Problem

In scRNAseq analysis, a major hyperparameter tuning step when using Seurat is, selecting the principal components (PC) in the FindNeighbors(..., dims = ___ ) and the resolution in FindClusters(...,resolution = ___ ) functions. Depending upon the data, this parameter optimization can be very tedious, as there is no consensus for setting a specific value for these. Because ultimately, these two values in these two functions can dramatically change the cell embedding/projection in the UMAP plot and the number of clusters. And based on the number of cell clusters, further analysis such as, cell marker gene identification, renaming the cell clusters number to an actual cell name, and performing further down-stream analysis like differential cell type abundance or differential expression among cell clusters. Of course, one can take a look at the ElbowPlot() and see at which PC, the standard deviation is the lowest. But sometimes, it is good to test out all the different PCs and resulting cell clusters from it in a iterative approach.

Solution:

One idea can be, to check all possible combinations of PCS and resolutions simulatneously, to see how many cell clusters appear in the data. From this, the researcher can make a more informed decision on testing and selecting these two parameters.

Please go to analysis-script.md for the analysis and plots. Or click here