scRNA-seq_analysis

pipelines/13_pseudotime/pdt_scanpy.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Aug 14 15:01:36 2018
+
+@author: doru
+"""
+print("starting .py script")
+
+import sys
+args = sys.argv
+root_cell_type = args[1]
+CWD = args[2]
+print("printing args")
+print(args)
+args
+# use the args below if you have a root cell type containing spaces and @@'s
+#root_cell_type = args[1] + " " + args[2]
+#CWD = args[3]
+
+import matplotlib; matplotlib.use('Agg');
+import scanpy.api as sc;
+import pandas as pd
+import numpy as np
+
+print("printing root_cell_type")
+print(root_cell_type)
+print("printing CWD")
+print(CWD)
+
+sc.settings.verbosity = 3
+
+scObj = sc.read("{CWD}/material/raw_data.mtx".format(CWD=CWD), cache = False).T
+
+# load gene names
+scObj.var_names = pd.read_csv("{CWD}/material/genenames.csv".format(CWD=CWD)).iloc[:, 1]
+
+# load cell names
+scObj.obs_names = pd.read_csv("{CWD}/material/cellnames.csv".format(CWD=CWD)).iloc[:, 1]
+
+# add cell labels
+cell_labels = pd.read_csv("{CWD}/material/cell_labels.csv".format(CWD=CWD), 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)
+
+# set root
+scObj.uns['iroot'] = np.flatnonzero(scObj.obs['cell_labels'] == root_cell_type)[0]
+
+# compute dpt
+print("computing sc.tl.dpt")
+sc.tl.dpt(scObj, n_dcs = 20)
+
+# pdt is at scObj.obs["dpt_pseudotime"]
+print("displaying pdt table stored in scObj")
+print(scObj.obs["dpt_pseudotime"])
+pdt = scObj.obs["dpt_pseudotime"].to_csv("{CWD}/material/pseudotime.csv".format(CWD=CWD))
+
+# save the pseudotime
+dm  = scObj.obsm["X_diffmap"]
+dm = pd.DataFrame(data = dm, index = None, columns = None)
+dm.to_csv("{CWD}/material/dm.csv".format(CWD=CWD), columns = None, header = None)
+
+