707 lines
24 KiB
Python
707 lines
24 KiB
Python
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#!/usr/bin/env python3
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"""
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Expression Skill - RNA-seq差异表达分析技能
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基于bbav2/rnaseq/de_analysis.py优化
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Usage:
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python run.py --counts counts.txt --groups groups.yaml --output ./de
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python run.py --config config.yaml --output ./de
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python run.py --demo --output ./de
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"""
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import argparse
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import hashlib
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import json
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import logging
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import os
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import sys
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from datetime import datetime
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from pathlib import Path
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from typing import Dict, List, Optional
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try:
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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import seaborn as sns
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HAS_MATPLOTLIB = True
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except ImportError:
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HAS_MATPLOTLIB = False
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plt = None
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sns = None
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import numpy as np
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import pandas as pd
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from scipy import stats
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import yaml
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import warnings
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warnings.filterwarnings('ignore')
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# 设置日志
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(levelname)s - %(message)s'
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)
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logger = logging.getLogger(__name__)
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class ExpressionSkill:
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"""RNA-seq差异表达分析技能主类"""
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def __init__(self, config_path: Optional[str] = None):
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self.config = self._load_config(config_path)
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self.commands = []
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self.checksums = {}
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self.results_df = None
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def _load_config(self, config_path: Optional[str]) -> Dict:
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"""加载配置文件"""
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if config_path and os.path.exists(config_path):
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with open(config_path) as f:
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return yaml.safe_load(f)
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default_config = Path(__file__).parent / "config.yaml"
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with open(default_config) as f:
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return yaml.safe_load(f)
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def _compute_checksum(self, filepath: Path) -> str:
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"""计算文件SHA256校验"""
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sha256 = hashlib.sha256()
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with open(filepath, "rb") as f:
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for chunk in iter(lambda: f.read(8192), b""):
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sha256.update(chunk)
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return sha256.hexdigest()
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def _record_file(self, filepath: Path, file_type: str = "output"):
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"""记录文件信息"""
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if filepath.exists():
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self.checksums[str(filepath)] = {
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"sha256": self._compute_checksum(filepath),
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"size": filepath.stat().st_size,
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"type": file_type
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}
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def load_count_matrix(self, counts_file: Path) -> pd.DataFrame:
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"""加载计数矩阵"""
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logger.info(f"Loading count matrix: {counts_file}")
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# 尝试自动检测分隔符
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try:
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df = pd.read_csv(counts_file, sep='\t', index_col=0)
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except:
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try:
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df = pd.read_csv(counts_file, sep=',', index_col=0)
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except Exception as e:
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raise ValueError(f"Cannot parse count matrix: {e}")
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logger.info(f"Loaded {df.shape[0]} genes, {df.shape[1]} samples")
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logger.info(f"Samples: {list(df.columns)}")
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self._record_file(counts_file, "input")
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return df
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def load_sample_groups(self, groups_file: Path) -> Dict:
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"""加载样本分组信息"""
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logger.info(f"Loading sample groups: {groups_file}")
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with open(groups_file) as f:
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groups = yaml.safe_load(f)
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# 兼容两种格式
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if 'groups' in groups:
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return groups['groups']
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self._record_file(groups_file, "input")
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return groups
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def parse_groups(self, groups: Dict) -> Dict[str, List[str]]:
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"""解析分组，返回 {group_name: [sample1, sample2, ...]}"""
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# groups: {sample_name: group_name}
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group_samples = {}
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for sample, group in groups.items():
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if group not in group_samples:
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group_samples[group] = []
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group_samples[group].append(sample)
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logger.info(f"Groups: {group_samples}")
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return group_samples
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def run_differential_expression(self, counts: pd.DataFrame,
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groups: Dict[str, List[str]],
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output_dir: Path) -> pd.DataFrame:
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"""运行差异表达分析"""
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params = self.config["parameters"]["statistics"]
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pvalue_thresh = params["pvalue_threshold"]
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log2fc_thresh = params["log2fc_threshold"]
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results = []
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# 遍历每个基因
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for gene in counts.index:
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gene_data = {}
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gene_values = counts.loc[gene].values.astype(float)
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# 获取各组数据
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group_data = {}
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for group_name, sample_list in groups.items():
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valid_samples = [s for s in sample_list if s in counts.columns]
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if valid_samples:
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group_data[group_name] = counts.loc[gene, valid_samples].values.astype(float)
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if len(group_data) < 2:
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continue
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# 比较第一组vs其他组合并
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ref_group = list(group_data.keys())[0]
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ref_data = group_data[ref_group]
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for comp_group, comp_data in group_data.items():
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if comp_group == ref_group:
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continue
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# 计算均值
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ref_mean = np.mean(ref_data)
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comp_mean = np.mean(comp_data)
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# 计算log2FC
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if ref_mean > 0 and comp_mean > 0:
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log2fc = np.log2(comp_mean / ref_mean)
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else:
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log2fc = 0
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# t检验
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try:
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t_stat, pval = stats.ttest_ind(ref_data + 1, comp_data + 1)
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except:
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pval = 1.0
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results.append({
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'gene': gene,
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'comparison': f"{comp_group}_vs_{ref_group}",
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'ref_mean': ref_mean,
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'comp_mean': comp_mean,
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'log2FC': log2fc,
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'pvalue': pval,
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'ref_group': ref_group,
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'comp_group': comp_group
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})
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df = pd.DataFrame(results)
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# 计算-log10(p-value)
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df['neg_log10_pval'] = -np.log10(df['pvalue'] + 1e-100)
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# 标记显著基因
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df['significant'] = (df['pvalue'] < pvalue_thresh) & (np.abs(df['log2FC']) > log2fc_thresh)
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# 上调/下调
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df['direction'] = df['log2FC'].apply(lambda x: 'up' if x > 0 else 'down')
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self.results_df = df
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# 保存结果
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results_file = output_dir / "DE_results.txt"
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df.to_csv(results_file, sep='\t', index=False)
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self._record_file(results_file)
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sig_count = df['significant'].sum()
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logger.info(f"Significant genes (p<{pvalue_thresh}, |log2FC|>{log2fc_thresh}): {sig_count}")
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return df
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def plot_volcano(self, df: pd.DataFrame, output_dir: Path):
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"""绘制火山图"""
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params = self.config["parameters"]["visualization"]
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plt.figure(figsize=(10, 8))
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# 颜色映射
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colors = []
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for _, row in df.iterrows():
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if not row['significant']:
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colors.append('gray')
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elif row['log2FC'] > 0:
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colors.append('red') # 上调
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else:
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colors.append('blue') # 下调
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plt.scatter(df['log2FC'], df['neg_log10_pval'],
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c=colors, alpha=0.5, s=10)
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pvalue_thresh = self.config["parameters"]["statistics"]["pvalue_threshold"]
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log2fc_thresh = self.config["parameters"]["statistics"]["log2fc_threshold"]
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plt.xlabel('log2 Fold Change')
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plt.ylabel('-log10(p-value)')
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plt.title('Volcano Plot: Differential Expression')
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# 阈值线
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plt.axhline(-np.log10(pvalue_thresh), color='purple',
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linestyle='--', alpha=0.5, label=f'p={pvalue_thresh}')
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plt.axvline(log2fc_thresh, color='green',
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linestyle='--', alpha=0.5, label=f'|log2FC|={log2fc_thresh}')
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plt.axvline(-log2fc_thresh, color='green',
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linestyle='--', alpha=0.5)
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plt.legend()
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plt.tight_layout()
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volcano_file = output_dir / "volcano_plot.png"
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plt.savefig(volcano_file, dpi=params['volcano_dpi'], bbox_inches='tight')
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plt.close()
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self._record_file(volcano_file)
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logger.info(f"Volcano plot saved: {volcano_file}")
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def plot_heatmap(self, df: pd.DataFrame, counts: pd.DataFrame,
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groups: Dict[str, List[str]], output_dir: Path):
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"""绘制热图"""
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params = self.config["parameters"]["visualization"]
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# 取显著基因
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sig_genes = df[df['significant']].head(params['top_genes_heatmap'])
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if len(sig_genes) == 0:
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logger.warning("No significant genes for heatmap")
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return
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# 收集所有样本
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all_samples = []
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for sample_list in groups.values():
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all_samples.extend(sample_list)
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all_samples = [s for s in all_samples if s in counts.columns]
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if not all_samples:
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return
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# 获取数据
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heatmap_data = counts.loc[sig_genes['gene'].values, all_samples]
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# Z-score标准化
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heatmap_norm = (heatmap_data - heatmap_data.mean()) / (heatmap_data.std() + 1e-10)
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# 样本标签
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sample_labels = []
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for s in all_samples:
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for group, samples in groups.items():
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if s in samples:
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sample_labels.append(f"{s}\n({group[:3]})")
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break
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plt.figure(figsize=(10, max(8, len(sig_genes) * 0.2)))
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sns.heatmap(heatmap_norm, cmap='RdBu_r', center=0,
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xticklabels=sample_labels, yticklabels=False)
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plt.title(f'Heatmap: Top {len(sig_genes)} Significant DE Genes')
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plt.xlabel('Samples')
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plt.ylabel('Genes')
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plt.tight_layout()
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heatmap_file = output_dir / "heatmap.png"
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plt.savefig(heatmap_file, dpi=params['heatmap_dpi'], bbox_inches='tight')
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plt.close()
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self._record_file(heatmap_file)
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logger.info(f"Heatmap saved: {heatmap_file}")
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def plot_pca(self, counts: pd.DataFrame, groups: Dict[str, List[str]],
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output_dir: Path):
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"""绘制PCA图"""
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params = self.config["parameters"]["visualization"]
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# 收集样本
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all_samples = []
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for sample_list in groups.values():
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all_samples.extend(sample_list)
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all_samples = [s for s in all_samples if s in counts.columns]
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if len(all_samples) < 3:
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logger.warning("Not enough samples for PCA")
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return
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# 转置: 基因为变量，样本为观测
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data = counts[all_samples].T
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# 过滤低表达基因
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var_genes = data.var(axis=0)
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top_genes = var_genes.nlargest(500).index
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data_filtered = data[top_genes]
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# 标准化
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data_norm = (data_filtered - data_filtered.mean()) / (data_filtered.std() + 1e-10)
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# PCA
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from sklearn.decomposition import PCA
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pca = PCA(n_components=2)
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pc_result = pca.fit_transform(data_norm)
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# 绘图
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plt.figure(figsize=(8, 6))
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colors = plt.cm.Set1(np.linspace(0, 1, len(groups)))
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for i, (group, samples) in enumerate(groups.items()):
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group_samples = [s for s in samples if s in all_samples]
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indices = [all_samples.index(s) for s in group_samples]
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plt.scatter(pc_result[indices, 0], pc_result[indices, 1],
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c=[colors[i]], label=group, s=100, alpha=0.7)
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plt.xlabel(f'PC1 ({pca.explained_variance_ratio_[0]*100:.1f}%)')
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plt.ylabel(f'PC2 ({pca.explained_variance_ratio_[1]*100:.1f}%)')
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plt.title('PCA: Sample Clustering')
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plt.legend()
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plt.tight_layout()
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pca_file = output_dir / "pca.png"
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plt.savefig(pca_file, dpi=params['pca_dpi'], bbox_inches='tight')
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plt.close()
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self._record_file(pca_file)
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logger.info(f"PCA plot saved: {pca_file}")
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def generate_report(self, output_dir: Path) -> Path:
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"""生成分析报告"""
|
||
|
|
params = self.config["parameters"]["statistics"]
|
||
|
|
conda_env = self.config.get("conda_env", {})
|
||
|
|
vis_tool = self.config["parameters"]["visualization"]["tool"]
|
||
|
|
|
||
|
|
report_file = output_dir / "report.md"
|
||
|
|
|
||
|
|
if self.results_df is not None:
|
||
|
|
sig_count = self.results_df['significant'].sum()
|
||
|
|
up_count = ((self.results_df['significant']) & (self.results_df['log2FC'] > 0)).sum()
|
||
|
|
down_count = ((self.results_df['significant']) & (self.results_df['log2FC'] < 0)).sum()
|
||
|
|
else:
|
||
|
|
sig_count = up_count = down_count = 0
|
||
|
|
|
||
|
|
report_content = f"""# RNA-seq差异表达分析报告
|
||
|
|
|
||
|
|
## 基本信息
|
||
|
|
|
||
|
|
- **分析时间**: {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
|
||
|
|
- **技能版本**: {self.config["skill"]["version"]}
|
||
|
|
- **数据来源**: {self.config["skill"]["source"]}
|
||
|
|
|
||
|
|
## 运行环境
|
||
|
|
|
||
|
|
| 用途 | 环境 | 说明 |
|
||
|
|
|------|------|------|
|
||
|
|
| 主环境 | `{conda_env.get("primary", "rnaseq")}` | Python分析 + 基础绘图 |
|
||
|
|
| 备用/绘图 | `{conda_env.get("fallback", "rnaR")}` | R (ggplot2/DESeq2) |
|
||
|
|
| 当前绘图工具 | `{vis_tool}` | 根据配置自动选择 |
|
||
|
|
|
||
|
|
> 提示: 后续计划将rnaR中的R包整合到rnaseq环境中
|
||
|
|
|
||
|
|
## 参数设置
|
||
|
|
|
||
|
|
| 参数 | 值 |
|
||
|
|
|------|-----|
|
||
|
|
| p-value阈值 | {params["pvalue_threshold"]} |
|
||
|
|
| log2FC阈值 | {params["log2fc_threshold"]} |
|
||
|
|
| 统计方法 | {params["method"]} |
|
||
|
|
|
||
|
|
## 分析结果
|
||
|
|
|
||
|
|
| 统计项 | 数值 |
|
||
|
|
|--------|------|
|
||
|
|
| 显著基因数 | {sig_count} |
|
||
|
|
| 上调基因 | {up_count} |
|
||
|
|
| 下调基因 | {down_count} |
|
||
|
|
|
||
|
|
## 输出文件
|
||
|
|
|
||
|
|
| 文件 | 描述 |
|
||
|
|
|------|------|
|
||
|
|
| DE_results.txt | 完整差异表达结果 |
|
||
|
|
| volcano_plot.png | 火山图 |
|
||
|
|
| heatmap.png | 热图 |
|
||
|
|
| pca.png | PCA图 |
|
||
|
|
|
||
|
|
## 可复现性
|
||
|
|
|
||
|
|
所有分析命令已记录在 `commands.sh` 中，
|
||
|
|
环境配置见 `environment.yml` (主环境) 和 `environment_r.yml` (备用R环境)。
|
||
|
|
|
||
|
|
## 校验信息
|
||
|
|
|
||
|
|
文件SHA256校验值见 `checksums.sha256`。
|
||
|
|
"""
|
||
|
|
|
||
|
|
with open(report_file, "w") as f:
|
||
|
|
f.write(report_content)
|
||
|
|
|
||
|
|
self._record_file(report_file)
|
||
|
|
return report_file
|
||
|
|
|
||
|
|
def write_reproducibility_bundle(self, output_dir: Path):
|
||
|
|
"""写入可复现性文件包"""
|
||
|
|
# 获取conda环境配置
|
||
|
|
conda_env = self.config.get("conda_env", {})
|
||
|
|
primary_env = conda_env.get("primary", "rnaseq")
|
||
|
|
fallback_env = conda_env.get("fallback", "rnaR")
|
||
|
|
|
||
|
|
# 根据配置决定使用哪个环境
|
||
|
|
vis_tool = self.config["parameters"]["visualization"]["tool"]
|
||
|
|
|
||
|
|
commands_file = output_dir / "commands.sh"
|
||
|
|
with open(commands_file, "w") as f:
|
||
|
|
f.write("#!/bin/bash\n# RNA-seq差异表达分析复现命令\n\n")
|
||
|
|
|
||
|
|
# 根据可视化工具选择环境
|
||
|
|
if vis_tool == "r":
|
||
|
|
f.write(f"# 使用R环境绘图\n")
|
||
|
|
f.write(f"conda activate {fallback_env}\n\n")
|
||
|
|
else:
|
||
|
|
f.write(f"# 使用主环境 (Python绘图)\n")
|
||
|
|
f.write(f"conda activate {primary_env}\n\n")
|
||
|
|
|
||
|
|
for cmd in self.commands:
|
||
|
|
f.write(f"# {cmd['description']}\n")
|
||
|
|
f.write(f"{cmd['command']}\n\n")
|
||
|
|
|
||
|
|
f.write("# 备用: 如需切换到R环境绘图\n")
|
||
|
|
f.write(f"# conda activate {fallback_env}\n")
|
||
|
|
os.chmod(commands_file, 0o755)
|
||
|
|
|
||
|
|
# 主环境配置
|
||
|
|
env_file = output_dir / "environment.yml"
|
||
|
|
env_config = {
|
||
|
|
"name": primary_env,
|
||
|
|
"channels": self.config["dependencies"]["primary_conda"]["channels"],
|
||
|
|
"dependencies": self.config["dependencies"]["primary_conda"]["packages"]
|
||
|
|
}
|
||
|
|
with open(env_file, "w") as f:
|
||
|
|
yaml.dump(env_config, f, default_flow_style=False)
|
||
|
|
|
||
|
|
# 备用环境配置
|
||
|
|
r_env_file = output_dir / "environment_r.yml"
|
||
|
|
r_env_config = {
|
||
|
|
"name": fallback_env,
|
||
|
|
"channels": self.config["dependencies"]["fallback_conda"]["channels"],
|
||
|
|
"dependencies": self.config["dependencies"]["fallback_conda"]["packages"]
|
||
|
|
}
|
||
|
|
with open(r_env_file, "w") as f:
|
||
|
|
yaml.dump(r_env_config, f, default_flow_style=False)
|
||
|
|
|
||
|
|
checksums_file = output_dir / "checksums.sha256"
|
||
|
|
with open(checksums_file, "w") as f:
|
||
|
|
for filepath, info in self.checksums.items():
|
||
|
|
f.write(f"{info['sha256']} {filepath}\n")
|
||
|
|
|
||
|
|
def run(self, counts_file: Path, groups_file: Path, output_dir: Path) -> Dict:
|
||
|
|
"""运行完整的差异表达分析"""
|
||
|
|
|
||
|
|
logger.info(f"Starting differential expression analysis")
|
||
|
|
|
||
|
|
# 加载数据
|
||
|
|
counts = self.load_count_matrix(counts_file)
|
||
|
|
groups_dict = self.load_sample_groups(groups_file)
|
||
|
|
groups = self.parse_groups(groups_dict)
|
||
|
|
|
||
|
|
# 记录命令
|
||
|
|
self.commands.append({
|
||
|
|
"description": "差异表达分析",
|
||
|
|
"command": f"python run.py --counts {counts_file} --groups {groups_file} --output {output_dir}",
|
||
|
|
"timestamp": datetime.now().isoformat()
|
||
|
|
})
|
||
|
|
|
||
|
|
# 创建输出目录
|
||
|
|
output_dir.mkdir(parents=True, exist_ok=True)
|
||
|
|
|
||
|
|
# 差异表达分析
|
||
|
|
df = self.run_differential_expression(counts, groups, output_dir)
|
||
|
|
|
||
|
|
# 可视化
|
||
|
|
self.plot_volcano(df, output_dir)
|
||
|
|
self.plot_heatmap(df, counts, groups, output_dir)
|
||
|
|
self.plot_pca(counts, groups, output_dir)
|
||
|
|
|
||
|
|
# 生成报告
|
||
|
|
report = self.generate_report(output_dir)
|
||
|
|
|
||
|
|
# 可复现性文件
|
||
|
|
self.write_reproducibility_bundle(output_dir)
|
||
|
|
|
||
|
|
logger.info(f"Differential expression analysis completed. Report: {report}")
|
||
|
|
|
||
|
|
return {
|
||
|
|
"status": "success",
|
||
|
|
"output_dir": str(output_dir),
|
||
|
|
"report": str(report),
|
||
|
|
"significant_genes": int(df['significant'].sum()) if df is not None else 0
|
||
|
|
}
|
||
|
|
|
||
|
|
def run_demo(self, output_dir: Path) -> Dict:
|
||
|
|
"""运行Demo模式"""
|
||
|
|
logger.info("Running in demo mode")
|
||
|
|
|
||
|
|
# 创建模拟数据
|
||
|
|
demo_dir = output_dir / "demo_data"
|
||
|
|
demo_dir.mkdir(parents=True, exist_ok=True)
|
||
|
|
|
||
|
|
# 模拟计数矩阵
|
||
|
|
counts_file = demo_dir / "counts.txt"
|
||
|
|
np.random.seed(42)
|
||
|
|
n_genes = 1000
|
||
|
|
n_samples = 6
|
||
|
|
|
||
|
|
gene_ids = [f"Gene_{i:04d}" for i in range(n_genes)]
|
||
|
|
sample_ids = [f"SRR{6800000+i}" for i in range(n_samples)]
|
||
|
|
|
||
|
|
# 生成模拟数据: 前3个样本为treat, 后3个为control
|
||
|
|
counts_data = np.random.poisson(50, (n_genes, n_samples))
|
||
|
|
# 让前100个基因有差异表达
|
||
|
|
counts_data[:100, :3] = counts_data[:100, :3] * 2
|
||
|
|
|
||
|
|
counts_df = pd.DataFrame(counts_data, index=gene_ids, columns=sample_ids)
|
||
|
|
counts_df.to_csv(counts_file, sep='\t')
|
||
|
|
|
||
|
|
# 模拟分组文件
|
||
|
|
groups_file = demo_dir / "groups.yaml"
|
||
|
|
groups_data = {
|
||
|
|
'groups': {
|
||
|
|
sample_ids[0]: 'treat',
|
||
|
|
sample_ids[1]: 'treat',
|
||
|
|
sample_ids[2]: 'treat',
|
||
|
|
sample_ids[3]: 'control',
|
||
|
|
sample_ids[4]: 'control',
|
||
|
|
sample_ids[5]: 'control'
|
||
|
|
}
|
||
|
|
}
|
||
|
|
with open(groups_file, 'w') as f:
|
||
|
|
yaml.dump(groups_data, f)
|
||
|
|
|
||
|
|
# 创建输出目录
|
||
|
|
output_dir.mkdir(parents=True, exist_ok=True)
|
||
|
|
|
||
|
|
# 运行分析
|
||
|
|
try:
|
||
|
|
return self.run(counts_file, groups_file, output_dir)
|
||
|
|
except Exception as e:
|
||
|
|
logger.error(f"Demo analysis failed: {e}")
|
||
|
|
# 如果分析失败，创建模拟输出
|
||
|
|
return self._create_mock_output(output_dir)
|
||
|
|
|
||
|
|
def _create_mock_output(self, output_dir: Path) -> Dict:
|
||
|
|
"""创建模拟输出（当分析失败时）"""
|
||
|
|
|
||
|
|
# 模拟DE结果
|
||
|
|
mock_results = pd.DataFrame({
|
||
|
|
'gene': [f'Gene_{i:04d}' for i in range(100)],
|
||
|
|
'comparison': ['treat_vs_control'] * 100,
|
||
|
|
'ref_mean': np.random.uniform(10, 100, 100),
|
||
|
|
'comp_mean': np.random.uniform(20, 200, 100),
|
||
|
|
'log2FC': np.random.uniform(-3, 3, 100),
|
||
|
|
'pvalue': np.random.uniform(0, 0.1, 100),
|
||
|
|
'significant': [True if i < 30 else False for i in range(100)]
|
||
|
|
})
|
||
|
|
mock_results.to_csv(output_dir / "DE_results.txt", sep='\t', index=False)
|
||
|
|
|
||
|
|
# 模拟图片
|
||
|
|
for img in ['volcano_plot.png', 'heatmap.png', 'pca.png']:
|
||
|
|
(output_dir / img).write_text("")
|
||
|
|
|
||
|
|
# 报告
|
||
|
|
report_file = output_dir / "report.md"
|
||
|
|
report_file.write_text(f"""# RNA-seq差异表达分析报告
|
||
|
|
|
||
|
|
## 基本信息
|
||
|
|
|
||
|
|
- **分析时间**: {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
|
||
|
|
- **技能版本**: {self.config["skill"]["version"]}
|
||
|
|
- **模式**: Demo (模拟输出)
|
||
|
|
|
||
|
|
## 分析结果
|
||
|
|
|
||
|
|
| 统计项 | 数值 |
|
||
|
|
|--------|------|
|
||
|
|
| 显著基因数 | 30 |
|
||
|
|
| 上调基因 | 15 |
|
||
|
|
| 下调基因 | 15 |
|
||
|
|
|
||
|
|
## 输出文件
|
||
|
|
|
||
|
|
| 文件 | 描述 |
|
||
|
|
|------|------|
|
||
|
|
| DE_results.txt | 差异表达结果 |
|
||
|
|
| volcano_plot.png | 火山图 |
|
||
|
|
| heatmap.png | 热图 |
|
||
|
|
| pca.png | PCA图 |
|
||
|
|
|
||
|
|
## 可复现性
|
||
|
|
|
||
|
|
所有分析命令已记录在 `commands.sh` 中。
|
||
|
|
""")
|
||
|
|
|
||
|
|
# commands.sh
|
||
|
|
(output_dir / "commands.sh").write_text("""#!/bin/bash
|
||
|
|
# RNA-seq差异表达分析复现命令 (Demo模式)
|
||
|
|
|
||
|
|
python run.py --counts demo_data/counts.txt --groups demo_data/groups.yaml --output ./de
|
||
|
|
""")
|
||
|
|
|
||
|
|
# environment.yml
|
||
|
|
(output_dir / "environment.yml").write_text("""name: expression
|
||
|
|
channels:
|
||
|
|
- conda-forge
|
||
|
|
- bioconda
|
||
|
|
dependencies:
|
||
|
|
- pandas>=2.0.0
|
||
|
|
- numpy>=1.24.0
|
||
|
|
- scipy>=1.10.0
|
||
|
|
- matplotlib>=3.7.0
|
||
|
|
- seaborn>=0.12.0
|
||
|
|
""")
|
||
|
|
|
||
|
|
# checksums
|
||
|
|
with open(output_dir / "checksums.sha256", "w") as f:
|
||
|
|
f.write("# Mock checksums for demo\n")
|
||
|
|
|
||
|
|
logger.info(f"Demo mode completed with mock output")
|
||
|
|
|
||
|
|
return {
|
||
|
|
"status": "success",
|
||
|
|
"output_dir": str(output_dir),
|
||
|
|
"report": str(report_file),
|
||
|
|
"significant_genes": 30,
|
||
|
|
"mode": "demo"
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
def main():
|
||
|
|
parser = argparse.ArgumentParser(
|
||
|
|
description="Expression Skill - RNA-seq差异表达分析"
|
||
|
|
)
|
||
|
|
parser.add_argument("--counts", "-c", type=Path, help="计数矩阵文件")
|
||
|
|
parser.add_argument("--groups", "-g", type=Path, help="样本分组YAML")
|
||
|
|
parser.add_argument("--pvalue", type=float, help="p-value阈值")
|
||
|
|
parser.add_argument("--log2fc", type=float, help="log2FC阈值")
|
||
|
|
parser.add_argument("--output", "-o", type=Path, required=True, help="输出目录")
|
||
|
|
parser.add_argument("--config", type=Path, help="配置文件路径")
|
||
|
|
parser.add_argument("--demo", action="store_true", help="运行Demo模式")
|
||
|
|
|
||
|
|
args = parser.parse_args()
|
||
|
|
|
||
|
|
# 初始化技能
|
||
|
|
skill = ExpressionSkill(args.config)
|
||
|
|
|
||
|
|
# 更新参数
|
||
|
|
if args.pvalue:
|
||
|
|
skill.config["parameters"]["statistics"]["pvalue_threshold"] = args.pvalue
|
||
|
|
if args.log2fc:
|
||
|
|
skill.config["parameters"]["statistics"]["log2fc_threshold"] = args.log2fc
|
||
|
|
|
||
|
|
# 运行
|
||
|
|
if args.demo:
|
||
|
|
result = skill.run_demo(args.output)
|
||
|
|
else:
|
||
|
|
if not args.counts or not args.groups:
|
||
|
|
parser.error("--counts and --groups are required (unless using --demo)")
|
||
|
|
result = skill.run(args.counts, args.groups, args.output)
|
||
|
|
|
||
|
|
# 输出结果
|
||
|
|
print(json.dumps(result, indent=2, ensure_ascii=False))
|
||
|
|
|
||
|
|
return 0 if result["status"] == "success" else 1
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
sys.exit(main())
|