Sachs

Protein expression in human immune system cells

datasets

Fit Bayesian Gaussian graphical models. The methods are separated into
two Bayesian approaches for inference: hypothesis testing and estimation. There are
extensions for confirmatory hypothesis testing, comparing Gaussian graphical models,
and node wise predictability. These methods were recently introduced in the Gaussian
graphical model literature, including
Williams (2019) <doi:10.31234/osf.io/x8dpr>,
Williams and Mulder (2019) <doi:10.31234/osf.io/ypxd8>,
Williams, Rast, Pericchi, and Mulder (2019) <doi:10.31234/osf.io/yt386>.

Philippe Rast

BGGM

Bayesian Gaussian Graphical Models

Donald Williams

Joris Mulder

Sachs function

A data frame containing 7466 cells (n = 7466) and flow cytometry
 measurements of 11 (p = 11) phosphorylated proteins and phospholipids
@references
 Sachs, K., Gifford, D., Jaakkola, T., Sorger, P., &amp; Lauffenburger, D. A. (2002).
 Bayesian network approach to cell signaling pathway modeling. Sci. STKE, 2002(148), pe38-pe38.

Sachs: Data: Sachs Network

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