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Various methods for causal inference were applied on a dataset from the paper "Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data".

causal-inference single-cell-data

causal-inference-flow-cytometry-data's Introduction

Causal-Discovery-Flow-Cytometry-Data

In this project, various methods for causal inference were applied on a dataset from the paper Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data, where they were able to almost reconstruct a known causal signaling pathway using Bayesian networks. The following algorithms were used:

  • PC-algorithm
  • Tabu search with modified BDe score (bnlearn)
  • Greedy interventional equivalence search (GIES) algorithm
  • Backshift algorithm

Additionaly, the methods were tested on a simulated dataset, with a similar causal structure as the Sachs data.

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