Update README.md

README.md

@@ -1,24 +1,34 @@
----
-license: apache-2.0
----
-
-### Introduction
-
-We propose the **MiniAtlas** dataset, containing more than 100,000 scATAC-seq with paired scRNA-seq as training data, across 19 tissues and 56 cell types, facilitating the training of foundation models. This dataset can be used to training single-cell multiomics fundation model.
-
-![image-20250204135812866](./assets/overview.png)
-
-### Subsets
-
-This dataset is divided into four subsets to accommodate different research needs and access limitations:
-
-1. `full_atlas_atac.h5ad` and `full_atlas_rna.h5ad` (~120k samples): full data of MiniAtlas, containing all tissues and cell types. 
-2. Evaluation set for different tissues: containing three tissues (Kidney, PBMC, BMMC), can be used to cell-type annotation or RNA-prediction fine-tuning and evaluation.
-
-### Citation
-
-If you find MiniAtlas useful for your research and applications, please cite using this BibTeX:
-
-```
-```
-
+---
+license: apache-2.0
+---
+
+### Introduction
+
+We propose the **MiniAtlas** dataset, containing more than 100,000 scATAC-seq with paired scRNA-seq as training data, across 19 tissues and 56 cell types, facilitating the training of foundation models. This dataset can be used to training single-cell multiomics fundation model.
+
+![image-20250204135812866](./assets/overview.png)
+
+### Subsets
+
+This dataset is divided into four subsets to accommodate different research needs and access limitations:
+
+1. `full_atlas_atac.h5ad` and `full_atlas_rna.h5ad` (~120k samples): full data of MiniAtlas, containing all tissues and cell types. 
+2. Evaluation set for different tissues: containing three tissues (Kidney, PBMC, BMMC), can be used to cell-type annotation or RNA-prediction fine-tuning and evaluation.
+
+### Citation
+
+If you find MiniAtlas useful for your research and applications, please cite using this BibTeX:
+
+```
+@article {Wu2025.02.05.636688,
+	author = {Wu, Juncheng and Wan, Changxin and Ji, Zhicheng and Zhou, Yuyin and Hou, Wenpin},
+	title = {EpiFoundation: A Foundation Model for Single-Cell ATAC-seq via Peak-to-Gene Alignment},
+	elocation-id = {2025.02.05.636688},
+	year = {2025},
+	doi = {10.1101/2025.02.05.636688},
+	URL = {https://www.biorxiv.org/content/early/2025/02/08/2025.02.05.636688},
+	eprint = {https://www.biorxiv.org/content/early/2025/02/08/2025.02.05.636688.full.pdf},
+	journal = {bioRxiv}
+}
+```
+