小黑屋 调教 scanpy和Seurat单细胞分析对比
尝试把曾敦朴的单细胞seurat分析的代码报复成scanpy版块的，包括样品读取，质控，harmony去除批次效应小黑屋 调教，降维聚类，marker随意。
seurat版块的R代码先望望seurat版块的R代码，内部调用了另外几个文献，lib.R，qc.R，harmony.R，check-all-markers.R，这几个文献往时应该齐有共享过.scRNA_scripts文献夹的r代码齐在上头的百度云网盘逢迎（https://pan.baidu.com/s/1MzfqW07P9ZqEA_URQ6rLbA?pwd=3heo）内部哦：
rm(list=ls())options(stringsAsFactors = F) source('scRNA_scripts/lib.R')###### step1:导入数据 ###### # 付费要领 800 元东说念主民币dir='GSE189357_RAW/output/' samples=list.files( dir )samples library(data.table)sceList = lapply(samples,function(pro){ # pro=samples[1] print(pro) sce=CreateSeuratObject(counts = Read10X(file.path(dir,pro) ) , project = pro , min.cells = 5, min.features = 300,) return(sce)})names(sceList) library(stringr)# samples=gsub('.txt.gz','',str_split(samples,'_',simplify = T)[,2])samplesnames(sceList) = samplessce.all <- merge(sceList[[1]], y= sceList[ -1 ] , add.cell.ids = samples) as.data.frame(sce.all@assays$RNA@counts[1:10, 1:2])head(sce.all@meta.data, 10)table(sce.all@meta.data$orig.ident) ###### step2:QC质控 ######dir.create("./1-QC")setwd("./1-QC")# 如若过滤的太狠，就需要去修改这个过滤代码source('../scRNA_scripts/qc.R')sce.all.filt = basic_qc(sce.all)print(dim(sce.all))print(dim(sce.all.filt))setwd('../')sp='human'###### step3: harmony整合多个单细胞样品 ######dir.create("2-harmony")getwd()setwd("2-harmony")source('../scRNA_scripts/harmony.R')# 默许 ScaleData 莫得添加"nCount_RNA", "nFeature_RNA"# 默许的sce.all.int = run_harmony(sce.all.filt)setwd('../')###### step4: 降维聚类分群和看绚丽基因库 ####### 原则上分别率是需要我方肉眼判断，取决于个东说念主教悔# 为了省力，咱们径直看 0.1和0.8即可table(Idents(sce.all.int))table(sce.all.int$seurat_clusters)table(sce.all.int$RNA_snn_res.0.1) table(sce.all.int$RNA_snn_res.0.8) getwd()dir.create('check-by-0.1')setwd('check-by-0.1')sel.clust = "RNA_snn_res.0.1"sce.all.int <- SetIdent(sce.all.int, value = sel.clust)table(sce.all.int@active.ident) source('../scRNA_scripts/check-all-markers.R')setwd('../') getwd()dir.create('check-by-0.8')setwd('check-by-0.8')sel.clust = "RNA_snn_res.0.8"sce.all.int <- SetIdent(sce.all.int, value = sel.clust)table(sce.all.int@active.ident) source('../scRNA_scripts/check-all-markers.R')setwd('../') getwd()dir.create('check-by-0.5')setwd('check-by-0.5')sel.clust = "RNA_snn_res.0.5"sce.all.int <- SetIdent(sce.all.int, value = sel.clust)table(sce.all.int@active.ident) source('../scRNA_scripts/check-all-markers.R')setwd('../') getwd()last_markers_to_checkscanpy版块的python代码装置各式库的时辰，径直pip install scanpy即可
如若是在jupyter notebook或者jupyter lab里，需要在前边加上！，敕令行里就不必
VR视角!pip install scanpy
#装置导入需要的模块import scanpy as scimport numpy as np import pandas as pd from glob import globimport reimport seaborn as snsimport matplotlib.pyplot as plt from tqdm import tqdmimport osfrom collections import Counterimport timeimport cosg as cosg%matplotlib inline
# 驱动计时a = time.perf_counter()
scanpy下的read_10x_mtx函数读取数据，传入文献夹旅途，保证文献夹下已经这三个文献即可
图片小黑屋 调教
轮回读取9个单细胞数据，用字典进行存储，key为样真名，value为scanpy读取后的对象用scanpy下的concat函数进行多个样本的澌灭
scanpy对象结构参考下图小黑屋 调教
adata.var是当作gene，列为统计意见的矩阵，进行完各式分析之后，大约如下图，刚读完数据的时辰，惟一行名，莫得列
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adata.obs是当作细胞，列为统计意见的矩阵图片
adata.uns内部存放的曲直结构化的数据，齐是字典的体式图片
比如sample color，存放的是每个样品对应的神志，pca内部存放有降维后的主要素的信息，louvain 0.01_colors内部存放的即是0.01的分别率下每个cluster对应的神志图片
adata.X内部存放的是抒发矩阵，当作cell，列为gene图片
### Step1 批量读取单细胞数据# 字符串前边加上r之后，旅途里的/和\就不必挑升修改了path = r'F:\新建文献夹\2022-GSE189357-LUAD-单细胞-疾病阐发\GSE189357_RAW\output'files = glob(path + '\*')# tqdm用来明白程度条，不加也不错data_dic = {}for i in tqdm(files): sample_name = i.split('\\')[-1] # cache=True 会存储读取文献时辰的缓存，下次再读取就很快了 adata = sc.read_10x_mtx(i,var_names='gene_symbols',cache=True) adata.var_names_make_unique() adata.obs_names_make_unique() data_dic[sample_name] = adata adata = sc.concat(data_dic,label='sample',axis=0)adata.obs_names_make_unique()os.getcwd()work_dir = r'F:新建文献夹/'os.chdir(work_dir)qc部分，参考曾敦朴seurat里qc.R剧本写的
这里只商酌了human gene，齐是大写的
#策画比例和要领质控 def basic_qc(adata): # 策画线粒体基因比例 adata.var['mt'] = adata.var_names.str.startswith('MT-') sc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], percent_top=None, log1p=False, inplace=True) mt_gene = adata.var.index[adata.var_names.str.startswith('MT-')] print('线粒体gene：',mt_gene) # 策画核糖体基因比例 adata.var['rp'] = [True if i.startswith('RPL') or i.startswith('RPS') else False for i in adata.var_names] sc.pp.calculate_qc_metrics(adata, qc_vars=['rp'], percent_top=None, log1p=False, inplace=True) rp_gene = [i for i in adata.var_names if i.startswith('RPL') or i.startswith('RPS')] print('核糖体gene：',rp_gene) # 策画红血细胞基因比例 adata.var['hb'] = [True if i.startswith('HB') and not i.startswith('HBP') else False for i in adata.var_names] sc.pp.calculate_qc_metrics(adata, qc_vars=['hb'], percent_top=None, log1p=False, inplace=True) hb_gene = [i for i in adata.var_names if i.startswith('HB') and not i.startswith('HBP')] print('红血细胞gene：',hb_gene) # 可视化细胞各gene比例 if not os.path.isdir('qc'): os.mkdir('qc') sc.pl.violin(adata,keys=['n_genes_by_counts','total_counts'],groupby='sample',show = False) plt.savefig('qc/vlnplot1.pdf') sc.pl.violin(adata,keys=['pct_counts_mt','pct_counts_rp','pct_counts_hb'],groupby='sample',show = False) plt.savefig('qc/vlnplot2.pdf') sc.pl.scatter(adata,x='total_counts', y='n_genes_by_counts',color='sample',show = False) plt.savefig('qc/scatterplot.pdf') sc.pl.highest_expr_genes(adata,n_top=20,show = False) plt.savefig('qc/TOP20_most_expressed_gene.pdf') # 过滤 print('过滤前：',adata.shape) adata = adata[adata.obs.n_genes_by_counts < 6000, :] adata = adata[adata.obs.pct_counts_mt < 20, :] adata = adata[adata.obs.pct_counts_rp > 3, :] adata = adata[adata.obs.pct_counts_hb < 1, :] print('过滤后：',adata.shape) # 可视化过滤后的效果 sc.pl.violin(adata,keys=['n_genes_by_counts','total_counts'],groupby='sample',show = False) plt.savefig('qc/vlnplot1_filtered.pdf') sc.pl.violin(adata,keys=['pct_counts_mt','pct_counts_rp','pct_counts_hb'],groupby='sample',show = False) plt.savefig('qc/vlnplot2_filtered.pdf') return adata#harmony整合def run_harmony(adata): # normalize sc.pp.normalize_total(adata, target_sum=1e4) sc.pp.log1p(adata) adata.raw = adata # 找高变gene sc.pp.highly_variable_genes(adata, min_mean=0.0125, max_mean=3, min_disp=0.5) # 只保留高变gene adata = adata[:, adata.var.highly_variable] # 把柄线粒体gene进行regress sc.pp.regress_out(adata, 'pct_counts_mt') # scale sc.pp.scale(adata) # pca sc.tl.pca(adata, svd_solver='arpack') sc.external.pp.harmony_integrate(adata,key='sample') sc.pp.neighbors(adata, n_neighbors=10, n_pcs=20,use_rep='X_pca_harmony') sc.tl.umap(adata) # 配置不同分别率 for i in [0.01, 0.05, 0.1, 0.2, 0.3, 0.5,0.8,1]: sc.tl.louvain(adata,resolution=i,key_added='louvain {}'.format(i)) if not os.path.isdir('harmony'): os.mkdir('harmony') sc.pl.umap(adata,color=['louvain 0.01','louvain 0.1','louvain 0.2'],show = False) plt.savefig('harmony/resolution_low.pdf') sc.pl.umap(adata,color=['louvain 0.5','louvain 0.8','louvain 1'],show = False) plt.savefig('harmony/resolution_high.pdf') return adatadef run_harmony(adata): # normalize sc.pp.normalize_total(adata, target_sum=1e4) sc.pp.log1p(adata) adata.raw = adata # 找高变gene sc.pp.highly_variable_genes(adata, min_mean=0.0125, max_mean=3, min_disp=0.5) # 只保留高变gene adata = adata[:, adata.var.highly_variable] # 把柄线粒体gene进行regress sc.pp.regress_out(adata, 'pct_counts_mt') # scale sc.pp.scale(adata) # pca sc.tl.pca(adata, svd_solver='arpack') sc.external.pp.harmony_integrate(adata,key='sample') sc.pp.neighbors(adata, n_neighbors=10, n_pcs=20,use_rep='X_pca_harmony') sc.tl.umap(adata) # 配置不同分别率 for i in [0.01, 0.05, 0.1, 0.2, 0.3, 0.5,0.8,1]: sc.tl.louvain(adata,resolution=i,key_added='louvain {}'.format(i)) if not os.path.isdir('harmony'): os.mkdir('harmony') sc.pl.umap(adata,color=['louvain 0.01','louvain 0.1','louvain 0.2'],show = False) plt.savefig('harmony/resolution_low.pdf') sc.pl.umap(adata,color=['louvain 0.5','louvain 0.8','louvain 1'],show = False) plt.savefig('harmony/resolution_high.pdf') return adata效果展示adata = basic_qc(adata)
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adata = run_harmony(adata)
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check_markers(0.1)check_markers(0.3)check_markers(0.8)
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