We have previously introduced the application of probabilistic models for elucidation of statistical relationships from single cell proteomic data (Sachs et al, Science 2005), an approach enabled by the key insight that each cell may be considered an observation of the underlying biological system. The application of this approach has been partially limited by the low dimensionality of the data modality (flow cytometry), which enabled measurements of only up to ~10 proteins of interest per cell. In 2011, we introduced a next generation single cell proteomic technology, mass cytometry or CyTOF (cytometry time of flight, Bendall et all, Science 2011), which enables quantification of 30-40 parameters per cell.
In this talk, I will describe the CyTOF technology and illuminate its advantages and potential pitfalls. I will then discuss my perspective on causal modeling of single cell data and what hurdles remain in this application.
Her work involves probabilistic modeling of signal transduction pathways. She uses probabilistic approaches to study how different proteins in a signaling pathway depend upon and influence each other.
This information is extracted from data, in which these proteins have been measured many times, each time in a single cell, using a technology called "flow cytometry".
