This appyter is the first part of the two-part ENKEFALOS analysis pipeline. It takes a list of differentially expressed genes (recommended to be from a neural tissue/cell sample) and helps identify genes that are significantly associated with neural electrophysiological and/or morphological measures using data derived from a study done by the Pavlidis Lab at the University of British Columbia. You can find the description of the data and how it was derived here. The second part of the ENKEFALOS appyter can be utilized for more downstream, single-gene analysis based on your results from this appyter's results.

Framework

There are several sections in this appyter, for which we have a brief overview below. If you would like a more comprehensive guide to how to use ENKEFALOS, please refer to our user guide here.

Appyter 1:

• Takes in your genes of interest (GOI) as well as a FDR threshold for analyses.
• Displays genes from your GOI that had significant correlations with electrophysiological/morphological measures.
• Prints out the number of enriched genes as well as what the genes with significant correlations are.
• Calls StringDB to create a gene interactome of your enriched genes. Will tabulate the number of interactions each gene has.

Reference

1. Bomkamp C, Tripathy SJ, Bengtsson Gonzales C, Hjerling-Leffler J, Craig AM, et al. (2019) Transcriptomic correlates of electrophysiological and morphological diversity within and across excitatory and inhibitory neuron classes. PLOS Computational Biology 15(6): e1007113.