How do I recalculate sequencing depth after filtering out beads that are not covered by tissue?

After you remove beads that are not covered by tissue, you can recalculate the sequencing depth using the following formula: median number of reads per bead / median number of UMIs per bead The "per bead" values of the "number of reads" and "number of UMIs" are located in the Seurat object metadata as $numReads and $nCount_RNA . Once you have determined the new "median reads/UMI" value, you can [...]

How do I crop out a region of interest from the spatial embedding of my Seeker data?

We recommend using free software called ExCellxGene. You can use a lasso tool to identify cell barcodes in a region of interest. See our official tutorial here: https://knowledgebase.curiobioscience.com/bioinformatics/excellxgene/" target="_blank https://knowledgebase.curiobioscience.com/bioinformatics/excellxgene/ Alternatively, Seurat has an interactive plotting function: [...]

How do I normalize my data using log-transformed count per million (CPM)?

You can follow these tutorials. Seurat: https://satijalab.org/seurat/articles/pbmc3k_tutorial#normalizing-the-data" target="_blank https://satijalab.org/seurat/articles/pbmc3k_tutorial#normalizing-the-data Scanpy: https://scanpy.readthedocs.io/en/stable/tutorials/basics/clustering.html#normalization" target="_blank [...]

What normalization method was used by the Seeker Primary Analysis Pipeline to generate the UMAP and clustering results in the HTML report?

SCTransform was used. [...]

What tool was used to generate the UMAP by the Seeker Primary Analysis Pipeline, and how many principal components were used?

We used the RunUMAP() function in the Seurat R package on the top 30 principal components (PC). If there were fewer than 30 PCs identified, we used all identified PCs. [...]