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| ##### DUODENUM CELL ANALYSIS SCRIPT | ||
| # Josh Wu | ||
| # 7 June, 2019 | ||
| # Contains analysis for Yu-Hwai Tsai | ||
| # Analysis of duodenum samples | ||
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| import sys | ||
| sys.path.insert(0,'C:/Users/Josh/Desktop/sca_run/sca_run') | ||
| from sca_run import * | ||
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| figdir = './figures_091020/' | ||
| an_run = sca_run() | ||
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| ############################################################################# | ||
| ## Change this to point toward your mount location for our MiStorage share ## | ||
| ############################################################################# | ||
| an_run.storage_mount_point = 'Z:/' | ||
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| # # IDs of samples as represented in the metadata table | ||
| # sca_dict.update(sample_list = ['2598-31', # 47 | ||
| # '2757-2', # 59 | ||
| # '2250-1','2250-2', # 127 | ||
| # '2292-2','2321-5', # Adult | ||
| # '2511-2', # 101 | ||
| # '2598-24', # 80 | ||
| # '2598-28', #132 | ||
| # '2856-1']) # 72 | ||
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| # an_run.sample_list = ['150-1','150-2','150-3','150-4','150-6','150-7','150-8'] | ||
| # run_save = pickle.load(open(''.join([figdir,'adata_save.p']))) | ||
| an_run.sample_list = ['2598-31', # 47 | ||
| '2757-2', # 59 | ||
| '2250-1','2250-2', # 127 | ||
| '2292-2','2321-5', # Adult | ||
| '2511-2', # 101 | ||
| '2598-24', # 80 | ||
| '2598-28', #132 | ||
| '2856-1'] # 72 | ||
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| ## List of interesting genes | ||
| an_run.add_gene_list(markers=['EPCAM'], | ||
| label='EPCAM_plot') | ||
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| an_run.add_gene_list(markers = ['CDX2','OLFM4','LGR5','SOX9','ELF3','VIL1','MUC2','CHGA','DEFA5', | ||
| 'LYZ','DPP4','MKI67','TPI1','SPDEF','MGAM','PDGFA'],#'IS','NGN3''IAP', | ||
| label='gene_list_1') | ||
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| an_run.add_gene_list(markers = ['NOTCH1','NOTCH2','NOTCH3', | ||
| 'NOTCH4','DLL1','DLL3','DLL4','JAG1','JAG2'], | ||
| label='notch_components') | ||
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| an_run.add_gene_list(markers = ['OLFM4','LGR5','SMOC2','IGFBP4','BMI1','LRIG1','TERT','MSI1','PHLDA1','PROM1', | ||
| 'TNFRSF19','EPHB2','POFUT1','SOX9','ASCL2'], | ||
| label='gene_list_2') | ||
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| an_run.add_gene_list(markers = ['LGR5'], | ||
| label='LGR5_plot') | ||
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| an_run.add_gene_list(markers = ['OLFM4'], | ||
| label='OLFM4_plot') | ||
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| an_run.add_gene_list(markers = ['CDX2','OLFM4','LGR5','NOTCH1','NOTCH2','NOTCH3', | ||
| 'NOTCH4','DLL1','DLL3','DLL4','JAG1','JAG2'], | ||
| label='basic_list') | ||
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| an_run.add_gene_list(markers = ['HES1','HES5','HEY1','HEY2','HEYL','CCND1','CDKN1A', | ||
| 'CDKN1B','CDKN2A','GATA3','PTCRA','DTX1'],#'MYRC' | ||
| label='target_genes') | ||
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| an_run.add_gene_list(markers = ['NUMB','YAP1','ADAM10','ADAM17','RBPJ','MAML1', | ||
| 'MAML2','MAML3'], | ||
| label='related_genes') | ||
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| an_run.add_gene_list(markers=['HES1'], | ||
| label='HES1_plot') | ||
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| ## Parameters used to filter the data - Mainly used to get rid of bad cells | ||
| an_run.set_filter_params(min_cells = 0, # Filter out genes expressed in fewer cells | ||
| min_genes = 500, # Filter out cells with fewer genes to remove dead cells | ||
| max_genes = 7000, # Filter out cells with more genes to remove most doublets | ||
| max_counts = 30000, # Filter out cells with more UMIs to catch a few remaining doublets | ||
| max_mito = 0.1) # Filter out cells with high mitochondrial gene content | ||
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| ## Parameters used for initial clustering analysis | ||
| an_run.set_analysis_params(n_neighbors = 30, # Size of the local neighborhood used for manifold approximation | ||
| n_pcs = 20, # Number of principle components to use in construction of neighborhood graph | ||
| spread = 1, # In combination with min_dist determines how clumped embedded points are | ||
| min_dist = 0.4, # Minimum distance between points on the umap graph | ||
| resolution = 0.5) # High resolution attempts to increases # of clusters identified | ||
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| an_run.set_plot_params(size = 10, | ||
| umap_obs = ['louvain','age','tissue','sampleName'], | ||
| exp_grouping = ['louvain','age'], | ||
| final_quality=True) | ||
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| ##### Trying out DeepImpute ##### | ||
| # adata = an_run.load_data() | ||
| # adata = an_run.filter_data(adata) | ||
| # counts_df = pd.DataFrame(adata.X.toarray()) | ||
| # print(counts_df) | ||
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| # from deepimpute.multinet import MultiNet | ||
| # model = MultiNet() | ||
| # model.fit(counts_df,NN_lim=10000) | ||
| # imputed_data = model.predict(counts_df) | ||
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| # adata.X = imputed_data.values | ||
| # print(adata.X) | ||
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| # pickle.dump(adata,open(''.join([figdir,'imputed_data.p']),"wb"),protocol=4) | ||
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| # adata_postFiltered = pickle.load(open(''.join([figdir,'imputed_data.p']),"rb")) | ||
| # from scipy.sparse import csr_matrix | ||
| # adata_postFiltered.X = csr_matrix(adata_postFiltered.X) | ||
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| # adata = an_run.preprocess_data(adata_postFiltered) | ||
| # an_run.adata_postFiltered = adata_postFiltered | ||
| # adata = an_run.run_analysis(adata) | ||
| # adata = an_run.plot_sca(adata,figdir=figdir) | ||
| # an_run.adata = adata.copy() | ||
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| # pickle.dump(an_run,open(''.join([figdir,'adata_save.p']),"wb"),protocol=4) | ||
| # an_run.write_summary(figdir=figdir) | ||
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| ## Basic pipeline for analysis - will filter data, process, cluster, etc. and output relevant figures | ||
| # an_run.pipe_basic(figdir)#load_save='adata_save.p') | ||
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| ## If you find some interesting clusters that you want to "zoom in" on and recluster, you can use the following code | ||
| # Will update | ||
| # New analysis parameters for the subset of parameters | ||
| analysis_params_ext = dict(n_neighbors = 15, | ||
| n_pcs = 11, | ||
| spread = 1, | ||
| min_dist = 0.4, | ||
| resolution = 0.4) | ||
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| an_run.size=20 | ||
| an_run.pipe_ext(analysis_params_ext, figdir=figdir, label='epithelium_7_12', | ||
| extracted=['7','12'],final_quality=True, load_save='adata_save.p') | ||
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| # an_run.pipe_ext(analysis_params_ext, figdir=figdir, label='epithelium_8_9_19', | ||
| # extracted=['8','9','19'],final_quality=True, load_save='adata_save.p') | ||
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| # an_run.pipe_ext(analysis_params_ext, figdir=''.join([figdir,'extracted/epithelium_8_9_19/']), label='LGR5_OLFM4_cells', | ||
| # extracted=['0','2','7','8','9','12'],final_quality=True, load_save='adata_save.p') | ||
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| # an_run.pipe_ext(analysis_params_ext, figdir=''.join([figdir,'extracted/epithelium_8_9_19/']), label='LGR5_cells', | ||
| # extracted=['0','2','7','8','9'],final_quality=True, load_save='adata_save.p') | ||
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| # an_run.pipe_ext(analysis_params_ext, figdir=''.join([figdir,'extracted/epithelium_8_9_19/']), label='OLFM4_cells', | ||
| # extracted=['0','8','12'],final_quality=True, load_save='adata_save.p') | ||
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| # an_run.pipe_ext(analysis_params_ext, figdir=''.join([figdir,'extracted/epithelium_8_9_19/']), label='C0', | ||
| # extracted=['0'],final_quality=True, load_save='adata_save.p') | ||
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| # an_run.pipe_ext(analysis_params_ext, figdir=''.join([figdir,'extracted/epithelium_8_9_19/']), label='C2', | ||
| # extracted=['2'],final_quality=True, load_save='adata_save.p') | ||
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| # an_run.pipe_ext(analysis_params_ext, figdir=''.join([figdir,'extracted/epithelium_8_9_19/']), label='C7', | ||
| # extracted=['7'],final_quality=True, load_save='adata_save.p') | ||
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| # an_run.pipe_ext(analysis_params_ext, figdir=''.join([figdir,'extracted/epithelium_8_9_19/']), label='C8', | ||
| # extracted=['8'],final_quality=True, load_save='adata_save.p') | ||
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| # an_run.pipe_ext(analysis_params_ext, figdir=''.join([figdir,'extracted/epithelium_8_9_19/']), label='C9', | ||
| # extracted=['9'],final_quality=True, load_save='adata_save.p') | ||
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| an_run.resolution = 1 | ||
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| # an_run.pipe_ext(analysis_params_ext, figdir=''.join([figdir,'extracted/epithelium_8_9_19/']), label='C12', | ||
| # extracted=['12'],final_quality=True, load_save='adata_save.p')#, preprocess=False) | ||
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| # run_save = pickle.load(open(''.join([figdir,'extracted/epithelium_8_9_19/adata_save.p']),"rb")) | ||
| # adata = run_save.adata.copy() | ||
| # adata.obs['OLFM4+'] = ['OLFM4+' if cluster else False for cluster in adata.obs['louvain'].isin(['0','8','12'])] | ||
| # # adata.obs['OLFM4+, LGR5+, SOX9+'] = ['OLFM4+, LGR5+, SOX9+' if cluster else False for cluster in adata.obs['louvain'].isin(['0','8'])] | ||
| # adata.obs['OLFM4+, LGR5+'] = ['OLFM4+, LGR5+' if cluster else False for cluster in adata.obs['louvain'].isin(['0','2','7','8','9','12'])] | ||
| # adata.obs['LGR5+'] = ['LGR5+' if cluster else False for cluster in adata.obs['louvain'].isin(['0','2','7','8','9'])] | ||
| # # adata.obs['SOX9+'] = ['SOX9+' for cluster in adata.obs['louvain']] | ||
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| # gene_groupings = ['OLFM4+','OLFM4+, LGR5+','LGR5+']#,'SOX9+'] | ||
| # notch_components = ['NOTCH1','NOTCH2','NOTCH3','NOTCH4','DLL1','DLL3','DLL4','JAG1','JAG2'] | ||
| # for grouping in gene_groupings: | ||
| # adata_plot = adata[adata.obs[grouping]==grouping].copy() | ||
| # feature_colors = [(210,210,210), (210,210,210), (245,245,200), (100,200,225), (0,45,125)] | ||
| # position=[0, 0.019999, 0.02, 0.55, 1] | ||
| # my_feature_cmap = an_run.make_cmap(feature_colors,bit=True,position=position) | ||
| # sc.pl.dotplot(adata_plot,notch_components,groupby=grouping, save=''.join(['_',grouping,'.pdf']), show=False, | ||
| # color_map=my_feature_cmap, use_raw=True, dendrogram=False) | ||
| # #, figsize=(4,6))#, dot_max=0.4)#, dendrogram=True) | ||
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| figdir = './figures_103119/LWRN-E/' | ||
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| #an_run.sample_list = ['150-5'] | ||
| an_run.sample_list = ['3011-6'] | ||
| ## Parameters used for initial clustering analysis | ||
| an_run.set_analysis_params(n_neighbors = 15, # Size of the local neighborhood used for manifold approximation | ||
| n_pcs = 11, # Number of principle components to use in construction of neighborhood graph | ||
| spread = 1, # In combination with min_dist determines how clumped embedded points are | ||
| min_dist = 0.4, # Minimum distance between points on the umap graph | ||
| resolution = 0.5) # High resolution attempts to increases # of clusters identified | ||
|
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| ## Basic pipeline for analysis - will filter data, process, cluster, etc. and output relevant figures | ||
| # an_run.pipe_basic(figdir)#,load_save='adata_save.p') |
Submodule sca_run
added at
e1af54