Skip to content

Latest commit

 

History

History
223 lines (146 loc) · 7.26 KB

CHANGELOG.md

File metadata and controls

223 lines (146 loc) · 7.26 KB
Raw

v0.0.12 (2024-10-01)

Bug fixes

  • Joint-RPCA re-centering bug.
    • see issue #97
  • Updated for the newest version of Pandas.
    • see issue #96
  • Reduce unecessary numpy warnings regarding log on unseen data.
    • see issue #78
  • Fix filter and ordering with TEMPTED
    • see issue #92 and issue #93

v0.0.11 (2024-06-24)

Bug fixes

  • Passes non-centered tables if non-centered flag is passed.
    • see issue #82
  • setup.py packages html files so visualization commands works from pip install.
    • see issue #83
    • see issue #87
  • Passes no filtering to the QIIME2/API TEMPTED command since filtering and transformation should already be done.
    • see issue #90

v0.0.10 (2023-12-01)

Bug fixes

  • Removed np.int types
    • fixes bug that prevents use in newer versions of numpy / QIIME2
      • see issue #71

Features [experimental]

  • rpca projection of new data and with cross validation
    • A new function for RPCA where new data can be projected into an existing ordination.
    • Also allows for internal CV, where the hold out data is projected into the training ordination, which is then used to re-build the test data, and the error between the projection and the real test data is calculated.
      • see issue #70
  • tempted.py and assocated tests/commands/tutorials
    • Added in a python implementation TEMPTED, more details on the methods inm this paper here.
  • joint-rpca and associated tests/commands/tutorials
    • Joint-RPCA is an extention of RPCA for multiple omics types.
  • qc_rarefaction
    • This function compares the mantel correlation of distances to the abs. differences in sample sum between rarefied and unrarefied input data. This is an easy check to ensure the results are not bieng significantly altered by non-rarefaction is cases of large differences (e.g., low-biomass) or where the sample sum differences can not be seperated from the phenotypes/low-rank structure of the data (i.e., deep sequencing of controls and shallow of sick).
      • see issue #70

Deprecated functionality

  • auto_rpca across the package and rank_estimate in optspace.py
    • was not performing correctly and no reasonable quick fix is available.
      • see issue #70
    • Change from auto_rpca to gemelli.rpca import rpca

v0.0.9 (2023-05-24)

Bug fixes

  • Fixes in preprocessing.py function
    • fixes bug that can drastically change taxonomic assigmennt
      • see issue #66

v0.0.8 (2022-03-28)

Bug fixes

  • Fixes in rpca.py function
    • fixes bug that passes QIIME2 Metadata instead of the expected pandas dataframe
      • see issue #57

v0.0.7 (2022-01-19)

Features [stable]

Features [experimental]

  • Phylogenetic RCLR and integration into RPCA/CTF
    • added phylogenetic weighting in rclr in preprocessing.py
    • added commands in scripts and q2 to perform these features with and without taxonomy
    • added tutorials in ipynb/tutorials to demonstrate the commands/workflows

Bug fixes

  • Fixes in optspace.py function rank_estimate and factorization.py function tenals
  • Fixes in preprocessing.py
    • fixes when the ordering/structure of the tensor could impact the rlcr results by sorting to ensure reproducibility.
    • added tests to ensure sorting of samples/features does not impact results.

v0.0.6 (2020-09-27)

Features

  • Robust Aitchison PCA
    • with manually chosen n_components
    • with auto chosen n_components
    • associated tests added for function, standalone, & QIIME2
  • Robust centered log-ratio (rclr) transformation
    • allows users to use the transform alone outside CTF/RPCA
    • associated tests added and extra tests for rclr added
  • Tutorials
    • Cross-sectional for RPCA (standalone & QIIME2)
    • Repeated measure for CTF (standalone & QIIME2)

Miscellaneous

  • auto_rpca rank estimation min rank now 3
  • added citations for CTF & RPCA (linked when appropriate)
  • updated the function descriptions

v0.0.5 (2019-06-12)

Features

  • The output of the standalone and QIIME2 -CLI now return a state ordination
    • Note that only the standalone can handle more than one state due to the lack of lists in QIIME2

Bug fixes

  • Fixes in _transformer.py and ctf.py to utilize QIIME2 metadata passing, this fixes the dtype bool dropping issues in metadata
  • Removed imports of QIIME2 metadata in ctf.py, this will allow the standalone commandline to run without having QIIME2 installed.
  • Update pandas future error of .loc[new_index, :] to .reindex(new_index)
  • Centered the subject biplot
  • Added flake8 testing to travis

v0.0.4 (2019-06-12)

Features

  • Tensor Building and RCLR transformation in preprocessing.rclr and preprocessing.build
    • N-mode tensor building and transformation
    • Mean of counts for subject-conditional pairs with several samples

Backward-incompatible changes [stable]

  • In preprocessing.build:
    • pervious -> current
    • build().sample_order -> build().subject_order
    • build().temporal_order -> build().condition_orders
      • as a list for N possible condition(s)
    • build().tensor -> build().counts

Backward-incompatible changes [experimental]

Performance enhancements

  • tensor building and transformation

Bug fixes

  • line 369-360 in factorization.tenals causes np.nan(s) in solution

    • fixed by added pseudocount if any nan in solution

  • line 178-179 in factorization.TenAls

    • was previously checking if all missing/zero not if there were no missing/zero as intended

Deprecated functionality [stable]

  • In preprocessing.rclr and preprocessing.build:
    • build().transform() -> preprocessing.rclr as standalone function

Deprecated functionality [experimental]

Miscellaneous

  • line 175 in factorization.TenAls to send ValueError if input is not numpy array

v0.0.3 (2019-05-17)

Features

  • Tensor factorization in factorization.tenals and factorization.TenALS

    • Accomplished by turning hard-coded operations into for loops and generalizing vectorization using tensor contractions

  • Khatri Rao Product

Backward-incompatible changes [stable]

Backward-incompatible changes [experimental]

  • factorization.tenals returns a list of matrices loadings instead of u1, u2, u3 tuple, along with other arguments

Performance enhancements

  • tensor contraction

Bug fixes

Deprecated functionality [stable]

Deprecated functionality [experimental]

Miscellaneous

  • various type and spacing fixes

Original "working" code (v0.0.1-2)

Features

Backward-incompatible changes [stable]

Backward-incompatible changes [experimental]

Performance enhancements

Bug fixes

Deprecated functionality [stable]

Deprecated functionality [experimental]

Miscellaneous