TWO-SIGMA-G: A New Competitive Gene Set Testing Framework for scRNA-seq Data Accounting for Inter-Gene and Cell-Cell Correlation

Van Buren, E.; Hu, M.; Cheng, L.; Wrobel, J.; Wilhelmsen, K.; Su, L.; Li, Y.; Wu, D.

doi:10.1101/2021.01.24.427979

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bioRxiv · 10.1101/2021.01.24.427979

TWO-SIGMA-G: A New Competitive Gene Set Testing Framework for scRNA-seq Data Accounting for Inter-Gene and Cell-Cell Correlation

Van Buren, E.·Hu, M.·Cheng, L.·Wrobel, J.·Wilhelmsen, K.·Su, L.·Li, Y.·Wu, D.

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Abstract

We propose TWO-SIGMA-G, a competitive gene set test for scRNA-seq data. TWO-SIGMA-G uses a mixed-effects regression model based on our previously published TWO-SIGMA to test for differential expression at the gene-level. This regression-based model provides flexibility and rigor at the gene-level in (1) handling complex experimental designs, (2) accounting for the correlation between biological replicates, and (3) accommodating the distribution of scRNA-seq data to improve statistical inference. Moreover, TWO-SIGMA-G uses a novel approach to adjust for inter-gene-correlation (IGC) at the set-level to control the set-level false positive rate. Simulations demonstrate that TWO-SIGMA-G preserves type-I error and increases power in the presence of IGC compared to other methods. Application to two datasets identified HIV-associated Interferon pathways in xenograft mice and pathways associated with Alzheimers disease progression in humans.

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Van Buren, E., Hu, M., Cheng, L., Wrobel, J., Wilhelmsen, K., Su, L., Li, Y., Wu, D.. 2021-01-26. TWO-SIGMA-G: A New Competitive Gene Set Testing Framework for scRNA-seq Data Accounting for Inter-Gene and Cell-Cell Correlation. https://doi.org/10.1101/2021.01.24.427979

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