scMarco

Visualize binarized gene expression for combinatorial marker discovery

scMarco (Chen et al. 2023) aims to streamline the selection of actionable marker combinations with a friendly exploration interface powered by a Bayesian mixture model (Davis et al. 2020; Özel et al. 2021) to binarize gene expression in scRNA-seq.

scMarco can be run locally on your personal computer if you have R and supporting packages including Shiny installed. It can also be deployed on the internet like any Shiny app if you wish to provide your datasets to the public.

To prepare your data for scMarco, we provide a Snakemake workflow to discover and binarize genes that are bimodal at per-cluster level, and instructions to convert binarized and normalized expression matrices to be imported to scMarco.

Once scMarco is configured, we recommend users to start from a cluster (From cluster) or from a gene that marks several clusters (From gene) of interest. Either tab will help you identify other clusters that could be marked when you only use one marker, and help you select another marker to specifically label it (Find distinct genes within).

Once marker combinations are found, you might want to see how other clusters in the same dataset express this combination (Co-exprssion plot).

We acknowledge that while binarization greatly reduces computational complexity, it has its limitation: Gene expression is a spectrum, and there would be clusters that are hard to categorize to either ON or OFF state. We thus encourage users to also examine log-normalized expression value and its dynamic (Plot expression trend) and decide whether a satisfactory pair of markers have been identified.

Contact

If you have any questions, please contact Yu-Chieh David Chen about suggestions and inquiries about fly lines and genetics; if you have questions, suggestions, or bugs noted about scMarco, please contact Yen-Chung Chen.

References

Chen, Yu-Chieh David, Yen-Chung Chen, Raghuvanshi Rajesh, Nathalie Shoji, Maisha Jacy, Haluk Lacin, Ted Erclik, and Claude Desplan. 2023. “Using Single-Cell RNA Sequencing to Generate Cell-Type-Specific Split-GAL4 Reagents Throughout Development.” bioRxiv. https://doi.org/10.1101/2023.02.03.527019.

Davis, Fred P, Aljoscha Nern, Serge Picard, Michael B Reiser, Gerald M Rubin, Sean R Eddy, and Gilbert L Henry. 2020. “A genetic, genomic, and computational resource for exploring neural circuit function.” eLife 9 (January): e50901. https://doi.org/10.7554/eLife.50901.

Özel, Mehmet Neset, Félix Simon, Shadi Jafari, Isabel Holguera, Yen-Chung Chen, Najate Benhra, Rana Naja El-Danaf, et al. 2021. “Neuronal Diversity and Convergence in a Visual System Developmental Atlas.” Nature 589 (7840): 88–95. https://doi.org/10.1038/s41586-020-2879-3.