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BCR tutorial (#542)
* Added beta-version v2 of bcr tutorial and adapted corresponding file accordingly; tested to add two citations into .bib file * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fixed issues with citation, leading to a fail of build-the-docs * Added missing citations and removed references to in-work functions * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fixed an issue with one citation * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Updated glossary to match new BCR functionalities and offer the user easy-access literature * Reference to new glossary entries * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fixed faulty citation * Update tutorial * update tutorial * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update notebook * Update tutorial * Discussion regarding bimodality of dataset * Remove sections that will be added in separate PR * Improve wording * Update CHANGELOG * Add BCR tutorial to CI * Fix glossary link * Glossary updates * Update references to preprocessing tools * Fix function reference --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Gregor Sturm <[email protected]> Co-authored-by: Gregor Sturm <[email protected]>
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.github/workflows/test-tutorials.yaml

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tutorial:
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- tutorial_3k_tcr.ipynb
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- tutorial_io.ipynb
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- tutorial_5k_bcr.ipynb
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os:
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- ubuntu-latest
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python:

CHANGELOG.md

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[keep a changelog]: https://keepachangelog.com/en/1.0.0/
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[semantic versioning]: https://semver.org/spec/v2.0.0.html
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## [Unreleased]
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### Documentation
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- Add a tutorial for BCR analysis with Scirpy ([#542](https://github.com/scverse/scirpy/pull/542)).
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## v0.19.0
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### Additions

docs/glossary.rst

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:term:`CDR3<CDR>` nucleotide sequences, but might recognize the same antigen
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because they have the same or similar CDR3 amino acid sequence.
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This is especially relevant for BCR, because clonally related cell are likely to differ due to
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:term:`somatic hypermutation <SHM>`. It is important to understand that there is currently no best practice or
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go-to approach on how to define clonotype cluster for BCR, as it remains an active research
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field (:cite:`Yaari.2015`). There exist many different approaches such as maximum-likelihood (:cite:`Ralph.2016`),
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hierarchical clustering (:cite:`Gupta.2017`), spectral clustering (:cite:`Nouri.2018`), natural language
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processing (:cite:`Lindenbaum.2021`) and network based approaches (:cite:`BashfordRogers.2013`). A recent
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comparison study indicates that computationally more sophisticated clonal inference approaches do not
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outperform simplistic, computational cheaper ones (:cite:`Balashova.2024`). That said, there is still a
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need for more in-depth comparison studies to confirm these results.
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See also: :func:`scirpy.tl.define_clonotype_clusters`.
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Private clonotype
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Immune receptor.
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BCR
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B-cell receptor. A BCR consiste of two Immunoglobulin (IG) heavy chains and
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B-cell receptor. A BCR consists of two Immunoglobulin (IG) heavy chains and
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two IG light chains. The two light chains contain a variable region, which is
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responsible for antigen recognition.
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under the `CC BY-4.0 <https://creativecommons.org/licenses/by/4.0/deed.en>`__ license,
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obtained from `wikimedia commons <https://commons.wikimedia.org/w/index.php?curid=49935883>`__
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SHM
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Common abbreviation for "Somatic hypermutation". This process is unique to BCR and occurs as part
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of affinity maturation upon antigen encounter. This process further increases the diversity of the
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variable domain of the BCR and selects for cells with higher affinity. SHM introduces around one point mutation per 1000
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base pairs (:cite:`Kleinstein.2003`) and is able to introduce (although rare) deletions and/or insertions (:cite:`Wilson.1998`).
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Furthermore, SHM is not a stochastic process, but biased in multiple ways (e.g. intrinsic hot-spot motifs (reviewed in :cite:`Schramm.2018`))
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Dual IR
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:term:`IRs<IR>` with more than one pair of :term:`VJ<V(D)J>` and
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:term:`VDJ<V(D)J>` sequences. While this was
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previously thought to be impossible due to the mechanism of allelic exclusion
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(:cite:`Brady2010-gh`), there is an increasing amound of evidence for a *bona fide*
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dual-IR population (:cite:`Schuldt2019`, :cite:`Ji2010-bn`, :cite:`Vettermann2010`).
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(:cite:`Brady2010-gh`), there is an increasing amount of evidence for a *bona fide*
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dual-IR population (:cite:`Schuldt2019`, :cite:`Shi.2019`, :cite:`RobertaPelanda.2014`,
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:cite:`Ji2010-bn`, :cite:`Vettermann2010`).
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Recent evidence suggest that also B cells with three or more productively rearranged
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H and/or L chains exist (:cite:`Zhu.2023`), which indicates how much of B cell development
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is still unclear.
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For more information on how *Scirpy* handles dual IRs, see the
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page about our :ref:`IR model<receptor-model>`.
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Alellically included B-cells
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A B cell with two pairs of :term:`IG` chains. See :term:`Dual IR`.
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Isotypically included B-cells
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Similar to :term:`Alellically included B-cells`, but expresses both IGL and
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IGK and thus rearrangements are not on alleles of the same gene (= isotypic inclusion).
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Clonotype modularity
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The clonotype modularity measures how densly connected the transcriptomics
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The clonotype modularity measures how densely connected the transcriptomics
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neighborhood graph underlying the cells in a clonotype is. Clonotypes with
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a high modularity consist of cells that are transcriptionally more similar
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than that of a clonotype with a low modularity.

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