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Methods for causal inference from gene perturbation experiments and validation.

Abstract：

:Inferring causal effects from observational and interventional data is a highly desirable but ambitious goal. Many of the computational and statistical methods are plagued by fundamental identifiability issues, instability, and unreliable performance, especially for large-scale systems with many measured variables. We present software and provide some validation of a recently developed methodology based on an invariance principle, called invariant causal prediction (ICP). The ICP method quantifies confidence probabilities for inferring causal structures and thus leads to more reliable and confirmatory statements for causal relations and predictions of external intervention effects. We validate the ICP method and some other procedures using large-scale genome-wide gene perturbation experiments in Saccharomyces cerevisiae The results suggest that prediction and prioritization of future experimental interventions, such as gene deletions, can be improved by using our statistical inference techniques.

journal_name

Proc Natl Acad Sci U S A

journal_title

Proceedings of the National Academy of Sciences of the United States of America

authors

Meinshausen N,Hauser A,Mooij JM,Peters J,Versteeg P,Bühlmann P

doi

10.1073/pnas.1510493113

subject

Has Abstract

pub_date

2016-07-05 00:00:00

pages

7361-8

issue

27

eissn

0027-8424

issn

1091-6490

pii

1510493113

journal_volume

113

pub_type

杂志文章

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