作用：把 Gifu T2T 基因组上的单核 RNA-seq（根的两个阶段 Dpi00 与 Dpi02，各两个重复）从 Cell Ranger 定量做到聚类与细胞类型注释、簇/细胞类型 DEG 与 scTenifoldKnk 虚拟敲除。
入口：下表 7 个 shell 脚本；Script/*.py 中需手动运行的为 03_1-cluster_statitcs.py、03_2-fliter_to_all_gene_h5ad.py、05_3-marker_cluster_DEGs_own_order.py 与 makesure_02dpi_samples_all_have_Early_signal_cell.py。
输入：sample.list（ProjectID CNName RunID SampleName）、四个 *_filtered.h5ad、Gifu T2T FASTA/GTF 与 .pep.fa、{Gmax,Gm2.1,GmZH,At}_marker_genes.pep.fa、eggNOG 注释；后续步骤按当前目录读 02_processData/ 与 04_Anno/，scTenifoldKnk 另需 zz_00_pea.rds 与 All_species_merged_by_GifuT2T.SNF_Symbol.tsv。
输出：00_ref/、01_cellranger_out/<SampleName>/、02_processData/（h5ad、zz_*、zz_00_pea.rds、00_02_pseudobulk_fpkm.csv）、03_figures/（降维图、clustree_plot_*.pdf、火山图）、04_Anno/（Seurat rds、marker CSV、blast_*/、富集结果）、06_batch_multiple_scTenifoldKnk/<group>/<gene>/。
运行：在本阶段根目录（含 02_processData/、03_figures/、04_Anno/）按表中顺序运行，编号脚本携带 #CSUB 作业指令、交由调度器提交；{TOKEN} 路径占位符见 scripts/README.md；注意 03_1-dimenRedu_adjusted.py 写出的文件名与 03_1-cluster_statitcs.py、03_2-fliter_to_all_gene_h5ad.py 读取的文件名不一致，需先统一。
工具：Cell Ranger 9.0.1；Python 3.9.19 + scanpy/anndata/scvi-tools/leidenalg/rpy2；R 4.4.1 + Seurat/scTenifoldKnk/DESeq2/clustree；BLAST+ 与 seqkit；参数：n_top_genes 4000（扫描 1000–5000）、Leiden 0.4（扫描 0.2–1.2）、每基因 100 次 scTenifoldKnk 重复（K ∈ {10,30,50,70,100}）。
| 脚本 |
用途 |
00_mkref.sh |
cellranger mkref --genome=Gifu（Gifu T2T FASTA + GTF）→ ./00_ref |
00_cellranger.sh |
对 sample.list 中每个样本执行一次 cellranger count → 01_cellranger_out/<SampleName> |
run_singlecell.sh |
一个驱动依次跑 Script/01_annaDataQC.py、02_integrate.py、03_1-dimenRedu_adjusted.py、05_3-markerAnno_Ps_v5_own.py、06_4-DEGs_cell_type.R |
05_cluster_DEGs_finder.sh |
先 05_1-…py 再 05_2-…R：Seurat 对象与簇 marker，随后按簇做富集 |
06_1.marker_gene_finder.sh |
DEG → 肽 FASTA → 对 Gmax/Gm2.1/GmZH/At marker 做 blastp 并合并结果 |
06_1.Individual_clusters_marker_gene_finder.sh |
取各簇 top-20 DEG 序列并抽 eggNOG 注释（启用簇 8） |
07_batch_multiple_scTenifoldKnk.sh |
每个候选基因生成一个 scTenifoldKnk 作业脚本与 scTenifoldKnk_run.list |
English
**Purpose**: Take the single-nucleus RNA-seq data on the Gifu T2T genome (the two root stages Dpi00 and Dpi02, two replicates each) from Cell Ranger quantification through clustering and cell-type annotation, cluster/cell-type DEGs and scTenifoldKnk virtual knockout.
**Entry point**: the seven shell scripts in the table below; the `Script/*.py` files with no entry script, run by hand, are `03_1-cluster_statitcs.py`, `03_2-fliter_to_all_gene_h5ad.py`, `05_3-marker_cluster_DEGs_own_order.py` and `makesure_02dpi_samples_all_have_Early_signal_cell.py`.
**Inputs**: `sample.list` (`ProjectID CNName RunID SampleName`), the four `*_filtered.h5ad` objects, Gifu T2T FASTA/GTF and `.pep.fa`, `{Gmax,Gm2.1,GmZH,At}_marker_genes.pep.fa`, eggNOG annotation; later steps read `02_processData/` and `04_Anno/` from the working directory, and scTenifoldKnk additionally needs `zz_00_pea.rds` and `All_species_merged_by_GifuT2T.SNF_Symbol.tsv`.
**Outputs**: `00_ref/`, `01_cellranger_out//`, `02_processData/` (h5ad files, `zz_*`, `zz_00_pea.rds`, `00_02_pseudobulk_fpkm.csv`), `03_figures/` (dimension-reduction plots, `clustree_plot_*.pdf`, volcano plots), `04_Anno/` (Seurat rds, marker CSVs, `blast_*/`, enrichment results) and `06_batch_multiple_scTenifoldKnk///`.
**Run**: from the stage root (holding `02_processData/`, `03_figures/`, `04_Anno/`) run the scripts in table order; the numbered scripts carry `#CSUB` directives and are submitted to the scheduler; for the ``{TOKEN}`` path placeholders see `scripts/README.md`; note that `03_1-dimenRedu_adjusted.py` writes a file name that `03_1-cluster_statitcs.py` and `03_2-fliter_to_all_gene_h5ad.py` do not read, so it must be unified first.
**Tools**: Cell Ranger 9.0.1; Python 3.9.19 with scanpy/anndata/scvi-tools/leidenalg/rpy2; R 4.4.1 with Seurat/scTenifoldKnk/DESeq2/clustree; BLAST+ and seqkit; settings as coded: `n_top_genes` 4000 (scanned 1000–5000), Leiden 0.4 (scanned 0.2–1.2), and 100 scTenifoldKnk replicates per gene (K ∈ {10,30,50,70,100}).
| Script | Purpose |
|---|---|
| `00_mkref.sh` | `cellranger mkref --genome=Gifu` from the Gifu T2T FASTA + GTF → `./00_ref` |
| `00_cellranger.sh` | one `cellranger count` per sample in `sample.list` → `01_cellranger_out/` |
| `run_singlecell.sh` | one driver running `Script/01_annaDataQC.py`, `02_integrate.py`, `03_1-dimenRedu_adjusted.py`, `05_3-markerAnno_Ps_v5_own.py`, `06_4-DEGs_cell_type.R` in order |
| `05_cluster_DEGs_finder.sh` | `05_1-…py` then `05_2-…R`: Seurat object and cluster markers, then per-cluster enrichment |
| `06_1.marker_gene_finder.sh` | DEGs → peptide FASTA → `blastp` against the Gmax/Gm2.1/GmZH/At markers, results merged |
| `06_1.Individual_clusters_marker_gene_finder.sh` | top-20 DEG sequences per cluster plus eggNOG annotation (active cluster `8`) |
| `07_batch_multiple_scTenifoldKnk.sh` | one scTenifoldKnk job script per candidate gene plus `scTenifoldKnk_run.list` |