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singlecellwdl.sublime-workspace
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singlecellwdl.sublime-workspace
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{
"auto_complete":
{
"selected_items":
[
[
"fig",
"fig_out"
],
[
"le",
"levels"
],
[
"spl",
"split.by"
],
[
"sample",
"sample_cols"
],
[
"scr",
"scrna_GEX"
],
[
"out",
"output_h5ad"
],
[
"len",
"length"
],
[
"me",
"metadata_df"
],
[
"T",
"TRUE"
],
[
"Def",
"DefaultAssay"
]
]
},
"buffers":
[
{
"contents": "scp -r [email protected]:/storage1/fs1/allegra.petti/Active/Users/khan.saad/cNMF_python/Get_transposed_counts.R scripts/Get_cNMF_counts.R .\n\n\nscp -r [email protected]:/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis/cNMF_analysis_prepare.py .\n\n\nscp -r [email protected]:/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis/cnmf.py .\n\nscp -r [email protected]:/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis/corrplot_K10_vs_modscore.rds .\n\nscp -r [email protected]:/storage1/fs1/tannerjohanns/Active/khan.saad/Mouse_TILs_analysis/MouseTIL_Seurat_mc0.05/TCR_genes_remdf.csv .\n\nscp -r [email protected]:/storage1/fs1/tannerjohanns/Active/khan.saad/Mouse_TILs_analysis/MouseTIL_Seurat_mc0.05/Dimplot_highlight_colored_clonotypes_M_IB-\\*.png .\n\n\nscp -r [email protected]:/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis/top_genes_K9_ngene50.csv .\n\n\nLSF_DOCKER_PRESERVE_ENVIRONMENT=false bsub -G compute-allegra.petti -J module2_rhp -oo logs/module2_rhp.%J.out -q general -M 128GB -n 1 -R 'rusage[mem=128GB] span[hosts=1]' -a 'docker(bhuvic/singlecell:v2)' Rscript /storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis/module2_rhp_functions.R /storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis/scwhann_scrna_only/scwhann_scrna_only_rpca.RDS /storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis/scwhann_scrna_only/nmf_h_coef_vs.RDS /storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis/scwhann_scrna_only/nmf_w_basis_vs.RDS /storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis/scwhann_scrna_only\n\n\nnmf_paramdf_list[['ngenes_30_inter_min10_intra_min18_intra_max10']][['nmf_corr_complexity_tumor']]\n\n scp -r Single_cell_WDLworkflows [email protected]:/scratch1/fs1/allegra.petti/khan.saad/WDL_workflow/\n\n\n scp -r [email protected]:/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis/schwann_subcluster_scRNA_nosuspcells_nomyelination .\n\n scp -r [email protected]:/scratch1/fs1/allegra.petti/khan.saad/WDL_workflow/Single_cell_WDLworkflows/*.json .\n\n\n scp -r [email protected]:/storage1/fs1/allegra.petti/Active/Users/khan.saad/WDL_pipelines/singleR_immune_nonimmune/08466c92-5f18-454e-b198-7229d806122f/call-run_singleR_immune_nonimmune/execution/glob-c312ce54c800e4f7b2886ad33e8b7f44/B148_singleR_immune_res.rds .\n\n scp -r [email protected]:/storage1/fs1/allegra.petti/Active/Users/khan.saad/WDL_pipelines/singleR_immune_nonimmune/08466c92-5f18-454e-b198-7229d806122f/call-run_singleR_immune_nonimmune/execution/glob-c312ce54c800e4f7b2886ad33e8b7f44/B148_singleR_nonimmune_res.rds .\n\n scp -r [email protected]:/storage1/fs1/allegra.petti/Active/Users/khan.saad/WDL_pipelines/singleR_immune_nonimmune/08466c92-5f18-454e-b198-7229d806122f/call-run_singleR_immune_nonimmune/execution/glob-c312ce54c800e4f7b2886ad33e8b7f44/B148_singleR_seurat_obj_immune.rds .\n\n scp -r [email protected]:/storage1/fs1/allegra.petti/Active/Users/khan.saad/WDL_pipelines/singleR_immune_nonimmune/08466c92-5f18-454e-b198-7229d806122f/call-run_singleR_immune_nonimmune/execution/glob-c312ce54c800e4f7b2886ad33e8b7f44/B148_singleR_seurat_obj_nonimmune.rds .\n\nscp -r [email protected]:/scratch1/fs1/allegra.petti/khan.saad/WDL_workflow/Single_cell_WDLworkflows/example_inputs/\\* ./Single_cell_WDLworkflows/example_inputs\n\nsingleR_files <- c('/storage1/fs1/allegra.petti/Active/Users/khan.saad/WDL_pipelines/scatter_gather_singleR/20f49d16-401c-492a-8b67-c9c7e0b5b0f2/call-run_singleR/shard-1/execution/Brain_immune_atlas_singleR_preds.rds','/storage1/fs1/allegra.petti/Active/Users/khan.saad/WDL_pipelines/scatter_gather_singleR/20f49d16-401c-492a-8b67-c9c7e0b5b0f2/call-run_singleR/shard-0/execution/Haemopedia_singleR_preds.rds')\n\nsingleR_tsv_file <- '/scratch1/fs1/allegra.petti/khan.saad/WDL_workflow/Single_cell_WDLworkflows/example_inputs/SingleR_singleref_scatter_mouse.tsv'\n\nSeurat_file <- '/storage1/fs1/allegra.petti/Active/GBM/Stegh/Seurat_analysis/RDS_files/Cycling.SCT.PCA.UMAP.TSNE.CLUST.doublet_calls.220315.rds'\n\n scp -r /Users/khan.saad/Vest_schwanomma_proj/NMF_analysis/subset_Renorm_reclust.R [email protected]:\n\nscatter_gather_singleR/20f49d16-401c-492a-8b67-c9c7e0b5b0f2/call-add_singleR_results_to_seurat/inputs/403717767/SingleR_singleref_scatter_mouse.tsv\n\n/storage1/fs1/allegra.petti/Active/Users/khan.saad/WDL_pipelines/scatter_gather_singleR/20f49d16-401c-492a-8b67-c9c7e0b5b0f2/call-add_singleR_results_to_seurat/inputs/1114994943/add_singleR_to_multisample_seurat.R:\n\n scp -r [email protected]:/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis/universe_genes.rds .\n\n scp -r [email protected]:/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis/universe_genes_tnksubclus.rds .\n\n DEGs.Wilcox.integrated_snn_res.0.5.T_NK_subcluster_scRNA_doublets_rem_onlypos.20220216.txt\n\n scp -r Single_cell_WDLworkflows [email protected]:/scratch1/fs1/allegra.petti/khan.saad/WDL_workflow/\n\nseurat_obj <- readRDS('/storage1/fs1/allegra.petti/Active/GBM/Stegh/Seurat_analysis/RDS_files/Cycling.SCT.PCA.UMAP.TSNE.CLUST.220312.rds')\n\nplot_qc_inputs.json\n\n\n/storage1/fs1/allegra.petti/Active/Users/khan.saad/WDL_pipelines/scatter_doublet/2a763d72-6eb1-4b41-8119-223edce1b0fa/call-run_doublet/shard-2/execution/OE1_plus_2_Doublet_collection_results.rds\n\n./scatter_gather_doublets.json\n./subworkflows/scatter_gather_Doubletcall.wdl\n\n/storage1/fs1/allegra.petti/Active/Users/khan.saad/WDL_pipelines/scatter_doublet/1316cd00-6ae5-49f3-8bd2-49cc3d8a2168/call-add_doublets_metadata_tomultisamp\nle_seurat/execution/stderr.\n\ndoublet_results_dfs$doublet_prediction\n\n/storage1/fs1/allegra.petti/Active/Users/khan.saad/WDL_pipelines/QC_plots/d54ade36-460e-4182-a262-3d1fb5467a0b/call-plot_qc/execution/glob-0131b28e008140b45cfc92ed88b1ad1e/Stegh_GBM.Post_filtering_QC.220314.pdf\n\n \"QC_plots.plot_qc.output_pdfs\": [\"\n\n /storage1/fs1/allegra.petti/Active/Users/khan.saad/WDL_pipelines/QC_plots/0e68d8b3-a1e9-4fa5-b14a-2190f3cb4722/call-plot_qc/execution/glob-0131b28e008140b45cfc92ed88b1a\n\n./tasks/plot_qc.wdl\n\nrsync -ave ssh --include=\"*/\" --exclude=\"*.npz\" --exclude=\"*.rda\" --exclude=\"*.RDS\" --exclude=\"*.rds\" --exclude=\"*.RData\" --exclude=\"*.npz\" --exclude=\"*Old_analysis_snRNAndscRNA*/\" --exclude=\"*_shelve.out\" --exclude=\"*.h5ad\" --exclude=\"*.tsv\" [email protected]:/storage1/fs1/tannerjohanns/Active/khan.saad/Mouse_TILs_analysis .\n\n\nrsync -ave ssh --include=\"*/\" --exclude=\"*.npz\" --exclude=\"*.rda\" --exclude=\"*.RDS\" --exclude=\"*.rds\" --exclude=\"*.RData\" --exclude=\"*.npz\" --exclude=\"*Old_analysis_snRNAndscRNA*/\" --exclude=\"*_shelve.out\" --exclude=\"*.h5ad\" --exclude=\"*.tsv\" [email protected]:/scratch1/fs1/allegra.petti/khan.saad/STEGH_analysis .\n\nrsync -ave ssh --include=\"*/\" --exclude=\"*.npz\" --exclude=\"*.rda\" --exclude=\"*.RDS\" --exclude=\"*.rds\" --exclude=\"*.RData\" --exclude=\"*.npz\" --exclude=\"*Old_analysis_snRNAndscRNA*/\" --exclude=\"*_shelve.out\" --exclude=\"*.h5ad\" --exclude=\"*.tsv\" [email protected]:/storage1/fs1/allegra.petti/Active/Meningioma_dunn/Seurat_analysis .\n\nrsync -ave ssh --include=\"*/\" --exclude=\"*.npz\" --exclude=\"*.rda\" --exclude=\"*.RDS\" --exclude=\"*.rds\" --exclude=\"*.RData\" --exclude=\"*.npz\" --exclude=\"*Old_analysis_snRNAndscRNA*/\" --exclude=\"*_shelve.out\" --exclude=\"*.h5ad\" --exclude=\"*.tsv\" [email protected]:/storage1/fs1/tannerjohanns/Active/khan.saad/Mouse_TILs_analysis/Final_figures .\n\n scp -r [email protected]:/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis/schwann_subcluster_scRNA_nosuspcells_nomyelination/DEGs.Wilcox.schwann_subcluster_scRNA_nosuspcells_nomyelination.integrated_snn_res.0.5_onlypos.20220215.txt .\n\n scp -r [email protected]:/storage1/fs1/tannerjohanns/Active/khan.saad/Mouse_TILs_analysis/Final_figures/Figure1b_genelist.rds .\n\n\n\n /storage1/fs1/allegra.petti/Active/Users/khan.saad/WDL_pipelines/Seurat_rPCA_integration/d66827ea-d69b-4e48-b893-35f123cee776/call-run_rpca_integration\n\n\n/storage1/fs1/allegra.petti/Active/Users/khan.saad/WDL_pipelines/Seurat_rPCA_integration/d66827ea-d69b-4e48-b893-35f123cee776/call-run_rpca_integration/execution/\n\nglob-ef5df339533c1334f081dc8cc75ee4f3/DEGs.Wilcox.schwann_subcluster_scRNA_noriboclus.integrated_snn_res.0.5_onlypos.20220209.txt\n\nscp -r [email protected]:/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/metamodule_symbol_list_for_enrichment_analysis_intramin_35_intra_max_10_inter_min_10_n50genes.rds ./NMF_analysis_iteration2/\n\nscp -r [email protected]:/storage1/fs1/alberthkim/Active/users/khan.saad/Scenic_iteration2/QC_plots_schwann_sublcuster.pdf .\n\n\nscp /Users/khan.saad/WDL_workflows/Single_cell_WDLworkflows/scripts/Get_cNMF_counts.R [email protected]:/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis\n\n\nscp -r [email protected]:/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis/schwann_scrna_noriboclus_cnmf/schwann_scrna_noriboclus_cnmf.k_selection.png .\n\nrsync -ave ssh --include=\"*/\" --exclude=\"*.npz\" --exclude=\"*.rda\" --exclude=\"*.RDS\" --exclude=\"*.rds\" --exclude=\"*.RData\" --exclude=\"*.txt\" --exclude=\"*.npz\" --exclude=\"*Old_analysis_snRNAndscRNA*/\" --exclude=\"*_shelve.out\" --exclude=\"*.h5ad\" --exclude=\"*.csv\" --exclude=\"*.tsv\" --exclude=\"*.txt\" [email protected]:/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis .\n\n\nrsync -ave ssh --include=\"*/\" --exclude=\"*.npz\" --exclude=\"*.rda\" --exclude=\"*.RDS\" --exclude=\"*.rds\" --exclude=\"*.RData\" --exclude=\"*.txt\" --exclude=\"*.npz\" --exclude=\"*Old_analysis_snRNAndscRNA*/\" --exclude=\"*_shelve.out\" --exclude=\"*.h5ad\" --exclude=\"*.tsv\" --exclude=\"*.txt\" [email protected]:/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis .\n\n/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis/scwhann_scrna_noriboclus_cnmf_out.h5ad\n\nadata = sc.read('/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis/scwhann_scrna_noriboclus_cnmf_out.h5ad')\n\nadata2 = sc.read('/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis/scwhann_scrna.nmf_out.h5ad')\n\nsc.read('/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis/scwhann_scrna_noriboclus_cnmf_out.h5ad')\n\nhvgs_file = '/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis/schwann_scrna_noriboclus_cnmf/schwann_scrna_noriboclus_cnmf.overdispersed_genes.txt'\n\nsc.write('scwhann_scrna_noriboclus_cnmf_out_norm.h5ad', adata)\n\nhvgs = open(hvgs_file).read().split('\\n')\n\n#bsub -oo logs/prepare_nmf.%J -q siteman -G compute-allegra.petti -g /khan.saad/R_seurat -M 128GB -n 1 -R 'rusage[mem=128GB] span[hosts=1]' -a 'docker(quay.io/dkotliar/cnmf:0.2)' /opt/miniconda3/envs/cnmf_env/bin/python cNMF_analysis_prepare.py schwann_subclus_scRNA_noriboclus_RNA_assay_counts_matrix.20220210.txt /storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis scwhann_scrna_noriboclus_cnmf_out.h5ad\n\n/storage1/fs1/allegra.petti/Active/Users/a.douglas/DOUGLAS_ROTATION/\n\nscwhann_scrna_noriboclus_cnmf_out_rawnorm.h5ad\n\nrsync -ave ssh --include=\"*.ipynb\" --exclude=\"*.*\" [email protected]:/storage1/fs1/allegra.petti/Active/Users/a.douglas/DOUGLAS_ROTATION/ .\n\nusage = pd.read_csv('/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis/schwann_scrna_noriboclus_cnmf/schwann_scrna_cnmf.usages.k_10.dt_0_05.consensus.txt',sep='\\t', index_col=0)\n\n\n\nusage = pd.read_csv('/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis/schwann_scrna_noriboclus_cnmf/schwann_scrna_noriboclus_cnmf.usages.k_7.dt_0_05.consensus.txt',sep='\\t', index_col=0)\n\nusage.columns = ['Usage_%s_K7' % i for i in usage.columns]\n\nusage_k8 = pd.read_csv('/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis/schwann_scrna_noriboclus_cnmf/schwann_scrna_noriboclus_cnmf.usages.k_8.dt_0_05.consensus.txt',sep='\\t', index_col=0)\n\nusage_k8.columns = ['Usage_%s_K8' % i for i in usage_k8.columns]\nusage_k8.head()\n\nusage_k9 = pd.read_csv('/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis/schwann_scrna_noriboclus_cnmf/schwann_scrna_noriboclus_cnmf.usages.k_9.dt_0_10.consensus.txt',sep='\\t', index_col=0)\n\nusage_k10 = pd.read_csv('/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis/schwann_scrna_noriboclus_cnmf/schwann_scrna_noriboclus_cnmf.usages.k_10.dt_0_05.consensus.txt',sep='\\t', index_col=0)\n\n\nusage_k9.columns = ['Usage_%s_K9' % i for i in usage_k9.columns]\nusage_k9.head()\n\nusage_k10.columns = ['Usage_%s_K10' % i for i in usage_k10.columns]\nusage_k10.head()\n\nusage_norm = usage.div(usage.sum(axis=1), axis=0)\n\nusage_k9_norm = usage_k9.div(usage_k9.sum(axis=1), axis=0)\n\nusage_k8_norm = usage_k8.div(usage_k8.sum(axis=1), axis=0)\n\nusage_k10_norm = usage_k10.div(usage_k10.sum(axis=1), axis=0)\n\nadata.obs = pd.merge(left=adata.obs, right=usage_norm, how='left', left_index=True, right_index=True)\n\nsc.pl.umap(adata, color=usage_norm.columns,ncols=3, vmin=0, vmax=1,save='umap_usage_K5_schwann_noriboclus.png')\n\nadata.obs = pd.merge(left=adata.obs, right=usage_k9_norm, how='left', left_index=True, right_index=True)\n\nsc.pl.umap(adata, color=usage_k9_norm.columns,ncols=3, vmin=0, vmax=1,save='umap_usage_K9_schwann_noriboclus.png')\n\n\nadata.obs = pd.merge(left=adata.obs, right=usage_k8_norm, how='left', left_index=True, right_index=True)\n\nsc.pl.umap(adata, color=usage_k8_norm.columns,ncols=3, vmin=0, vmax=1,save='umap_usage_K8_schwann_noriboclus.png')\n\n\nadata.obs = pd.merge(left=adata.obs, right=usage_k10_norm, how='left', left_index=True, right_index=True)\n\nsc.pl.umap(adata, color=usage_k10_norm.columns,ncols=3, vmin=0, vmax=1,save='umap_usage_K10_schwann_noriboclus.png')\n\n\n\ngene_scores_K5 = pd.read_csv('/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis/schwann_scrna_noriboclus_cnmf/schwann_scrna_noriboclus_cnmf.gene_spectra_score.k_5.dt_0_10.txt',sep='\\t', index_col=0).T\n\ngene_scores_K8 = pd.read_csv('/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis/schwann_scrna_noriboclus_cnmf/schwann_scrna_noriboclus_cnmf.gene_spectra_score.k_8.dt_0_05.txt',sep='\\t', index_col=0).T\n\ngene_scores_K9 = pd.read_csv('/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis/schwann_scrna_noriboclus_cnmf/schwann_scrna_noriboclus_cnmf.gene_spectra_score.k_9.dt_0_10.txt',sep='\\t', index_col=0).T\n\ngene_scores_K10 = pd.read_csv('/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis/schwann_scrna_noriboclus_cnmf/schwann_scrna_noriboclus_cnmf.gene_spectra_score.k_10.dt_0_05.txt',sep='\\t', index_col=0).T\n\n\ndef get_top_genes(gene_scores,ngenes,num_comp,adata):\n\ttop_genes = []\n\tfor gep in gene_scores.columns:\n\t\ttop_genes.append(list(gene_scores.sort_values(by=gep, ascending=False).index[:ngenes]))\n\ttop_genes = pd.DataFrame(top_genes, index=gene_scores.columns).T\n\ttop_genes.columns = ['program_%s_%s' % (i,num_comp) for i in top_genes.columns]\n\ttop1_gene_byprog = list(top_genes.iloc[0])\n\toutplot= 'umap_top1genes_byprogram_schwann_noriboclus_'+num_comp+'.png'\n\tsc.pl.umap(adata,use_raw=True, color=top1_gene_byprog,ncols=3,save=outplot)\n\toutfile= 'top_genes_byprogram_schwann_noriboclus'+num_comp+'_ngene'+str(ngenes)+'.csv'\n\ttop_genes.to_csv(outfile, index=False)\n\n\n\nget_top_genes(gene_scores=gene_scores_K10,ngenes=50,num_comp='K10',adata=adata)\n\ntop50_K10_genes.iloc[0]\n\ngene_scores.head()\n\ntop_genes = []\nngenes = 50\nfor gep in gene_scores.columns:\n\ttop_genes.append(list(gene_scores.sort_values(by=gep, ascending=False).index[:ngenes]))\ntop_genes = pd.DataFrame(top_genes, index=gene_scores.columns).T\ntop_genes\n\n\n/rdcw/fs1/allegra.petti/Active/spatial_snRNAseq_gbm/scrnaseq/FASTQS\n\n/rdcw/fs1/allegra.petti/Active/spatial_snRNAseq_gbm/scrnaseq/FASTQS/B186/\n\n/storage1/fs1/allegra.petti/Active/spatial_snRNAseq_gbm/scrnaseq/SAMPLES/B186/outs/\n\nsouth asians\n\n\n\nscp [email protected]:/storage1/fs1/alberthkim/Active/data/VS/merged_analysis/merged_1k_genes/objects/cleaned_subclus_predictions/schwann_subclus_scRNA_noriboclus/\\*.jpg . \n\n\nscp [email protected]:/storage1/fs1/alberthkim/Active/data/VS/merged_analysis/merged_1k_genes/objects/cleaned_subclus_predictions/schwann_subclus_scRNA_noriboclus/DEGs.Wilcox.schwann_subcluster_scRNA_noriboclus.integrated_snn_res.0.5_onlypos.20220209.txt .\n\n\nscp [email protected]:/storage1/fs1/alberthkim/Active/users/khan.saad/Scenic_iteration2/universe_genes.rds .\n\n\n/storage1/fs1/allegra.petti/Active/Users/khan.saad/WDL_pipelines/filter_n_cluster/8d6549bd-41fe-43ba-b7e1-801d249202b4/call-clus_n_pca/execution/B148.Cycling.SCT.PCA.UMAP.TSNE.CLUST.20220126.rds\n\n\n/storage1/fs1/allegra.petti/Active/Users/khan.saad/WDL_pipelines/SingleR_scsorter/be860bc3-e7a4-4e6c-ae21-83142b070dd5/call-run_singleR_scsorter/execution/\n\n/storage1/fs1/allegra.petti/Active/Users/khan.saad/WDL_pipelines/filter_n_cluster/8d6549bd-41fe-43ba-b7e1-801d249202b4/call-clus_n_pca/execution/B148.Cycling.SCT.PCA.UMAP.TSNE.CLUST.20220126.rds\n\nb148 <- readRDS('/storage1/fs1/allegra.petti/Active/Users/khan.saad/WDL_pipelines/filter_n_cluster/8d6549bd-41fe-43ba-b7e1-801d249202b4/call-clus_n_pca/execution/B148.Cycling.SCT.PCA.UMAP.TSNE.CLUST.20220126.rds')\n\nb148_immune_singleR <- readRDS('/storage1/fs1/allegra.petti/Active/Users/khan.saad/WDL_pipelines/SingleR_scsorter/be860bc3-e7a4-4e6c-ae21-83142b070dd5/call-run_singleR_scsorter/execution/B148_singleR_immune_res.rds')\n\n\nb148_non_immune_singleR <- readRDS('/storage1/fs1/allegra.petti/Active/Users/khan.saad/WDL_pipelines/SingleR_scsorter/be860bc3-e7a4-4e6c-ae21-83142b070dd5/call-run_singleR_scsorter/execution/B148_singleR_nonimmune_res.rds')\n\n/storage1/fs1/allegra.petti/Active/Users/khan.saad/WDL_pipelines/SingleR_scsorter/be860bc3-e7a4-4e6c-ae21-83142b070dd5/call-run_singleR_scsorter/execution/B148_singleR_nonimmune_res.rds\n\nhead(rownames(myorig[['br_imm_atl']]))\n\nmyorig <- b148_immune_singleR@listData$orig.results\n\nhead(b148_non_immune_singleR[['orig.results']][['stb']][['scores']])\n\nmyorig[[i]][['scores']]\n\nfor(i in names(myorig)){\n\trownames(myorig[[i]][['scores']]) <- rownames(myorig[[i]])\n\tsingleR_scores <- CreateAssayObject(data = t(as.matrix(myorig[[i]][['scores']])))\n\tscrna[[sprintf(\"%s.singleR_scores\", i)]] <- singleR_scores\n}\n\nsprintf(\"%s.singleR_scores\", i)\n\nrownames(myorig[['br_imm_atl']])\n\n\nmyorig$stb\nhead(myorig$stb$scores)\nhistory()\nmyorig$stb$scores\n\n\n\n\nmod_scores_seurat <- CreateAssayObject(data = t(as.matrix([email protected][26:34])))\nscrna[[\"mod_scores_seurat\"]] <- mod_scores_seurat\n\n\n\n/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis/corrplot_K10_vs_modscore.rds\n/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis/seurat_obj_suspcellsrem_nomyelination_clus.rds\n/storage1/fs1/alberthkim/Active/users/khan.saad/NMF_analysis_iteration2/cNMF_analysis/seurat_obj_suspcellsrem.rds\n\n\n\n\n\n",
"file": "scripts/scp -r [email protected]:",
"file_size": 13612,
"file_write_time": 132894230791925636,
"settings":
{
"buffer_size": 20043,
"line_ending": "Unix"
}
},
{
"contents": "import anndata2ri\nfrom rpy2.robjects import r\nanndata2ri.activate()\nimport scib\nimport pandas as pd\nimport scanpy as sc\nfrom datetime import date\nimport anndata\nimport random\n\ntoday = date.today()\n\nsc.set_figure_params(dpi=600)\nsc.settings.verbosity = 1\nsc.set_figure_params(figsize=(10, 10))\nsc.logging.print_header()\n\n# from matplotlib import pyplot as plt\n\n# with plt.rc_context(): # Use this to set figure params like size and dpi\n# sc.pl.plotting_function(..., show=False)\n# plt.savefig(\"path/to/file.extension\", bbox_inches=\"tight\")\n\n# with rc_context({'figure.figsize': (4, 4)}):\n# sc.pl.umap(pbmc, color='CD79A')\n\ndef phase_without_regressing_by_cellcycle(anndata,phase_pal=phase_pal,s_genes=s_genes,g2m_genes=g2m_genes):\n\t#without regressing out cell cycle\n\tsc.pp.normalize_total(anndata, target_sum=1e6)\n\tsc.pp.log1p(anndata)\n\tsc.pp.scale(anndata)\n\tsc.tl.score_genes_cell_cycle(anndata, s_genes=s_genes, g2m_genes=g2m_genes)\n\t#calculate pca on anndata\n\tsc.tl.pca(anndata)\n\tfig_out = 'Before_cellcycle_regression_removecellswithzerocounts'+'.png'\n\tsc.pl.pca_scatter(anndata, color='phase',palette=phase_pal,save=fig_out)\n\nanndata = scib.preprocessing.read_seurat('/storage1/fs1/allegra.petti/Active/GBM/CellState/GBM.9/Redo_PCA_clustering/GBM_cellstate_selgenes.220516.rds')\n\nanndata.raw = anndata # at the point during preprocessing at which you wish store a copy for visualization and differential testing\nsc.pp.filter_cells(anndata, min_counts=1)\n\nOrganism='human'\n\nif(str(Organism))=='human':\n\tcell_cycle_df = pd.read_csv(\"/storage1/fs1/allegra.petti/Active/10xGenomics/key.gene.lists/CellCycleTirosh.txt\",sep=\"\\t\",header=0)\n\ts_genes = list(cell_cycle_df['Gene.Symbol'][cell_cycle_df['List']=='G1/S'])\n\tg2m_genes = list(cell_cycle_df['Gene.Symbol'][cell_cycle_df['List']=='G2/M'])\n\tcell_cycle_genes = list(cell_cycle_df['Gene.Symbol'])\n\tcell_cycle_genes = [x for x in cell_cycle_genes if x in anndata.var_names]\n\t# cell_cycle_genes = [x.strip() for x in open('/data/regev_lab_cell_cycle_genes.txt')]\nelse:\n\tcell_cycle_df = pd.read_csv(\"/storage1/fs1/allegra.petti/Active/10xGenomics/key.gene.lists/CellCycleTirosh_mouse.txt\",sep=\"\\t\",header=None)\n\tcell_cycle_df =['List', 'Gene.Symbol']\n\ts_genes = list(cell_cycle_df['Gene.Symbol'][cell_cycle_df['List']=='G1/S'])\n\tg2m_genes = list(cell_cycle_df['Gene.Symbol'][cell_cycle_df['List']=='G2/M'])\n\nphase_pal = {\n\t'G1':\"#4477AA\",\n\t'G2M':\"#DDCC77\",\n\t'S' : \"#CC6677\"\n}\n\nphase_without_regressing_by_cellcycle(anndata)\n\nanndata = scib.preprocessing.read_seurat('/storage1/fs1/allegra.petti/Active/GBM/CellState/GBM.9/Redo_PCA_clustering/GBM_cellstate_selgenes.220516.rds')\nsc.pp.filter_cells(anndata, min_counts=1)\n\nphase_without_regressing_by_cellcycle(anndata)\n#with regressing cell cycle\nanndata = scib.preprocessing.read_seurat('/storage1/fs1/allegra.petti/Active/GBM/CellState/GBM.9/Redo_PCA_clustering/GBM_cellstate_selgenes.220516.rds')\nanndata.raw = anndata # at the point during preprocessing at which you wish store a copy for visualization and differential testing\nsc.pp.filter_cells(anndata, min_counts=1)\n\nsc.tl.score_genes_cell_cycle(anndata, s_genes=s_genes, g2m_genes=g2m_genes)\n# Regress out effects of cellcycle\nsc.tl.pca(anndata)\n\nanndata.var['mt'] = anndata.var_names.str.startswith('MT-') # annotate the group of mitochondrial genes as 'mt'\nsc.pp.calculate_qc_metrics(anndata, qc_vars=['mt'], percent_top=None, log1p=False, inplace=True)\n\n\nfig_out = '_multipanel_QC'+'.png'\n\nsc.pl.violin(anndata, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt'],\n jitter=0.4, multi_panel=True,save=fig_out)\n\nfig_out = '_topgenes'+'.png'\n#top 20 genes\nsc.pl.highest_expr_genes(anndata, n_top=20,save=fig_out)\n\nfig_out = '_total_counts_vs_pct_counts_mt'+'.png'\n\nsc.pl.scatter(anndata, x='total_counts', y='pct_counts_mt',save=fig_out)\n\nfig_out = '_total_counts_vs_n_genes_by_counts'+'.png'\n\nsc.pl.scatter(anndata, x='total_counts', y='n_genes_by_counts',save=fig_out)\n\nsc.pp.normalize_total(anndata, target_sum=1e6)\nsc.pp.log1p(anndata)\nsc.pp.highly_variable_genes(anndata, min_mean=0.0125, max_mean=3, min_disp=0.5)\n\nfig_out = '_HVGs'+'.png'\nsc.pl.highly_variable_genes(anndata,save=fig_out)\n\nsc.pp.regress_out(anndata, ['S_score', 'G2M_score'],n_jobs=10)\nsc.pp.scale(anndata)\nfig_out = '_after_cellcycle_regression'+'.png'\nsc.pl.pca_scatter(anndata, color='phase',palette=phase_pal,save=fig_out)\n\nsc.tl.pca(anndata, svd_solver='arpack')\n\nfig_out = '_after_cellcycle_regression_Principal_component_variance'+'.png'\n\nsc.pl.pca_variance_ratio(anndata, log=True,save=fig_out)\n\nsc.pp.neighbors(anndata, n_neighbors=10, n_pcs=50)\nsc.tl.leiden(anndata)\n\nsc.tl.paga(anndata)\nsc.pl.paga(anndata, plot=False) # remove `plot=False` if you want to see the coarse-grained graph\nsc.tl.umap(anndata, init_pos='paga')\n\nsc.tl.umap(anndata)\n\nfig_out = '_after_cellcycle_regression_selgenes_UMAP'+'.png'\n\nsc.pl.umap(anndata, color=['PTPRC', 'NKG7', 'TREM2'],save=fig_out)\n\n# As we set the .raw attribute of adata, the previous plots \n# showed the “raw” (normalized, logarithmized, but uncorrected)\n# gene expression. You can also plot the scaled and corrected \n# gene expression by explicitly stating that you don’t want to use .raw.\n\nfig_out = '_after_cellcycle_regression_selgenes_noraw'+'.png'\n\n\nsc.pl.umap(anndata, color=['PTPRC', 'NKG7', 'TREM2'], use_raw=False,save=fig_out)\n\n\nfig_out = '_after_cellcycle_regression_selgenes_clus'+'.png'\n\n\nsc.pl.umap(anndata, color=['leiden','PTPRC', 'NKG7', 'TREM2'],save=fig_out)\n\nfig_out = '_QC_umapplots'+'.png'\n\nsc.pl.umap(anndata, color=['n_genes_by_counts','total_counts', 'pct_counts_mt'],save=fig_out)\n\n\n# phase_pal = {\n# \t'G1':\"#4477AA\",\n# \t'G2M':\"#DDCC77\",\n# \t'S' : \"#CC6677\"\n# }\n\nfig_out = '_cellcycle_umapplots'+'.png'\n\nsc.pl.umap(anndata,color='phase',palette=phase_pal,save=fig_out)\n\n\nsample_palette = {\n\t'B150':\"#88CCEE\",\n\t'B152':\"#CC6677\",\n\t'B178' : \"#DDCC77\",\n\t'B183' : \"#117733\",\n\t'B185' : \"#332288\",\n\t'B186' : \"#AA4499\",\n\t'ST073021' : \"#44AA99\" ,\n\t'WU1225_core' : \"#999933\",\n\t'WU1225_edge' : \"#882255\",\n\t'WU1226_core_CD45neg' : \"#661100\",\n\t'WU1226_edge_CD45neg' : \"#888888\",\n}\n\nfig_out = '_sample_umapplots'+today.strftime(\"%m%d%y\")+'.png'\n\nsc.pl.umap(anndata,color='orig.ident',palette=sample_palette,save=fig_out,s=50)\n\nfig_out = '_SingleR_scsorter_umapplots'+'.png'\n\nsc.pl.umap(anndata,color='Cell_Types_SingleR_scsorter_v2',save=fig_out)\n\n# df.colName.value_counts()\n\n # B150 B152 B178 B183\n # \"#88CCEE\" \"#CC6677\" \"#DDCC77\" \"#117733\"\n # B185 B186 ST073021 WU1225_core\n # \"#332288\" \"#AA4499\" \"#44AA99\" \"#999933\"\n # WU1225_edge WU1226_core_CD45neg WU1226_edge_CD45neg\n # \"#882255\" \"#661100\" \"#888888\"\n\nresults_file = 'Scanpy_end2end_analysis/GBM.CellState.' + today.strftime(\"%m%d%y\") + '.h5ad' # the file that will store the analysis results\n\n\nanndata.write(results_file)\n\n\nanndata = anndata.read_h5ad('/storage1/fs1/allegra.petti/Active/GBM/CellState/GBM.9/Scanpy_end2end_analysis/GBM.CellState.051722.h5ad')\n\nanndata.obs['Cell_Types_SingleR_scsorter_v2'].value_counts()\n\n\ncelltype_colorpalette = {\n\t'nef.Malignant.NPC1':\"#fb298a\",\n\t'nef.Malignant.MES1':\"#2b9b00\",\n\t'nef.Malignant.AC' : \"#f846ba\",\n\t'nef.Malignant.OPC' : \"#01ca75\",\n\t'br_imm_atl.Mg-TAM' : \"#ff84f5\",\n\t'br_imm_atl.Mo-TAM' : \"#006a16\",\n\t'nef.Malignant.MES2' : \"#fc3557\",\n\t'nef.Malignant.NPC2' : \"#00cb90\",\n\t'br_imm_atl.T cells' : \"#ff5a76\",\n\t'br_imm_atl.prol. TAM' : \"#00c7d9\",\n\t'stb.NPC' : \"#f1552b\",\n\t'br_imm_atl.Monocytes' : \"#008db1\",\n\t'No.Prediction' : \"#e0e0e0\",\n\t'nef.Malignant.Unknown' : \"#c0aaff\",\n\t'br_imm_atl.DC' : \"#d6c94a\",\n\t'br_imm_atl.NK cells' : \"#324f97\",\n\t'stb.InN1b' : \"#793882\",\n\t'stb.ExN1_4' : \"#9dd48d\",\n\t'stb.Micro' : \"#a31925\",\n\t'stb.Astro1' : \"#018b6a\",\n\t'stb.Perc' : \"#ff8670\",\n\t'br_imm_atl.B cells' : \"#185f2a\",\n\t'stb.OPC1' : \"#8b3358\",\n\t'stb.Astro4' : \"#5c6100\",\n\t'stb.Endo' : \"#ffaa5d\",\n\t'stb.Olig3' : \"#8f360e\",\n\t'stb.InN5' : \"#ffb377\",\n}\n\nfig_out = '_SingleR_scsorter_umapplots.'+today.strftime(\"%m%d%y\")+'.png'\n\n# s=50, frameon=False, ncols=4,\n# ,palette=sample_palette,save=fig_out\n\nsc.set_figure_params(dpi=300)\nsc.settings.verbosity = 1\nsc.set_figure_params(figsize=(20, 20))\nsc.set_figure_params(scanpy=True, fontsize=14)\n\n\n\nsc.pl.umap(anndata,color='Cell_Types_SingleR_scsorter_v2',palette=celltype_colorpalette,s=50,save=fig_out)\n\n\n\nsc.pl.umap(anndata,color='orig.ident',palette=sample_palette,save=fig_out,s=50)\n\n\nfig_out = '_SingleR_scsorter_umapplots_grouped'+today.strftime(\"%m%d%y\")+'.png'\n\n\ndef cluster_small_multiples(adata, clust_key,figout,palette,size=50, frameon=False, legend_loc=None,**kwargs):\n\ttmp = adata.copy()\n\tfor i,clust in enumerate(adata.obs[clust_key].cat.categories):\n\t\ttmp.obs[clust] = adata.obs[clust_key].isin([clust]).astype('category')\n\t\ttmp.uns[clust+'_colors'] = ['#d3d3d3', adata.uns[clust_key+'_colors'][i]]\n\t\tncol_arg = min(len(adata.obs[clust_key].cat.categories.tolist()),3)\n\tsc.pl.umap(tmp, groups=tmp.obs[clust].cat.categories[1:].values,color=adata.obs[clust_key].cat.categories.tolist(),ncols=ncol_arg,save=fig_out,size=size, frameon=frameon, legend_loc=legend_loc, **kwargs)\n\n# sc.pl.umap(anndata,color='Cell_Types_SingleR_scsorter_v2',palette=celltype_colorpalette,s=50,save=fig_out,frameon=False, ncols=4,groups=list(celltype_colorpalette.keys()))\n\nfig_out = '_sample_umapplots_grouped'+today.strftime(\"%m%d%y\")+'.png'\n\n# sc.pl.umap(anndata, groups=[[c] for c in anndata.obs['Cell_Types_SingleR_scsorter_v2'].cat.categories],save=fig_out,color='Cell_Types_SingleR_scsorter_v2',palette=celltype_colorpalette, ncols=4)#, simply passing a list of lists to groups (without copying a whole anndata object). Will that be sufficient?\n\n\n\nsc.set_figure_params(figsize=(20, 20))\n\ncluster_small_multiples(adata=anndata,clust_key='orig.ident',figout=fig_out,palette=sample_palette,size=20)\n# sc.pl.umap(tmp, groups=tmp.obs[clust].cat.categories[1:].values, color=adata.obs[clust_key].cat.categories.tolist(), size=size, frameon=frameon, legend_loc=legend_loc, **kwargs)\n\n# sc.pl.umap(anndata,color='orig.ident',palette=sample_palette,save=fig_out,s=50,frameon=False, ncols=4,groups=anndata.obs['orig.ident'].cat.categories.tolist())\nfig_out = '_celltypes_umapplots_grouped'+today.strftime(\"%m%d%y\")+'.png'\n\ncluster_small_multiples(adata=anndata,clust_key='Cell_Types_SingleR_scsorter_v2',figout=fig_out,palette=sample_palette,size=20)\n\nsc.tl.leiden(anndata, key_added = \"leiden_1.0\") # default resolution in 1.0\nsc.tl.leiden(anndata, resolution = 0.5, key_added = \"leiden_0.5\")\nsc.tl.leiden(anndata, resolution = 0.7, key_added = \"leiden_0.7\")\nsc.tl.leiden(anndata, resolution = 1.2, key_added = \"leiden_1.2\")\nsc.tl.leiden(anndata, resolution = 1.5, key_added = \"leiden_1.5\")\n\nresults_file = 'GBM.CellState.' + today.strftime(\"%m%d%y\") + 'clustered.h5ad' # the file that will store the analysis results\nanndata.write(results_file)\n\n\ndef make_custom_palette(category_list):\n\t#rainbow palettes from R in a python list length 50\n\trainbow_palette = [\"#FF0000\",\"#FF1F00\",\"#FF3D00\",\"#FF5C00\",\"#FF7A00\",\"#FF9900\",\"#FFB800\",\"#FFD600\",\"#FFF500\",\"#EBFF00\",\"#CCFF00\",\"#ADFF00\",\"#8FFF00\",\"#70FF00\",\"#52FF00\",\"#33FF00\",\"#14FF00\",\"#00FF0A\",\"#00FF29\",\"#00FF47\",\"#00FF66\",\"#00FF85\",\"#00FFA3\",\"#00FFC2\",\"#00FFE0\",\"#00FFFF\",\"#00E0FF\",\"#00C2FF\",\"#00A3FF\",\"#0085FF\",\"#0066FF\",\"#0047FF\",\"#0029FF\",\"#000AFF\",\"#1400FF\",\"#3300FF\",\"#5200FF\",\"#7000FF\",\"#8F00FF\",\"#AD00FF\",\"#CC00FF\",\"#EB00FF\",\"#FF00F5\",\"#FF00D6\",\"#FF00B8\",\"#FF0099\",\"#FF007A\",\"#FF005C\",\"#FF003D\",\"#FF001F\"]\n\tthirty_color_pal = [\"#fb298a\",\"#2b9b00\",\"#f846ba\",\"#01ca75\",\"#ff84f5\",\"#006a16\",\"#fc3557\",\"#00cb90\",\"#ff5a76\",\"#00c7d9\",\"#f1552b\",\"#008db1\",\"#a33300\",\"#c0aaff\",\"#d6c94a\",\"#324f97\",\"#bc6400\",\"#793882\",\"#9dd48d\",\"#a31925\",\"#018b6a\",\"#ff8670\",\"#185f2a\",\"#8b3358\",\"#5c6100\",\"#ffaa5d\",\"#8f360e\",\"#ffb377\",\"#735800\",\"#9e734d\"]\n\tif(len(category_list) > len(thirty_color_pal)):\n\t\tlen_sel = len(category_list) - len(thirty_color_pal)\n\t\tsel_rainbow = random.sample(rainbow_palette, len_sel)\n\t\tsel_pal = thirty_color_pal+sel_rainbow\n\telse:\n\t\tsel_pal = thirty_color_pal\n\tpal_dict = {}\n\tcounter = 0\n\tfor i in category_list:\n\t\tpal_dict[i] = sel_pal[counter]\n\t\tcounter = counter+1\n\treturn(pal_dict)\n\n\nsc.set_figure_params(figsize=(10, 10))\n# anndata.obs['Cell_Types_SingleR_scsorter_v2'].cat.categories.tolist()\n\ndef plot_clusters_annot(adata,key_word,fig_out,pal,sup_pal):\n\ttmp = adata.copy()\n\tleg_title = 'clustering of cells '+str(key_word)\n\tif(pal==True):\n\t\tsc.pl.umap(adata, color=key_word, add_outline=True,legend_loc='on data',\n\t\tlegend_fontsize=12, legend_fontoutline=2,size=30,legend_fontweight='bold',frameon=False,\n\t\ttitle=leg_title, palette=sup_pal,save=fig_out)\n\telse:\n\t\tsup_pal = make_custom_palette(category_list=adata.obs[key_word].cat.categories.tolist())\n\t\tsc.pl.umap(adata, color=key_word, add_outline=True,legend_loc='on data',\n\t\tlegend_fontsize=12, legend_fontoutline=2,size=30,legend_fontweight='bold',frameon=False,\n\t\ttitle=leg_title,palette=sup_pal,save=fig_out)\n\nfor i in [\"leiden_0.5\",\"leiden_0.7\",\"leiden_1.0\",\"leiden_1.2\",\"leiden_1.5\"]:\n\tfig_out = '_Clusters_'+str(i)+'_umapplots_annot'+today.strftime(\"%m%d%y\")+'.png'\n\tplot_clusters_annot(adata=anndata,key_word=i,fig_out=fig_out,pal=False,sup_pal='na')\n\n\n\nfig_out = str('_Cell_Types_SingleR_scsorter_v2')+'_umapplots_annot'+today.strftime(\"%m%d%y\")+'.png'\n\nsc.pl.umap(anndata, color=['Cell_Types_SingleR_scsorter_v2'], add_outline=True,legend_loc='right margin',\nlegend_fontsize=12, legend_fontoutline=2,size=30,legend_fontweight='bold',frameon=False,\ntitle='Cell_Types_SingleR_scsorter_v2', palette=celltype_colorpalette,save=fig_out)\n\n\nfig_out = str('_orig.ident')+'_umapplots_annot'+today.strftime(\"%m%d%y\")+'.png'\n\nsc.pl.umap(anndata, color=['orig.ident'], add_outline=True,legend_loc='right margin',\nlegend_fontsize=12, legend_fontoutline=2,size=30,legend_fontweight='bold',frameon=False,\ntitle='orig.ident', palette=sample_palette,save=fig_out)\n\n###Add batch variable to observations\n\nanndata.obs['Batch'] = '5_prime'\n\nold_3prime_batch = list(anndata.obs.index[anndata.obs['orig.ident']=='B152'])+list(anndata.obs.index[anndata.obs['orig.ident']=='B150'])\n\nanndata.obs.loc[old_3prime_batch, 'Batch'] = '3_prime'\n\nbatch_palette = {\n\t'5_prime':\"#D95F02\",\n\t'3_prime':\"#7570B3\"}\n\nfig_out = str('_Batch')+'_umapplots_annot'+today.strftime(\"%m%d%y\")+'.png'\n\nsc.pl.umap(anndata, color=['Batch'], add_outline=True,legend_loc='right margin',\nlegend_fontsize=12, legend_fontoutline=2,size=30,legend_fontweight='bold',frameon=False,\ntitle='Batch', palette=batch_palette,save=fig_out)\n\nfig_out = str('_Batch_5prime')+'_umapplots_annot'+today.strftime(\"%m%d%y\")+'.png'\n\nsc.pl.umap(anndata, color=['Batch'], add_outline=True,legend_loc='right margin',\nlegend_fontsize=12, legend_fontoutline=2,size=30,legend_fontweight='bold',frameon=False,\ntitle='Batch',groups=['5_prime'],save=fig_out)\n\n\nfig_out = str('_Batch_3prime')+'_umapplots_annot'+today.strftime(\"%m%d%y\")+'.png'\n\nsc.pl.umap(anndata, color=['Batch'], add_outline=True,legend_loc='right margin',\nlegend_fontsize=12, legend_fontoutline=2,size=30,legend_fontweight='bold',frameon=False,\ntitle='Batch',groups=['3_prime'],save=fig_out)\n\n\n\nanndata = anndata.read_h5ad('/storage1/fs1/allegra.petti/Active/GBM/CellState/GBM.9/Scanpy_end2end_analysis/GBM.CellState.051722clustered.h5ad')\n\nsc.set_figure_params(scanpy=True, fontsize=20,figsize=(20, 20))\n\nfig_out = str('_n_counts')+'_umapplots_annot'+today.strftime(\"%m%d%y\")+'.png'\n\nsc.pl.umap(anndata, color=['n_counts'], add_outline=True,legend_loc='right margin',\nlegend_fontsize=12, legend_fontoutline=2,size=10,legend_fontweight='bold',frameon=False,\ntitle='nCount',save=fig_out)\n\nsc.set_figure_params(scanpy=True, fontsize=20,figsize=(30, 30))\n\nfor i in [\"leiden_0.5\",\"leiden_0.7\",\"leiden_1.0\",\"leiden_1.2\",\"leiden_1.5\"]:\n\t# fig_out = '_Clusters_'+str(i)+'_umapplots_annot'+today.strftime(\"%m%d%y\")+'.png'\n\tfig_out = str('_n_counts_')+i+'_violinplots.'+today.strftime(\"%m%d%y\")+'.png'\n\tsc.pl.violin(anndata, ['n_counts'], groupby=i,save=fig_out)\n# \tsc.pl.umap(anndata, color=['n_counts'], add_outline=True,legend_loc='right margin',\n# legend_fontsize=12, legend_fontoutline=2,size=10,legend_fontweight='bold',frameon=False,\n# title='nCount',save=fig_out)\n\nsc.set_figure_params(scanpy=True, fontsize=14,figsize=(25, 25))\n\nfor i in [\"orig.ident\",\"Batch\"]:\n\t# fig_out = '_Clusters_'+str(i)+'_umapplots_annot'+today.strftime(\"%m%d%y\")+'.png'\n\tfig_out = str('_n_counts_')+i+'_violinplots.'+today.strftime(\"%m%d%y\")+'.png'\n\tsc.pl.violin(anndata, ['n_counts'], groupby=i,save=fig_out)\n\nsc.set_figure_params(scanpy=True, fontsize=14,figsize=(20, 20))\n\nfor i in anndata.obs['Cell_Types_SingleR_scsorter_v2'].cat.categories.tolist():\n\tfig_out = str('Cell_Types_SingleR_scsorter_v2_')+i+'_umapplots.'+today.strftime(\"%m%d%y\")+'.png'\n\tsc.pl.umap(anndata, color=['Cell_Types_SingleR_scsorter_v2'], add_outline=True,legend_loc='right margin',\n\tlegend_fontsize=12, legend_fontoutline=2,size=30,legend_fontweight='bold',frameon=False,\n\ttitle=str('Cell type prediction ')+str(i),groups=[i],save=fig_out)\n\nsc.set_figure_params(scanpy=True, fontsize=20,figsize=(20, 20))\n\n#to return to raw anndata\n#anndata.raw.to_adata()\n\nfor i in anndata.obs['orig.ident'].cat.categories.tolist():\n\tfig_out = str('orig.ident_')+i+'_umapplots.'+today.strftime(\"%m%d%y\")+'.png'\n\tsc.pl.umap(anndata, color=['orig.ident'], add_outline=True,legend_loc='right margin',\n\tlegend_fontsize=12, legend_fontoutline=2,size=30,legend_fontweight='bold',frameon=False,\n\ttitle=str('orig.ident ')+str(i),groups=[i],save=fig_out)\n\n#####Batch correction approaches#######################\n\n#Trying batch correction methods in scib\n\nout = anndata.copy() #always make copy of anndata object.\n\n# out.raw.to_adata()\n\nsce.pp.harmony_integrate(out, 'Batch')\n\nresults_file = 'GBM.CellState.' + today.strftime(\"%m%d%y\") + '.clustered.batch_corrected.harmony.h5ad' # the file that will store the analysis results\nout.write(results_file)\n\n# sc.pl.embedding(out, basis='X_pca_harmony', color=['Batch'],save=fig_out)\n\n\nadata = out.copy() #always make copy of anndata object.\n\nadata.obsm['X_pca'] = adata.obsm['X_pca_harmony']\n\nsc.pl.umap(adata, color=['Batch'], add_outline=True,legend_loc='right margin',\nlegend_fontsize=12, legend_fontoutline=2,size=30,legend_fontweight='bold',frameon=False,\ntitle='Batch',save=fig_out)\n\n##### Combat batch correction ######################\nann_combat = anndata.read_h5ad('/storage1/fs1/allegra.petti/Active/GBM/CellState/GBM.9/Scanpy_end2end_analysis/GBM.CellState.batchcorr_combat051922.h5ad')\n\n# out = scib.integration.runScvi(out, 'Batch')\n\n# scvi.data.setup_anndata(out, layer=\"counts\", batch_key = 'Batch')\n\n# seurat_ann = scib.preprocessing.read_seurat('/storage1/fs1/allegra.petti/Active/GBM/CellState/GBM.9/Redo_PCA_clustering/GBM_cellstate_selgenes.220516.rds')\n\n\nsc.pp.highly_variable_genes(ann_combat)\nprint(\"Highly variable genes: %d\"%sum(ann_combat.var.highly_variable))\nsc.pl.highly_variable_genes(ann_combat)\nsc.pp.pca(ann_combat, n_comps=50, use_highly_variable=True, svd_solver='arpack')\nsc.pp.neighbors(ann_combat, n_pcs =50)\nsc.tl.umap(ann_combat)\n\nfig_out = str('_Batch_combat_corrected')+'_umapplots_annot'+today.strftime(\"%m%d%y\")+'.png'\nsc.pl.umap(ann_combat, color=['Batch'], add_outline=True,legend_loc='right margin',\nlegend_fontsize=12, legend_fontoutline=2,size=30,legend_fontweight='bold',frameon=False,\ntitle='Batch (Combat batch correction)',save=fig_out)\n\nsc.tl.leiden(ann_combat,resolution = 1.2, key_added = \"leiden_1.2_combat\")\n\nfig_out = '_Clusters_'+str('leiden_1.2_combat')+'_umapplots_annot'+today.strftime(\"%m%d%y\")+'.png'\nplot_clusters_annot(adata=ann_combat,key_word='leiden_1.2_combat',fig_out=fig_out,pal=False,sup_pal='na')\n\n\n##########################\n\n########## Harmony batch correction ################\n\nadata_harmony = anndata.read_h5ad('/storage1/fs1/allegra.petti/Active/GBM/CellState/GBM.9/Scanpy_end2end_analysis/GBM.CellState.051922.clustered.batch_corrected.harmony.h5ad')\nsc.pp.neighbors(adata_harmony, n_pcs =50, use_rep = \"X_pca_harmony\")\nsc.tl.umap(adata_harmony)\n\nfig_out = str('_Batch_harmonycorrected')+'_umapplots_annot'+today.strftime(\"%m%d%y\")+'.png'\n\n# sc.pp.neighbors(out,use_rep='X_pca_harmony',n_neighbors=10, n_pcs=50)\nsc.tl.leiden(adata_harmony,resolution = 1.2, key_added = \"leiden_1.2_harmony\")\n\nfig_out = '_Clusters_'+str('leiden_1.2_harmony')+'_umapplots_annot'+today.strftime(\"%m%d%y\")+'.png'\nplot_clusters_annot(adata=adata_harmony,key_word='leiden_1.2_harmony',fig_out=fig_out,pal=False,sup_pal='na')\n\nfig_out = str('_Batch_harmonycorrected')+'_umapplots_annot'+today.strftime(\"%m%d%y\")+'.png'\n\nsc.pl.umap(adata_harmony, color=['Batch'], add_outline=True,legend_loc='right margin',\nlegend_fontsize=12, legend_fontoutline=2,size=30,legend_fontweight='bold',frameon=False,\ntitle='Batch (harmony batch correction)',save=fig_out)\n\nfig_out = str('orig.ident_harmony_')+'_umapplots.'+today.strftime(\"%m%d%y\")+'.png'\nsc.pl.umap(adata_harmony, color=['orig.ident'], add_outline=True,legend_loc='right margin',\nlegend_fontsize=12, legend_fontoutline=2,size=30,legend_fontweight='bold',frameon=False,\ntitle=str('orig.ident (harmony batch correction)'),save=fig_out)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import colors\n\n#Define a nice colour map for gene expression\ncolors2 = plt.cm.Reds(np.linspace(0, 1, 128))\ncolors3 = plt.cm.Greys_r(np.linspace(0.7,0.8,20))\ncolorsComb = np.vstack([colors3, colors2])\nmymap = colors.LinearSegmentedColormap.from_list('my_colormap', colorsComb)\n\nfig_out = str('CD45_expression_harmony_')+'_umapplots.'+today.strftime(\"%m%d%y\")+'.png'\nsc.pl.umap(adata_harmony, color=['PTPRC'], use_raw=False, color_map=mymap, size = 30,\n\ttitle=str('CD45 expression'),save=fig_out)\n\nfig_out = str('Tcellmarkers_harmony_')+'_umapplots.'+today.strftime(\"%m%d%y\")+'.png'\nsc.pl.umap(adata_harmony, color=['CD8A','CD8B', 'CD4'], use_raw=False, color_map=mymap, size = 30,\n\ttitle=str('T cell markers'),save=fig_out)\n\nfig_out = str('NK_Tmarkers_harmony_')+'_umapplots.'+today.strftime(\"%m%d%y\")+'.png'\nsc.pl.umap(adata_harmony, color=['NKG7','CD160', 'GNLY','GZMA'], use_raw=False, color_map=mymap,\n\ttitle=str('NK-T/NK markers'),save=fig_out)\n\nfig_out = str('macs_monomarkers_harmony_')+'_umapplots.'+today.strftime(\"%m%d%y\")+'.png'\nsc.pl.umap(adata_harmony, color=['FCGR3A','CD14','TREM2'], use_raw=False, color_map=mymap,\n\ttitle=str('Monocytes/Macs/Micro-glia'),save=fig_out)\n\nfig_out = str('DC_monocytic_markers_harmony_')+'_umapplots.'+today.strftime(\"%m%d%y\")+'.png'\nsc.pl.umap(adata_harmony, color=['CD1C', 'CST3', 'FCER1A'], use_raw=False, color_map=mymap,\n\ttitle=str('Monocyte-derived Dendritic cells'),save=fig_out)\n\nfig_out = str('plasmocytoidDC_markers_')+'_umapplots.'+today.strftime(\"%m%d%y\")+'.png'\nsc.pl.umap(adata_harmony, color=['GZMB', 'IL3RA'], use_raw=False, color_map=mymap,\ntitle=str('Plasmacytoid dendritic cells'),save=fig_out)\n\nfig_out = str('Bcell_markers_')+'_umapplots.'+today.strftime(\"%m%d%y\")+'.png'\nsc.pl.umap(adata_harmony, color=['MS4A1','VPREB3'], use_raw=False, color_map=mymap,\ntitle=str('B cells'),save=fig_out)\n\n# results_file = 'GBM.CellState.' + today.strftime(\"%m%d%y\") + 'clustered.h5ad' # the file that will store the analysis results\n# adata_harmony.write(results_file)\n\nresults_file = 'GBM.CellState.' + today.strftime(\"%m%d%y\") + '.clustered.batch_corrected.harmony.h5ad' # the file that will store the analysis results\nadata_harmony.write(results_file)\n\n##########################\n# out = scib.ig.runCombat(out, 'Batch')\n\nConvert(\"/storage1/fs1/allegra.petti/Active/GBM/CellState/GBM.9/Scanpy_end2end_analysis/GBM.CellState.051922.clustered.batch_corrected.harmony.h5ad\", dest = \"h5seurat\", overwrite = TRUE)\n",
"file": "scripts/scanpy_end2end.py",
"file_size": 23312,
"file_write_time": 132974425408046142,
"settings":
{
"buffer_size": 23845,
"line_ending": "Unix"
}
},
{
"contents": ".libPaths( c(\"/storage1/fs1/allegra.petti/Active/R_libs_scratch/RLibs_4.0.3\",.libPaths()) )\nlibrary(Seurat)\nlibrary(ggplot2)\nlibrary(cowplot)\nlibrary(dplyr)\nlibrary(Matrix)\nlibrary(RColorBrewer)\nlibrary(ggthemes)\n\nargs <- commandArgs(trailingOnly = TRUE)\nif(length(args) < 6) {\n args <- c(\"--help\")\n}\n\nseurat_loc <- as.character(args[1])\nsub_col <- as.character(args[2])\nident_names <- as.character(args[3])\ninverse <- as.character(args[4])\noutput_suffix <- as.character(args[5])\norganism <- as.character(args[6])\n# output.stats <- as.character(args[4])\n# output_meta <- as.character(args[5])\n\nseurat_obj <- readRDS(seurat_loc)\n\nprint(names([email protected]))\n\ndate = gsub(\"2022-\",\"22\",Sys.Date(),perl=TRUE);\ndate = gsub(\"-\",\"\",date);\n\n\nget_significant_pcs <- function(scrna_GEX) {\n control='Cycling'\n if(length(unique(scrna_GEX$orig.ident))==1){\n nPC=20 \n }else{\n nPC=50\n }\n # print('Does the error happen inside get_significant_pcs?')\n scrna_GEX <- RunPCA(scrna_GEX, npcs = nPC, verbose = FALSE)\n scrna_GEX <- JackStraw(object = scrna_GEX, num.replicate = 100, dims=nPC)\n scrna_GEX <- ScoreJackStraw(object = scrna_GEX, dims = 1:nPC)\n jpeg(sprintf(\"PCA.jackstraw.%s.%s.jpg\", control, date), width = 10, height = 6, units=\"in\", res=300);\n js <- JackStrawPlot(object = scrna_GEX, dims = 1:nPC)\n print(js);\n dev.off();\n pc.pval <- scrna_GEX@reductions$pca@[email protected]\n print(pc.pval);\n nPC=length( pc.pval[,'Score'][pc.pval[,'Score'] <= 0.05]) \n # print('No the error does not happen inside get_significant_pcs!')\n #redefine nPCs based on number of significant prinicipal components in jackstraw plot\n return(nPC)\n}\n\nsubset_renormalize_recluster <- function(seurat_obj,sub_col,ident_names,inverse,date) {\n DefaultAssay(seurat_obj) <- \"RNA\"\n # print('Does the error happen inside subset_renormalize_recluster?')\n Idents(seurat_obj) <- sub_col\n if(inverse=='TRUE'){\n scrna_GEX <- subset(seurat_obj,idents=ident_names,invert = TRUE)\n }else{\n scrna_GEX <- subset(seurat_obj,idents=ident_names)\n }\n\n # ##################################################################################\n # # This bit is only for testing purposes for the rscript within wdl would be disabled\n # # in the main workflow\n # set.seed(100)\n # random_sample_of_cells = sample(Cells(scrna_GEX),length(Cells(scrna_GEX)) * 0.1)\n # #select 10% of all cells randomly for testing the script.\n # scrna_GEX <- subset(scrna_GEX,cells=random_sample_of_cells)\n # ##################################################################################\n\n if(organism=='human'){\n cell.cycle.tirosh <- read.table(\"/storage1/fs1/allegra.petti/Active/10xGenomics/key.gene.lists/CellCycleTirosh.txt\", sep='\\t', header=TRUE);\n s.genes = cell.cycle.tirosh$`Gene.Symbol`[which(cell.cycle.tirosh$List == \"G1/S\")];\n g2m.genes = cell.cycle.tirosh$`Gene.Symbol`[which(cell.cycle.tirosh$List == \"G2/M\")];\n } else{\n cell.cycle.tirosh <- read.table(\"/storage1/fs1/allegra.petti/Active/10xGenomics/key.gene.lists/CellCycleTirosh_mouse.txt\", sep='\\t', header=FALSE);\n s.genes = cell.cycle.tirosh$V2[which(cell.cycle.tirosh$V1 == \"G1/S\")];\n g2m.genes = cell.cycle.tirosh$V2[which(cell.cycle.tirosh$V1 == \"G2/M\")];\n }\n\n scrna_GEX <- CellCycleScoring(object=scrna_GEX, s.features=s.genes, g2m.features=g2m.genes, set.ident=FALSE)\n\n scrna_GEX <- NormalizeData(object = scrna_GEX, normalization.method = \"LogNormalize\", scale.factor = 1e6); # 1e6 is new as of 1/8/20\n\n scrna_GEX <- FindVariableFeatures(object = scrna_GEX, selection.method = 'vst', mean.cutoff = c(0.1,8), dispersion.cutoff = c(1, Inf))\n\n control='Cycling'\n\n if (control == \"Cycling\") { # This removes all signal associated with the cell cycle\n scrna_GEX <- ScaleData(object = scrna_GEX, features = rownames(x = scrna_GEX), vars.to.regress = c(\"S.Score\",\"G2M.Score\"), display.progress=FALSE);\n } else if (control == \"CyclingRB\") {\n scrna_GEX <- ScaleData(object = scrna_GEX, features = rownames(x = scrna_GEX), vars.to.regress = c(\"S.Score\",\"G2M.Score\",\"percent.ribo\"), display.progress=FALSE);\n } else if (control == \"CyclingDiff\") {\n scrna_GEX <- ScaleData(object = scrna_GEX, features = rownames(x = scrna_GEX), vars.to.regress = c(\"CC.Difference\"), display.progress=FALSE);\n } else {\n scrna_GEX <- ScaleData(object = scrna_GEX, features = rownames(x = scrna_GEX), display.progress=FALSE);\n }\n\n nPC <- get_significant_pcs(scrna_GEX)\n scrna_GEX <- RunPCA(scrna_GEX, npcs = nPC, verbose = FALSE)\n\n scrna_GEX <- RunUMAP(object = scrna_GEX, reduction = \"pca\", dims = 1:nPC)\n scrna_GEX <- RunTSNE(object = scrna_GEX, reduction = \"pca\", dims = 1:nPC)\n\n scrna_GEX <- JackStraw(object = scrna_GEX, num.replicate = 100, dims=nPC)\n scrna_GEX <- ScoreJackStraw(object = scrna_GEX, dims = 1:nPC)\n jpeg(sprintf(\"PCA.jackstraw.%s.%s.jpg\",control, date), width = 10, height = 6, units=\"in\", res=300);\n js <- JackStrawPlot(object = scrna_GEX, dims = 1:nPC)\n print(js);\n dev.off();\n\n jpeg(sprintf(\"UMAP.%s.%s.jpg\",control, date), width = 10, height = 8, units=\"in\", res=300);\n p2 <- DimPlot(object = scrna_GEX, reduction = \"umap\", group.by = \"Sample\", pt.size=0.1)\n print(p2);\n dev.off();\n\n print (\"VizDimLoadings Running...\");\n jpeg(sprintf(\"VizDimLoadings.%s.%s.jpg\",control, date), width = 8, height = 30, units=\"in\", res=300);\n vdl <- VizDimLoadings(object = scrna_GEX, dims = 1:3)\n print(vdl);\n dev.off();\n\n print (\"ProjectDim Running...\");\n scrna_GEX <- ProjectDim(object = scrna_GEX)\n\n # saveRDS(scrna_GEX, file = sprintf(\"%s.SCT.PCA.UMAP.TSNE.%s.rds\",control, date))\n\n print (\"DimHeatmap Running...\");\n jpeg(sprintf(\"PCA.heatmap.top.%s.%s.jpg\",control, date), width = 8.5, height = 11, units=\"in\", res=300);\n hm <- DimHeatmap(object = scrna_GEX, dims = 1, cells = 500, balanced = TRUE);\n print(hm);\n dev.off();\n\n jpeg(sprintf(\"PCA.heatmap.multi.%s.%s.jpg\",control, date), width = 8.5, height = 24, units=\"in\", res=300);\n hm.multi <- DimHeatmap(object = scrna_GEX, dims = 1:10, cells = 500, balanced = TRUE);\n print(hm.multi);\n dev.off();\n\n scrna_GEX <- FindNeighbors(scrna_GEX, reduction = \"pca\", dims = 1:nPC)\n scrna_GEX <- FindClusters(scrna_GEX, resolution = 0.5)\n scrna_GEX[[sprintf(\"ClusterNames_%.1f_%dPC\",0.5, nPC)]] <- Idents(scrna_GEX)\n scrna_GEX <- FindClusters(scrna_GEX, resolution = 0.7)\n scrna_GEX[[sprintf(\"ClusterNames_%.1f_%dPC\",0.7, nPC)]] <- Idents(scrna_GEX)\n scrna_GEX <- FindClusters(scrna_GEX, resolution = 0.9)\n scrna_GEX[[sprintf(\"ClusterNames_%.1f_%dPC\",0.9, nPC)]] <- Idents(scrna_GEX)\n\n scrna_GEX <- FindClusters(scrna_GEX, resolution = 1.2)\n scrna_GEX[[sprintf(\"ClusterNames_%.1f_%dPC\",1.2, nPC)]] <- Idents(scrna_GEX)\n\n #for now 0.7 is used as a default cluster resolution but something \n #better needs to be replace this. Perhaps will use multiK \n #https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8375188/\n #https://github.com/siyao-liu/MultiK\n #in the next version \n\n Idents(scrna_GEX) <- sprintf(\"ClusterNames_%.1f_%dPC\",0.7, nPC)\n DEGs <- FindAllMarkers(object=scrna_GEX); # output is a matrix!\n write.table(DEGs, file=sprintf(\"DEGs.Wilcox.PCA.%d.cluster.%.1f.%s.xls\", nPC, 0.7, date), quote=FALSE, sep=\"\\t\", row.names=FALSE) # must save cluster-specific marker genes\n\t\n n.graph = length(unique(Idents(scrna_GEX)))\n print(n.graph)\n rainbow.colors = rainbow(n.graph, s=0.6, v=0.9);\n names(rainbow.colors) <- sort(unique(Idents(scrna_GEX)))\n print(rainbow.colors)\n print(length(rainbow.colors))\n\n jpeg(sprintf(\"UMAP.clusters.%d.%.1f.%s.jpg\", nPC, 0.7, date), width = 10, height = 8, units=\"in\", res=300);\n p2 <- DimPlot(object = scrna_GEX, reduction = \"umap\", group.by = sprintf(\"ClusterNames_%.1f_%dPC\",0.7, nPC), cols = rainbow.colors, pt.size=0.1) + theme(axis.title.x=element_blank(),axis.title.y=element_blank(),axis.text.x=element_blank(),axis.text.y=element_blank(),axis.ticks.x=element_blank(),axis.ticks.y=element_blank());\n print(p2);\n dev.off();\n \n jpeg(sprintf(\"UMAP.clusters.labeled.%d.%.1f.%s.jpg\",nPC, 0.7, date), width = 10, height = 8, units=\"in\", res=300);\n p2 <- DimPlot(object = scrna_GEX, reduction = \"umap\", group.by = sprintf(\"ClusterNames_%.1f_%dPC\",0.7, nPC), cols = rainbow.colors, pt.size=0.1, label=TRUE,label.size = 5) + theme(axis.title.x=element_blank(),axis.title.y=element_blank(),axis.text.x=element_blank(),axis.text.y=element_blank(),axis.ticks.x=element_blank(),axis.ticks.y=element_blank());\n print(p2);\n dev.off();\n\n feature.pal = rev(colorRampPalette(brewer.pal(11,\"Spectral\"))(50));\n \n mito.genes <- grep(pattern = \"^MT-\", x = rownames(x = scrna_GEX), value = TRUE,ignore.case = TRUE);\n percent.mito <- Matrix::colSums(x = GetAssayData(object = scrna_GEX, slot = 'counts')[mito.genes, ]) / Matrix::colSums(x = GetAssayData(object = scrna_GEX, slot = 'counts'));\n scrna_GEX[['percent.mito']] <- percent.mito;\n\n # ribosomal genes\n ribo.genes <- grep(pattern = \"^RP[SL][[:digit:]]\", x = rownames(x = scrna_GEX), value = TRUE,ignore.case = TRUE);\n percent.ribo <- Matrix::colSums(x = GetAssayData(object = scrna_GEX, slot = 'counts')[ribo.genes, ]) / Matrix::colSums(x = GetAssayData(object = scrna_GEX, slot = 'counts'));\n scrna_GEX[['percent.ribo']] <- percent.ribo;\n\n scrna_GEX[['percent.ribo']] <- percent.ribo;\n\n\n print(\"Making additional UMAP plots\");\n # color UMAP plots by parameters of interest:\n print (\"color by UMI\");\n jpeg(sprintf(\"umap.%d.%.1f.colorby.UMI.%s.%s.jpg\", nPC, 0.7, control, date), width = 10, height = 8, units=\"in\", res=300);\n fp2 <- FeaturePlot(object = scrna_GEX, features = c(\"nCount_RNA\"), cols = feature.pal, pt.size=0.1, reduction = \"umap\")+ theme(axis.title.x=element_blank(),axis.title.y=element_blank(),axis.text.x=element_blank(),axis.text.y=element_blank(),axis.ticks.x=element_blank(),axis.ticks.y=element_blank());\n print(fp2);\n dev.off();\n\n print (\"color by % mito\");\n jpeg(sprintf(\"umap.%d.%.1f.colorby.MC.%s.%s.jpg\", nPC, 0.7, control, date), width = 10, height = 8, units=\"in\", res=300);\n fp2 <- FeaturePlot(object = scrna_GEX, features = c(\"percent.mito\"), cols = feature.pal, pt.size=0.1, reduction = \"umap\")+ theme(axis.title.x=element_blank(),axis.title.y=element_blank(),axis.text.x=element_blank(),axis.text.y=element_blank(),axis.ticks.x=element_blank(),axis.ticks.y=element_blank());\n print(fp2);\n dev.off();\n\n print (\"color by % RB\");\n jpeg(sprintf(\"umap.%d.%.1f.colorby.RB.%s.%s.jpg\", nPC, 0.7, control, date), width = 10, height = 8, units=\"in\", res=300);\n fp2 <- FeaturePlot(object = scrna_GEX, features = c(\"percent.ribo\"), cols = feature.pal, pt.size=0.1, reduction = \"umap\") + theme(axis.title.x=element_blank(),axis.title.y=element_blank(),axis.text.x=element_blank(),axis.text.y=element_blank(),axis.ticks.x=element_blank(),axis.ticks.y=element_blank());\n print(fp2);\n dev.off();\n\n print (\"color by nGene\");\n jpeg(sprintf(\"umap.%d.%.1f.colorby.nGene.%s.%s.jpg\", nPC, 0.7, control, date), width = 10, height = 8, units=\"in\", res=300);\n fp2 <- FeaturePlot(object = scrna_GEX, features = c(\"nFeature_RNA\"), cols = feature.pal, pt.size=0.1, reduction = \"umap\") + theme(axis.title.x=element_blank(),axis.title.y=element_blank(),axis.text.x=element_blank(),axis.text.y=element_blank(),axis.ticks.x=element_blank(),axis.ticks.y=element_blank());\n print(fp2);\n dev.off();\n\n print (\"color by Phase\");\n jpeg(sprintf(\"umap.%d.%.1f.colorby.Phase.%s.%s.jpg\", nPC, 0.7, control, date), width = 10, height = 8, units=\"in\", res=300);\n phase.colors = ptol_pal()(3)\n umapplot <- DimPlot(object = scrna_GEX, cols=phase.colors, group.by=\"Phase\", pt.size=0.1, reduction = \"umap\") + theme(axis.title.x=element_blank(),axis.title.y=element_blank(),axis.text.x=element_blank(),axis.text.y=element_blank(),axis.ticks.x=element_blank(),axis.ticks.y=element_blank())\n print(umapplot);\n dev.off();\n\n \n print (\"color UMAP by Principal Components\");\n jpeg(sprintf(\"UMAP.%d.%.1f.colorby.PCs.%s.jpg\", nPC, 0.7, date), width = 12, height = 6, units=\"in\", res=100);\n redblue=c(\"blue\",\"gray\",\"red\");\n fp1 <- FeaturePlot(object = scrna_GEX, features = 'PC_1', cols=redblue, pt.size=0.1, reduction = \"umap\")+ theme(axis.title.x=element_blank(),axis.title.y=element_blank(),axis.text.x=element_blank(),axis.text.y=element_blank(),axis.ticks.x=element_blank(),axis.ticks.y=element_blank());\n fp2 <- FeaturePlot(object = scrna_GEX, features = 'PC_2', cols=redblue, pt.size=0.1, reduction = \"umap\")+ theme(axis.title.x=element_blank(),axis.title.y=element_blank(),axis.text.x=element_blank(),axis.text.y=element_blank(),axis.ticks.x=element_blank(),axis.ticks.y=element_blank());\n fp3 <- FeaturePlot(object = scrna_GEX, features = 'PC_3', cols=redblue, pt.size=0.1, reduction = \"umap\")+ theme(axis.title.x=element_blank(),axis.title.y=element_blank(),axis.text.x=element_blank(),axis.text.y=element_blank(),axis.ticks.x=element_blank(),axis.ticks.y=element_blank());\n fp4 <- FeaturePlot(object = scrna_GEX, features = 'PC_4', cols=redblue, pt.size=0.1, reduction = \"umap\")+ theme(axis.title.x=element_blank(),axis.title.y=element_blank(),axis.text.x=element_blank(),axis.text.y=element_blank(),axis.ticks.x=element_blank(),axis.ticks.y=element_blank());\n fp5 <- FeaturePlot(object = scrna_GEX, features = 'PC_5', cols=redblue, pt.size=0.1, reduction = \"umap\")+ theme(axis.title.x=element_blank(),axis.title.y=element_blank(),axis.text.x=element_blank(),axis.text.y=element_blank(),axis.ticks.x=element_blank(),axis.ticks.y=element_blank());\n fp6 <- FeaturePlot(object = scrna_GEX, features = 'PC_6', cols=redblue, pt.size=0.1, reduction = \"umap\")+ theme(axis.title.x=element_blank(),axis.title.y=element_blank(),axis.text.x=element_blank(),axis.text.y=element_blank(),axis.ticks.x=element_blank(),axis.ticks.y=element_blank());\n fp7 <- FeaturePlot(object = scrna_GEX, features = 'PC_7', cols=redblue, pt.size=0.1, reduction = \"umap\")+ theme(axis.title.x=element_blank(),axis.title.y=element_blank(),axis.text.x=element_blank(),axis.text.y=element_blank(),axis.ticks.x=element_blank(),axis.ticks.y=element_blank());\n fp8 <- FeaturePlot(object = scrna_GEX, features = 'PC_8', cols=redblue, pt.size=0.1, reduction = \"umap\")+ 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