Colloquium Series: Nancy Zhang

Our upcoming event for the Statistics Department Colloquium Series is scheduled for Monday, March 10 from 12:00 – 1:00pm (ET) and will be an in-person presentation Science Center Rm. 316. Lunch will be provided to guests following the talk. This week's speaker will be Nancy Zhang of the Statistics department at UPenn Wharton.

Data Integration in Spatial and Single Cell Omics: What is Erased, and Can you Recover it?

In single-cell and spatial biology, data integration refers to the alignment of cells across samples and modalities, and is an ubiquitous challenge affecting all downstream analyses. The goal in cell integration is to find cells across data sets that share the same biological state that may be obscured by technical differences.

In this talk, I will cast the cell integration problem on a continuum of weak to strong linkage, depending on the strength of feature sharing between experiments. First, I will examine integration across data modalities of weak linkage. This arises when there are few shared features between the data being integrated, for example, between single-cell RNA sequencing data and spatial proteomics data. For this, I will present MaxFuse, a method that leverages higher order relationships between all features, including unshared features, to achieve accurate integration. Next, we consider the scenario of data alignment across the same modality in clinical scale studies. For this setting, I will show that existing paradigms are overly aggressive, erasing disease and treatment effects and introducing severe data distortion. I will introduce a "pool-of-controls" experimental design concept to disentangle biological variation from unwanted variation. Based on this, I will describe CellANOVA, a novel statistical model and scalable algorithm that recovers biological signals lost during batch integration and corrects integration related data distortion. Through these two contrasting paradigms, I will share the key lessons learned and the remaining challenges in this field.

Citations:

Zhang, Z., Mathew, D., Lim, T.L. et al. Recovery of biological signals lost in single-cell batch integration with CellANOVA. Nat Biotechnol (2024). https://doi.org/10.1038/s41587-024-02463-1

Chen, S., Zhu, B., Huang, S. et al. Integration of spatial and single-cell data across modalities with weakly linked features. Nat Biotechnol 42, 1096–1106 (2024). https://doi.org/10.1038/s41587-023-01935-0