CSN

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