cytometryContinuous: The continuous cytometry network

In itsrainingdata/sparsebn: Learning Sparse Bayesian Networks from High-Dimensional Data

Description

Data and network for analyzing the flow cytometry experiment from Sachs et al. (2005) [1]. This dataset contains the raw measurements from these experiments.

Usage

data(cytometryContinuous)

Format

A list with three components:

• dag An edgeList containing the consensus network (11 nodes, 17 edges).

• data A data.frame with 11 variables and 7466 observations.

• ivn A list specifying which nodes are under intervention in each observation. Compatible with the input to sparsebnData.

Details

The dataset consists of n = 7466 observations of p = 11 continuous variables corresponding to different proteins and phospholipids in human immune system cells, and each observation indicates the measured level of each biomolecule in a single cell under different experimental interventions. Based on this data, a consensus network was reconstructed and validated, which is included as well.

References

[1] Sachs, Karen, et al. "Causal protein-signaling networks derived from multiparameter single-cell data." Science 308.5721 (2005): 523-529.

Examples

# Create a valid sparsebnData object from the cytometry data
data(cytometryContinuous)
dat <- sparsebnData(cytometryContinuous$data, type = "c", ivn = cytometryContinuous$ivn)

itsrainingdata/sparsebn documentation built on Sept. 8, 2020, 3:15 a.m.