.ipynb
+.. _`Single Cell RNA-seq Data Visualization`: http://nbviewer.jupyter.org/github/MaayanLab/single_cell_RNAseq_Visualization/blob/master/Single%20Cell%20RNAseq%20Visualization%20Example.ipynb
+.. _`CCLE`: https://portals.broadinstitute.org/ccle/home

docs/_build_html/_sources/clustergrammer_js.txt

@@ -102,14 +102,61 @@ Clustergrammer-JS API
 
   Clustergrammer's attributes and functions are listed below:
 
+  .. js:attribute:: params
+
+    The Clustergrammer parameters object, which contains all the parameters necessary to generate the visualization.
+
   .. js:function:: update_cats(row_data)
 
     Update the visualization row categories.
 
-    :param row_data: The row category data that will be used to ...
+    :param row_data: Row category data.
+
+  .. js:function:: reset_cats()
+
+    Reset the row categories to their original state.
+
+  .. js:function:: resize_viz:
+
+    Call this function to resize the visualization to fit in its resized container (if the user has resized the container).
+
+  .. js:function:: d3_tip_custom
+
+    Generate a D3 tooltip for SVG elements.
+
+  .. js:function:: update_view(filter_type, inst_state)
+
+    Update the heatmap with a specified row filter 'view'.
+
+    :param filter_type: The available filter types sum or variance: e.g. N_row_sum, N_row_var
+
+    :param inst_state: The value of the row filter, e.g. 500
+
+  .. js:function:: filter_viz_using_names(names)
+
+    Update the visualization to show the row and column names specified in the names object.
+
+    :param names: Object with ``row`` and ``col`` attributes that specify the row and column names that will be visible after updating.
+
+  .. js:function:: filter_viz_using_names(nodes)
+
+    Update the visualization to show the row and column names specified in the nodes object.
+
+    :param names: Object with ``row`` and ``col`` attributes that specify the row and column nodes that will be visible after updating.
+
+  .. js:function:: zoom(pan_x, pan_y, zoom)
+
+    Zoom and pan into the visualization.
+
+    :param pan_x: Panning in the `x` direction
+
+    :param pan_y: Panning in the `y` direction
+
+    :param zoom: The zoom level applied to the visualization.
 
-    :param resize_viz: Call this function to resize the visualization to fit in its resized container (if the user has resized the container).
+  .. js:function:: export_matrix()
 
+    Save the current matrix (e.g. after cropping) as a tab-separated-file.
 
 
 .. _visualization_json:

docs/_build_html/_sources/interacting_with_viz.txt

@@ -2,13 +2,13 @@
 
 Interacting with the Visualization
 ----------------------------------
-Data visualizations benefit enormously from user interaction that allow users to explore their data and interactively generate new views. Clustergrammer produces highly interactive heatmaps that enable users to intuitively explore and perform complex transformations on their data. Clustergrammer visualizations are built using the :ref:`clustergrammer_js` library and are consistent across the :ref:`clustergrammer_web` and the :ref:`clustergrammer_widget`. This section will cover the types of interactions that are available to the user.
+Data visualizations benefit enormously from user interactions that allow users to explore their data and interactively generate new views. Clustergrammer produces highly interactive heatmaps that enable users to intuitively explore and perform complex transformations on their data. Clustergrammer visualizations are built using the :ref:`clustergrammer_js` library and are consistent across the :ref:`clustergrammer_web` and the :ref:`clustergrammer_widget`. This section will cover the types of interactions that are available to the user.
 
 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/column labels, which is commonly referred to as a heatmap or clustergram (this documentation uses the terms 'heatmap' and 'clustergram' interchangeably; see `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 visualizations 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. in thousand-dimensional space). However, static heatmaps are of limited use for visualizing large datasets (e.g. for large matrices, visualization elements and labels become too small to read). Furthermore, static heatmaps prevent users from interactively exploring their data, e.g. reordering rows/columns.
+Heatmaps are powerful visualizations 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. in thousand-dimensional space). However, static heatmaps are of limited use for visualizing large datasets (e.g. for large matrices, 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, in part, to address these issues.
 
 .. _interactive_demo:
 

docs/_build_html/_static/basic.css

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