NEMix: Single-cell Nested Effects Models for Probabilistic Pathway Stimulation

Nested effects models have been used successfully for learning subcellular networks from high-dimensional perturbation effects that result from RNA interference (RNAi) experiments. Here, we further develop the basic nested effects model using high-content single-cell imaging data from RNAi screens of cultured cells infected with human rhinovirus. RNAi screens with single-cell readouts are becoming increasingly common, and they often reveal high cell-to-cell variation. As a consequence of this cellular heterogeneity, knock-downs result in variable effects among cells and lead to weak average phenotypes on the cell population level. To address this confounding factor in network inference, we explicitly model the stimulation status of a signaling pathway in individual cells. We extend the framework of nested effects models to probabilistic combinatorial knock-downs and propose NEMix, a nested effects mixture model that accounts for unobserved pathway activation. We analyzed the identifiability of NEMix and developed a parameter inference scheme based on the Expectation Maximization algorithm. In an extensive simulation study, we show that NEMix improves learning of pathway structures over classical NEMs significantly in the presence of hidden pathway stimulation. We applied our model to single-cell imaging data from RNAi screens monitoring human rhinovirus infection, where limited infection efficiency of the assay results in uncertain pathway stimulation. Using a subset of genes with known interactions, we show that the inferred NEMix network has high accuracy and outperforms the classical nested effects model without hidden pathway activity. NEMix is implemented as part of the R/Bioconductor package ‘nem’ and available at www.cbg.ethz.ch/software/NEMix.Author Summary: Experiments monitoring individual cells show that cells can behave differently even under same experimental conditions. Summarizing measurements over a population of cells can lead to weak and widely deviating signals, and subsequently applied modeling approaches, like network inference, will suffer from this information loss. Nested effects models, a method tailored to reconstruct signaling networks from high-dimensional read-outs of gene silencing experiments, have so far been only applied on the cell population level. These models assume the pathway under consideration to be activated in all cells. The signal flow is only disrupted, when genes are silenced. However, if this assumption is not met, inference results can be incorrect, because observed effects are interpreted wrongly. We extended nested effects models, to use the power of single-cell resolution data sets. We introduce a new unobserved factor, which describes the pathway activity of single cells. The pathway activity is learned for each cell during network inference. We apply our model to gene silencing screens, investigating human rhino virus infection of single cells from microscopy imaging features. Comparing the learned network to the known KEGG pathway of the genes shows that our method recovers networks significantly better than classical nested effects models without capturing of hidden signaling.