From 185af45a7ee3620398c75a835b8a48bf639631e4 Mon Sep 17 00:00:00 2001 From: Yuta Nagano <52748151+yutanagano@users.noreply.github.com> Date: Fri, 7 Jun 2024 14:16:56 +0100 Subject: [PATCH 01/31] Lint with black --- docs/conf.py | 21 ++++++++------------- src/sceptr/__init__.py | 1 - src/sceptr/model.py | 2 +- src/sceptr/sceptr/__init__.py | 1 - src/sceptr/variant.py | 3 +-- tox.ini | 8 ++++++++ 6 files changed, 18 insertions(+), 18 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index ad0217e..39814de 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -8,27 +8,22 @@ # -- Project information ----------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information -project = 'sceptr' -copyright = '2024, Yuta Nagano' -author = 'Yuta Nagano' +project = "sceptr" +copyright = "2024, Yuta Nagano" +author = "Yuta Nagano" version = sceptr.VERSION # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration -extensions = [ - "sphinx.ext.autodoc", - "sphinx.ext.autosummary", - "sphinx.ext.napoleon" -] - -templates_path = ['_templates'] -exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] +extensions = ["sphinx.ext.autodoc", "sphinx.ext.autosummary", "sphinx.ext.napoleon"] +templates_path = ["_templates"] +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] # -- Options for HTML output ------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output -html_theme = 'sphinx_book_theme' -html_static_path = ['_static'] +html_theme = "sphinx_book_theme" +html_static_path = ["_static"] diff --git a/src/sceptr/__init__.py b/src/sceptr/__init__.py index 08c6dfe..8d8dc1d 100644 --- a/src/sceptr/__init__.py +++ b/src/sceptr/__init__.py @@ -4,5 +4,4 @@ SCEPTR is a small, fast, and performant TCR representation model for alignment-free TCR analysis. """ - VERSION = "1.0.0-beta.1" diff --git a/src/sceptr/model.py b/src/sceptr/model.py index 86f6868..3e65d6e 100644 --- a/src/sceptr/model.py +++ b/src/sceptr/model.py @@ -23,7 +23,7 @@ class Sceptr: name : str The name of the model variant. """ - + name: str = None distance_bins = np.linspace(0, 2, num=21) diff --git a/src/sceptr/sceptr/__init__.py b/src/sceptr/sceptr/__init__.py index 364d715..c84e701 100644 --- a/src/sceptr/sceptr/__init__.py +++ b/src/sceptr/sceptr/__init__.py @@ -3,7 +3,6 @@ The functional API uses the default SCEPTR model. """ - from sceptr import variant from numpy import ndarray from pandas import DataFrame diff --git a/src/sceptr/variant.py b/src/sceptr/variant.py index 81d5e92..6fe8125 100644 --- a/src/sceptr/variant.py +++ b/src/sceptr/variant.py @@ -5,7 +5,6 @@ Each of their function signatures are equivalent to the functional API, so you can just plug and play! """ - from sceptr._model_saves import load_variant @@ -186,7 +185,7 @@ def finetuned(): .. note :: This model is fine-tuned explicity for discriminating between the pMHC specificities listed above. While it does indeed become much better than the default model at that specific task, its general performance is otherwise *demonstrably worse* compared to the default. - + Returns ------- :py:class:`~sceptr.model.Sceptr` diff --git a/tox.ini b/tox.ini index 95ab726..0425e01 100644 --- a/tox.ini +++ b/tox.ini @@ -22,3 +22,11 @@ skip_install = true deps = black>=23 commands = black . + +[testenv:docs] +description = build sphinx docs +deps = + sphinx>=6 + sphinx-book-theme +commands = + sphinx-build docs docs/_build From 799fb33a3d5a458f2357d7d5e4e6436ec1ae3881 Mon Sep 17 00:00:00 2001 From: Yuta Nagano <52748151+yutanagano@users.noreply.github.com> Date: Fri, 7 Jun 2024 15:38:01 +0100 Subject: [PATCH 02/31] Add license --- LICENSE.txt | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 LICENSE.txt diff --git a/LICENSE.txt b/LICENSE.txt new file mode 100644 index 0000000..8c7ba7b --- /dev/null +++ b/LICENSE.txt @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024 Yuta Nagano + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. From 3de04e0f2cd3c4692ae6ae63f27ed0e27260ac6c Mon Sep 17 00:00:00 2001 From: Yuta Nagano <52748151+yutanagano@users.noreply.github.com> Date: Fri, 7 Jun 2024 15:38:10 +0100 Subject: [PATCH 03/31] Add some badges --- README.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/README.md b/README.md index 2dd78b3..44f5154 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,10 @@ # SCEPTR +![Tests](https://github.com/yutanagano/sceptr/actions/workflows/tests.yaml/badge.svg) + +[![License](https://img.shields.io/badge/license-MIT-blue)](https://github.com/yutanagano/tidytcells?tab=MIT-1-ov-file#readme) + + > [!NOTE] > The latest version of SCEPTR no longer supports Python versions earlier than 3.9. From 0e90e133297508291c245a7dfbbad8fc8af57e49 Mon Sep 17 00:00:00 2001 From: Yuta Nagano <52748151+yutanagano@users.noreply.github.com> Date: Fri, 7 Jun 2024 15:40:25 +0100 Subject: [PATCH 04/31] Remove unnecessary badges --- README.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/README.md b/README.md index 44f5154..113544c 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,7 @@ # SCEPTR ![Tests](https://github.com/yutanagano/sceptr/actions/workflows/tests.yaml/badge.svg) - [![License](https://img.shields.io/badge/license-MIT-blue)](https://github.com/yutanagano/tidytcells?tab=MIT-1-ov-file#readme) - > [!NOTE] > The latest version of SCEPTR no longer supports Python versions earlier than 3.9. From 0b03411a769b274af071fe464a68ecb88d176a16 Mon Sep 17 00:00:00 2001 From: Yuta Nagano <52748151+yutanagano@users.noreply.github.com> Date: Sat, 8 Jun 2024 20:57:50 +0100 Subject: [PATCH 05/31] Further polish the documentation --- docs/index.rst | 15 +++++++--- docs/installation.rst | 16 +++++----- docs/sceptr_sceptr.rst | 2 ++ docs/usage.rst | 55 ++++++++++++++++++----------------- src/sceptr/model.py | 8 ++--- src/sceptr/sceptr/__init__.py | 8 ++--- src/sceptr/variant.py | 4 +-- 7 files changed, 59 insertions(+), 49 deletions(-) diff --git a/docs/index.rst b/docs/index.rst index 6e7d705..65a444c 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -3,11 +3,18 @@ You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. -SCEPTR: A fast and performant TCR representation model -====================================================== +SCEPTR +====== -SCEPTR (Simple Contrastive Embedding of the Primary sequence of T cell Receptors) is a BERT-like attention model trained on T cell receptor (TCR) data. -It maps TCRs to vector representations, which enables alignment-free downstream TCR and TCR repertoire analysis such as TCR clustering or classification. +**SCEPTR** (\ **S**\ imple **C**\ ontrastive **E**\ mbedding of the **P**\ rimary sequence of **T** cell **R**\ eceptors) is a small, fast, and performant TCR representation model that can be used for alignment-free downstream TCR and TCR repertoire analysis such as TCR clustering or classification. +Our `manuscript (coming soon) `_ demonstrates SCEPTR's state-of-the-art performance (as of 2024) on downstream TCR specificity prediction. + +SCEPTR is a BERT-like transformer-based neural network implemented in `Pytorch `_. +With the default model providing best-in-class performance with only 153,108 parameters (typical protein language models have tens or hundreds of millions), SCEPTR runs fast- even on a CPU! +And if your computer does have a `CUDA-enabled GPU `_, the ``sceptr`` package will automatically detect and use it, giving you blazingly fast performance without the hassle. + +``sceptr``'s :ref:`API ` exposes three intuitive functions: :py:func:`~sceptr.sceptr.calc_vector_representations`, :py:func:`~sceptr.sceptr.calc_cdist_matrix`, and :py:func:`~sceptr.sceptr.calc_pdist_vector`-- and it's all you need to make full use of the SCEPTR models. +What's even better is that they are fully compliant with `pyrepseq `_'s `tcr_metric `_ API, so ``sceptr`` will fit snugly into the rest of your repertoire analysis toolkit. .. toctree:: :maxdepth: 2 diff --git a/docs/installation.rst b/docs/installation.rst index 2e655ff..755af80 100644 --- a/docs/installation.rst +++ b/docs/installation.rst @@ -12,13 +12,13 @@ From `Source `_ ----------------------------------------------------- .. important:: - To install `sceptr` from source, you must have `git-lfs `_ installed and set up on your system. + To install ``sceptr`` from source, you must have `git-lfs `_ installed and set up on your system. This is because you must be able to download the trained model weights directly from the Git LFS servers during your install. -Using `pip` -........... +Using ``pip`` +............. -From your Python environment, run the following replacing `` with the appropriate version specifier (e.g. `v1.0.0-beta.1`). +From your Python environment, run the following replacing ```` with the appropriate version specifier (e.g. ``v1.0.0-beta.1``). The latest release tags can be found by checking the 'releases' section on the github repository page. .. code-block:: bash @@ -34,22 +34,22 @@ You can also clone the repository, and from within your Python environment, navi $ pip install . -Note that even for manual installation, you still need `git-lfs` to properly de-reference the stub files at `git-clone`-ing time. +Note that even for manual installation, you still need ``git-lfs`` to properly de-reference the stub files at `git-clone`-ing time. Troubleshooting ............... -A recent security update to `git` has resulted in some difficulties cloning repositories that rely on `git-lfs`. +A recent security update to ``git`` has resulted in some difficulties cloning repositories that rely on ``git-lfs``. This can result in an error message with a message along the lines of: .. code-block:: bash $ fatal: active `post-checkout` hook found during `git clone` -If this happens, you can temporarily set the `GIT_CLONE_PROTECTION_ACTIVE` environment variable to `false` by prepending `GIT_CLONE_PROTECTION_ACTIVE=false` before the install command like below: +If this happens, you can temporarily set the ``GIT_CLONE_PROTECTION_ACTIVE`` environment variable to ``false`` by prepending ``GIT_CLONE_PROTECTION_ACTIVE=false`` before the install command like below: .. code-block:: bash $ GIT_CLONE_PROTECTION_ACTIVE=false pip install git+https://github.com/yutanagano/sceptr.git@ -This is `a known issue `_ for `git` version `2.45.1` and is fixed from version `2.45.2`. +This is `a known issue `_ for ``git`` version ``2.45.1`` and is fixed from version ``2.45.2``. diff --git a/docs/sceptr_sceptr.rst b/docs/sceptr_sceptr.rst index 7465ff7..67d8d28 100644 --- a/docs/sceptr_sceptr.rst +++ b/docs/sceptr_sceptr.rst @@ -1,3 +1,5 @@ +.. _api: + `sceptr.sceptr` =============== diff --git a/docs/usage.rst b/docs/usage.rst index 0ad2e0b..27d7bf6 100644 --- a/docs/usage.rst +++ b/docs/usage.rst @@ -1,6 +1,34 @@ Usage ===== +Functional API (:py:mod:`sceptr.sceptr`) +---------------------------------------- + +.. tip:: + To use the functional API, import the `sceptr` submodule like so: + + >>> from sceptr import sceptr + + Attempting to access the submodule as an attribute of the top level module + + >>> import sceptr + >>> # ... load data, etc ... + >>> sceptr.sceptr.calc_vector_representations(df) + + will result in an error. + +The eponymous :py:mod:`sceptr.sceptr` submodule is the easiest way to use SCEPTR. +It loads the default SCEPTR variant and exposes its methods directly as module-level functions. + +Model Variants (:py:mod:`sceptr.variant`) +----------------------------------------- + +For more curious users, model variants are available to load and use through the :py:mod:`sceptr.variant` submodule. +The module exposes functions, each named after a particular model variant, which when called, will return a :py:class:`~sceptr.model.Sceptr` object corresponding to the selected model variant. +This :py:class:`~sceptr.model.Sceptr` object will then have the methods: `calc_pdist_vector`, `calc_cdist_matrix`, and `calc_vector_representations` available to use, with function signatures exactly as defined above for the functional API in the :py:mod:`sceptr.sceptr` submodule. + +.. _data_format: + Prescribed data format ---------------------- @@ -29,30 +57,3 @@ Incomplete rows are allowed (e.g. only beta chain data available) as long as the +-------------+-----------------+-----------------------------------------------------------------------------------------------------+ |TRBJ |`str` |IMGT symbol for the beta chain J gene | +-------------+-----------------+-----------------------------------------------------------------------------------------------------+ - - -Functional API (:py:mod:`sceptr.sceptr`) ----------------------------------------- - -.. tip:: - To use the functional API, import the `sceptr` submodule like so: - - >>> from sceptr import sceptr - - Attempting to access the submodule as an attribute of the top level module - - >>> import sceptr - >>> # ... load data, etc ... - >>> sceptr.sceptr.calc_vector_representations(df) - - will result in an error. - -The eponymous :py:mod:`sceptr.sceptr` submodule is the easiest way to use SCEPTR. -It loads the default SCEPTR variant and exposes its methods directly as module-level functions. - -Model Variants (:py:mod:`sceptr.variant`) ------------------------------------------ - -For more curious users, model variants are available to load and use through the :py:mod:`sceptr.variant` submodule. -The module exposes functions, each named after a particular model variant, which when called, will return a :py:class:`~sceptr.model.Sceptr` object corresponding to the selected model variant. -This :py:class:`~sceptr.model.Sceptr` object will then have the methods: `calc_pdist_vector`, `calc_cdist_matrix`, and `calc_vector_representations` available to use, with function signatures exactly as defined above for the functional API in the :py:mod:`sceptr.sceptr` submodule. diff --git a/src/sceptr/model.py b/src/sceptr/model.py index 3e65d6e..50b6055 100644 --- a/src/sceptr/model.py +++ b/src/sceptr/model.py @@ -42,7 +42,7 @@ def calc_vector_representations(self, instances: DataFrame) -> ndarray: Parameters ---------- instances : DataFrame - DataFrame in the presribed format. + DataFrame in the :ref:`prescribed format `. Returns ------- @@ -86,9 +86,9 @@ def calc_cdist_matrix(self, anchors: DataFrame, comparisons: DataFrame) -> ndarr Parameters ---------- anchors : DataFrame - DataFrame in the prescribed format, representing TCRs from collection A. + DataFrame in the :ref:`prescribed format `. comparisons : DataFrame - DataFrame in the prescribed format, representing TCRs from collection B. + DataFrame in the :ref:`prescribed format `. Returns ------- @@ -110,7 +110,7 @@ def calc_pdist_vector(self, instances: DataFrame) -> ndarray: Parameters ---------- instances : DataFrame - DataFrame in the prescribed format. + DataFrame in the :ref:`prescribed format `. Returns ------- diff --git a/src/sceptr/sceptr/__init__.py b/src/sceptr/sceptr/__init__.py index c84e701..45ed16b 100644 --- a/src/sceptr/sceptr/__init__.py +++ b/src/sceptr/sceptr/__init__.py @@ -18,9 +18,9 @@ def calc_cdist_matrix(anchors: DataFrame, comparisons: DataFrame) -> ndarray: Parameters ---------- anchors : DataFrame - DataFrame in the prescribed format, representing TCRs from collection A. + DataFrame in the :ref:`prescribed format `. comparisons : DataFrame - DataFrame in the prescribed format, representing TCRs from collection B. + DataFrame in the :ref:`prescribed format `. Returns ------- @@ -38,7 +38,7 @@ def calc_pdist_vector(instances: DataFrame) -> ndarray: Parameters ---------- instances : DataFrame - DataFrame in the prescribed format. + DataFrame in the :ref:`prescribed format `. Returns ------- @@ -56,7 +56,7 @@ def calc_vector_representations(instances: DataFrame) -> ndarray: Parameters ---------- instances : DataFrame - DataFrame in the presribed format. + DataFrame in the :ref:`prescribed format `. Returns ------- diff --git a/src/sceptr/variant.py b/src/sceptr/variant.py index 6fe8125..99cea3e 100644 --- a/src/sceptr/variant.py +++ b/src/sceptr/variant.py @@ -1,7 +1,7 @@ """ For the curious/power users, several model variants are available to load and use through this submodule. The submodule exposes functions, each named after a particular variant, which when called will return a :py:class:`~sceptr.model.Sceptr` instance corresponding to the selected model variant. -:py:class:`~sceptr.model.Sceptr` instances expose the same methods as in the functional API: namely `calc_pdist_vector`, `calc_cdist_matrix`, and `calc_vector_representations`. +:py:class:`~sceptr.model.Sceptr` instances expose the same methods as in the functional API: namely :py:func:`~sceptr.model.Sceptr.calc_pdist_vector`, :py:func:`~sceptr.model.Sceptr.calc_cdist_matrix`, and :py:func:`~sceptr.model.Sceptr.calc_vector_representations`. Each of their function signatures are equivalent to the functional API, so you can just plug and play! """ @@ -180,7 +180,7 @@ def dropout_noise_only(): def finetuned(): """ Load the fine-tuned variant of SCEPTR. - This variant is fine-tuned using supervised contrastive learning for six pMHCs with peptides GILGFVFTL, NLVPMVATV, SPRWYFYYL, TFEYVSQPFLMDLE, TTDPSFLGRY and YLQPRTFLL (from [VDJdb](https://vdjdb.cdr3.net/))| + This variant is fine-tuned using supervised contrastive learning for six pMHCs with peptides GILGFVFTL, NLVPMVATV, SPRWYFYYL, TFEYVSQPFLMDLE, TTDPSFLGRY and YLQPRTFLL (from `VDJdb `_) .. note :: This model is fine-tuned explicity for discriminating between the pMHC specificities listed above. From 9273f16851619f41f4ebfc8549ffbc9b1c02de57 Mon Sep 17 00:00:00 2001 From: Yuta Nagano <52748151+yutanagano@users.noreply.github.com> Date: Sat, 8 Jun 2024 21:09:50 +0100 Subject: [PATCH 06/31] Move functional API into root module --- src/sceptr/__init__.py | 67 +++++++++++++++++++++++++++++++++-- src/sceptr/sceptr/__init__.py | 67 ----------------------------------- tests/test_functional_api.py | 2 +- 3 files changed, 66 insertions(+), 70 deletions(-) delete mode 100644 src/sceptr/sceptr/__init__.py diff --git a/src/sceptr/__init__.py b/src/sceptr/__init__.py index 8d8dc1d..91ec496 100644 --- a/src/sceptr/__init__.py +++ b/src/sceptr/__init__.py @@ -1,7 +1,70 @@ """ -Simple Contrastive Embedding of the Primary sequence of T cell Receptors -======================================================================== SCEPTR is a small, fast, and performant TCR representation model for alignment-free TCR analysis. +The top module provides easy access to SCEPTR through a functional API which uses the default :py:class:`~sceptr.model.Sceptr` model. """ +from sceptr import variant +from numpy import ndarray +from pandas import DataFrame + + VERSION = "1.0.0-beta.1" + + +default_model = variant.default() + + +def calc_cdist_matrix(anchors: DataFrame, comparisons: DataFrame) -> ndarray: + """ + Generate a cdist matrix between two collections of TCRs. + + Parameters + ---------- + anchors : DataFrame + DataFrame in the :ref:`prescribed format `. + comparisons : DataFrame + DataFrame in the :ref:`prescribed format `. + + Returns + ------- + ndarray + A 2D numpy ndarray representing a cdist matrix between TCRs from `anchors` and `comparisons`. + The returned array will have shape (X, Y) where X is the number of TCRs in `anchors` and Y is the number of TCRs in `comparisons`. + """ + return default_model.calc_cdist_matrix(anchors, comparisons) + + +def calc_pdist_vector(instances: DataFrame) -> ndarray: + """ + Generate a pdist set of distances between each pair of TCRs in the input data. + + Parameters + ---------- + instances : DataFrame + DataFrame in the :ref:`prescribed format `. + + Returns + ------- + ndarray + A 2D numpy ndarray representing a pdist vector of distances between each pair of TCRs in `instances`. + The returned array will have shape (1/2 * N * (N-1),), where N is the number of TCRs in `instances`. + """ + return default_model.calc_pdist_vector(instances) + + +def calc_vector_representations(instances: DataFrame) -> ndarray: + """ + Map a table of TCRs provided as a pandas DataFrame in the above format to a set of vector representations. + + Parameters + ---------- + instances : DataFrame + DataFrame in the :ref:`prescribed format `. + + Returns + ------- + ndarray + A 2D numpy ndarray object where every row vector corresponds to a row in `instances`. + The returned array will have shape (N, D) where N is the number of TCRs in `instances` and D is the dimensionality of the SCEPTR model. + """ + return default_model.calc_vector_representations(instances) diff --git a/src/sceptr/sceptr/__init__.py b/src/sceptr/sceptr/__init__.py deleted file mode 100644 index 45ed16b..0000000 --- a/src/sceptr/sceptr/__init__.py +++ /dev/null @@ -1,67 +0,0 @@ -""" -This submodule provides easy access to SCEPTR's functional API. -The functional API uses the default SCEPTR model. -""" - -from sceptr import variant -from numpy import ndarray -from pandas import DataFrame - - -default_model = variant.default() - - -def calc_cdist_matrix(anchors: DataFrame, comparisons: DataFrame) -> ndarray: - """ - Generate a cdist matrix between two collections of TCRs. - - Parameters - ---------- - anchors : DataFrame - DataFrame in the :ref:`prescribed format `. - comparisons : DataFrame - DataFrame in the :ref:`prescribed format `. - - Returns - ------- - ndarray - A 2D numpy ndarray representing a cdist matrix between TCRs from `anchors` and `comparisons`. - The returned array will have shape (X, Y) where X is the number of TCRs in `anchors` and Y is the number of TCRs in `comparisons`. - """ - return default_model.calc_cdist_matrix(anchors, comparisons) - - -def calc_pdist_vector(instances: DataFrame) -> ndarray: - """ - Generate a pdist set of distances between each pair of TCRs in the input data. - - Parameters - ---------- - instances : DataFrame - DataFrame in the :ref:`prescribed format `. - - Returns - ------- - ndarray - A 2D numpy ndarray representing a pdist vector of distances between each pair of TCRs in `instances`. - The returned array will have shape (1/2 * N * (N-1),), where N is the number of TCRs in `instances`. - """ - return default_model.calc_pdist_vector(instances) - - -def calc_vector_representations(instances: DataFrame) -> ndarray: - """ - Map a table of TCRs provided as a pandas DataFrame in the above format to a set of vector representations. - - Parameters - ---------- - instances : DataFrame - DataFrame in the :ref:`prescribed format `. - - Returns - ------- - ndarray - A 2D numpy ndarray object where every row vector corresponds to a row in `instances`. - The returned array will have shape (N, D) where N is the number of TCRs in `instances` and D is the dimensionality of the SCEPTR model. - """ - return default_model.calc_vector_representations(instances) diff --git a/tests/test_functional_api.py b/tests/test_functional_api.py index bf4bc7a..c9397f3 100644 --- a/tests/test_functional_api.py +++ b/tests/test_functional_api.py @@ -1,4 +1,4 @@ -from sceptr import sceptr +import sceptr import numpy as np import pandas as pd import pytest From 7e2667d56001960df7fe469c60bd24d3ffa8ea59 Mon Sep 17 00:00:00 2001 From: Yuta Nagano <52748151+yutanagano@users.noreply.github.com> Date: Sat, 8 Jun 2024 21:26:38 +0100 Subject: [PATCH 07/31] Defer default model loading in the root module until necessary --- src/sceptr/__init__.py | 19 ++++++++++++------- 1 file changed, 12 insertions(+), 7 deletions(-) diff --git a/src/sceptr/__init__.py b/src/sceptr/__init__.py index 91ec496..02a822e 100644 --- a/src/sceptr/__init__.py +++ b/src/sceptr/__init__.py @@ -1,9 +1,11 @@ """ SCEPTR is a small, fast, and performant TCR representation model for alignment-free TCR analysis. -The top module provides easy access to SCEPTR through a functional API which uses the default :py:class:`~sceptr.model.Sceptr` model. +The root module provides easy access to SCEPTR through a functional API which uses the default :py:class:`~sceptr.model.Sceptr` model. """ from sceptr import variant +from sceptr.model import Sceptr +import sys from numpy import ndarray from pandas import DataFrame @@ -11,9 +13,6 @@ VERSION = "1.0.0-beta.1" -default_model = variant.default() - - def calc_cdist_matrix(anchors: DataFrame, comparisons: DataFrame) -> ndarray: """ Generate a cdist matrix between two collections of TCRs. @@ -31,7 +30,7 @@ def calc_cdist_matrix(anchors: DataFrame, comparisons: DataFrame) -> ndarray: A 2D numpy ndarray representing a cdist matrix between TCRs from `anchors` and `comparisons`. The returned array will have shape (X, Y) where X is the number of TCRs in `anchors` and Y is the number of TCRs in `comparisons`. """ - return default_model.calc_cdist_matrix(anchors, comparisons) + return get_default_model().calc_cdist_matrix(anchors, comparisons) def calc_pdist_vector(instances: DataFrame) -> ndarray: @@ -49,7 +48,7 @@ def calc_pdist_vector(instances: DataFrame) -> ndarray: A 2D numpy ndarray representing a pdist vector of distances between each pair of TCRs in `instances`. The returned array will have shape (1/2 * N * (N-1),), where N is the number of TCRs in `instances`. """ - return default_model.calc_pdist_vector(instances) + return get_default_model().calc_pdist_vector(instances) def calc_vector_representations(instances: DataFrame) -> ndarray: @@ -67,4 +66,10 @@ def calc_vector_representations(instances: DataFrame) -> ndarray: A 2D numpy ndarray object where every row vector corresponds to a row in `instances`. The returned array will have shape (N, D) where N is the number of TCRs in `instances` and D is the dimensionality of the SCEPTR model. """ - return default_model.calc_vector_representations(instances) + return get_default_model().calc_vector_representations(instances) + + +def get_default_model() -> Sceptr: + if "_DEFAULT_MODEL" not in dir(sys.modules[__name__]): + setattr(sys.modules[__name__], "_DEFAULT_MODEL", variant.default()) + return _DEFAULT_MODEL From e61f15f15de6ef567b964063a76545f0a1097aec Mon Sep 17 00:00:00 2001 From: Yuta Nagano <52748151+yutanagano@users.noreply.github.com> Date: Sat, 8 Jun 2024 21:27:55 +0100 Subject: [PATCH 08/31] Rename VERSION attribute to __version__ --- docs/conf.py | 2 +- pyproject.toml | 2 +- src/sceptr/__init__.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 39814de..b24fffc 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -11,7 +11,7 @@ project = "sceptr" copyright = "2024, Yuta Nagano" author = "Yuta Nagano" -version = sceptr.VERSION +version = sceptr.__version__ # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration diff --git a/pyproject.toml b/pyproject.toml index f78309f..5654f14 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -47,7 +47,7 @@ dev = [ include-package-data = true [tool.setuptools.dynamic] -version = {attr = "sceptr.VERSION"} +version = {attr = "sceptr.__version__"} [tool.pytest.ini_options] filterwarnings = [ diff --git a/src/sceptr/__init__.py b/src/sceptr/__init__.py index 02a822e..61725d2 100644 --- a/src/sceptr/__init__.py +++ b/src/sceptr/__init__.py @@ -10,7 +10,7 @@ from pandas import DataFrame -VERSION = "1.0.0-beta.1" +__version__ = "1.0.0-beta.1" def calc_cdist_matrix(anchors: DataFrame, comparisons: DataFrame) -> ndarray: From 59a50e6f5db1e3d2f2fb44e3e1f2175d1577a45b Mon Sep 17 00:00:00 2001 From: Yuta Nagano <52748151+yutanagano@users.noreply.github.com> Date: Sat, 8 Jun 2024 21:35:43 +0100 Subject: [PATCH 09/31] Update documentation to reflect refactor --- docs/api.rst | 2 +- docs/index.rst | 2 +- docs/sceptr.rst | 7 +++++++ docs/sceptr_sceptr.rst | 7 ------- docs/usage.rst | 21 +++------------------ 5 files changed, 12 insertions(+), 27 deletions(-) create mode 100644 docs/sceptr.rst delete mode 100644 docs/sceptr_sceptr.rst diff --git a/docs/api.rst b/docs/api.rst index 15c1396..644a09d 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -4,5 +4,5 @@ API reference .. toctree:: :maxdepth: 2 - sceptr_sceptr + sceptr sceptr_variant diff --git a/docs/index.rst b/docs/index.rst index 65a444c..626ca57 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -13,7 +13,7 @@ SCEPTR is a BERT-like transformer-based neural network implemented in `Pytorch < With the default model providing best-in-class performance with only 153,108 parameters (typical protein language models have tens or hundreds of millions), SCEPTR runs fast- even on a CPU! And if your computer does have a `CUDA-enabled GPU `_, the ``sceptr`` package will automatically detect and use it, giving you blazingly fast performance without the hassle. -``sceptr``'s :ref:`API ` exposes three intuitive functions: :py:func:`~sceptr.sceptr.calc_vector_representations`, :py:func:`~sceptr.sceptr.calc_cdist_matrix`, and :py:func:`~sceptr.sceptr.calc_pdist_vector`-- and it's all you need to make full use of the SCEPTR models. +``sceptr``'s :ref:`API ` exposes three intuitive functions: :py:func:`~sceptr.calc_vector_representations`, :py:func:`~sceptr.calc_cdist_matrix`, and :py:func:`~sceptr.calc_pdist_vector`-- and it's all you need to make full use of the SCEPTR models. What's even better is that they are fully compliant with `pyrepseq `_'s `tcr_metric `_ API, so ``sceptr`` will fit snugly into the rest of your repertoire analysis toolkit. .. toctree:: diff --git a/docs/sceptr.rst b/docs/sceptr.rst new file mode 100644 index 0000000..8006793 --- /dev/null +++ b/docs/sceptr.rst @@ -0,0 +1,7 @@ +.. _api: + +`sceptr` +=============== + +.. automodule:: sceptr + :members: diff --git a/docs/sceptr_sceptr.rst b/docs/sceptr_sceptr.rst deleted file mode 100644 index 67d8d28..0000000 --- a/docs/sceptr_sceptr.rst +++ /dev/null @@ -1,7 +0,0 @@ -.. _api: - -`sceptr.sceptr` -=============== - -.. automodule:: sceptr.sceptr - :members: diff --git a/docs/usage.rst b/docs/usage.rst index 27d7bf6..8144d32 100644 --- a/docs/usage.rst +++ b/docs/usage.rst @@ -1,31 +1,16 @@ Usage ===== -Functional API (:py:mod:`sceptr.sceptr`) ----------------------------------------- +Functional API (Recommended) +---------------------------- -.. tip:: - To use the functional API, import the `sceptr` submodule like so: - - >>> from sceptr import sceptr - - Attempting to access the submodule as an attribute of the top level module - - >>> import sceptr - >>> # ... load data, etc ... - >>> sceptr.sceptr.calc_vector_representations(df) - - will result in an error. - -The eponymous :py:mod:`sceptr.sceptr` submodule is the easiest way to use SCEPTR. -It loads the default SCEPTR variant and exposes its methods directly as module-level functions. +The functional API is accessible from the root module, and is the easiest way to use SCEPTR. Model Variants (:py:mod:`sceptr.variant`) ----------------------------------------- For more curious users, model variants are available to load and use through the :py:mod:`sceptr.variant` submodule. The module exposes functions, each named after a particular model variant, which when called, will return a :py:class:`~sceptr.model.Sceptr` object corresponding to the selected model variant. -This :py:class:`~sceptr.model.Sceptr` object will then have the methods: `calc_pdist_vector`, `calc_cdist_matrix`, and `calc_vector_representations` available to use, with function signatures exactly as defined above for the functional API in the :py:mod:`sceptr.sceptr` submodule. .. _data_format: From 3c07879059fc3809390cefd46d69ebf5b23742aa Mon Sep 17 00:00:00 2001 From: Yuta Nagano <52748151+yutanagano@users.noreply.github.com> Date: Sat, 8 Jun 2024 21:48:27 +0100 Subject: [PATCH 10/31] Move Sceptr model class documentation to its own page --- docs/api.rst | 1 + docs/sceptr.rst | 4 ++-- docs/sceptr_variant.rst | 10 ++-------- 3 files changed, 5 insertions(+), 10 deletions(-) diff --git a/docs/api.rst b/docs/api.rst index 644a09d..ce011cb 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -6,3 +6,4 @@ API reference sceptr sceptr_variant + sceptr_model diff --git a/docs/sceptr.rst b/docs/sceptr.rst index 8006793..10492bb 100644 --- a/docs/sceptr.rst +++ b/docs/sceptr.rst @@ -1,7 +1,7 @@ .. _api: -`sceptr` -=============== +``sceptr`` +========== .. automodule:: sceptr :members: diff --git a/docs/sceptr_variant.rst b/docs/sceptr_variant.rst index d86da58..bdb5342 100644 --- a/docs/sceptr_variant.rst +++ b/docs/sceptr_variant.rst @@ -1,11 +1,5 @@ -`sceptr.variant` -================ +``sceptr.variant`` +================== .. automodule:: sceptr.variant :members: - -`Sceptr` object API -------------------- - -.. autoclass:: sceptr.model.Sceptr - :members: From b0fbd8dde567cccb253a270249029b45e53be3f7 Mon Sep 17 00:00:00 2001 From: Yuta Nagano <52748151+yutanagano@users.noreply.github.com> Date: Sat, 8 Jun 2024 21:49:06 +0100 Subject: [PATCH 11/31] Make separate page for model submodule --- docs/sceptr_model.rst | 5 +++++ 1 file changed, 5 insertions(+) create mode 100644 docs/sceptr_model.rst diff --git a/docs/sceptr_model.rst b/docs/sceptr_model.rst new file mode 100644 index 0000000..d348a6c --- /dev/null +++ b/docs/sceptr_model.rst @@ -0,0 +1,5 @@ +``sceptr.model`` +================ + +.. automodule:: sceptr.model + :members: From 5250991b9de50c3fdbe27823a9c9ba2b3b072ce0 Mon Sep 17 00:00:00 2001 From: Yuta Nagano <52748151+yutanagano@users.noreply.github.com> Date: Sat, 8 Jun 2024 22:24:15 +0100 Subject: [PATCH 12/31] Further polish documentation --- docs/api.rst | 2 +- docs/conf.py | 2 +- docs/index.rst | 11 +++-------- docs/installation.rst | 8 +------- src/sceptr/model.py | 2 +- 5 files changed, 7 insertions(+), 18 deletions(-) diff --git a/docs/api.rst b/docs/api.rst index ce011cb..f4164d7 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -2,7 +2,7 @@ API reference ============= .. toctree:: - :maxdepth: 2 + :maxdepth: 1 sceptr sceptr_variant diff --git a/docs/conf.py b/docs/conf.py index b24fffc..f1d36a2 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -11,7 +11,7 @@ project = "sceptr" copyright = "2024, Yuta Nagano" author = "Yuta Nagano" -version = sceptr.__version__ +release = sceptr.__version__ # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration diff --git a/docs/index.rst b/docs/index.rst index 626ca57..730e31b 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -1,8 +1,3 @@ -.. sceptr documentation master file, created by - sphinx-quickstart on Fri Jun 7 10:32:22 2024. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - SCEPTR ====== @@ -11,10 +6,10 @@ Our `manuscript (coming soon) `_ demonstrates SCEPTR's state-of-the SCEPTR is a BERT-like transformer-based neural network implemented in `Pytorch `_. With the default model providing best-in-class performance with only 153,108 parameters (typical protein language models have tens or hundreds of millions), SCEPTR runs fast- even on a CPU! -And if your computer does have a `CUDA-enabled GPU `_, the ``sceptr`` package will automatically detect and use it, giving you blazingly fast performance without the hassle. +And if your computer does have a `CUDA-enabled GPU `_, the sceptr package will automatically detect and use it, giving you blazingly fast performance without the hassle. -``sceptr``'s :ref:`API ` exposes three intuitive functions: :py:func:`~sceptr.calc_vector_representations`, :py:func:`~sceptr.calc_cdist_matrix`, and :py:func:`~sceptr.calc_pdist_vector`-- and it's all you need to make full use of the SCEPTR models. -What's even better is that they are fully compliant with `pyrepseq `_'s `tcr_metric `_ API, so ``sceptr`` will fit snugly into the rest of your repertoire analysis toolkit. +sceptr's :ref:`API ` exposes three intuitive functions: :py:func:`~sceptr.calc_vector_representations`, :py:func:`~sceptr.calc_cdist_matrix`, and :py:func:`~sceptr.calc_pdist_vector`-- and it's all you need to make full use of the SCEPTR models. +What's even better is that they are fully compliant with `pyrepseq `_'s `tcr_metric `_ API, so sceptr will fit snugly into the rest of your repertoire analysis toolkit. .. toctree:: :maxdepth: 2 diff --git a/docs/installation.rst b/docs/installation.rst index 755af80..92bc502 100644 --- a/docs/installation.rst +++ b/docs/installation.rst @@ -15,9 +15,6 @@ From `Source `_ To install ``sceptr`` from source, you must have `git-lfs `_ installed and set up on your system. This is because you must be able to download the trained model weights directly from the Git LFS servers during your install. -Using ``pip`` -............. - From your Python environment, run the following replacing ```` with the appropriate version specifier (e.g. ``v1.0.0-beta.1``). The latest release tags can be found by checking the 'releases' section on the github repository page. @@ -25,16 +22,13 @@ The latest release tags can be found by checking the 'releases' section on the g $ pip install git+https://github.com/yutanagano/sceptr.git@ -Manual install -.............. - You can also clone the repository, and from within your Python environment, navigate to the project root directory and run: .. code-block:: bash $ pip install . -Note that even for manual installation, you still need ``git-lfs`` to properly de-reference the stub files at `git-clone`-ing time. +Note that even for manual installation, you still need ``git-lfs`` to properly de-reference the stub files at ``git-clone``-ing time. Troubleshooting ............... diff --git a/src/sceptr/model.py b/src/sceptr/model.py index 50b6055..11bd265 100644 --- a/src/sceptr/model.py +++ b/src/sceptr/model.py @@ -16,7 +16,7 @@ class Sceptr: """ Loads a trained state of a SCEPTR (variant) and provides an easy interface for generating TCR representations and making inferences from them. - Instances can be obtained through the variants submodule. + Instances can be obtained through the :py:mod:`sceptr.variant` submodule. Attributes ---------- From e946f2d0ae3d14084979b6ff99608157a3be559f Mon Sep 17 00:00:00 2001 From: Yuta Nagano <52748151+yutanagano@users.noreply.github.com> Date: Sun, 9 Jun 2024 14:54:07 +0100 Subject: [PATCH 13/31] Flesh out usage page of docs --- docs/sceptr_model.rst | 2 +- docs/usage.rst | 85 +++++++++++++++++++++++++++++++++--------- src/sceptr/__init__.py | 14 +++---- src/sceptr/model.py | 12 +++--- 4 files changed, 82 insertions(+), 31 deletions(-) diff --git a/docs/sceptr_model.rst b/docs/sceptr_model.rst index d348a6c..be1c3fc 100644 --- a/docs/sceptr_model.rst +++ b/docs/sceptr_model.rst @@ -1,5 +1,5 @@ ``sceptr.model`` ================ -.. automodule:: sceptr.model +.. autoclass:: sceptr.model.Sceptr() :members: diff --git a/docs/usage.rst b/docs/usage.rst index 8144d32..1bf0bd5 100644 --- a/docs/usage.rst +++ b/docs/usage.rst @@ -4,41 +4,92 @@ Usage Functional API (Recommended) ---------------------------- -The functional API is accessible from the root module, and is the easiest way to use SCEPTR. +The functional :ref:`API ` is accessible from the root module, and is the easiest way to use SCEPTR. +When using the functional API, you will be using the default SCEPTR model (see the :ref:`model variants ` section below). +To begin analysing TCR data with sceptr, you must first load the TCR data into memory in the :ref:`prescribed format ` using `pandas `_. + +.. tip:: + SCEPTR only recognises TCR V gene symbols that are IMGT-compliant, and also known to be functional (i.e. known pseudogenes or ORFs are not allowed). + For easy standardisation of TCR gene nomenclature in your data, as well as filtering your data for functional V/J genes, check out `tidytcells `_. + +>>> from pandas import DataFrame +>>> tcrs = DataFrame( +... data = { +... "TRAV": ["TRAV38-1*01", "TRAV3*01", "TRAV13-2*01", "TRAV38-2/DV8*01"], +... "CDR3A": ["CAHRSAGGGTSYGKLTF", "CAVDNARLMF", "CAERIRKGQVLTGGGNKLTF", "CAYRSAGGGTSYGKLTF"], +... "TRBV": ["TRBV2*01", "TRBV25-1*01", "TRBV9*01", "TRBV2*01"], +... "CDR3B": ["CASSEFQGDNEQFF", "CASSDGSFNEQFF", "CASSVGDLLTGELFF", "CASSPGTGGNEQYF"], +... }, +... index = [0,1,2,3] +... ) +>>> print(tcrs) + TRAV CDR3A TRBV CDR3B +0 TRAV38-1*01 CAHRSAGGGTSYGKLTF TRBV2*01 CASSEFQGDNEQFF +1 TRAV3*01 CAVDNARLMF TRBV25-1*01 CASSDGSFNEQFF +2 TRAV13-2*01 CAERIRKGQVLTGGGNKLTF TRBV9*01 CASSVGDLLTGELFF +3 TRAV38-2/DV8*01 CAYRSAGGGTSYGKLTF TRBV2*01 CASSPGTGGNEQYF + +:py:mod:`sceptr` exposes three intuitive functions: :py:func:`~sceptr.calc_cdist_matrix`, :py:func:`~sceptr.calc_pdist_vector`, and :py:func:`~sceptr.calc_vector_representations`. +As the name suggests, :py:func:`~sceptr.calc_cdist_matrix` gives you an easy way to calculate a cross-distance matrix between two sets of TCRs. + +>>> import sceptr +>>> cdist_matrix = sceptr.calc_cdist_matrix(tcrs.iloc[:2], tcrs.iloc[2:]) +>>> print(cdist_matrix) +[[1.2849896 0.75219345] + [1.4653426 1.4646543 ]] + +If you're only interested in calculating distances within a set, :py:func:`~sceptr.calc_pdist_vector` gives you a one-dimensional array of within-set distances. + +>>> pdist_vector = sceptr.calc_pdist_vector(tcrs) +>>> print(pdist_vector) +[1.4135991 1.2849895 0.7521934 1.4653426 1.4646543 1.287208 ] + +.. tip:: + The end result of using the :py:func:`~sceptr.calc_cdist_matrix` and :py:func:`~sceptr.calc_pdist_vector` functions are equivalent to generating sceptr's TCR representations first with :py:func:`~sceptr.calc_vector_representations`, then using `scipy `_'s `cdist `_ or `pdist `_ functions to get the corresponding matrix or vector, respectively. + But on machines with `CUDA-enabled GPUs `_, directly using sceptr's :py:func:`~sceptr.calc_cdist_matrix` and :py:func:`~sceptr.calc_pdist_vector` functions will run faster, as it internally runs all computations on the GPU. + +If you want to directly operate on sceptr's TCR representations, you can use :py:func:`~sceptr.calc_vector_representations`. + +>>> reps = sceptr.calc_vector_representations(tcrs) +>>> print(reps.shape) +(4,64) + +.. _model_variants: Model Variants (:py:mod:`sceptr.variant`) ----------------------------------------- -For more curious users, model variants are available to load and use through the :py:mod:`sceptr.variant` submodule. -The module exposes functions, each named after a particular model variant, which when called, will return a :py:class:`~sceptr.model.Sceptr` object corresponding to the selected model variant. +The :py:mod:`sceptr.variant` submodule allows users access a variety of non-default SCEPTR model variants, and use them for TCR analysis. +The submodule exposes functions which return :py:class:`~sceptr.model.Sceptr` objects with the model state of the chosen variant loaded. +These model instances expose the same functions as those used in the functional API, so you can just plug and play. +For example: + +>>> from sceptr import variant +>>> sceptr_tiny = variant.tiny() +>>> tiny_reps = sceptr_tiny.calc_vector_representations(tcrs) +>>> print(tiny_reps.shape) +(4, 16) .. _data_format: Prescribed data format ---------------------- -.. important:: - SCEPTR only recognises TCR V/J gene symbols that are IMGT-compliant, and also known to be functional (i.e. known pseudogenes or ORFs are not allowed). - For easy standardisation of TCR gene nomenclature in your data, as well as filtering your data for functional V/J genes, check out `tidytcells `_. - -SCEPTR expects to receive TCR data in the form of `pandas `_ `DataFrame `_ instances. -Therefore, all TCR data should be represented as a `DataFrame` with the following structure and data types. +SCEPTR expects to receive TCR data in the form of `pyrepseq standard format `_-compliant `pandas `_ `DataFrame `_\ s. +All TCR data should be represented as a DataFrame with the following structure and data types. The column order is irrelevant. Each row should represent one TCR. Incomplete rows are allowed (e.g. only beta chain data available) as long as the SCEPTR :py:mod:`~sceptr.variant` that is being used has at least some partial information to go on. +Extra columns are also allowed. +-------------+-----------------+-----------------------------------------------------------------------------------------------------+ | Column name | Column datatype | Column contents | +=============+=================+=====================================================================================================+ -|TRAV |`str` |IMGT symbol for the alpha chain V gene | -+-------------+-----------------+-----------------------------------------------------------------------------------------------------+ -|CDR3A |`str` |Amino acid sequence of the alpha chain CDR3, including the first C and last W/F residues, in all caps| -+-------------+-----------------+-----------------------------------------------------------------------------------------------------+ -|TRAJ |`str` |IMGT symbol for the alpha chain J gene | +|TRAV |str |IMGT symbol for the alpha chain V gene | +-------------+-----------------+-----------------------------------------------------------------------------------------------------+ -|TRBV |`str` |IMGT symbol for the beta chain V gene | +|CDR3A |str |Amino acid sequence of the alpha chain CDR3, including the first C and last W/F residues, in all caps| +-------------+-----------------+-----------------------------------------------------------------------------------------------------+ -|CDR3B |`str` |Amino acid sequence of the beta chain CDR3, including the first C and last W/F residues, in all caps | +|TRBV |str |IMGT symbol for the beta chain V gene | +-------------+-----------------+-----------------------------------------------------------------------------------------------------+ -|TRBJ |`str` |IMGT symbol for the beta chain J gene | +|CDR3B |str |Amino acid sequence of the beta chain CDR3, including the first C and last W/F residues, in all caps | +-------------+-----------------+-----------------------------------------------------------------------------------------------------+ diff --git a/src/sceptr/__init__.py b/src/sceptr/__init__.py index 61725d2..88537a7 100644 --- a/src/sceptr/__init__.py +++ b/src/sceptr/__init__.py @@ -1,6 +1,6 @@ """ SCEPTR is a small, fast, and performant TCR representation model for alignment-free TCR analysis. -The root module provides easy access to SCEPTR through a functional API which uses the default :py:class:`~sceptr.model.Sceptr` model. +The root module provides easy access to SCEPTR through a functional API which uses the default model. """ from sceptr import variant @@ -28,14 +28,14 @@ def calc_cdist_matrix(anchors: DataFrame, comparisons: DataFrame) -> ndarray: ------- ndarray A 2D numpy ndarray representing a cdist matrix between TCRs from `anchors` and `comparisons`. - The returned array will have shape (X, Y) where X is the number of TCRs in `anchors` and Y is the number of TCRs in `comparisons`. + The returned array will have shape :math:`(X, Y)` where :math:`X` is the number of TCRs in `anchors` and :math:`Y` is the number of TCRs in `comparisons`. """ return get_default_model().calc_cdist_matrix(anchors, comparisons) def calc_pdist_vector(instances: DataFrame) -> ndarray: - """ - Generate a pdist set of distances between each pair of TCRs in the input data. + r""" + Generate a pdist vector of distances between each pair of TCRs in the input data. Parameters ---------- @@ -46,14 +46,14 @@ def calc_pdist_vector(instances: DataFrame) -> ndarray: ------- ndarray A 2D numpy ndarray representing a pdist vector of distances between each pair of TCRs in `instances`. - The returned array will have shape (1/2 * N * (N-1),), where N is the number of TCRs in `instances`. + The returned array will have shape :math:`(\frac{1}{2}N(N-1),)`, where :math:`N` is the number of TCRs in `instances`. """ return get_default_model().calc_pdist_vector(instances) def calc_vector_representations(instances: DataFrame) -> ndarray: """ - Map a table of TCRs provided as a pandas DataFrame in the above format to a set of vector representations. + Map a table of TCRs provided as a pandas DataFrame in the above format to their corresponding vector representations. Parameters ---------- @@ -64,7 +64,7 @@ def calc_vector_representations(instances: DataFrame) -> ndarray: ------- ndarray A 2D numpy ndarray object where every row vector corresponds to a row in `instances`. - The returned array will have shape (N, D) where N is the number of TCRs in `instances` and D is the dimensionality of the SCEPTR model. + The returned array will have shape :math:`(N, 64)` where :math:`N` is the number of TCRs in `instances`. """ return get_default_model().calc_vector_representations(instances) diff --git a/src/sceptr/model.py b/src/sceptr/model.py index 11bd265..1f7c7fa 100644 --- a/src/sceptr/model.py +++ b/src/sceptr/model.py @@ -37,7 +37,7 @@ def __init__( def calc_vector_representations(self, instances: DataFrame) -> ndarray: """ - Map a table of TCRs provided as a pandas DataFrame in the above format to a set of vector representations. + Map a table of TCRs provided as a pandas DataFrame in the above format to their corresponding vector representations. Parameters ---------- @@ -48,7 +48,7 @@ def calc_vector_representations(self, instances: DataFrame) -> ndarray: ------- ndarray A 2D numpy ndarray object where every row vector corresponds to a row in `instances`. - The returned array will have shape (N, D) where N is the number of TCRs in `instances` and D is the dimensionality of the SCEPTR model. + The returned array will have shape :math:`(N, D)` where :math:`N` is the number of TCRs in `instances` and :math:`D` is the dimensionality of the current model variant. """ torch_representations = self._calc_torch_representations(instances) return torch_representations.cpu().numpy() @@ -94,7 +94,7 @@ def calc_cdist_matrix(self, anchors: DataFrame, comparisons: DataFrame) -> ndarr ------- ndarray A 2D numpy ndarray representing a cdist matrix between TCRs from `anchors` and `comparisons`. - The returned array will have shape (X, Y) where X is the number of TCRs in `anchors` and Y is the number of TCRs in `comparisons`. + The returned array will have shape :math:`(X, Y)` where :math:`X` is the number of TCRs in `anchors` and :math:`Y` is the number of TCRs in `comparisons`. """ anchor_representations = self._calc_torch_representations(anchors) comparison_representations = self._calc_torch_representations(comparisons) @@ -104,8 +104,8 @@ def calc_cdist_matrix(self, anchors: DataFrame, comparisons: DataFrame) -> ndarr return cdist_matrix.cpu().numpy() def calc_pdist_vector(self, instances: DataFrame) -> ndarray: - """ - Generate a pdist set of distances between each pair of TCRs in the input data. + r""" + Generate a pdist vector of distances between each pair of TCRs in the input data. Parameters ---------- @@ -116,7 +116,7 @@ def calc_pdist_vector(self, instances: DataFrame) -> ndarray: ------- ndarray A 2D numpy ndarray representing a pdist vector of distances between each pair of TCRs in `instances`. - The returned array will have shape (1/2 * N * (N-1),), where N is the number of TCRs in `instances`. + The returned array will have shape :math:`(\frac{1}{2}N(N-1),)`, where :math:`N` is the number of TCRs in `instances`. """ representations = self._calc_torch_representations(instances) pdist_vector = torch.pdist(representations, p=2) From 8c7a5de1792dc18db24e034b31894057343f20c7 Mon Sep 17 00:00:00 2001 From: Yuta Nagano <52748151+yutanagano@users.noreply.github.com> Date: Sun, 9 Jun 2024 15:02:32 +0100 Subject: [PATCH 14/31] Update README --- README.md | 182 ++++-------------------------------------------------- 1 file changed, 13 insertions(+), 169 deletions(-) diff --git a/README.md b/README.md index 113544c..bafdc84 100644 --- a/README.md +++ b/README.md @@ -1,182 +1,26 @@ # SCEPTR +
+ ![Tests](https://github.com/yutanagano/sceptr/actions/workflows/tests.yaml/badge.svg) [![License](https://img.shields.io/badge/license-MIT-blue)](https://github.com/yutanagano/tidytcells?tab=MIT-1-ov-file#readme) -> [!NOTE] -> The latest version of SCEPTR no longer supports Python versions earlier than 3.9. - -**S**imple **C**ontrastive **E**mbedding of the **P**rimary sequence of **T** cell **R**eceptors (**SCEPTR**) is a BERT-like attention model trained on T cell receptor (TCR) data. -It maps TCRs to vector representations, which can be used for downstream TCR and TCR repertoire analysis such as TCR clustering or classification. - -## Installation - -### From [PyPI](https://pypi.org/project/sceptr/) (Recommended) - -```bash -pip install sceptr -``` - -### From Source - -> [!IMPORTANT] -> To install `sceptr` from source, you must have [`git-lfs`](https://git-lfs.com/) installed and set up on your system. -> This is because you must be able to download the trained model weights directly from the Git LFS servers during your install. - -#### Using `pip` - -From your Python environment, run the following replacing `` with the appropriate version specifier (e.g. `v1.0.0-alpha.1`). -The latest release tags can be found by checking the 'releases' section on the github repository page. - -```bash -pip install git+https://github.com/yutanagano/sceptr.git@ -``` - -#### Manual install - -You can also clone the repository, and from within your Python environment, navigate to the project root directory and run: - -```bash -pip install . -``` +Check out the [documentation page](about:blank). -Note that even for manual installation, you still need `git-lfs` to properly de-reference the stub files at `git-clone`-ing time. +
-#### Troubleshooting +**SCEPTR** (**S**imple **C**ontrastive **E**mbedding of the **P**rimary sequence of **T** cell **R**eceptors) is a small, fast, and performant TCR representation model that can be used for alignment-free downstream TCR and TCR repertoire analysis such as TCR clustering or classification. +Our [manuscript (coming soon)](about:blank) demonstrates SCEPTR's state-of-the-art performance (as of 2024) on downstream TCR specificity prediction. -A recent security update to `git` has resulted in some difficulties cloning repositories that rely on `git-lfs`. -This can result in an error message with a message along the lines of: +SCEPTR is a BERT-like transformer-based neural network implemented in [Pytorch](https://pytorch.org). +With the default model providing best-in-class performance with only 153,108 parameters (typical protein language models have tens or hundreds of millions), SCEPTR runs fast- even on a CPU! +And if your computer does have a [CUDA-enabled GPU](https://en.wikipedia.org/wiki/CUDA), the sceptr package will automatically detect and use it, giving you blazingly fast performance without the hassle. -``` -fatal: active `post-checkout` hook found during `git clone` -``` +sceptr's API exposes three intuitive functions: `calc_vector_representations`, `calc_cdist_matrix`, and `calc_pdist_vector`-- and it's all you need to make full use of the SCEPTR models. +What's even better is that they are fully compliant with [pyrepseq](https://pyrepseq.readthedocs.io)'s [tcr_metric](https://pyrepseq.readthedocs.io/en/latest/api.html#pyrepseq.metric.tcr_metric.TcrMetric) API, so sceptr will fit snugly into the rest of your repertoire analysis toolkit. -If this happens, you can temporarily set the `GIT_CLONE_PROTECTION_ACTIVE` environment variable to `false` by prepending `GIT_CLONE_PROTECTION_ACTIVE=false` before the install command like below: +## Installation ```bash -GIT_CLONE_PROTECTION_ACTIVE=false pip install git+https://github.com/yutanagano/sceptr.git@ +pip install sceptr ``` - -This is [a known issue](https://github.com/git-lfs/git-lfs/issues/5749) for `git` version `2.45.1` and [is fixed](https://lore.kernel.org/git/xmqqr0dheuw5.fsf@gitster.g/T/#u) from version `2.45.2`. - -## Prescribed data format - -> [!IMPORTANT] -> SCEPTR only recognises TCR V/J gene symbols that are IMGT-compliant, and also known to be functional (i.e. known pseudogenes or ORFs are not allowed). -> For easy standardisation of TCR gene nomenclature in your data, as well as filtering your data for functional V/J genes, check out [tidytcells](https://pypi.org/project/tidytcells/). - -SCEPTR expects to receive TCR data in the form of [pandas](https://pandas.pydata.org/) [`DataFrame`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html?highlight=dataframe#pandas.DataFrame) instances. -Therefore, all TCR data should be represented as a `DataFrame` with the following structure and data types. -The column order is irrelevant. -Each row should represent one TCR. -Incomplete rows are allowed (e.g. only beta chain data available) as long as the SCEPTR variant that is being used has at least some partial information to go on. - -| Column name | Column datatype | Column contents | -|---|---|---| -|TRAV|`str`|IMGT symbol for the alpha chain V gene| -|CDR3A|`str`|Amino acid sequence of the alpha chain CDR3, including the first C and last W/F residues, in all caps| -|TRAJ|`str`|IMGT symbol for the alpha chain J gene| -|TRBV|`str`|IMGT symbol for the beta chain V gene| -|CDR3B|`str`|Amino acid sequence of the beta chain CDR3, including the first C and last W/F residues, in all caps| -|TRBJ|`str`|IMGT symbol for the beta chain J gene| - -## Usage - -### Functional API (`sceptr.sceptr`) - -The eponymous `sceptr` submodule is the easiest way to use SCEPTR. -It loads the default SCEPTR variant (currently `ab_sceptr`) and exposes its methods directly as module-level functions. - -> [!TIP] -> To use the functional API, import the `sceptr` submodule like so: -> ``` -> from sceptr import sceptr -> ``` -> Attempting to access the submodule as an attribute of the top level module -> ``` -> import sceptr -> -> sceptr.sceptr.calc_vector_representations() #...do something... -> ``` -> will result in an error. - ---- - -#### `sceptr.sceptr.calc_vector_representations(instances: DataFrame) -> ndarray` - -Map a table of TCRs provided as a pandas `DataFrame` in the above format to a set of vector representations. - -Parameters: - -- tcrs (`DataFrame`): DataFrame in the presribed format. - -Returns: - -A 2D [numpy](https://numpy.org/) [`ndarray`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html) object where every row vector corresponds to a row in the original TCR `DataFrame`. -The returned array will have shape (N, D) where N is the number of TCRs in the input data and D is the dimensionality of the SCEPTR model. - ---- - -#### `sceptr.sceptr.calc_cdist_matrix(anchors: DataFrame, comparisons: DataFrame) -> ndarray` - -Generate a cdist matrix between two collections of TCRs. - -Parameters: - -- anchor_tcrs (`DataFrame`): DataFrame in the prescribed format, representing TCRs from collection A. -- comparison_tcrs (`DataFrame`): DataFrame in the prescribed format, representing TCRs from collection B. - -Returns: - -A 2D numpy `ndarray` representing a cdist matrix between TCRs from collection A and B. -The returned array will have shape (X, Y) where X is the number of TCRs in collection A and Y is the number of TCRs in collection B. - ---- - -#### `sceptr.sceptr.calc_pdist_vector(instances: DataFrame) -> ndarray` - -Generate a pdist set of distances between each pair of TCRs in the input data. - -Parameters: - -- tcrs (`DataFrame`): DataFrame in the prescribed format. - -Returns - -A 2D numpy `ndarray` representing a pdist vector of distances between each pair of TCRs in the input data. -The returned array will have shape (1/2 * N * (N-1),), where N is the number of TCRs in the input data. - ---- - -### Loading specific SCEPTR variants (`sceptr.variant`) - -For more curious users, model variants are available to load and use through the `sceptr.variant` submodule. - -The module exposes functions, each named after a particular model variant, which when called, will return a `Sceptr` object corresponding to the selected model variant. -This `Sceptr` object will then have the methods: `calc_pdist_vector`, `calc_cdist_matrix`, and `calc_vector_representations` available to use, with function signatures exactly as defined above for the functional API in the `sceptr.sceptr` submodule. - -#### Paired-chain variants - -|Name|Description| -|---|---| -|`sceptr.variant.default`|default model used by the functional API| -|`sceptr.variant.mlm_only`|default model trained without autocontrastive learning| -|`sceptr.variant.left_aligned`|similar to default model but with learnable token embeddings and a sinusoidal position information embedding method more similar to the original NLP BERT/transformer models| -|`sceptr.variant.cdr3_only`|only uses the CDR3 loops as input| -|`sceptr.variant.cdr3_only_mlm_only`|only uses CDR3 loops as input, and did not receive autocontrastive learning| -|`sceptr.variant.large`|larger variant with model dimensionality 128| -|`sceptr.variant.small`|smaller variant with model dimensionality 32| -|`sceptr.variant.tiny`|smaller variant with model dimensionality 16| -|`sceptr.variant.blosum`|variant using BLOSUM62 embeddings instead of one-hot| -|`sceptr.variant.average_pooling`|variant using the average-pooling method to generate the TCR representation vector| -|`sceptr.variant.shuffled_data`|variant trained on the Tanno et al. dataset with randomised alpha/beta pairing| -|`sceptr.variant.synthetic_data`|variant trained using synthetic TCR sequences generated by OLGA| -|`sceptr.variant.dropout_noise_only`|variant trained without residue/chain dropping during autocontrastive learning| -|`sceptr.variant.finetuned`|variant fine-tuned using supervised contrastive learning for six pMHCs with peptides GILGFVFTL, NLVPMVATV, SPRWYFYYL, TFEYVSQPFLMDLE, TTDPSFLGRY and YLQPRTFLL (from [VDJdb](https://vdjdb.cdr3.net/))| - -#### Single-chain variants - -|Name|Description -|---|---| -|`sceptr.variant.a_sceptr`|alpha-chain only variant| -|`sceptr.variant.b_sceptr`|beta-chain only variant| From 4816fa7d1cc86453c8fbdbc12796a19bebf9171b Mon Sep 17 00:00:00 2001 From: Yuta Nagano <52748151+yutanagano@users.noreply.github.com> Date: Sun, 9 Jun 2024 15:04:04 +0100 Subject: [PATCH 15/31] Center badges and documentation link --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index bafdc84..af7672e 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # SCEPTR -
+
![Tests](https://github.com/yutanagano/sceptr/actions/workflows/tests.yaml/badge.svg) [![License](https://img.shields.io/badge/license-MIT-blue)](https://github.com/yutanagano/tidytcells?tab=MIT-1-ov-file#readme) From ce6fde7e77c3877262d8e8ea4b8cc1388d59cfd5 Mon Sep 17 00:00:00 2001 From: Yuta Nagano <52748151+yutanagano@users.noreply.github.com> Date: Sun, 9 Jun 2024 15:05:13 +0100 Subject: [PATCH 16/31] Improve readme heading --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index af7672e..1df894b 100644 --- a/README.md +++ b/README.md @@ -1,11 +1,11 @@ -# SCEPTR -
+# SCEPTR + ![Tests](https://github.com/yutanagano/sceptr/actions/workflows/tests.yaml/badge.svg) [![License](https://img.shields.io/badge/license-MIT-blue)](https://github.com/yutanagano/tidytcells?tab=MIT-1-ov-file#readme) -Check out the [documentation page](about:blank). +### Check out the [documentation page](about:blank).
From 8a5349170066907cf36e0c349361cd0788c15c27 Mon Sep 17 00:00:00 2001 From: Yuta Nagano <52748151+yutanagano@users.noreply.github.com> Date: Sun, 9 Jun 2024 15:16:27 +0100 Subject: [PATCH 17/31] Add pypi version badge to README --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 1df894b..a207bd5 100644 --- a/README.md +++ b/README.md @@ -2,6 +2,7 @@ # SCEPTR +[![Latest release](https://img.shields.io/pypi/v/sceptr)](https://pypi.org/p/sceptr) ![Tests](https://github.com/yutanagano/sceptr/actions/workflows/tests.yaml/badge.svg) [![License](https://img.shields.io/badge/license-MIT-blue)](https://github.com/yutanagano/tidytcells?tab=MIT-1-ov-file#readme) From 7e27a7a2ba309bedaeb2e1a6f619d54430f373a8 Mon Sep 17 00:00:00 2001 From: Yuta Nagano <52748151+yutanagano@users.noreply.github.com> Date: Sun, 9 Jun 2024 15:18:30 +0100 Subject: [PATCH 18/31] Add $ to beginning of pip install line on README --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a207bd5..223dc17 100644 --- a/README.md +++ b/README.md @@ -23,5 +23,5 @@ What's even better is that they are fully compliant with [pyrepseq](https://pyre ## Installation ```bash -pip install sceptr +$ pip install sceptr ``` From abedb48362e561ebd8968d2f71ecdad78ca14aff Mon Sep 17 00:00:00 2001 From: Yuta Nagano <52748151+yutanagano@users.noreply.github.com> Date: Sun, 9 Jun 2024 15:20:22 +0100 Subject: [PATCH 19/31] Remove back $ from pip install line to make it directly copyable --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 223dc17..a207bd5 100644 --- a/README.md +++ b/README.md @@ -23,5 +23,5 @@ What's even better is that they are fully compliant with [pyrepseq](https://pyre ## Installation ```bash -$ pip install sceptr +pip install sceptr ``` From 5da0065869015258f2259219d4c77725ea46b416 Mon Sep 17 00:00:00 2001 From: yutanagano <52748151+yutanagano@users.noreply.github.com> Date: Sun, 9 Jun 2024 21:05:51 +0100 Subject: [PATCH 20/31] Pre-emptively link to documentation in README --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a207bd5..9027bdb 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ ![Tests](https://github.com/yutanagano/sceptr/actions/workflows/tests.yaml/badge.svg) [![License](https://img.shields.io/badge/license-MIT-blue)](https://github.com/yutanagano/tidytcells?tab=MIT-1-ov-file#readme) -### Check out the [documentation page](about:blank). +### Check out the [documentation page](https://sceptr.readthedocs.io).
From a64495966018160cb986af46d8219efa76ee8bd2 Mon Sep 17 00:00:00 2001 From: Yuta Nagano <52748151+yutanagano@users.noreply.github.com> Date: Sun, 9 Jun 2024 21:18:37 +0100 Subject: [PATCH 21/31] Add links to repository in sphinx documentation --- docs/conf.py | 6 ++++++ pyproject.toml | 2 ++ 2 files changed, 8 insertions(+) diff --git a/docs/conf.py b/docs/conf.py index f1d36a2..6b2f58f 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -26,4 +26,10 @@ # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output html_theme = "sphinx_book_theme" +html_theme_options = { + "repository_url": "https://github.com/yutanagano/sceptr", + "path_to_docs": "docs", + "use_repository_button": True, + "use_issues_button": True, +} html_static_path = ["_static"] diff --git a/pyproject.toml b/pyproject.toml index 5654f14..31446a8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -31,6 +31,8 @@ dependencies = [ dynamic = ["version"] [project.urls] +Homepage = "https://sceptr.readthedocs.io" +Documentation = "https://sceptr.readthedocs.io" Repository = "https://github.com/yutanagano/sceptr" Issues = "https://github.com/yutanagano/sceptr/issues" From b4453035ba2b3114d9d19f3f463e434d17ac4305 Mon Sep 17 00:00:00 2001 From: Yuta Nagano <52748151+yutanagano@users.noreply.github.com> Date: Sun, 9 Jun 2024 21:50:19 +0100 Subject: [PATCH 22/31] Add schematic figure from preprint onto home page --- docs/about_sceptr.png | Bin 0 -> 301854 bytes docs/index.rst | 9 +++++++++ 2 files changed, 9 insertions(+) create mode 100644 docs/about_sceptr.png diff --git a/docs/about_sceptr.png b/docs/about_sceptr.png new file mode 100644 index 0000000000000000000000000000000000000000..fb996c1b0363b99fbfa88d0f9968ae68261616cd GIT binary patch literal 301854 zcmX_HbzBtR*Iv4$Q(9V5=?-a;2FV3!5b0)VLAs=*5u}#xTtGy+Q@T5rUYd8&@9+I% z*$=ZbckY~X?>WzTo-^TUD)QKv4etZ-|wHyvi%&?-cqJVrG&HCy>Jz>3OwVa$!b`$)mpzLLu)Zo5xUq#H3+l%H3 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HcmV?d00001

diff --git a/docs/index.rst b/docs/index.rst
index 730e31b..b778f69 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -11,6 +11,15 @@ And if your computer does have a `CUDA-enabled GPU ` exposes three intuitive functions: :py:func:`~sceptr.calc_vector_representations`, :py:func:`~sceptr.calc_cdist_matrix`, and :py:func:`~sceptr.calc_pdist_vector`-- and it's all you need to make full use of the SCEPTR models.
 What's even better is that they are fully compliant with `pyrepseq `_'s `tcr_metric `_ API, so sceptr will fit snugly into the rest of your repertoire analysis toolkit.
 
+.. figure:: about_sceptr.png
+   :width: 700px
+   :alt: Schematic diagrams showing a visual introduction to the architecture of SCEPTR, as well as how it was trained-- namely, autocontrastive learning and masked-language modelling.
+
+   A visual introduction to how SCEPTR works, taken from our SCEPTR preprint.
+   SCEPTR is a TCR language model (a,b) pre-trained using masked-language modelling and autocontrastive learning (c,d).
+   \* (a) The default model uses the ```` pooling method, but there is also a variant that is trained to use average-pooling (see :py:func:`sceptr.variant.average_pooling`).
+   Please see the manuscript for more details.
+
 .. toctree::
    :maxdepth: 2
    :caption: Contents:

From d6d3455f1f61f5a31c8acb66f6c3171651156933 Mon Sep 17 00:00:00 2001
From: Yuta Nagano <52748151+yutanagano@users.noreply.github.com>
Date: Sun, 9 Jun 2024 21:54:20 +0100
Subject: [PATCH 23/31] Fix typos in documentation

---
 docs/index.rst         | 2 +-
 src/sceptr/__init__.py | 2 +-
 src/sceptr/model.py    | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/index.rst b/docs/index.rst
index b778f69..e02e573 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -17,7 +17,7 @@ What's even better is that they are fully compliant with `pyrepseq `` pooling method, but there is also a variant that is trained to use average-pooling (see :py:func:`sceptr.variant.average_pooling`).
+   (a) The default model uses the ```` pooling method, but there is also a variant that is trained to use average-pooling (see :py:func:`sceptr.variant.average_pooling`).
    Please see the manuscript for more details.
 
 .. toctree::
diff --git a/src/sceptr/__init__.py b/src/sceptr/__init__.py
index 88537a7..92f9593 100644
--- a/src/sceptr/__init__.py
+++ b/src/sceptr/__init__.py
@@ -45,7 +45,7 @@ def calc_pdist_vector(instances: DataFrame) -> ndarray:
     Returns
     -------
     ndarray
-        A 2D numpy ndarray representing a pdist vector of distances between each pair of TCRs in `instances`.
+        A 1D numpy ndarray representing a pdist vector of distances between each pair of TCRs in `instances`.
         The returned array will have shape :math:`(\frac{1}{2}N(N-1),)`, where :math:`N` is the number of TCRs in `instances`.
     """
     return get_default_model().calc_pdist_vector(instances)
diff --git a/src/sceptr/model.py b/src/sceptr/model.py
index 1f7c7fa..23bae7d 100644
--- a/src/sceptr/model.py
+++ b/src/sceptr/model.py
@@ -115,7 +115,7 @@ def calc_pdist_vector(self, instances: DataFrame) -> ndarray:
         Returns
         -------
         ndarray
-            A 2D numpy ndarray representing a pdist vector of distances between each pair of TCRs in `instances`.
+            A 1D numpy ndarray representing a pdist vector of distances between each pair of TCRs in `instances`.
             The returned array will have shape :math:`(\frac{1}{2}N(N-1),)`, where :math:`N` is the number of TCRs in `instances`.
         """
         representations = self._calc_torch_representations(instances)

From f5de61d2504aeefd3f0d71b19aa0be93032e1d24 Mon Sep 17 00:00:00 2001
From: Andreas Tiffeau-Mayer 
Date: Mon, 10 Jun 2024 17:01:43 +0100
Subject: [PATCH 24/31] Readme changes

---
 README.md | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 9027bdb..183da68 100644
--- a/README.md
+++ b/README.md
@@ -10,15 +10,16 @@
-**SCEPTR** (**S**imple **C**ontrastive **E**mbedding of the **P**rimary sequence of **T** cell **R**eceptors) is a small, fast, and performant TCR representation model that can be used for alignment-free downstream TCR and TCR repertoire analysis such as TCR clustering or classification. -Our [manuscript (coming soon)](about:blank) demonstrates SCEPTR's state-of-the-art performance (as of 2024) on downstream TCR specificity prediction. +**SCEPTR** (**S**imple **C**ontrastive **E**mbedding of the **P**rimary sequence of **T** cell **R**eceptors) is a small, fast, and accurate TCR representation model that can be used for alignment-free TCR analysis, including for TCR-pMHC interaction prediction and TCR clustering (metaclonotype discovery). + +Our [manuscript (coming soon)](about:blank) demonstrates that SCEPTR can be used for few-shot TCR specificity prediction with its accuracy exceeding prior baselines. SCEPTR is a BERT-like transformer-based neural network implemented in [Pytorch](https://pytorch.org). With the default model providing best-in-class performance with only 153,108 parameters (typical protein language models have tens or hundreds of millions), SCEPTR runs fast- even on a CPU! And if your computer does have a [CUDA-enabled GPU](https://en.wikipedia.org/wiki/CUDA), the sceptr package will automatically detect and use it, giving you blazingly fast performance without the hassle. sceptr's API exposes three intuitive functions: `calc_vector_representations`, `calc_cdist_matrix`, and `calc_pdist_vector`-- and it's all you need to make full use of the SCEPTR models. -What's even better is that they are fully compliant with [pyrepseq](https://pyrepseq.readthedocs.io)'s [tcr_metric](https://pyrepseq.readthedocs.io/en/latest/api.html#pyrepseq.metric.tcr_metric.TcrMetric) API, so sceptr will fit snugly into the rest of your repertoire analysis toolkit. +What's even better is that they are fully compliant with [pyrepseq](https://pyrepseq.readthedocs.io)'s [tcr_metric](https://pyrepseq.readthedocs.io/en/latest/api.html#pyrepseq.metric.tcr_metric.TcrMetric) API, so sceptr will fit snugly into the rest of your repertoire analysis workflow. ## Installation From 7f4aa4cee9968b20fa36461f29079e48a61a0820 Mon Sep 17 00:00:00 2001 From: yutanagano <52748151+yutanagano@users.noreply.github.com> Date: Mon, 10 Jun 2024 17:10:12 +0100 Subject: [PATCH 25/31] Update README.md --- README.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 183da68..c85e95a 100644 --- a/README.md +++ b/README.md @@ -11,14 +11,13 @@ **SCEPTR** (**S**imple **C**ontrastive **E**mbedding of the **P**rimary sequence of **T** cell **R**eceptors) is a small, fast, and accurate TCR representation model that can be used for alignment-free TCR analysis, including for TCR-pMHC interaction prediction and TCR clustering (metaclonotype discovery). - -Our [manuscript (coming soon)](about:blank) demonstrates that SCEPTR can be used for few-shot TCR specificity prediction with its accuracy exceeding prior baselines. +Our [manuscript (coming soon)](about:blank) demonstrates that SCEPTR can be used for few-shot TCR specificity prediction with improved accuracy over previous methods. SCEPTR is a BERT-like transformer-based neural network implemented in [Pytorch](https://pytorch.org). With the default model providing best-in-class performance with only 153,108 parameters (typical protein language models have tens or hundreds of millions), SCEPTR runs fast- even on a CPU! And if your computer does have a [CUDA-enabled GPU](https://en.wikipedia.org/wiki/CUDA), the sceptr package will automatically detect and use it, giving you blazingly fast performance without the hassle. -sceptr's API exposes three intuitive functions: `calc_vector_representations`, `calc_cdist_matrix`, and `calc_pdist_vector`-- and it's all you need to make full use of the SCEPTR models. +sceptr's API exposes three intuitive functions: `calc_vector_representations`, `calc_cdist_matrix`, and `calc_pdist_vector`- and it's all you need to make full use of the SCEPTR models. What's even better is that they are fully compliant with [pyrepseq](https://pyrepseq.readthedocs.io)'s [tcr_metric](https://pyrepseq.readthedocs.io/en/latest/api.html#pyrepseq.metric.tcr_metric.TcrMetric) API, so sceptr will fit snugly into the rest of your repertoire analysis workflow. ## Installation From 93854f599054c6fdb2155cee1363238e7e69250e Mon Sep 17 00:00:00 2001 From: Yuta Nagano <52748151+yutanagano@users.noreply.github.com> Date: Mon, 10 Jun 2024 20:46:16 +0100 Subject: [PATCH 26/31] Add readthedocs configuration --- .readthedocs.yaml | 28 ++++++++++++++++++++++++++++ docs/requirements.txt | 1 + 2 files changed, 29 insertions(+) create mode 100644 .readthedocs.yaml create mode 100644 docs/requirements.txt diff --git a/.readthedocs.yaml b/.readthedocs.yaml new file mode 100644 index 0000000..8e7f229 --- /dev/null +++ b/.readthedocs.yaml @@ -0,0 +1,28 @@ +# .readthedocs.yaml +# Read the Docs configuration file +# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details + +# Required +version: 2 + +# Set the OS, Python version and other tools you might need +build: + os: ubuntu-22.04 + tools: + python: "3.12" + +# Build documentation in the "docs/" directory with Sphinx +sphinx: + configuration: docs/conf.py + +# Optionally build your docs in additional formats such as PDF and ePub +# formats: +# - pdf +# - epub + +# Optional but recommended, declare the Python requirements required +# to build your documentation +# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html +python: + install: + - requirements: docs/requirements.txt diff --git a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 0000000..31c781b --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1 @@ +sphinx-book-theme From 1582dae82d3c94319f5d47b1d1522513a398707f Mon Sep 17 00:00:00 2001 From: Yuta Nagano <52748151+yutanagano@users.noreply.github.com> Date: Mon, 10 Jun 2024 20:53:31 +0100 Subject: [PATCH 27/31] Simplify rtd yaml --- .readthedocs.yaml | 15 --------------- 1 file changed, 15 deletions(-) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 8e7f229..8e3f9ba 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -1,28 +1,13 @@ -# .readthedocs.yaml -# Read the Docs configuration file -# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details - -# Required version: 2 -# Set the OS, Python version and other tools you might need build: os: ubuntu-22.04 tools: python: "3.12" -# Build documentation in the "docs/" directory with Sphinx sphinx: configuration: docs/conf.py -# Optionally build your docs in additional formats such as PDF and ePub -# formats: -# - pdf -# - epub - -# Optional but recommended, declare the Python requirements required -# to build your documentation -# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html python: install: - requirements: docs/requirements.txt From 46b91da9a9ad69c8cee696ffbde9b46cfc904450 Mon Sep 17 00:00:00 2001 From: Yuta Nagano <52748151+yutanagano@users.noreply.github.com> Date: Mon, 10 Jun 2024 20:57:38 +0100 Subject: [PATCH 28/31] Switch to optional dependencies for rtd import --- .readthedocs.yaml | 5 ++++- docs/requirements.txt | 1 - pyproject.toml | 1 + 3 files changed, 5 insertions(+), 2 deletions(-) delete mode 100644 docs/requirements.txt diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 8e3f9ba..962b5e9 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -10,4 +10,7 @@ sphinx: python: install: - - requirements: docs/requirements.txt + - method: pip + path: . + extra_requirements: + - docs diff --git a/docs/requirements.txt b/docs/requirements.txt deleted file mode 100644 index 31c781b..0000000 --- a/docs/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -sphinx-book-theme diff --git a/pyproject.toml b/pyproject.toml index 31446a8..4ecb0d7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -44,6 +44,7 @@ dev = [ "sphinx", "sphinx_book_theme", ] +docs = ["sphinx_book_theme"] [tool.setuptools] include-package-data = true From a990d6f816c7f3893035ab0e3465198cc5b56f0a Mon Sep 17 00:00:00 2001 From: Yuta Nagano <52748151+yutanagano@users.noreply.github.com> Date: Mon, 10 Jun 2024 20:57:48 +0100 Subject: [PATCH 29/31] Add docs badge to README --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index c85e95a..d5f40af 100644 --- a/README.md +++ b/README.md @@ -4,6 +4,7 @@ [![Latest release](https://img.shields.io/pypi/v/sceptr)](https://pypi.org/p/sceptr) ![Tests](https://github.com/yutanagano/sceptr/actions/workflows/tests.yaml/badge.svg) +[![Documentation Status](https://readthedocs.org/projects/sceptr/badge/?version=latest)](https://sceptr.readthedocs.io) [![License](https://img.shields.io/badge/license-MIT-blue)](https://github.com/yutanagano/tidytcells?tab=MIT-1-ov-file#readme) ### Check out the [documentation page](https://sceptr.readthedocs.io). From 7c44f851021e2f9a1d2e224baa7ffaeb96eb796e Mon Sep 17 00:00:00 2001 From: Yuta Nagano <52748151+yutanagano@users.noreply.github.com> Date: Mon, 10 Jun 2024 21:04:06 +0100 Subject: [PATCH 30/31] Bump version to 1.0.0 --- src/sceptr/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/sceptr/__init__.py b/src/sceptr/__init__.py index 92f9593..e4be567 100644 --- a/src/sceptr/__init__.py +++ b/src/sceptr/__init__.py @@ -10,7 +10,7 @@ from pandas import DataFrame -__version__ = "1.0.0-beta.1" +__version__ = "1.0.0" def calc_cdist_matrix(anchors: DataFrame, comparisons: DataFrame) -> ndarray: From 663db2398ee623f70dfbb95970b9ab7c9305fd9e Mon Sep 17 00:00:00 2001 From: Yuta Nagano <52748151+yutanagano@users.noreply.github.com> Date: Mon, 10 Jun 2024 21:05:22 +0100 Subject: [PATCH 31/31] Bump dev status to stable on pyproject toml --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 4ecb0d7..d751877 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,7 +15,7 @@ description = "Fast and performant TCR representation model" readme = "README.md" keywords = ["TCR", "TR", "T cell", "transformer", "bert", "MLM", "immunology", "bioinformatics"] classifiers = [ - "Development Status :: 4 - Beta", + "Development Status :: 5 - Production/Stable", "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent",