尝试把曾憨厚的单细胞seurat分析的代码诊疗成scanpy版块的，包括样品读取，质控，harmony去除批次效应，降维聚类，marker坚贞。
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，这几个文献曩昔应该齐有共享过.scRNA_scripts文献夹的r代码齐在上头的百度云网盘一语气（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 ###### # 付费花样 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")# 若是过滤的太狠，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: 降维聚类分群和看象征基因库 ####### 原则上分歧率是需要我方肉眼判断，取决于个东说念主教训# 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代码安设各式库的时辰，径直pip install scanpy即可
若是是在jupyter notebook或者jupyter lab里，需要在前边加上！，呐喊行里就无须
!pip install scanpy
软件开发#安设导入需要的模块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
# 运行计时a = time.perf_counter()
scanpy下的read_10x_mtx函数读取数据，传入文献夹旅途，保证文献夹下依然这三个文献即可
图片联系我们
上期龙头开出奇数号码05，近10期龙头奇偶比7：3，本期龙头预测关注偶数号码，独胆参考08。
轮回读取9个单细胞数据，用字典进行存储，呼和浩特管理系统开发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吞并
scanpy对象结构参考下图
adata.var是看成gene，列为统计缱绻的矩阵，进行完各式分析之后，偶而如下图，刚读完数据的时辰，只好行名，莫得列
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adata.obs是看成细胞，列为统计缱绻的矩阵图片
adata.uns内部存放的吊问结构化的数据，齐是字典的样式图片
比如sample color，存放的是每个样品对应的表情，pca内部存放有降维后的主要素的信息，louvain 0.01_colors内部存放的等于0.01的分歧率下每个cluster对应的表情图片
adata.X内部存放的是抒发矩阵，看成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\就无须独特修改了path = r'F:\新建文献夹\2022-GSE189357-LUAD-单细胞-疾病阐明\GSE189357_RAW\output'files = glob(path + '\*')# tqdm用来潜入流程条，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，参考曾憨厚seurat里qc.R剧本写的
这里只探求了human gene，齐是大写的
#狡计比例和圭臬质控 def basic_qc(adata): # 狡计线粒体基因比例 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) # 狡计核糖体基因比例 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) # 狡计红血细胞基因比例 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) # 拓荒不同分歧率 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) # 拓荒不同分歧率 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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