@article {Subedi2023-fl,
	title = {Single-cell pair-wise relationships untangled by composite embedding model},
	journal = {iScience},
	volume = {26},
	number = {2},
	year = {2023},
	month = {feb},
	pages = {106025},
	abstract = {In multicellular organisms, cell identity and functions are primed and refined through interactions with other surrounding cells. Here, we propose a scalable machine learning method, termed SPRUCE, which is designed to systematically ascertain common cell-cell communication patterns embedded in single-cell RNA-seq data. We applied our approach to investigate tumor microenvironments consolidating multiple breast cancer datasets and found seven frequently observed interaction signatures and underlying gene-gene interaction networks. Our results implicate that a part of tumor heterogeneity, especially within the same subtype, is better understood by differential interaction patterns rather than the static expression of known marker genes.},
	keywords = {Cancer systems biology, machine learning, Transcriptomics},
	author = {Subedi, Sishir and Park, Yongjin P}
}
@article {Zhang2023-qg,
	title = {Unraveling dynamically encoded latent transcriptomic patterns in pancreatic cancer cells by topic modeling},
	journal = {Cell Genomics},
	volume = {3},
	number = {9},
	year = {2023},
	pages = {100388},
	abstract = {Summary Building a comprehensive topic model has become an important research tool in single-cell genomics. With a topic model, we can decompose and ascertain distinctive cell topics shared across multiple cells, and the gene programs implicated by each topic can later serve as a predictive model in translational studies. Here, we present a Bayesian topic model that can uncover short-term RNA velocity patterns from a plethora of spliced and unspliced single-cell RNA-sequencing (RNA-seq) counts. We showed that modeling both types of RNA counts can improve robustness in statistical estimation and can reveal new aspects of dynamic changes that can be missed in static analysis. We showcase that our modeling framework can be used to identify statistically significant dynamic gene programs in pancreatic cancer data. Our results discovered that seven dynamic gene programs (topics) are highly correlated with cancer prognosis and generally enrich immune cell types and pathways.},
	keywords = {machine learning, pancreatic cancer, pancreatic ductal adenocarcinoma, RNA velocity, single-cell RNA-seq, topic model, variational autoencoder},
	author = {Zhang, Yichen and Khalilitousi, Mohammadali (sam) and Park, Yongjin P}
}
@article {9349,
	title = {Switching nonparametric regression models for multi-curve data},
	journal = {Canadian Journal of Statistics},
	volume = {45},
	year = {2017},
	pages = {442{\textendash}460},
	keywords = {EM algorithm, Functional data analysis, latent variables, machine learning, MSC 2010: Primary 62G08, nonparametric regression, power usage, secondary 62G05, switching nonparametric regression model},
	issn = {1708-945X},
	doi = {10.1002/cjs.11331},
	url = {http://dx.doi.org/10.1002/cjs.11331},
	author = {de Souza, Camila P. E. and Heckman, Nancy E. and Xu, Fan}
}
@article { ISI:000343808700001,
	title = {Switching nonparametric regression models},
	journal = {JOURNAL OF NONPARAMETRIC STATISTICS},
	volume = {26},
	number = {4},
	year = {2014},
	month = {OCT 2},
	pages = {617-637},
	publisher = {TAYLOR \& FRANCIS LTD},
	type = {Article},
	address = {4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND},
	abstract = {We propose a methodology to analyse data arising from a curve that, over its domain, switches among J states. We consider a sequence of response variables, where each response y depends on a covariate x according to an unobserved state z. The states form a stochastic process and their possible values are j=1, horizontal ellipsis , J. If z equals j the expected response of y is one of J unknown smooth functions evaluated at x. We call this model a switching nonparametric regression model. We develop an Expectation-Maximisation algorithm to estimate the parameters of the latent state process and the functions corresponding to the J states. We also obtain standard errors for the parameter estimates of the state process. We conduct simulation studies to analyse the frequentist properties of our estimates. We also apply the proposed methodology to the well-known motorcycle dataset treating the data as coming from more than one simulated accident run with unobserved run labels.},
	keywords = {EM algorithm, latent variables, machine learning, mixture of Gaussian processes, motorcycle data, nonparametric regression},
	issn = {1048-5252},
	doi = {10.1080/10485252.2014.941364},
	author = {de Souza, Camila P. E. and Heckman, Nancy E.}
}
