scRNA-seq_analysis

pipelines/11_plot_dr/dm.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Aug 14 15:01:36 2018
+
+@author: doru
+"""
+
+import sys
+args = sys.argv
+working_folder = args[1]
+
+import matplotlib; matplotlib.use('Agg');
+import scanpy.api as sc;
+import pandas as pd
+
+sc.settings.verbosity = 3
+
+scObj = sc.read("{CW}/raw_data.mtx".format(CW = working_folder), cache = False).T
+
+# load gene names
+scObj.var_names = pd.read_csv("{CW}/genenames.csv".format(CW = working_folder)).iloc[:, 1]
+
+# load cell names
+scObj.obs_names = pd.read_csv("{CW}/cellnames.csv".format(CW = working_folder)).iloc[:, 1]
+
+# add cell labels
+cell_labels = pd.read_csv("{CW}/cell_labels.csv".format(CW = working_folder), index_col = 0)
+
+scObj.obs["cell_labels"] = cell_labels
+
+# filter out genes present in less than 3 cells
+sc.pp.filter_genes(scObj, min_cells=3)
+
+# log-normalize the data
+scObj.raw = sc.pp.log1p(scObj, copy=True)
+sc.pp.normalize_per_cell(scObj, counts_per_cell_after=1e4)
+
+# variable genes
+filter_result = sc.pp.filter_genes_dispersion(
+        scObj.X, min_mean=0.0125, max_mean=3, min_disp=0.5)
+# subset data on variable genes
+scObj = scObj[:, filter_result.gene_subset]
+# not sure?
+sc.pp.log1p(scObj)
+
+# scale the data
+sc.pp.scale(scObj, max_value=10)
+
+# run pca
+sc.tl.pca(scObj)
+
+# compunte neighborhood graph
+sc.pp.neighbors(scObj, n_neighbors = 15, n_pcs = 20, knn = True, random_state = 10, method = "gauss")
+
+# compute diffusion map
+sc.tl.diffmap(scObj, n_comps = 20)
+
+# save diffusion map to disk
+dm  = scObj.obsm["X_diffmap"]
+dm = pd.DataFrame(data = dm, index = None, columns = None)
+dm.to_csv("{CW}/dm.csv".format(CW = working_folder), columns = None, header = None)
+
+