mirror of
https://github.com/haniffalab/scRNA-seq_analysis.git
synced 2024-10-23 08:29:24 -07:00
64 lines
1.6 KiB
Python
Executable file
64 lines
1.6 KiB
Python
Executable file
#!/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)
|
|
|
|
|