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Bioinformatics. 2019 Dec 24. pii: btz949. doi: 10.1093/bioinformatics/btz949. [Epub ahead of print]

Causal network perturbations for instance-specific analysis of single cell and disease samples.

Author information

1
Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
2
Joint CMU-Pitt PhD Program in Computational Biology, Pittsburgh, Pennsylvania, USA.

Abstract

MOTIVATION:

Complex diseases involve perturbation in multiple pathways and a major challenge in clinical genomics is characterizing pathway perturbations in individual samples. This can lead to patient-specific identification of the underlying mechanism of disease thereby improving diagnosis and personalizing treatment. Existing methods rely on external databases to quantify pathway activity scores. This ignores the data dependencies and that pathways are incomplete or condition-specific.

RESULTS:

ssNPA is a new approach for subtyping samples based on deregulation of their gene networks. ssNPA learns a causal graph directly from control data. Sample-specific network neighborhood deregulation is quantified via the error incurred in predicting the expression of each gene from its Markov blanket. We evaluate the performance of ssNPA on liver development single-cell RNAseq data, where the correct cell timing is recovered; and two TCGA datasets, where ssNPA patient clusters have significant survival differences. In all analyses ssNPA consistently outperforms alternative methods, highlighting the advantage of network-based approaches.

AVAILABILITY:

http://www.benoslab.pitt.edu/Software/ssnpa/.

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

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