Skip to content

changelog.md

Latest commit

 

History

History
52 lines (43 loc) · 3.23 KB

changelog.md

File metadata and controls

52 lines (43 loc) · 3.23 KB

Changelog

Version 0.5.0rc2

  • For better compatibility with scanpy, lower bounds on dependencies have been relaxed.

Version 0.5.0rc1

This version improves compatibility with scanpy:

  • Added n_genes arguments allowing to return only the top N genes per group when return_as_scanpy=True. This allowed to match scanpy's sorting method (partial sort) resulting in better reproducibility of scanpy results.
  • Fixed genes ordering in the scanpy formatter, by removing redundant sorting of perturbation names as encode_and_count_groups already returns sorted unique perturbation names. This ensures that gene names are sorted the same way everywhere.
  • Added explicit testing of genes ordering. In the PBMC dataset, lots of genes end up with identical z-scores but different logfoldchanges. This was not caught by previous tests.
  • Fold change is now computed with (numerator + 1.e-9) / (denominator + 1.e-9) to avoid division by zero, and to be more consistent with scanpy's implementation. This has no effect on the ranking of genes, but allows to get finite fold change values for all genes.

It also includes some performance improvements:

  • Improved CSR chunking mechanism for the OVO test, resulting in faster execution and much smaller memory footprint. A direct implication is that batch_size can grow much larger now.
    • On TAHOE's plate3 (in RAM) with batch_size=1024, this reduced memory footprint from 35GB to 1.5GB, and runtime from 1:17 to 0:50 with 8 CPUs.
    • The reduced footprint allows to scale more aggressively n_threads. With 32 threads, TAHOE's plate3 runs in 21 seconds, while eating only 2.5GB of RAM.

Also, it adds support for OVO test on lazy CSR (h5-based) datasets, through a specific parallelization scenario where groups are processed one by one.

Version 0.4.0

  • Added option to return scanpy-friendly output with return_as_scanpy arg. asymptotic_wilcoxon returns either:
    • A pandas.DataFrame with columns feature, p_value, fold_change, and statistic (default), if return_as_scanpy=False
    • A dictionary containing the same keys as scanpy.tl.rank_genes_groups, if return_as_scanpy=True. Similarly as scanpy, genes are ordered by decreasing z-score.
  • Improved the batching mechanism, fixed the 'auto' mode that was excluding the very last gene in previous versions.

Version 0.3.0

  • Rust backend is available for all tests. Compare Rust vs Numba with poetry run pytest-benchmark compare 0003 0005:
    • CSR OVO approx 20% faster
    • CSR OVR approx 80% faster
    • Dense OVO approx 70% faster
    • Dense OVR approx 100% faster (twice faster)
  • Moved results allocation into thread operator for both Numba and Rust
    • Compare before/after with poetry run pytest-benchmark compare 0003 0008, approx 15% speedup on 8 threads.
    • Enables better scaling to larger machines: 32 threads is approximately 27 times faster than 1 thread.

Version 0.2.0

  • H5-based, disk-backed, CSC and dense datasets are now supported natively.
  • Non tie-corrected tests are now supported as well.

Version 0.1.1

  • Changed reference_group to reference for better transparence with the scanpy API.

Version 0.1.0

First version