Data-driven approaches for improving the interpretability of patch-seq data
Patch-seq, enabling simultaneous measurement of a transcriptomic, electrophysiological, and morphological features, has recently emerged as a powerful tool for neuronal characterization. However, we show the method is susceptible to technical artifacts, including the presence of mRNA contaminants from multiple cells, that limit the interpretability of the data. We present a straightforward marker gene-based approach for controlling for these artifacts and show that our method improves the correspondence between gene expression and electrophysiological features.
neuroscience↗