Credit to https://github.com/ASoelvsten/scRNA.
#load library###
import pyreadr
from os import walk
import gzip
import numpy as np
import pandas as pd
import scipy
import matplotlib.pyplot as plt
import sys
import os
os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE"
#folder of nnRNA
sys.path.insert(1, '~/nnRNA')
import nnRNA
import nnRNA_wrapper
import sim
import applytodata as app
#output path
store_path="~/nnout/"
#load toy example
df = pd.read_csv("~/sim500run2"+"/"+"toyexample.csv")
DData = df.to_numpy()
#prepare count matrix (genes in rows and cells in columns)
scRNA_input=DData.astype(float)
#estimate cell-specific capture efficiencies
inputbeta = sim.empirBETA(scRNA_input,meanBeta=0.06)
#no gene name provided, so just set to be None
Gname_input=None
#create directory for storing output fron nnRNA
os.chdir(store_path)
os.getcwd()
file_name='sim500run2'
#Run nnRNA
#set `allele_double=True` for UMI data, otherwise `allele_double=False` for allele specific scRNA-seq data.
dfnn_c=nnRNA_wrapper.nnRNA_wrapper(scRNA=scRNA_input,Gname=Gname_input, file_name=file_name,store_path=store_path,allele_double=False,repeats=1,threshold_gc=[5000,2,100],prior="Fano",inputbeta_vec=inputbeta)Output will be in your output directory with name NN_${file_name}.csv. The first columns should be index or gene symboles. After that, the 2nd to 5th columns correspond to log10 for kon, koff, ksyn and burst size respectively. The remaining columns are the corresponding credibility intervals