@article {Subedi2024-es,
	title = {A scalable approach to topic modelling in single-cell data by approximate pseudobulk projection},
	journal = {Life science alliance},
	volume = {7},
	number = {10},
	year = {2024},
	publisher = {Life Sci Alliance},
	abstract = {Probabilistic topic modelling has become essential in many types of single-cell data analysis. Based on probabilistic topic assignments in each cell, we identify the latent representation of cellular states. A dictionary matrix, consisting of topic-specific gene frequency vectors, provides interpret {\textellipsis}},
	author = {Subedi, S and Sumida, T S and Park, Y P}
}
