v0.3.5

What's Changed

• fix container script bug by @zktuong in #350
• reorder content table on docs by @zktuong in #352
• fix entry of anndata with NaN 'sequence_id' values by @amoschoomy in #351
• add dependabot dependency review for PR by @zktuong in #353
• add else statement to check contigs when there's no sequence by @MeganS92 in #354
• pip prod(deps): update pandas requirement from <=2.1.4,>=1.0.3 to >=1.0.3,<=2.2.0 by @dependabot in #355
• pip dev(deps-dev): update sphinx-autodoc-typehints requirement from <=1.25.2 to <=1.25.3 by @dependabot in #356
• pip dev(deps-dev): update scirpy requirement from <=0.14 to <=0.15.0 by @dependabot in #357
• convert to use umi_count by @zktuong in #358

New Contributors

• @MeganS92 made their first contribution in #354

Full Changelog: v0.3.4...v0.3.5

v0.3.4

Summary

• Speed up network generation in generate_network
• Add soft filtering and normalisation to vdj_psuedobulk functions - @ktpolanski
• Created a new column in .data (extra) to flag if contig is considered extra.
• New clone id definition to insert VDJ and VJ to the id to reduce ambiguity - need to check if it does it properly for cells with no clone ids. This also means that now clone ids can be created for orphan chains.
• New to_scirpy/from_scirpy functions that will now convert them to the new scverse airr formats - @amoschoomy
• Container build is now simplified and uses mamba to manage all the dependencies.
• New option to run preprocessing with ogrdb references in both the base package and the container.
• New reference download function in the container folder to ensure the latest references are pulled for every new iteration of the container.
• Deprecate support for python3.7 tests.

What's Changed

• create a remove_malformed option by @zktuong in #315
• fix future warning for multimappers by @zktuong in #316
• fix cleaning up step in the singularity image by @zktuong in #317
• Speed up network generation by @zktuong in #319
• remove d gene VJ columns by @zktuong in #320
• Running palantir by @zktuong in #321
• update workflow dependencies by @zktuong in #323
• Fix #325 by @zktuong in #326
• add concat in tutorial by @zktuong in #327
• Update 3_dandelion_findingclones-10x_data.ipynb by @tnieuwe in #328
• disable 3.7 tests by @zktuong in #330
• add logic to deal with contigs with no clone id during network generation step by @zktuong in #335
• Soft filtering in setup_vdj_pseudobulk() by @ktpolanski in #334
• attempting to reduce code bloat for find_clones by @zktuong in #329
• fix bug with check contigs by @zktuong in #341
• add an option to the singularity container to skip tigger step. by @zktuong in #343
• add new option to check contigs by @zktuong in #344
• simplify build container by @zktuong in #346
• Awkward to pandas by @amoschoomy in #342
• Fix yml by @zktuong in #349
• fix container build definition file by @zktuong in #348

dependabot updates

• pip prod(deps): update pandas requirement from <=2.1.0,>=1.0.3 to >=1.0.3,<=2.1.1 by @dependabot in #314
• pip dev(deps-dev): update readthedocs-sphinx-ext requirement from <=2.2.2 to <=2.2.3 by @dependabot in #318
• pip prod(deps): update pandas requirement from <=2.1.1,>=1.0.3 to >=1.0.3,<=2.1.2 by @dependabot in #324
• pip dev(deps-dev): update sphinx-autodoc-typehints requirement from <=1.24.0 to <=1.24.1 by @dependabot in #331
• pip prod(deps): update pandas requirement from <=2.1.2,>=1.0.3 to >=1.0.3,<=2.1.3 by @dependabot in #332
• pip dev(deps-dev): update sphinx-autodoc-typehints requirement from <=1.24.1 to <=1.25.2 by @dependabot in #333
• pip dev(deps-dev): update sphinx-rtd-theme requirement from <=1.2.2 to <=2.0.0 by @dependabot in #338
• pip prod(deps): update pandas requirement from <=2.1.3,>=1.0.3 to >=1.0.3,<=2.1.4 by @dependabot in #339
• pip dev(deps-dev): update readthedocs-sphinx-ext requirement from <=2.2.3 to <=2.2.4 by @dependabot in #340
• pip dev(deps-dev): update readthedocs-sphinx-ext requirement from <=2.2.4 to <=2.2.5 by @dependabot in #345
• Bump tj-actions/changed-files from 35 to 41 in /.github/workflows by @dependabot in #347

New Contributors

• @tnieuwe made their first contribution in #328
• @amoschoomy made their first contribution in #342

Full Changelog: v0.3.3...v0.3.4

v0.3.3

What's Changed

• Mainly updates and bug fixes to tl.clone_overlap and pl.clone_overlap.
• simplified pre-processing functions to call command line arguments instead of running within the code.

Detailed notes:

• Update docs for clone overlap by @zktuong in #276
• Allow additional arguments in define_clones by @zktuong in #280
• add an if statement to check if actor is dependabot by @zktuong in #289
• pip dev(deps-dev): update sphinx-autodoc-typehints requirement from <=1.23.0 to <=1.23.3 by @dependabot in #284
• pip dev(deps-dev): update sphinx-rtd-theme requirement from <=1.2.0 to <=1.2.2 by @dependabot in #285
• pip dev(deps-dev): update readthedocs-sphinx-ext requirement from <=2.2.0 to <=2.2.2 by @dependabot in #286
• pip dev(deps-dev): update nbsphinx requirement from <=0.9.1 to <=0.9.2 by @dependabot in #287
• enable auto-merge for dependabot by @zktuong in #290
• refactoring how external scripts and locations are called by @zktuong in #288
• fix reassign_alleles by @zktuong in #293
• remove deprecated function from docs by @zktuong in #297
• pip dev(deps-dev): update sphinx-autodoc-typehints requirement from <=1.23.3 to <=1.24.0 by @dependabot in #296
• fix weekly tests by @zktuong in #301
• pip prod(deps): update mizani requirement from <0.10.0 to <0.11.0 by @dependabot in #302
• add options to plotting clone overlap by @zktuong in #307
• add requirements.txt by @zktuong in #309
• should be cartesian product instead of combination by @zktuong in #312

Full Changelog: v0.3.2...v0.3.3

v0.3.2

What's Changed

Mainly to fix compatibility with dependencies.

• minor wording/renaming tweaks in tutorial by @ktpolanski in #252
• ensure additional column names are present in strict mode by @zktuong in #253
• fix weekly tests by @zktuong in #255
• fix singularity preprocessing for org by @zktuong in #256
• Easy docs2 by @zktuong in #260
• Update _network.py by @zktuong in #258
• fix requirements by @zktuong in #262
• Create dependabot.yml by @zktuong in #263
• pip prod(deps): update pandas requirement from <1.5.0,>=1.0.3 to >=1.0.3,<2.1.0 by @dependabot in #264
• getting rid of CI warnings by @zktuong in #265
• update actions by @zktuong in #266
• barplot bug by @zktuong in #267
• fix query behaviour for merge by @zktuong in #268
• Update colab doc by @zktuong in #271

New Contributors

• @dependabot made their first contribution in #264

Full Changelog: v0.3.1...v0.3.2

v0.3.1

What's Changed

Just to update pypi - Some bug fixes to accompany the revision
Doesn't affect the container image (but i should add a tag on sylabs to also call it 0.3.1 just to be consisten).

• Enforce pickle priorty to be 4 by @zktuong in #216
• minor doc aesthetic updates by @zktuong in #218
• add citation to preprint by @zktuong in #221
• Does this help? by @zktuong in #222
• add missing function to api by @zktuong in #223
• update-docstrings by @zktuong in #224
• Minor behavior update by @zktuong in #228
• fix re-indexing issue by @zktuong in #229
• parse main calls by @zktuong in #231
• add mouse-preprocess by @zktuong in #237
• vdj mapping causing an issue? by @zktuong in #241
• revert awk change by @zktuong in #242
• fix macos tests by @zktuong in #243
• add logic for jmultimap checking by @zktuong in #244
• Revert "add logic for jmultimap checking" by @zktuong in #245
• fix empty columns by @zktuong in #246
• Update _core.py by @zktuong in #247
• increased transparency tutorial by @ktpolanski in #248
• update email by @zktuong in #249
• Return gamma delta notebook by @zktuong in #250

Full Changelog: v0.3.0...v0.3.1

v0.3.0

What's Changed

This release adds a number of new features and minor restructuring to accompany Dandelion's manuscript (uploading soon). Kudos to @suochenqu and @ktpolanski

1. data strategy to handle non-productive contigs, partial contigs and 'J multi-mappers'
2. new V(D)J pseudotime trajectory inference!
3. revamped tutorials and documents

Detailed PRs

• multimappers by @zktuong in #165
• Update environment.yml by @zktuong in #168
• fix-typo by @zktuong in #172
• add J multimap to BCR workflow by @zktuong in #178
• fix pandas dependency by @zktuong in #181
• fix unreference variable 182 by @zktuong in #183
• add trajectory utils by @suochenqu in #185
• select left most J call in multimappers by @zktuong in #186
• update container definitions by @zktuong in #187
• fix the column names by @zktuong in #188
• Update _trajectory.py by @zktuong in #189
• Pseudobulking improvements by @ktpolanski in #193
• Further tweaks by @ktpolanski in #194
• Update api.rst by @zktuong in #197
• save calculate threshold plot by @zktuong in #200
• Adjust toggling of productive/non-productive filtering for setup pseudobulk by @zktuong in #201
• compute_pseudobulk_gex by @zktuong in #202
• change update_metadata to always reinitialise by @zktuong in #203
• two quick pseudobulking fixes by @ktpolanski in #204
• Customise setup pseudobulk by @zktuong in #205
• refactor pseudobulking, update gex by @ktpolanski in #206
• add tests by @zktuong in #208
• singularity changeo pipeline by @ktpolanski in #207
• quickstart tutorial by @ktpolanski in #209
• place nxviz as an external submodule by @zktuong in #210
• update notebooks by @zktuong in #211
• Update external by @zktuong in #212
• fix api documentation styling by @zktuong in #214

New Contributors

• @suochenqu made their first contribution in #185

Full Changelog: v0.2.4...v0.3.0

v0.2.4

What's Changed

• slicing and check contigs by @zktuong in #159
• add new functions and rework github actions by @zktuong in #161

New features

slicing functionality

• the Dandelion object can now be sliced like a AnnData, or pandas DataFrame!

  vdj[vdj.data['productive'] == 'T']
  Dandelion class object with n_obs = 38 and n_contigs = 94
      data: 'sequence_id', 'sequence', 'rev_comp', 'productive', 'v_call', 'd_call', 'j_call', 'sequence_alignment', 'germline_alignment', 'junction', 'junction_aa', 'v_cigar', 'd_cigar', 'j_cigar', 'stop_codon', 'vj_in_frame', 'locus', 'junction_length', 'np1_length', 'np2_length', 'cell_id', 'c_call', 'consensus_count', 'duplicate_count', 'rearrangement_status'
      metadata: 'locus_VDJ', 'locus_VJ', 'productive_VDJ', 'productive_VJ', 'v_call_VDJ', 'd_call_VDJ', 'j_call_VDJ', 'v_call_VJ', 'j_call_VJ', 'c_call_VDJ', 'c_call_VJ', 'junction_VDJ', 'junction_VJ', 'junction_aa_VDJ', 'junction_aa_VJ', 'v_call_B_VDJ', 'd_call_B_VDJ', 'j_call_B_VDJ', 'v_call_B_VJ', 'j_call_B_VJ', 'productive_B_VDJ', 'productive_B_VJ', 'v_call_abT_VDJ', 'd_call_abT_VDJ', 'j_call_abT_VDJ', 'v_call_abT_VJ', 'j_call_abT_VJ', 'productive_abT_VDJ', 'productive_abT_VJ', 'v_call_gdT_VDJ', 'd_call_gdT_VDJ', 'j_call_gdT_VDJ', 'v_call_gdT_VJ', 'j_call_gdT_VJ', 'productive_gdT_VDJ', 'productive_gdT_VJ', 'duplicate_count_B_VDJ', 'duplicate_count_B_VJ', 'duplicate_count_abT_VDJ', 'duplicate_count_abT_VJ', 'duplicate_count_gdT_VDJ', 'duplicate_count_gdT_VJ', 'isotype', 'isotype_status', 'locus_status', 'chain_status', 'rearrangement_status_VDJ', 'rearrangement_status_VJ'

  vdj[vdj.metadata['productive_VDJ'] == 'T']
  Dandelion class object with n_obs = 17 and n_contigs = 36
      data: 'sequence_id', 'sequence', 'rev_comp', 'productive', 'v_call', 'd_call', 'j_call', 'sequence_alignment', 'germline_alignment', 'junction', 'junction_aa', 'v_cigar', 'd_cigar', 'j_cigar', 'stop_codon', 'vj_in_frame', 'locus', 'junction_length', 'np1_length', 'np2_length', 'cell_id', 'c_call', 'consensus_count', 'duplicate_count', 'rearrangement_status'
      metadata: 'locus_VDJ', 'locus_VJ', 'productive_VDJ', 'productive_VJ', 'v_call_VDJ', 'd_call_VDJ', 'j_call_VDJ', 'v_call_VJ', 'j_call_VJ', 'c_call_VDJ', 'c_call_VJ', 'junction_VDJ', 'junction_VJ', 'junction_aa_VDJ', 'junction_aa_VJ', 'v_call_B_VDJ', 'd_call_B_VDJ', 'j_call_B_VDJ', 'v_call_B_VJ', 'j_call_B_VJ', 'productive_B_VDJ', 'productive_B_VJ', 'v_call_abT_VDJ', 'd_call_abT_VDJ', 'j_call_abT_VDJ', 'v_call_abT_VJ', 'j_call_abT_VJ', 'productive_abT_VDJ', 'productive_abT_VJ', 'v_call_gdT_VDJ', 'd_call_gdT_VDJ', 'j_call_gdT_VDJ', 'v_call_gdT_VJ', 'j_call_gdT_VJ', 'productive_gdT_VDJ', 'productive_gdT_VJ', 'duplicate_count_B_VDJ', 'duplicate_count_B_VJ', 'duplicate_count_abT_VDJ', 'duplicate_count_abT_VJ', 'duplicate_count_gdT_VDJ', 'duplicate_count_gdT_VJ', 'isotype', 'isotype_status', 'locus_status', 'chain_status', 'rearrangement_status_VDJ', 'rearrangement_status_VJ'

  vdj[vdj.metadata_names.isin(['cell1', 'cell2', 'cell3', 'cell4', 'cell5'])]
  Dandelion class object with n_obs = 5 and n_contigs = 20
  data: 'sequence_id', 'sequence', 'rev_comp', 'productive', 'v_call', 'd_call', 'j_call', 'sequence_alignment', 'germline_alignment', 'junction', 'junction_aa', 'v_cigar', 'd_cigar', 'j_cigar', 'stop_codon', 'vj_in_frame', 'locus', 'junction_length', 'np1_length', 'np2_length', 'cell_id', 'c_call', 'consensus_count', 'duplicate_count', 'rearrangement_status'
  metadata: 'locus_VDJ', 'locus_VJ', 'productive_VDJ', 'productive_VJ', 'v_call_VDJ', 'd_call_VDJ', 'j_call_VDJ', 'v_call_VJ', 'j_call_VJ', 'c_call_VDJ', 'c_call_VJ', 'junction_VDJ', 'junction_VJ', 'junction_aa_VDJ', 'junction_aa_VJ', 'v_call_B_VDJ', 'd_call_B_VDJ', 'j_call_B_VDJ', 'v_call_B_VJ', 'j_call_B_VJ', 'productive_B_VDJ', 'productive_B_VJ', 'v_call_abT_VDJ', 'd_call_abT_VDJ', 'j_call_abT_VDJ', 'v_call_abT_VJ', 'j_call_abT_VJ', 'productive_abT_VDJ', 'productive_abT_VJ', 'v_call_gdT_VDJ', 'd_call_gdT_VDJ', 'j_call_gdT_VDJ', 'v_call_gdT_VJ', 'j_call_gdT_VJ', 'productive_gdT_VDJ', 'productive_gdT_VJ', 'duplicate_count_B_VDJ', 'duplicate_count_B_VJ', 'duplicate_count_abT_VDJ', 'duplicate_count_abT_VJ', 'duplicate_count_gdT_VDJ', 'duplicate_count_gdT_VJ', 'isotype', 'isotype_status', 'locus_status', 'chain_status', 'rearrangement_status_VDJ', 'rearrangement_status_VJ'

  vdj[vdj.data_names.isin(['contig1','contig2','contig3','contig4','contig5'])]
  Dandelion class object with n_obs = 2 and n_contigs = 5
  data: 'sequence_id', 'sequence', 'rev_comp', 'productive', 'v_call', 'd_call', 'j_call', 'sequence_alignment', 'germline_alignment', 'junction', 'junction_aa', 'v_cigar', 'd_cigar', 'j_cigar', 'stop_codon', 'vj_in_frame', 'locus', 'junction_length', 'np1_length', 'np2_length', 'cell_id', 'c_call', 'consensus_count', 'duplicate_count', 'rearrangement_status'
  metadata: 'locus_VDJ', 'locus_VJ', 'productive_VDJ', 'productive_VJ', 'v_call_VDJ', 'd_call_VDJ', 'j_call_VDJ', 'v_call_VJ', 'j_call_VJ', 'c_call_VDJ', 'c_call_VJ', 'junction_VDJ', 'junction_VJ', 'junction_aa_VDJ', 'junction_aa_VJ', 'v_call_B_VDJ', 'd_call_B_VDJ', 'j_call_B_VDJ', 'v_call_B_VJ', 'j_call_B_VJ', 'productive_B_VDJ', 'productive_B_VJ', 'v_call_abT_VDJ', 'd_call_abT_VDJ', 'j_call_abT_VDJ', 'v_call_abT_VJ', 'j_call_abT_VJ', 'productive_abT_VDJ', 'productive_abT_VJ', 'v_call_gdT_VDJ', 'd_call_gdT_VDJ', 'j_call_gdT_VDJ', 'v_call_gdT_VJ', 'j_call_gdT_VJ', 'productive_gdT_VDJ', 'productive_gdT_VJ', 'duplicate_count_B_VDJ', 'duplicate_count_B_VJ', 'duplicate_count_abT_VDJ', 'duplicate_count_abT_VJ', 'duplicate_count_gdT_VDJ', 'duplicate_count_gdT_VJ', 'isotype', 'isotype_status', 'locus_status', 'chain_status', 'rearrangement_status_VDJ', 'rearrangement_status_VJ'

  • not sure implementing it like adata[:, adata.var.something] make sense as it's not really row information in the data slot?
  • also the base slot in Dandelion is .data, and doesn't make sense for .metadata to be the 'row'
  • maybe scverse/scirpy#327 can come up with a better strategy and i can adopt that later on.

ddl.pp.check_contigs

• created a new function ddl.pp.check_contigs as a way to just check if contigs are ambiguous, rather than outright removing them. I envisage that this will eventually replace simple mode in ddl.pp.filter_contigs in the future.
  • new column in .data: ambiguous, T/F to indicate whether contig is considered ambiguous or not (different from cell level ambiguous).
  • the .metadata and several other functions ignores any contigs marked as T to maintain the same behaviour
  • The largest difference between ddl.pp.check_contigs and ddl.pp.filter_contigs is that the onus is on the user to remove any 'bad' cells from the GEX data (illustrated in the tutorial) with check_contigs whereas this happens semi-automatically with filter_contigs.

ddl.update_metadata now comes with a 'by_celltype' option

• This brings a new feature - B cell, alpha-beta T cell and gamma-delta T cell associated columns for V,D,J,C and productive columns!
  • this is achieved through a new .retrieve_celltype subfunction in the Query class, which breaks up the retrieval into the 3 major groups if by_celltype = True.
  • No longer the need to guess which belongs to which and allows for easy slicing! This does cause a bit of .obs bloating.
  • Which leads to the removal of constant_status_VDJ, constant_status_VJ, productive_status_VDJ, productive_status_VJ as the metadata is getting bloated with the slight rework of Dandelion metadata slot to account for the new B/abT/gdT columns

tl.productive_ratio

• Calculates a cell-level representation of productive vs non-productive contigs.
  • Plotting is achieved through pl.productive_ratio

tl.vj_usage_pca

• Computes PCA on a cell-level representation of V/J gene usage across designated groupings
  • uses scanpy.pp.pca internally
  • Plotting can be achieved through scanpy.pl.pca

bug fixes

• fix cell ordering issue scverse/scirpy#347
• small refactor of ddl.pp.filter_contigs
  • moved some of the repetitive loops into callable functions
  • deprecate filter_vj_chains argument and replaced with filter_extra_vdj_chains and filter_extra_vj_chains to hopefully enable more interpretable behaviour. fixes #158
  • umi adjustment step was buggy but i have now made the behaviour consistent with how it functions in ddl.pp.check_contigs
• rearrangement_status_VDJ and rearrangement_status_VJ (renamed from rearrangement_VDJ_status and rearrangement_VJ_status) from now gives a single value for whether a chimeric rearrangement occured e.g. TRDV pairing with TRAJ and TRAC as in this paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4267242/
• fixed issues with progress bars getting out of hand
• fixed issue with ddl.tl.find_clones crashing if more than 1 type of loci is found in the data.
  • now a B, abT and gdT prefix will be appended to BCR/TR-ab/TR-gd clones.
• check_contigs, find_clones and define_clones were removing non-productive contigs even though there's no need to. May cause issues with filter_contigs... but there's a problem for next time.
• fix issue with min_size in network not behaving as intended. switch to using connected components to find which nodes to trim

other changes

• new column chain_status, to summarise the reworked locus_status column.
  • Should contain values like ambiguous, Orphan VDJ, Single pair etc, similar to chain_pairing in scirpy.
• Also fixed the ordering of metadata to make it more presentable, instead of just randomly slotting into the...

v0.2.3

same as v0.2.2 but i seemed to have messed up the upload to pypi. so trying again.

What's Changed

• try and add youtube video to docs by @zktuong in #148
• testing_rpy2_update by @zktuong in #150
• Speed upgrade - Refactor generate network by @zktuong in #152
• remove nxviz from requirements by @zktuong in #157

Bug fixes and Improvements

• Speed up generate_network
  • pair-wise hamming distance is calculated on per clone/clonotype only if more than 1 cell is assigned to a clone/clonotype
  • .distance slot is removed and is now directly stored/converted from the .graph slot.
  • new options:
    • compute_layout: bool = True. If dataset is too large, generate_layout can be switched to False in which case only the networkx graph is returned. The data can still be visualised later with scirpy's plotting method (see below).
    • layout_method: Literal['sfdp', 'mod_fr'] = 'sfdp'. New default uses the ultra-fast C++ implemented sfdp_layout algorithm in graph-tools to generate final layout. sfdp stands for Scalable Force Directed Placement.
      • Minor caveat is that the repulsion is not as good - when there's a lot of singleton nodes, they don't separate well unless you some how work out which of the parameters in sfdp_layout to tweak will produce an effective separate. changing gamma alone doesn't really seem to do much.
      • The original layout can still be generated by specifying layout_method = 'mod_fr'. Requires a separate installation of graph-tool via conda (not managed by pip) as it has several C++ dependencies.
      • pytest on macos may also stall because of a different backend being called - this is solved by changing tests that calls generate_network to run last.
  • added steps to reduce memory hogging.
  • min_size was doing the opposite previously and this is now fixed. #155
• Speed up transfer
  • Found a faster way to create the connectivity matrix.
  • this also now transfer a dictionary that scirpy can use to generate the plots scverse/scirpy#286
  • Fix #153
    • rename productive to productive_status.
• Fix #154
  • reorder the if-else statements.
• Speed up filter_contigs
  • tree construction is simplified and replaced for-loops with dictionary updates.
• Speed up initialise_metadata. Dandelion should now initialise and read faster.
  • Removed an unnecessary data sanitization step when loading data.
  • Now load_data will rename umi_count to duplicate_count
  • Speed up Query
    • tree construction is simplified and replaced for-loops with dictionary updates.
    • didn't need to use an airr validator as that slows things down.
• data initialised by Dandelion will be ordered based on productive first, then followed by umi count (largest to smallest).

Breaking Changes

• initialise_metadata/update_metadata/Dandelion
  • For-loops to initialise the object has veen vectorized, resulting in a minor speed uprade
  • This results in reduction of some columns in the .metadata which were probably bloated and not used.
    • vdj_status and vdj_status_summary removed and replaced with rearrangement_VDJ_status and rearrange_VJ_status
    • constant_status and constant_summary removed and replaced with constant_VDJ_status and constant_VJ_status.
    • productive and productive_summary combined and replaced with productive_status.
    • locus_status and locus_status_summary combined and replaced with locus_status.
    • isotype_summary replaced with isotype_status.
• where there was previously unassigned or '' has been changed to :str: None in .metadata.
  • Not changed to NoneType as there's quite a bit of text processing internally that gets messed up if swapped.
  • No_contig will still be populated after transfer to AnnData to reflect cells with no TCR/BCR info.
• deprecate use of nxviz<0.7.4
  • reworked code to use the updated version at https://github.com/zktuong/nxviz/tree/custom_color_mapping_circos_nodes_and_edges

Minor changes

• Rename and deprecate read_h5/write_h5. Use of read_h5ddl/write_h5ddl will be enforced in the next update.

Full Changelog: v0.2.1...v0.2.2

v0.2.2

What's Changed

• try and add youtube video to docs by @zktuong in #148
• testing_rpy2_update by @zktuong in #150
• Speed upgrade - Refactor generate network by @zktuong in #152
• remove nxviz from requirements by @zktuong in #157

Bug fixes and Improvements

• Speed up generate_network
  • pair-wise hamming distance is calculated on per clone/clonotype only if more than 1 cell is assigned to a clone/clonotype
  • .distance slot is removed and is now directly stored/converted from the .graph slot.
  • new options:
    • compute_layout: bool = True. If dataset is too large, generate_layout can be switched to False in which case only the networkx graph is returned. The data can still be visualised later with scirpy's plotting method (see below).
    • layout_method: Literal['sfdp', 'mod_fr'] = 'sfdp'. New default uses the ultra-fast C++ implemented sfdp_layout algorithm in graph-tools to generate final layout. sfdp stands for Scalable Force Directed Placement.
      • Minor caveat is that the repulsion is not as good - when there's a lot of singleton nodes, they don't separate well unless you some how work out which of the parameters in sfdp_layout to tweak will produce an effective separate. changing gamma alone doesn't really seem to do much.
      • The original layout can still be generated by specifying layout_method = 'mod_fr'. Requires a separate installation of graph-tool via conda (not managed by pip) as it has several C++ dependencies.
      • pytest on macos may also stall because of a different backend being called - this is solved by changing tests that calls generate_network to run last.
  • added steps to reduce memory hogging.
  • min_size was doing the opposite previously and this is now fixed. #155
• Speed up transfer
  • Found a faster way to create the connectivity matrix.
  • this also now transfer a dictionary that scirpy can use to generate the plots scverse/scirpy#286
  • Fix #153
    • rename productive to productive_status.
• Fix #154
  • reorder the if-else statements.
• Speed up filter_contigs
  • tree construction is simplified and replaced for-loops with dictionary updates.
• Speed up initialise_metadata. Dandelion should now initialise and read faster.
  • Removed an unnecessary data sanitization step when loading data.
  • Now load_data will rename umi_count to duplicate_count
  • Speed up Query
    • tree construction is simplified and replaced for-loops with dictionary updates.
    • didn't need to use an airr validator as that slows things down.
• data initialised by Dandelion will be ordered based on productive first, then followed by umi count (largest to smallest).

Breaking Changes

• initialise_metadata/update_metadata/Dandelion
  • For-loops to initialise the object has veen vectorized, resulting in a minor speed uprade
  • This results in reduction of some columns in the .metadata which were probably bloated and not used.
    • vdj_status and vdj_status_summary removed and replaced with rearrangement_VDJ_status and rearrange_VJ_status
    • constant_status and constant_summary removed and replaced with constant_VDJ_status and constant_VJ_status.
    • productive and productive_summary combined and replaced with productive_status.
    • locus_status and locus_status_summary combined and replaced with locus_status.
    • isotype_summary replaced with isotype_status.
• where there was previously unassigned or '' has been changed to :str: None in .metadata.
  • Not changed to NoneType as there's quite a bit of text processing internally that gets messed up if swapped.
  • No_contig will still be populated after transfer to AnnData to reflect cells with no TCR/BCR info.
• deprecate use of nxviz<0.7.4
  • reworked code to use the updated version at https://github.com/zktuong/nxviz/tree/custom_color_mapping_circos_nodes_and_edges

Minor changes

• Rename and deprecate read_h5/write_h5. Use of read_h5ddl/write_h5ddl will be enforced in the next update.

Full Changelog: v0.2.1...v0.2.2

v0.2.1

What's Changed

• Update documentation by @zktuong in #142
• Add google colab button by @zktuong in #143
• Fix pytest issue on macos by @zktuong in #144
• v0.2.1 by @zktuong in #147
  • minor QoL changes
  • remove skbio from dependencies

Full Changelog: v0.2.0...v0.2.1