Data preprocessing and batch effect correction

Seurat was used for most processing steps where other software is not specified⁵⁸. All cell counts were pruned of cells with UMI counts below 250, cells with more than 10% mitochondrial genes, and genes expressed in fewer than 0.1% of cells. We then normalized and scaled the data with regression on UMI count to account for batch effect and percent mitochondrial genes and calculated principle components using the top 2000 most variable genes. For muscle data sets we then corrected the principle components for batch effect using Harmony⁵⁹. There are inevitably differences between experimental batches when running single cell RNA sequencing. Harmony and other batch effect correction algorithms are designed to align different batches prior to downstream clustering and dimensional reduction. UMAP and shared nearest neighbor graph construction with subsequent Louvain clustering was then run on principle components.