domains / families / science / spatial-preprocessing
Quality control, filtering, and normalization for spatial transcriptomics (Visium, Visium HD, Xenium, MERFISH/MERSCOPE, CosMx, Slide-seq) with Squidpy and Scanpy. Use when setting QC floors that do NOT delete real low-count imaging cells (an scRNA min_counts=500 floor deletes nearly every Xenium cell, whose vector is tens-to-low-hundreds of transcripts); deciding whether to normalize at all when library size carries spatial biology rather than pure technical depth; choosing cell-volume/area normalization over Pearson residuals for skewed targeted panels; reading negative-control-probe / blank-barcode false-discovery rates; and inspecting QC spatially on the tissue rather than only in violins.
The two skills cover the same task but disagree on the core procedure. The first copy (OpenClaw) provides a standard, platform-agnostic pipeline using Scanpy/Squidpy with fixed thresholds (min_counts=500, min_genes=200) and standard normalization (CP10k/log1p or Pearson residuals). The second copy (GPTomics) explicitly argues that these standard thresholds and normalization methods are incorrect for imaging-based spatial platforms (Xenium, MERFISH, CosMx), stating that they delete real cells and introduce bias. It mandates a 'platform-class fork' decision and different QC/normalization strategies (e.g., area/volume normalization) for imaging data. Because the first copy's procedure is actively contradicted by the second for a major subset of spatial data, crowning one would hide a critical alternative workflow.