Pristina atlas analysis 12 counts extraction.ipynb

{"cells":[{"cell_type":"markdown","id":"72ece39b","metadata":{"id":"72ece39b"},"source":["# Importing modules and settings"]},{"cell_type":"code","execution_count":null,"id":"238c8e41","metadata":{"id":"238c8e41"},"outputs":[],"source":["import numpy as np\n","import pandas as pd\n","import scanpy as sc"]},{"cell_type":"code","execution_count":null,"id":"a6ca374b","metadata":{"id":"a6ca374b"},"outputs":[],"source":["from matplotlib.pyplot import rc_context"]},{"cell_type":"markdown","id":"4133c199","metadata":{"id":"4133c199"},"source":["General settings of Scanpy"]},{"cell_type":"code","execution_count":null,"id":"82e95800","metadata":{"id":"82e95800"},"outputs":[],"source":["pd.options.display.float_format = \"{:,.2f}\".format"]},{"cell_type":"code","execution_count":null,"id":"bf9038d0","metadata":{"id":"bf9038d0"},"outputs":[],"source":["pd.set_option('display.max_colwidth', 500)"]},{"cell_type":"code","execution_count":null,"id":"3be21d53","metadata":{"id":"3be21d53"},"outputs":[],"source":["sc.settings.verbosity = 4\n","sc.logging.print_header()\n","sc.settings.set_figure_params(dpi=80, facecolor='white')\n"]},{"cell_type":"markdown","id":"37ab1998","metadata":{"id":"37ab1998"},"source":["# Declaring the input and output files"]},{"cell_type":"code","execution_count":null,"id":"292c5538","metadata":{"id":"292c5538"},"outputs":[],"source":["name_of_analysis = 'pristina_counts'"]},{"cell_type":"code","execution_count":null,"id":"e404e135","metadata":{"id":"e404e135"},"outputs":[],"source":["adata_processed = sc.read_h5ad('pristina_atlas_coloured_subcl.h5ad')"]},{"cell_type":"code","execution_count":null,"id":"f742fb8e","metadata":{"id":"f742fb8e"},"outputs":[],"source":["adata = sc.read_10x_mtx(\n","    \"./pristina/\",\n","    var_names='gene_symbols',\n","    cache=True)"]},{"cell_type":"code","execution_count":null,"id":"72707a11","metadata":{"id":"72707a11"},"outputs":[],"source":["adata_processed"]},{"cell_type":"code","execution_count":null,"id":"127586d1","metadata":{"id":"127586d1"},"outputs":[],"source":["adata.var_names_make_unique()"]},{"cell_type":"code","execution_count":null,"id":"0fb8b85b","metadata":{"id":"0fb8b85b"},"outputs":[],"source":["adata.var_names"]},{"cell_type":"code","execution_count":null,"id":"31607fb9","metadata":{"id":"31607fb9"},"outputs":[],"source":["adata.var"]},{"cell_type":"code","execution_count":null,"id":"8cb2aabc","metadata":{"id":"8cb2aabc"},"outputs":[],"source":["adata"]},{"cell_type":"code","execution_count":null,"id":"37b14bf3","metadata":{"id":"37b14bf3"},"outputs":[],"source":["adata.obs"]},{"cell_type":"code","execution_count":null,"id":"a6c31ed0","metadata":{"id":"a6c31ed0"},"outputs":[],"source":["clusteringlayer = 'leiden_1.5'"]},{"cell_type":"code","execution_count":null,"id":"a032869d","metadata":{"id":"a032869d"},"outputs":[],"source":["with rc_context({'figure.figsize': (12, 12)}):\n","    sc.pl.umap(adata_processed, color=clusteringlayer, legend_loc='on data', legend_fontoutline = 5, title= 'Clustering layer '+str(clusteringlayer), size = 30,\n","        frameon=False, add_outline = True)"]},{"cell_type":"code","execution_count":null,"id":"6dddf562","metadata":{"id":"6dddf562"},"outputs":[],"source":[]},{"cell_type":"markdown","id":"e5f64011","metadata":{"id":"e5f64011"},"source":["# Function to extract counts"]},{"cell_type":"code","execution_count":null,"id":"1804b235","metadata":{"id":"1804b235"},"outputs":[],"source":["def get_counts_per_cluster(adata_unprocessed, adata_processed, clusteringlayer):\n","    \n","    \n","#This function takes the unprocessed version and the processed version of a single cell experiment, with a given clustering layer:\n","#The parameters are:\n","#adata_unprocessed: the anndata object just after reading with read 10x\n","#adata_processed: the anndata object after processing\n","#clusteringlayer: a key in adata_processed.obs that gives the clusters\n","\n","\n","\n","    adata_processed_raw = adata_processed.raw.to_adata() #recovering the .raw object\n","    filt_cells = adata_unprocessed.obs.index.isin(adata_processed.obs.index) #filtering for cells present in processed\n","    filt_genes = adata_unprocessed.var.index.isin(adata_processed_raw.var.index) #filtering for genes present in processed\n","    adata_filtered = adata_unprocessed[filt_cells,filt_genes].copy() #slicing the unprocessed object\n","    adata_filtered.obs[clusteringlayer] = adata_processed.obs[clusteringlayer] # copying the cluster information\n","    \n","    # create empty dataframe of the right dimensions where we will store the result\n","    counts = pd.DataFrame(index = adata_filtered.var.index, columns = adata_filtered.obs[clusteringlayer].cat.categories)\n","    \n","    for clust in adata_filtered.obs[clusteringlayer].cat.categories: # iterating over each cluster\n","        filt_clust = adata_filtered.obs[clusteringlayer] == clust # obtaining a filtering expression for cluster \"clust\"\n","        adata_filt_clust = adata_filtered[filt_clust] #filtering to obtain (n cells in cluster clust) x (genes) anndata view\n","        mat = adata_filt_clust.X # obtaining the matrix of this\n","        mat_sum = mat.sum(axis = 0) # obtaining the sum of the matrix (for each gene)\n","        mat_sum_ser = pd.Series(np.array(mat_sum).flatten(), dtype = 'int', index = adata_filtered.var.index) # transforming into pandas series\n","        counts[clust] = mat_sum_ser # storing this series into the corresponding column of the result dataframe\n","    return counts\n","    \n"]},{"cell_type":"code","execution_count":null,"id":"f433cfec","metadata":{"id":"f433cfec"},"outputs":[],"source":["counts = get_counts_per_cluster(adata, adata_processed, clusteringlayer)"]},{"cell_type":"code","execution_count":null,"id":"4d37d92b","metadata":{"id":"4d37d92b"},"outputs":[],"source":["counts"]},{"cell_type":"code","execution_count":null,"id":"ab9c73d7","metadata":{"id":"ab9c73d7"},"outputs":[],"source":["counts.to_csv('./figures/pristina_atlas_counts/Pristina_atlas_counts.tsv', sep=\"\\t\")"]},{"cell_type":"code","execution_count":null,"id":"5ef289f4","metadata":{"id":"5ef289f4"},"outputs":[],"source":["adata_processed.obs['broad_names']"]},{"cell_type":"code","execution_count":null,"id":"5233df26","metadata":{"id":"5233df26"},"outputs":[],"source":["counts_broad = get_counts_per_cluster(adata, adata_processed, 'broad_names')"]},{"cell_type":"code","execution_count":null,"id":"1ede44a8","metadata":{"id":"1ede44a8"},"outputs":[],"source":["counts_broad"]},{"cell_type":"code","execution_count":null,"id":"e2b100e9","metadata":{"id":"e2b100e9"},"outputs":[],"source":["counts_broad.to_csv('./figures/pristina_atlas_counts/Pristina_atlas_counts_broad.tsv', sep=\"\\t\")"]},{"cell_type":"code","execution_count":null,"id":"1058e55e","metadata":{"id":"1058e55e"},"outputs":[],"source":["adata_processed.obs['Experiment'].cat.categories"]},{"cell_type":"code","execution_count":null,"id":"5f7950cf","metadata":{"id":"5f7950cf"},"outputs":[],"source":["adata_processed[adata_processed.obs['Experiment'] == 'lib_12']"]},{"cell_type":"code","execution_count":null,"id":"6923a1dd","metadata":{"id":"6923a1dd"},"outputs":[],"source":["for i in adata_processed.obs['Experiment'].cat.categories:\n","    adata_processed_e = adata_processed[adata_processed.obs['Experiment'] == i]\n","    counts_exp = get_counts_per_cluster(adata, adata_processed_e, clusteringlayer)\n","    counts_exp.to_csv('./figures/pristina_atlas_counts/Pristina_atlas_counts_exp_'+i+'.tsv', sep=\"\\t\")\n","    counts_exp_broad = get_counts_per_cluster(adata, adata_processed_e, 'broad_names')\n","    counts_exp_broad.to_csv('./figures/pristina_atlas_counts/Pristina_atlas_counts_broad_exp_'+i+'.tsv', sep=\"\\t\")    "]},{"cell_type":"code","execution_count":null,"id":"da0944c4","metadata":{"id":"da0944c4"},"outputs":[],"source":[]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.8"}},"nbformat":4,"nbformat_minor":5}