CSN

@AUTHOR Gialitsis and Voutsadaki

Implemented as project for the course Machine Learning in Computational Biology 2020

File Descriptions:

    Already Implemented:
           csndm.m: this function performs the transformation from gene expression matrix to network degree matrix (ndm).
           csnedge.m: this function calculates the normalized statistic of edge x-y corresponding to two genes
           csnet.m: The function performs the transformation from gene expression matrix to cell-specific network (csn)
    Implemented By Us:
            buettner_analysis.r : this script performs the differential expression analysis on the gene expression data 
            all_in_one_CSN.ipynb : this jupyter notebook constructs and visualizes the representative cell specific network according to various centrality measures 

Libraries and Dependencies python3.6 Install GeoParse(2.0.1): !pip install GEOparse:

    Requirement pandas>=0.17 (1.0.5)
    Requirement requests>=2.21.0 (2.23.0)
    Requirement numpy>=1.7 (1.18.5)
    Requirement tqdm>=4.31.1 (4.41.1)
    Requirement python-dateutil>=2.6.1 (2.8.1)
    Requirement pytz>=2017.2 (2018.9)
    Requirement idna<3,>=2.5 (2.9)
    Requirement certifi>=2017.4.17(2020.6.20)
    Requirement  chardet<4,>=3.0.2 (3.0.4)
    Requirement urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 (1.24.3)
    Requirement six>=1.5 (1.12.0)

Import Libraries:

import pandas as pd
import copy
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cm as cm
import numpy as np
import networkx as nx
import sys
from scipy.optimize import curve_fit
import numpy as np
from scipy.stats import norm
import GEOparse
import pandas

Execution:

1. Unzip Data folder
2. Open notebook all_in_one_CSN.ipynb (we used Google Collaboratory)
3. Import files ReprCSN.csv and gea_norm.csv in the working directory
4. Runtime -> Run All

' Forked Readme Below (ownership belongs to the owners of the respective authors)' Cell-specific Network Constructed by Single-cell RNA Sequencing Data

function ndm = csndm(data,alpha,boxsize,normalize)

Construction of network degree matrix

The function performs the transformation from gene expression matrix to network degree matrix (ndm).

data: Gene expression matrix (TPM/FPKM/RPKM/count), rows = genes, columns = cells

alpha: Significant level (eg. 0.001, 0.01, 0.05 ...), Default = 0.01

boxsize: Size of neighborhood, Default = 0.1

normalize: 1: result is normalized (Default); 0: result is not normalized

function csn = csnet(data,c,alpha,boxsize,weighted)

Construction of cell-specific network

The function performs the transformation from gene expression matrix to cell-specific network (csn).

data: Gene expression matrix, rows = genes, columns = cells

c: Construct the CSNs for all cells, set c = [] (Default); Construct the CSN for cell k, set c = k

alpha: Significant level (eg. 0.001, 0.01, 0.05 ...), larger alpha leads to more edges, Default = 0.01

boxsize: Size of neighborhood, Default = 0.1

weighted: 1: edge is weighted; 0: edge is not weighted (Default)

csn: Cell-specific network, the kth CSN is in csn{k}, rows = genes, columns = genes

Note that too many cells or genes may lead to out of memory.

function edge = csnedge(gx,gy,boxsize)

The normalized statistic of edge x-y

gx gy: Gene expression values of gene x and gene y. If there are n cells, gx and gy are 1-by-n vectors.

boxsize: Size of neighborhood, Default = 0.1

edge: 1-by-n vector, the normalized statistic of edge x-y in all cells