[Bioinformatics] scRNA-seq data analysis (1): quality control ~ cell screening ~ high expression gene screening

1. Experiment introduction

  Quality control ~ Cell screening ~ Highly expressed gene screening

2. Experimental environment

1. Configure the virtual environment

  You can use the following commands:

conda create -n bio python==3.9

conda activate bio

pip install -r requirements.txt

  Among them, requirements.txt:

numpy==1.21.5
pandas==1.4.4
scanpy==1.9.6

2. Library version introduction

software package	This experimental version
numpy	1.21.5
pandas	1.4.4
python	3.8.16
scanpy	1.9.6
scipy	1.10.1
seaborn	0.12.2

3. Experimental content

0. Import necessary libraries

import numpy as np
import pandas as pd
import scanpy as sc

• Scanpy is a Python library for single-cell RNA sequencing data analysis, providing many functions and tools to process and analyze single-cell data

1. Quality control

# 设置Scanpy参数
sc.settings.verbosity = 3
sc.logging.print_header()
sc.settings.set_figure_params(dpi=80, facecolor='white')

# 定义结果文件路径
results_file = 'write/pbmc3k.h5ad'

# 读取单细胞数据
adata = sc.read_10x_mtx(
    'data/filtered_gene_bc_matrices/hg19/',  # 数据目录
    var_names='gene_symbols',                # 使用基因符号作为变量名
    cache=True)                              # 写入缓存文件以便后续更快读取

# 确保基因名唯一
adata.var_names_make_unique()

# 绘制展示高度表达的基因
sc.pl.highest_expr_genes(adata, n_top=20)

2. Cell screening

# 过滤细胞和基因
sc.pp.filter_cells(adata, min_genes=200)
sc.pp.filter_genes(adata, min_cells=3)

# 标记线粒体基因
adata.var['mt'] = adata.var_names.str.startswith('MT-')

# 计算质量控制指标
sc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], percent_top=None, log1p=False, inplace=True)

# 绘制质量控制指标的小提琴图和散点图
sc.pl.violin(adata, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt'], jitter=0.4, multi_panel=True)
sc.pl.scatter(adata, x='total_counts', y='pct_counts_mt')
sc.pl.scatter(adata, x='total_counts', y='n_genes_by_counts')

3. Highly expressed gene screening

# 进一步的过滤和归一化
adata = adata[adata.obs.n_genes_by_counts < 2500, :]
adata = adata[adata.obs.pct_counts_mt < 5, :]

# 总计数归一化
sc.pp.normalize_total(adata, target_sum=1e4)

# 对数变换
sc.pp.log1p(adata)

# 特征选择：识别高度变异的基因
sc.pp.highly_variable_genes(adata, min_mean=0.0125, max_mean=3, min_disp=0.5)

# 绘制高度变异基因的图
sc.pl.highly_variable_genes(adata)

# 设置.raw属性
adata.raw = adata

# 实际过滤数据
adata = adata[:, adata.var.highly_variable]

# 数据回归处理和标准化
sc.pp.regress_out(adata, ['total_counts', 'pct_counts_mt'])
sc.pp.scale(adata, max_value=10)