Installing Python libraries

To run SPRING Viewer locally, make sure Python 2.7 is installed (and that it's your active version). You will also need the following Python libraries:

scikit-learn
numpy
scipy
h5py
networkx
fa2

We recommend Anaconda to manage your Python libraries. You can download it here (be sure to get the Python 2.7 version): https://conda.io/miniconda.html. Libraries can then be installed using the command conda. To do so, open Terminal (Mac) or Anaconda Prompt (Windows) and enter:

conda install scikit-learn numpy scipy h5py

The remaining libraries can be installed using pip. Note that if you're a Windows user, you'll first need to install Microsoft Visual C++ compiler for Python (available from http://aka.ms/vcpython27). Enter the following into Terminal or Anaconda Prompt:

pip install networkx fa2

Setting up a SPRING data directory

See the example notebook.

A SPRING data set consist of a main directory and any number of subdirectories, with each subdirectory corresponding to one SPRING plot (i.e. subplot) that draws on a data matrix stored in the main directory. The main directory should have the following files, as well as one subdirectory for each SPRING plot.

counts_norm.npz
counts_norm_sparse_cells.hdf5
counts_norm_sparse_genes.hdf5
genes.txt

Each subdirectory should contain:

categorical_coloring_data.json
cell_filter.npy
cell_filter.txt
color_data_gene_sets.csv
color_stats.json
coordinates.txt
edges.csv
graph_data.json
run_info.json

Place the main directory somehwere inside folder that contains this README and the other SPRING file. We recommend that you create a special datasets directory. For example, if you have a main data set called human_bone_marrow and another called frog_embryo, you could place them in ./datasets/human_bone_marrow/ and ./datasets/frog_embryo/.

Running SPRING Viewer

1. Open Terminal (Mac) or Anaconda Prompt (Windows) and change directories (cd) to the directory containing this README file (SPRING_dev/).
2. Start a local server by entering the following: python -m CGIHTTPServer 8000
3. Open web browser (preferably Chrome; best to use incognito mode to ensure no cached data is used).
4. View data set by navigating to corresponding URL: http://localhost:8000/springViewer_1_6_dev.html?path_to/main/subplot. In the example above, if you wanted to view a SPRING plot called HSC in the main directory human_bone_marrow, then you would navigate to http://localhost:8000/springViewer_1_6_dev.html?datasets/human_bone_marrow/HSC