作用：Gifu 组织图谱分析——把 SNF 家族基因与全图谱中相关性最高的 top-5“新”基因配对，并计算 Gifu 与 MG20 的跨材料表达相关性。
入口：snf_new_corr.sh、spearman.R；getCount.sh 只有计数任务的投递头，没有命令体。
输入：snf_new_corr.sh 需要 expr.tsv（TPM 矩阵，首列为基因 ID）与 genes.tsv（GeneID、Category，Category 含 SNF 或 New）；spearman.R 需要 Gifu_all_samples_mean.tsv 与 MG20_all_samples_mean.tsv。
输出：<outprefix>.snf_new_corr/ 下的 *.SNF_vs_New.all_pairs.tsv、*.SNF_top5_newgenes.tsv、*.SNF_top5_matrix.tsv、*.summary.txt、*.SNF_top5_heatmap.pdf|.png、*.SNF_top5_dotplot.pdf|.png；spearman.R 写 Gifu_MG20.sample_expression_spearman.tsv 与 Gifu_MG20.organ_expression_spearman.tsv 及各自的 PDF。
运行：bash snf_new_corr.sh expr.tsv genes.tsv outprefix（须以本目录为工作目录）；Rscript spearman.R
工具：Python 3 + pandas/numpy/scipy（Pearson r 与 t 检验 p）；R + ggplot2/readr/dplyr/tidyr 与 tidyverse；集群投递头 #CSUB -q c01 -n 64。
English
**Purpose**: Tissue-atlas analysis for the Gifu accession — pairing SNF-family genes with the top-5 most correlated "new" genes across the atlas, and computing the cross-accession expression correlation between Gifu and MG20.
**Entry point**: `snf_new_corr.sh`, `spearman.R`; `getCount.sh` holds only the job header of the counting step and has no command body.
**Inputs**: `snf_new_corr.sh` needs `expr.tsv` (TPM matrix, first column = gene ID) and `genes.tsv` (GeneID and Category; Category containing `SNF` or `New`); `spearman.R` needs `Gifu_all_samples_mean.tsv` and `MG20_all_samples_mean.tsv`.
**Outputs**: in `.snf_new_corr/` — `*.SNF_vs_New.all_pairs.tsv`, `*.SNF_top5_newgenes.tsv`, `*.SNF_top5_matrix.tsv`, `*.summary.txt`, `*.SNF_top5_heatmap.pdf|.png` and `*.SNF_top5_dotplot.pdf|.png`; `spearman.R` writes `Gifu_MG20.sample_expression_spearman.tsv` and `Gifu_MG20.organ_expression_spearman.tsv`, each with a PDF.
**Run**: `bash snf_new_corr.sh expr.tsv genes.tsv outprefix` (run it with this directory as the working directory); `Rscript spearman.R`
**Tools**: Python 3 with pandas/numpy/scipy (Pearson r and t-test p); R with ggplot2/readr/dplyr/tidyr and tidyverse; cluster job header `#CSUB -q c01 -n 64`.