There are three primary ways to use SenePy: 1) as an input list in any tool that takes a gene list, such as gene set enrichment. 2) senescence scoring directly on single-cell data. 3) Database to search for senescence markers in a specific cell type. (5/5)

In addition to aging, SenePy is widely applicable in many disease contexts. We show how it can be applied to cancer, heart disease, and infection. Here is an example of senescent-like foci in infarction spatial data (4/5)

These signatures recapitulate in vivo cellular senescence better than available gene sets derived from in vitro studies (3/5)

We derive cell-type-specific weighted signatures of cellular senescence for humans and mice and universal signatures of genes enriched in multiple signatures. We combine these signatures with a scoring tool to identify senescence in your data. (2/5) github.com/jaleesr/SenePy

Exactly. Also related is how most preprocessing workflows don’t account for cell types/condition and treat everything as one distribution.

Should we instead normalize cell a/b to some shared technical/depth factor? Eg, cell A is 0.4x depth compared to other A cells. So scale cell B to 0.4x to other B cells. Then combine? In my mind this is more true to reality