=================================== Welcome to scProca's documentation! =================================== .. image:: https://readthedocs.org/projects/scproca/badge/?version=latest :target: https://scproca.readthedocs.io/en/latest/?badge=latest :alt: Documentation Status **scProca** is a package designed to integrate and generate single-cell proteomics from transcriptomics, implemented in PyTorch. ---- News ---- .. raw:: html

2025-9-29: Now published in IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/JBHI.2025.3615771 .

------------ Introduction ------------ .. image:: scProca.png :align: center Overview of scProca. (A) Schematic representation of scProca within the framework of deep generative models. scProca is capable of inferring batch-corrected, integrated latent variables from scRNA-seq and CITE-seq data, and generating the expression profiles of ADT for scRNA-seq cells. (B) The variational auto-encoder with cross-attention introduced in scProca. Cross-attention is used to incorporate CITE-seq cells as references, completing representation of scRNA-seq cells in the ADT embedding space. .. image:: examples.png :align: center UMAP visualization of the low-dimensional representations obtained by scProca on the SLN dataset, colored by batch annotation and cell types. Color by batch annotation: (A) Only SLN111-D1 serves as CITE-seq data, while the others serve as scRNA-seq data. (B) Both SLN111-D1 and SLN111-D2 serve as CITE-seq data, while both SLN208-D1 and SLN208-D2 serve as scRNA-seq data. Color by cell types: (C) Same setup as (A). (D) Same setup as (B). .. toctree:: :maxdepth: 1 :caption: Installation and Reproducibility pre .. toctree:: :maxdepth: 1 :caption: Jupyter notebooks for examples Integrate and generate CITE-seq PBMC Datasets from scRNA-seq PBMC Datasets Integrate and generate CITE-seq SLN Datasets from scRNA-seq SLN Datasets Integrate and generate CITE-seq SLN Datasets from scRNA-seq SLN Datasets using experimental batches .. toctree:: :maxdepth: 1 :caption: Main functions and parameters api -------- Citation -------- .. code-block:: bibtex @article{xiong2025scproca, author = {Xiong, Jiankang and Zheng, Shuqiao and Gong, Fuzhou and Ma, Liang and Wan, Lin}, journal = {IEEE Journal of Biomedical and Health Informatics}, title = {{scProca}: A Cross-Attention-Enhanced Deep Generative Model for Single-Cell Transcriptomics and Proteomics Integration and Imputation}, year = {2025}, pages = {1-11}, keywords = {Proteomics;Proteins;Transcriptomics;RNA;Mathematical models;Biomedical measurement;Sequential analysis;Imputation;Data models;Training;Multi-omics integration;Single-cell imputation;Deep generative model;Attention mechanisms;Transcriptomics and proteomics}, doi = {10.1109/JBHI.2025.3615771} }