theislab
/

Nicheformer

@@ -1,21 +1,29 @@
-MIT License
-Copyright (c) 2024 Nicheformer Contributors
-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.

+BSD 3-Clause License
+Copyright (c) 2024, Theislab
+All rights reserved.
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

README.md CHANGED Viewed

@@ -1,87 +1,3 @@
----
-license: mit
-base_model:
-- aletlvl/Nicheformer
-tags:
-- single-cell
-- biology
-- transcriptomics
----
-# Nicheformer
-Nicheformer is a transformer-based model designed for understanding and predicting cellular niches and their interactions. The model uses masked language modeling to learn representations of cellular contexts and their relationships.
-## Model Description
-Nicheformer is built on a transformer architecture with the following key features:
-- **Architecture**: Transformer encoder with customizable number of layers and attention heads
-- **Pre-training**: Masked Language Modeling (MLM) objective with dynamic masking
-- **Input Processing**: Handles cell type, assay, and modality information
-- **Positional Encoding**: Supports both learnable and fixed positional embeddings
-- **Masking Strategy**:
-  - 80% of selected tokens are replaced with [MASK]
-  - 10% are replaced with random tokens
-  - 10% remain unchanged
-### Model Architecture
-- Transformer encoder layers: 12
-- Hidden dimension: 512
-- Attention heads: 16
-- Feedforward dimension: 1024
-- Maximum sequence length: 1500
-- Vocabulary size: 25000
-- Masking probability: 15%
-## Usage
-```python
-from transformers import AutoModelForMaskedLM, AutoTokenizer
-import anndata as ad
-# Load model and tokenizer
-model = AutoModelForMaskedLM.from_pretrained("aletlvl/Nicheformer")
-tokenizer = AutoTokenizer.from_pretrained("aletlvl/Nicheformer")
-# Load your single-cell data
-adata = ad.read_h5ad("your_data.h5ad")
-# Tokenize the data
-inputs = tokenizer(adata)
-# Get predictions
-outputs = model(**inputs)
-```
-## Training Data
-The model was trained on single-cell gene expression data from various tissues and organisms. It supports:
-- **Modalities**: spatial and dissociated
-- **Species**: human and mouse
-- **Technologies**:
-  - MERFISH
-  - CosMx
-  - Xenium
-  - 10x Genomics (various versions)
-  - CITE-seq
-  - Smart-seq v4
-## Limitations
-- The model is specifically designed for gene expression data and may not generalize to other types of biological data
-- Performance may vary depending on the quality and type of input data
-- The model works best with data from supported species and technologies
-## License
-This model is released under the MIT License. See the LICENSE file for more details.
-## Contact
-For questions and issues, please open an issue on the GitHub repository or contact the maintainers.
 # nicheformer
 This is the official repository for **Nicheformer: a foundation model for single-cell and spatial omics**
@@ -119,10 +35,19 @@ git clone https://github.com/theislab/nicheformer.git
 cd nicheformer
 pip install -e .
 ```
 ## Contact
 For questions and help requests, you can reach out (preferably) on GitHub or email to the corresponding author.
-[issue-tracker]: https://github.com/theislab/nicheformer/issues

 # nicheformer
 This is the official repository for **Nicheformer: a foundation model for single-cell and spatial omics**
 cd nicheformer
 pip install -e .
 ```
+## Nicheformer data
+We provide examplary data loading scripts in the data subdirectory that can be used as templates for loading the spatial omics datasets and datasets retreived from GEO.
+## Pretraining weights
+We provide the Nicheformer pretraining weights on Mendeley data, they can be downloaded from [here](https://data.mendeley.com/preview/87gm9hrgm8?a=d95a6dde-e054-4245-a7eb-0522d6ea7dff).
 ## Contact
 For questions and help requests, you can reach out (preferably) on GitHub or email to the corresponding author.
+[issue-tracker]: https://github.com/theislab/nicheformer/issues
+[changelog]: https://nicheformer.readthedocs.io/latest/changelog.html
+[link-docs]: https://nicheformer.readthedocs.io
+[link-api]: https://nicheformer.readthedocs.io/latest/api.html