CompCCAT: Correlation of Connectome And Transcriptome

In aet21/SCENT: Single Cell Entropy

Correlation of Connectome And Transcriptome

Description

This function leverages Pearson correlation between gene expresion level and gene connectome derived from PPI network to fastly estimate signaling entropy rate.

Usage

CompCCAT(exp.m, ppiA.m)

Arguments

exp.m	A scRNA-Seq data matrix with rows labeling genes and columns labeling single cells and with rownames annotated to a gene-identifier identical to that used in the ppiA.m argument. scRNA-Seq data matrix should have undergone prior QC to remove poor quality cells and each cell normalized by library size. If data has not been log-transformed, the function will log2-transform with a pseudocount of +1.
ppiA.m	The adjacency matrix of a user-given PPI network with rownames and colnames labeling genes (same gene identifier as in exp.m). Diagonal entries should be zero and the number of genes in the network should be large, i.e. at least over 8000, to ensure a reasonably large overlap of genes with those in the expression data matrix.

Value

CCAT	The estimated CCAT values as a vector.

References

Teschendorff Andrew E., Tariq Enver. Single-cell entropy for accurate estimation of differentiation potency from a cell’s transcriptome. Nature communications 8 (2017): 15599. doi: 10.1038/ncomms15599.

aet21/SCENT documentation built on Aug. 1, 2022, 12:05 p.m.