The identification of microglia subtypes is important for understanding the role of innate immunity in neurodegenerative diseases. Current methods of unsupervised cell type identification assume a small noise-to-signal ratio of transcriptome measurements to produce well-separated cell clusters. However, identification of subtypes can be obscured by gene expression noise, which diminishes the distances in transcriptome space between distinct cell types, blurs boundaries, and reduces reproducibility. Here we use Fokker-Planck (FP) diffusion maps to model cellular differentiation as a stochastic process whereby cells settle into local minima that correspond to cell subtypes, in a potential landscape constructed from transcriptome data using a nearest neighbor graph approach. By applying critical transition fields, we identify individual cells on the verge of transitioning between subtypes, revealing microglial cells in an inactivated, homeostatic state before radially transitioning into various specialized subtypes. Specifically, we show that cells from Alzheimer's disease patients are enriched in a microglia subtype associated to antigen presentation and T-cell recruitment, and are depleted in an anti-inflammatory subtype.