Stanford Advisors

All Publications

  • Supervised dimensionality reduction for exploration of single-cell data by HSS-LDA. Patterns (New York, N.Y.) Amouzgar, M., Glass, D. R., Baskar, R., Averbukh, I., Kimmey, S. C., Tsai, A. G., Hartmann, F. J., Bendall, S. C. 2022; 3 (8): 100536


    Single-cell technologies generate large, high-dimensional datasets encompassing a diversity of omics. Dimensionality reduction captures the structure and heterogeneity of the original dataset, creating low-dimensional visualizations that contribute to the human understanding of data. Existing algorithms are typically unsupervised, using measured features to generate manifolds, disregarding known biological labels such as cell type or experimental time point. We repurpose the classification algorithm, linear discriminant analysis (LDA), for supervised dimensionality reduction of single-cell data. LDA identifies linear combinations of predictors that optimally separate a priori classes, enabling the study of specific aspects of cellular heterogeneity. We implement feature selection by hybrid subset selection (HSS) and demonstrate that this computationally efficient approach generates non-stochastic, interpretable axesamenable to diverse biological processes such as differentiation over time and cell cycle. We benchmark HSS-LDA against several popular dimensionality-reduction algorithms and illustrate its utility and versatility for the exploration of single-cell mass cytometry, transcriptomics, and chromatin accessibility data.

    View details for DOI 10.1016/j.patter.2022.100536

    View details for PubMedID 36033591

  • Transition to invasive breast cancer is associated with progressive changes in the structure and composition of tumor stroma. Cell Risom, T., Glass, D. R., Averbukh, I., Liu, C. C., Baranski, A., Kagel, A., McCaffrey, E. F., Greenwald, N. F., Rivero-Gutiérrez, B., Strand, S. H., Varma, S., Kong, A., Keren, L., Srivastava, S., Zhu, C., Khair, Z., Veis, D. J., Deschryver, K., Vennam, S., Maley, C., Hwang, E. S., Marks, J. R., Bendall, S. C., Colditz, G. A., West, R. B., Angelo, M. 2022; 185 (2): 299-310.e18


    Ductal carcinoma in situ (DCIS) is a pre-invasive lesion that is thought to be a precursor to invasive breast cancer (IBC). To understand the changes in the tumor microenvironment (TME) accompanying transition to IBC, we used multiplexed ion beam imaging by time of flight (MIBI-TOF) and a 37-plex antibody staining panel to interrogate 79 clinically annotated surgical resections using machine learning tools for cell segmentation, pixel-based clustering, and object morphometrics. Comparison of normal breast with patient-matched DCIS and IBC revealed coordinated transitions between four TME states that were delineated based on the location and function of myoepithelium, fibroblasts, and immune cells. Surprisingly, myoepithelial disruption was more advanced in DCIS patients that did not develop IBC, suggesting this process could be protective against recurrence. Taken together, this HTAN Breast PreCancer Atlas study offers insight into drivers of IBC relapse and emphasizes the importance of the TME in regulating these processes.

    View details for DOI 10.1016/j.cell.2021.12.023

    View details for PubMedID 35063072

  • Evaluation of Geuenich et al.: Targeting a crucial bottleneck for analyzing single-cell multiplexed imaging data. Cell systems Averbukh, I., Greenwald, N. F., Liu, C. C., Angelo, M. 2021; 12 (12): 1121-1123


    One snapshot of the peer review process for "Automated assignment of cell identity from single-cell multiplexed imaging and proteomic data" (Geuenich et al., 2021).

    View details for DOI 10.1016/j.cels.2021.11.003

    View details for PubMedID 34914901