Python package release 0.9.5

Fixed:

• If pandas >= 2.0 was installed, constructing a DiscoverMatrix object gave rise to "AttributeError: 'DataFrame' object has no attribute '__array_wrap__'".

R package release 0.9.4

Added:

• The new fdr.method argument of the pairwise.discover.test function selects the false discovery rate estimation method used for multiple testing correction. Its default value selects a Benjamini-Hochberg procedure adapted for discrete test statistics. As an alternative, the standard Benjamini-Hochberg procedure can be selected. While the latter is much faster, it is also more conservative than the discrete version. The discrete method was always used in previous versions and is still the recommended choice.

Changed:

• Speed improvements in discover.matrix and pairwise.discover.test. discover.matrix finishes instantaneously for small to medium-sized data sets and takes only a few seconds for very large data sets. pairwise.discover.test (with discrete Benjamini-Hochberg) is about twice as fast as in previous versions.
• The matrixStats package is no longer a dependency.

Fixed:

• Re-enabled OpenMP, which distributes the computations in pairwise.discover.test across multiple CPUs. OpenMP was disabled since R 4.0.
• Fixed the underlying Fortran code to make it compile with GNU Fortran >= 10. Due to these changes the minimum supported GNU Fortran version is now version 5.

Python package release 0.9.4

Added:

• The new fdr_method argument of the pairwise_discover_test function selects the false discovery rate estimation method used for multiple testing correction. Its default value selects a Benjamini-Hochberg procedure adapted for discrete test statistics. As an alternative, the standard Benjamini-Hochberg procedure can be selected. While the latter is much faster, it is also more conservative than the discrete version. The discrete method was always used in previous versions and is still the recommended choice.

Changed:

• Speed improvements in DiscoverMatrix and pairwise_discover_test. DiscoverMatrix finishes instantaneously for small to medium-sized data sets and takes only a few seconds for very large data sets. pairwise_discover_test (with discrete Benjamini-Hochberg) is about twice as fast as in previous versions.
• Python 2.7 is no longer supported.

Fixed:

• If pandas >= 1.0 was installed, subsetting a DiscoverMatrix object gave rise to "AttributeError: 'DataFrame' objects has no attribute 'ix'".
• Fixed the underlying Fortran code to make it compile with GNU Fortran >= 10. Due to these changes the minimum supported GNU Fortran version is now version 5.

v0.9

This release was used for the analyses published in the paper 'A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co-occurrence'.