Processed Seurat Objects for Localized Marker Detector (Cluster-Independent Multiscale Marker Identification inSingle-cell RNA-seq Data using Localized Marker Detector)

These are processed Seurat objects for the biological datasets in Localized Marker Detector (https://github.com/KlugerLab/LocalizedMarkerDetector):<br><b>Tabular Muris bone marrow dataset (FACS-based and Droplet-based)</b>We used publicly available scRNA-seq mouse bone marrow datasets (FACS and Droplet-based) from the Tabular Muris Consortium, which were already pre-processed and annotated according to their workflow. In addition, we applied ALRA imputation to generate a denoised assay <code>alra</code> and added several cell annotations: (1) Cell cycle annotation using <code>CellCycleScoring</code> with the updated 2019 cell cycle gene set; (2) Module Activity Scores for the gene modules listed in our paper.<b>Mouse embryo skin dataset</b>We separated dermal cell populations from newly collected mouse embryo skin samples (aligned to the mouse genome mm10 using CellRanger (v.6.1.2)). Cells from the wildtype and SmoM2YFP mutant (SmoM2) for two consecutive days (embryonic day 13.5 and 14.5) were pooled for analysis. To avoid batch effects from pooling or integrating, we analyzed each condition separately: E13.5 SmoM2, E13.5 WT, E14.5 SmoM2, and E14.5 WT. For each condition, we performed standard normalization, selected the top 2,000 highly variable genes, and scaled the data using the Seurat v4 R package. We then applied PCA, retaining the number of PCs determined by the elbow plot: E13.5 SmoM2 (14 PCs), E13.5 WT (12 PCs), E14.5 SmoM2 (12 PCs), and E14.5 WT (11 PCs).<br>