DREMI: Conditional Density-based Analysis
of T cell Signaling in Single Cell Data

Smita Krishnaswamy, Matthew H. Spitzer, Michael Mingueneau, Sean C Bendall, Oren Litvin, Erica Stone, Dana Pe’er* and Garry P Nolan

Dremi is currently part of the Krishnaswamy Lab scprep stats toolkit. Click the following links to find the code on Github or read the article in Science.


Cellular circuits sense the environment, process signals, and compute decisions using networks of interacting proteins. To model such a system, the abundance of each activated protein species can be described as a stochastic function of the abundance of other proteins. High-dimensional single-cell technologies, like mass cytometry, offer an opportunity to characterize signaling circuit-wide. However, the challenge of developing and applying computational approaches to interpret such complex data remains.

Here, we developed computational methods, based on established statistical concepts, to characterize signaling network relationships by quantifying the strengths of network edges and deriving signaling response functions. In comparing signaling between naïve and antigen-exposed CD4+ T-lymphocytes, we find that although these two cell subtypes had similarly-wired networks, naïve cells transmitted more information along a key signaling cascade than did antigen-exposed cells.

We validated our characterization on mice lacking the extracellular-regulated MAP kinase (ERK2), which showed stronger influence of pERK on pS6 (phosphorylated-ribosomal protein S6), in naïve cells compared to antigen-exposed cells, as predicted. We demonstrate that by using cell-to-cell variation inherent in single cell data, we can algorithmically derive response functions underlying molecular circuits and drive the understanding of how cells process signals.



TIDES: Learning time-varying information flow from
single-cell epithelial to mesenchymal transition data

Smita Krishnaswamy, Nevena Zivanovic, Roshan Sharma, Dana Pe’er, Bernd Bodenmiller

You can access the publication in PLOS ONE by following the link below


TIDES or ( Trajectory Interpolated DREMI Scores) is an extension of our earlier Density Resampled Estimate of Mutual Information which quantifies time-varying edge behavior over a developmental trajectory. In particular it tracks times during development in which a regulatory relationship is strong (high mutual information) vs times when regulatory relationships are inactive (low mutual information) due to regulatory network rewiring that underlies differentiation. We also predict an overall metric of edge dynamism, which combined with TIDES allows us to predict the effect of drug perturbations on the Epithelial-to-Mesenchymal transition in breast cancer cells.