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

server/data_common/data_adaptor.py

@@ -241,8 +241,7 @@ class DataAdaptor(metaclass=ABCMeta):
         duplicate_columns = list(set(labels_df.columns) & set(obs_columns))
         if len(duplicate_columns) > 0:
             raise KeyError(
-                "Labels file may not contain column names which overlap "
-                f"with h5ad obs columns {duplicate_columns}"
+                "Labels file may not contain column names which overlap " f"with h5ad obs columns {duplicate_columns}"
             )
 
         # labels must have same count as obs annotations