Biological tissues are composed of different types of structurally organized cells which play distinct and cooperative functional roles in phenotypes. Recent spatial transcriptomics technologies have enabled spatially-resolved RNA profiling of single cells with cell identities and localizations for understanding cells’ organizations and functions. The project will develop new machine learning methods for mining RNA profiles collected from single cells and their spatial locations. The research community will benefit from the collection of tools for the analysis of spatial and single-cell genomic data in studying molecular characteristics of cellular structures in tissue. The new methods will be applied to the study of spatial cell heterogeneity of ovarian cancer and circadian rhythms in Brassica rapa. The two applications will improve understanding of cellular structure and pathology of ovarian tissues and the association of cell-specific circadian gene expression patterns with crop improvement traits. Underrepresented graduate and undergraduate students will be advised on research conduction.
Funding:
NSF BIO DBI IIBR Informatics (IIBR 2042159): Mining Spatial and Single-cell Transcriptomes to Understand Cell Locality and Heterogeneity in Tissues
Publications:
Broadbent, Charles; Song, Tianci; Kuang, Rui
Deciphering High-order Structures in Spatial Transcriptomes with Graph-guided Tucker Decomposition Proceedings Forthcoming
To appear In the Proceedings of International Conference on Intelligent Systems for Molecular Biology (ISMB) 2024, Forthcoming.
@proceedings{GraphTucker,
title = {Deciphering High-order Structures in Spatial Transcriptomes with Graph-guided Tucker Decomposition},
author = {Charles Broadbent and Tianci Song and Rui Kuang},
url = {https://github.com/kuanglab/GraphTucker/blob/main/document/GraphTucker_ISMB_2024_preprint.pdf
https://github.com/kuanglab/GraphTucker/blob/main/document/GraphTucker_ISMB_2024_Supplement.pdf},
year = {2024},
date = {2024-07-12},
urldate = {2024-07-12},
howpublished = {To appear In the Proceedings of International Conference on Intelligent Systems for Molecular Biology (ISMB) 2024},
keywords = {},
pubstate = {forthcoming},
tppubtype = {proceedings}
}
Song, Tianci; Broadbent, Charles; Kuang, Rui
GNTD: Reconstructing Spatial Transcriptomes with Graph-guided Neural Tensor Decomposition Informed by Spatial and Functional Relations Journal Article
In: Nature Communications, vol. 14, no. 8276, 2023.
@article{GNTD,
title = {GNTD: Reconstructing Spatial Transcriptomes with Graph-guided Neural Tensor Decomposition Informed by Spatial and Functional Relations},
author = {Tianci Song and Charles Broadbent and Rui Kuang},
url = {https://www.nature.com/articles/s41467-023-44017-0},
year = {2023},
date = {2023-04-01},
urldate = {2023-04-01},
journal = {Nature Communications},
volume = {14},
number = {8276},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Atkins, Thomas Karl; Song, Tianci; Kuang, Rui
FIST-nD: A tool for n-dimensional spatial transcriptomics data imputation via graph-regularized tensor completio Technical Report
2022.
@techreport{FIST_nD,
title = {FIST-nD: A tool for n-dimensional spatial transcriptomics data imputation via graph-regularized tensor completio},
author = {Thomas Karl Atkins and Tianci Song and Rui Kuang
},
url = {https://www.biorxiv.org/content/10.1101/2022.10.12.511928v1.article-metrics},
doi = {10.1101/2022.10.12.511928},
year = {2022},
date = {2022-10-16},
urldate = {2022-10-16},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Song, Tianci; Markham, Kathleen K.; Li, Zhuliu; Muller, Kristen E.; Greenham, Kathleen; Kuang, Rui
Detecting Spatially Co-expressed Gene Clusters with Functional Coherence by Graph-regularized Convolutional Neural Network Journal Article
In: Bioinformatics, vol. 38, no. 5, pp. 1344–1352, 2022.
@article{spatialGCNNb,
title = {Detecting Spatially Co-expressed Gene Clusters with Functional Coherence by Graph-regularized Convolutional Neural Network},
author = {Tianci Song and Kathleen K. Markham and Zhuliu Li and Kristen E. Muller and Kathleen Greenham and Rui Kuang},
url = {https://academic.oup.com/bioinformatics/article/38/5/1344/6448221},
year = {2022},
date = {2022-03-01},
urldate = {2021-11-30},
journal = {Bioinformatics},
volume = {38},
number = {5},
pages = {1344–1352},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Li, Zhuliu; Song, Tianci; Yong, Jeongsik; Kuang, Rui
Imputation of spatially-resolved transcriptomes by graph-regularized tensor completion Journal Article
In: PLoS computational biology, vol. 17, no. 4, pp. e1008218, 2021.
@article{li2021imputation,
title = {Imputation of spatially-resolved transcriptomes by graph-regularized tensor completion},
author = {Zhuliu Li and Tianci Song and Jeongsik Yong and Rui Kuang},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008218},
year = {2021},
date = {2021-01-01},
journal = {PLoS computational biology},
volume = {17},
number = {4},
pages = {e1008218},
publisher = {Public Library of Science San Francisco, CA USA},
keywords = {},
pubstate = {published},
tppubtype = {article}
}