Shaokun (Stella) An

Shaokun (Stella) An

she/her

Postdoc at Brigham and Women's Hospital, developing computational methods for disease research using single-cell data.

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publications

TSEE: an elastic embedding method to visualize the dynamic gene expression patterns of time series single-cell RNA sequencing data

Published in BMC Genomics, 2019

This paper presents TSEE, an elastic embedding method to visualize dynamic gene expression patterns in time series single-cell RNA sequencing data.

Recommended citation: An S, Ma L, Wan L. (2019). "TSEE: an elastic embedding method to visualize the dynamic gene expression patterns of time series single-cell RNA sequencing data." BMC Genomics. 20: 77-92.
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DensityPath: an algorithm to visualize and reconstruct cell state-transition path on density landscape for single-cell RNA sequencing data

Published in Bioinformatics, 2019

This paper introduces DensityPath, an algorithm to visualize and reconstruct cell state-transition paths on density landscapes for single-cell RNA sequencing data.

Recommended citation: Chen Z, An S, Bai X, et al. (2019). "DensityPath: an algorithm to visualize and reconstruct cell state-transition path on density landscape for single-cell RNA sequencing data." Bioinformatics. 35(15): 2593-2601.
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A New Context Tree Inference Algorithm for Variable Length Markov Chain Model with Applications to Biological Sequence Analyses

Published in Journal of Computational Biology, 2022

This paper introduces a new context tree inference algorithm for Variable Length Markov Chain Model, with applications in biological sequence analyses.

Recommended citation: An S, Ren J, Sun F, et al. (2022). "A New Context Tree Inference Algorithm for Variable Length Markov Chain Model with Applications to Biological Sequence Analyses." Journal of Computational Biology. 29(8): 839-856.
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scCausalVI disentangles single-cell perturbation responses with causality-aware generative model

Published in Cell Systems, 2025

This paper presents scCausalVI, a causality-aware generative model to learn cell-state-specific treatment effects to external stimulations. By incorporating structural causal modeling with cross-condition in silico prediction, scCausalVI enables inference of gene expression profiles under hypothetical scenarios.

Recommended citation: An, S., Cho, J. W., Cao, K., Xiong, J., Hemberg, M., & Wan, L. (2025). scCausalVI disentangles single-cell perturbation responses with causality-aware generative model. Cell Systems, 16(11), 101443.
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SpatialQuery: scalable discovery and molecular characterization of multicellular motifs from spatial omics data

Published in bioRxiv, 2026

SpatialQuery is a framework that identifies cellular motifs (recurrent multicellular co-localization patterns) and performs molecular analyses focused on the motifs. It uncovers genes modulated by spatial contexts through differential expression analysis, and detects coordinated expression changes through covariation analysis. Applications to spatial transcriptomics and proteomics data uncover cross-germ-layer signaling, disease-specific niches, and regional determinants of motif-associated transcriptional programs.

Recommended citation: An, S., Keller, M., Gehlenborg, N., & Hemberg, M. (2026). SpatialQuery: scalable discovery and molecular characterization of multicellular motifs from spatial omics data. bioRxiv. https://doi.org/10.64898/2026.04.22.720136
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