In her PhD work, Pe'er demonstrated that
Bayesian networks can describe interactions between thousands of genes, enabling the analysis of data from newly available
DNA microarrays, which generate thousands of noisy measurements of gene expression. The approach has been widely applied to genome-scale sequencing data. In her postdoctoral work, she used this framework to study protein signaling networks in multivariate
flow cytometry data. At Columbia, Pe'er applied Bayesian networks to integrate different data types for the study of
gene regulatory networks, determining how DNA sequence variation alters the regulation of gene expression, with a view towards
personalized medicine. The Pe'er research group has developed a series of methods for high-throughput single-cell data analysis, initially to address a new high-dimensional data type derived from
mass cytometry, which quantifies a few dozen proteins per cell for millions of cells at a time. They introduced the application of non-linear dimensionality reduction by
t-distributed stochastic neighbor embedding (t-SNE) to visualize high-dimensional single-cell RNA sequencing data, and the use of a
nearest neighbors graph to represent the data manifold of RNA-defined cell states. The Pe'er group used this formalization to identify discrete cell types or cell states by applying the
Louvain community detection method to cluster data, and demonstrated that cells can be ordered along
differentiation trajectories from individual samples, due to the asynchrony of cells found in tissue samples. In 2020, the Pe'er and Fabian Theis groups presented CellRank, an algorithm that uncovers cellular dynamics by combining trajectories based on cell-cell similarity with local RNA velocity information, which identifies nascent transcriptional states by the proportion of spliced-to-unspliced RNA transcripts. Pe'er applies these methods to model biological questions around cellular plasticity and single-cell phenotypic variation in cancer, developmental biology, and immunology, including
tumor microenvironments,
metastasis ==Selected publications==