Non-negative Independent Factor Analysis for single cell RNA-seq

Mao, W.; Baran Pouyan, M.; Kostka, D.; Chikina, M.

doi:10.1101/2020.01.31.927921

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bioRxiv · 10.1101/2020.01.31.927921

Non-negative Independent Factor Analysis for single cell RNA-seq

Mao, W.·Baran Pouyan, M.·Kostka, D.·Chikina, M.

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Abstract

MotivationSingle-cell RNA-seq analysis has emerged as a powerful tool for understanding inter-cellular heterogeneity. Due to the inherent noise of the data, computational techniques often rely on dimensionality reduction (DR) as both a pre-processing step and an analysis tool. Ideally, dimensionality reduction should preserve the biological information while discarding the noise. However if the dimensionality reduction is to be used directly to gain biological insight it must also be interpretable - that is the individual dimensions of the reduction should correspond to specific biological variables such as cell-type identity or pathway activity. Maximizing biological interpretability necessitates making assumption about the data structures and the choice of the model is critical. ResultsWe present a new probabilistic single-cell factor analysis model, Non-negative Independent Factor Analysis (NIFA), that incorporates different interpretability inducing assumptions into a single modeling framework. The key advantage of our NIFA model is that it simultaneously models uni- and multi-modal latent factors, and thus isolates discrete cell-type identity and continuous pathway activity into separate components. We apply our approach to a range of datasets where cell-type identity is known, and we show that NIFA-derived factors outperform results from ICA, PCA, NMF and scCoGAPS (an NMF method designed for single-cell data) in terms of disentangling biological sources of variation. Studying an immunotherapy dataset in detail, we show that NIFA is able to reproduce and refine previous findings in a single analysis framework and enables the discovery of new clinically relevant cell states. AvailabilityNFIA is a R package which is freely available at GitHub (https://github.com/wgmao/NIFA). Contactmchikina@pitt.edu Supplementary informationSupplementary data are available at Bioinformatics online.

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Mao, W., Baran Pouyan, M., Kostka, D., Chikina, M.. 2020-02-02. Non-negative Independent Factor Analysis for single cell RNA-seq. https://doi.org/10.1101/2020.01.31.927921

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