Enhancing experimental signals in single-cell RNA-sequencing data using graph signal processing

Burkhardt, D. B.; Stanley, J. S.; Perdigoto, A. L.; Gigante, S. A.; Herold, K. C.; Wolf, G.; Giraldez, A.; van Dijk, D.; Krishnaswamy, S.

doi:10.1101/532846

bioRxiv · 10.1101/532846

Enhancing experimental signals in single-cell RNA-sequencing data using graph signal processing

Burkhardt, D. B.·Stanley, J. S.·Perdigoto, A. L.·Gigante, S. A.·Herold, K. C.·Wolf, G.·Giraldez, A.·van Dijk, D.·Krishnaswamy, S.

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Abstract

Current methods for comparing scRNA-seq datasets collected in multiple conditions focus on discrete regions of the transcriptional state space, such as clusters of cells. Here, we quantify the effects of perturbations at the single-cell level using a continuous measure of the effect of a perturbation across the transcriptomic space. We describe this space as a manifold and develop a relative likelihood estimate of observing each cell in each of the experimental conditions using graph signal processing. This likelihood estimate can be used to identify cell populations specifically affected by a perturbation. We also develop vertex frequency clustering to extract populations of affected cells at the level of granularity that matches the perturbation response. The accuracy of our algorithm to identify clusters of cells that are enriched or depleted in each condition is on average 57% higher than the next best-performing algorithm tested. Gene signatures derived from these clusters are more accurate compared to six alternative algorithms in ground-truth comparisons.

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Burkhardt, D. B., Stanley, J. S., Perdigoto, A. L., Gigante, S. A., Herold, K. C., Wolf, G., Giraldez, A., van Dijk, D., Krishnaswamy, S.. 2019-01-28. Enhancing experimental signals in single-cell RNA-sequencing data using graph signal processing. https://doi.org/10.1101/532846

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