双虚拟敲除 CellOracle + scTenifoldKnk 全流程复现（二）
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TL;DR - Part 2 of a WeChat tutorial series reproducing a dual virtual-knockout scRNA-seq workflow (CellOracle virtual KO + scTenifoldKnk validation) from a 2026 Cell Prolif paper on STAT3 in dentinogenesis; this installment covers QC and cell-type annotation of the public dental pulp dataset GSE146123, which the original authors did not release annotated.
- QC in Seurat: computes mitochondrial (
^MT-), ribosomal (^Rp[sl]), and hemoglobin (^Hb[^(p)]) percentages, then filters to nFeature_RNA 200–6000, nCount_RNA 3–30000, percent_mito <10%, percent_hb <1%. - Thresholds were chosen after an AI-assisted literature survey of dental pulp/odontogenic tissue papers, whose reported MT% cutoffs ranged widely (5%, 15%, 20%, up to 40%), reflecting the low metabolic activity of mesenchymal pulp cells.
- Clustering pipeline: LogNormalize → 2000 HVGs → ScaleData → PCA → Harmony integration on
orig.ident→ UMAP/FindNeighbors on 20 dims, with resolutions swept 0.05–1 and inspected via clustree; resolution 0.5 was adopted. - Annotation targets the original paper's 9 major populations (endothelial 21.2%, mesenchymal 20.7%, perivascular 16.9%, glial, peri-odontoblastic layer, pulp, preodontoblasts, epithelial, immune), distinguishing "mesenchymal" (COL1A1/DCN/LUM+) from quiescent "pulp" cells (VIM/BGN/POSTN+).
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双虚拟敲除 CellOracle + scTenifoldKnk 全流程复现（二）
TL;DR - Part 2 of a WeChat tutorial series reproducing a dual virtual-knockout scRNA-seq workflow (CellOracle virtual KO + scTenifoldKnk validation) from a 2026 Cell Prolif paper on STAT3 in dentinogenesis; this installment covers QC and cell-type annotation of the public dental pulp dataset GSE146123, which the original authors did not release annotated.
- QC in Seurat: computes mitochondrial (
^MT-), ribosomal (^Rp[sl]), and hemoglobin (^Hb[^(p)]) percentages, then filters to nFeature_RNA 200–6000, nCount_RNA 3–30000, percent_mito <10%, percent_hb <1%. - Thresholds were chosen after an AI-assisted literature survey of dental pulp/odontogenic tissue papers, whose reported MT% cutoffs ranged widely (5%, 15%, 20%, up to 40%), reflecting the low metabolic activity of mesenchymal pulp cells.
- Clustering pipeline: LogNormalize → 2000 HVGs → ScaleData → PCA → Harmony integration on
orig.ident→ UMAP/FindNeighbors on 20 dims, with resolutions swept 0.05–1 and inspected via clustree; resolution 0.5 was adopted. - Annotation targets the original paper's 9 major populations (endothelial 21.2%, mesenchymal 20.7%, perivascular 16.9%, glial, peri-odontoblastic layer, pulp, preodontoblasts, epithelial, immune), distinguishing "mesenchymal" (COL1A1/DCN/LUM+) from quiescent "pulp" cells (VIM/BGN/POSTN+).