Prepare for first stable release

.readthedocs.yaml

@@ -0,0 +1,16 @@
+version: 2
+
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.12"
+
+sphinx:
+  configuration: docs/conf.py
+
+python:
+  install:
+    - method: pip
+      path: .
+      extra_requirements:
+        - docs

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.

README.md

@@ -1,179 +1,28 @@
-# SCEPTR
-
-> [!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.
+<div align="center">
 
-#### Using `pip`
-
-From your Python environment, run the following replacing `<VERSION_TAG>` 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@<VERSION_TAG>
-```
-
-#### Manual install
+# SCEPTR
 
-You can also clone the repository, and from within your Python environment, navigate to the project root directory and run:
+[![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)
 
-```bash
-pip install .
-```
+### Check out the [documentation page](https://sceptr.readthedocs.io).
 
-Note that even for manual installation, you still need `git-lfs` to properly de-reference the stub files at `git-clone`-ing time.
+</div>
 
-#### Troubleshooting
+**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 improved accuracy over previous methods.
 
-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 workflow.
 
-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@<VERSION_TAG>
+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/[email protected]/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|

docs/api.rst

@@ -2,7 +2,8 @@ API reference
 =============
 
 .. toctree::
-	:maxdepth: 2
+	:maxdepth: 1
 
-	sceptr_sceptr
+	sceptr
 	sceptr_variant
+	sceptr_model

docs/conf.py

@@ -8,27 +8,28 @@
 # -- Project information -----------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
 
-project = 'sceptr'
-copyright = '2024, Yuta Nagano'
-author = 'Yuta Nagano'
-version = sceptr.VERSION
+project = "sceptr"
+copyright = "2024, Yuta Nagano"
+author = "Yuta Nagano"
+release = 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_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"]

docs/index.rst

@@ -1,13 +1,24 @@
-.. 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
+======
 
-SCEPTR: A fast and performant TCR representation model
-======================================================
+**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 (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 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 :ref:`API <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 <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.
+
+.. 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 ``<cls>`` 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

docs/installation.rst

@@ -12,44 +12,38 @@ From `Source <https://github.com/yutanagano/sceptr>`_
 -----------------------------------------------------
 
 .. important::
-	To install `sceptr` from source, you must have `git-lfs <https://git-lfs.com/>`_ installed and set up on your system.
+	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 `<VERSION_TAG>` with the appropriate version specifier (e.g. `v1.0.0-beta.1`).
+From your Python environment, run the following replacing ``<VERSION_TAG>`` 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
 
 	$ pip install git+https://github.com/yutanagano/sceptr.git@<VERSION_TAG>
 
-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
 ...............
 
-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@<VERSION_TAG>
 
-This is `a known issue <https://github.com/git-lfs/git-lfs/issues/5749>`_ for `git` version `2.45.1` and is fixed from version `2.45.2`.
+This is `a known issue <https://github.com/git-lfs/git-lfs/issues/5749>`_ for ``git`` version ``2.45.1`` and is fixed from version ``2.45.2``.

docs/sceptr.rst

@@ -0,0 +1,7 @@
+.. _api:
+
+``sceptr``
+==========
+
+.. automodule:: sceptr
+	:members:

docs/sceptr_model.rst

@@ -0,0 +1,5 @@
+``sceptr.model``
+================
+
+.. autoclass:: sceptr.model.Sceptr()
+	:members: