Alternatives to imputation for single-cell RNA-Seq analysis to increase statistical power of DE analysis

I have a small scRNA-Seq dataset (n = 357, inhibitory neurons). This set of cells is split almost evenly between two conditions (Case and Control). I would like to test for differential expression with MAST [1]. However, with such a small number of cells and high heterogeneity of different inhibitory neuron cell types, I am able to get only a single gene as differentially expressed (False Discovery Rate 0.05, Log Fold Change 0.15).

The best solution would be to enrich this subset of neurons in a new sample, however, are there other approaches that can be used to extract more information from this dataset? I’m not considering imputation at the moment since it seems to produce a large number of false positives [2].

---

1. Finak, Greg, et al. "MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data." Genome biology 16.1 (2015): 1-13.
2. Andrews, Tallulah S., and Martin Hemberg. "False signals induced by single-cell imputation." F1000Research 7 (2018).