Many of our matrices are log normalized, which tends to eliminate
the number of non zero values (if there were any). This prevents
the matrix from being stored as a sparse matrix. The solution here
is to use a simple transformation to make it sparse again. The most
common value from each column is subtracted from that column. These
values that were subtracted are saved in an array called X_col_shift.
The cellxgene code needs to understand how to undo the transformation when
operating over the X matrix.
- added script to create a synthetic dataset for testing
- added a script to convert an existing CXG dataset to a sparse CXG dataset
Support for sparse tiledb arrays for the X matrix
1. cxgtool can now output sparse matrices
2. cxg_adaptor and diffexp_cxg updated to handle sparse matrices
3. added a test in test_diffexp to test sparse diffexp and get_X_array
* Add user-defined category-label colors
Fixes https://github.com/chanzuckerberg/cellxgene/issues/1152
As described in https://github.com/chanzuckerberg/cellxgene/issues/1307
* Respond to feedback from @bkmartinjr in nodejs
* Respond to feedback from @bkmartinjr in python
* Add tests to the server module
* Autoformat python, run linter
* Make colors_get error handling specific
* Respond to feedback from @bkmartinjr
* Respond to feedback from @bkmartinjr
* Fix whitespace
* Fix python lint errrors
* Update documentation
* Add --disable-user-colors option to launch and cxgtool.py
* Fix python formatting
* Rename '--disable-user-colors' to '--disable-custom-colors'
* Add user-generated annotations tests to the server
Partially completes https://github.com/chanzuckerberg/cellxgene/issues/969
* Auto-format python code
* @skip_if: passing lambdas > than property strings
* Respond to feedback from @bkmartinjr
