hematoData

<p>Binarized expression data of 33 transcription factors involved
in early differentiation of primitive erythroid and endothelial
cells (3934 cells).</p>

datasets

data

We report an information-theoretic method which learns a large class of causal or non-causal graphical models from purely observational data, while including the effects of unobserved latent variables, commonly found in many datasets. Starting from a complete graph, the method iteratively removes dispensable edges, by uncovering significant information contributions from indirect paths, and assesses edge-specific confidences from randomization of available data. The remaining edges are then oriented based on the signature of causality in observational data. This approach can be applied on a wide range of datasets and provide new biological insights on regulatory networks from single cell expression data, genomic alterations during tumor development and co-evolving residues in protein structures. For more information you can refer to: Verny et al. Plos Comput Biol. (2017) <doi:10.1371/journal.pcbi.1005662>.

Nadir Sella

miic

Multivariate Information Inductive Causation

Louis Verny

Severine Affeldt

Herv<c3><a9> Isambert

hematoData function

Format

Early blood development: single cell binary gene expression data — hematoData

Early blood development: single cell binary gene expression data

hematoData: Early blood development: single cell binary gene expression data

Description

Usage

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References