bmccandless 76523d4f32 sparse column shift encoding. (#1502) ...

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

2020-06-02 08:23:52 -07:00

..

__init__.py

server refactor (#1140)

2020-02-19 10:22:35 -08:00

diffexp_cxg.py

sparse column shift encoding. (#1502)

2020-06-02 08:23:52 -07:00

diffexp_generic.py

Specialize diffexp for tiledb (#1384)

2020-04-12 09:56:55 -07:00

scanpy.py

tighten up error reporting (#1269)

2020-03-22 09:55:47 -07:00