CCAT: Correlation of Connectome And Transcriptome
In ChenWeiyan/LandSCENT: Landscape Single Cell Entropy

Description Usage Arguments Value References Examples

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

CCAT(
  Integration.l = NULL,
  data.m = NULL,
  ppiA.m = NULL,
  log_trans = FALSE,
  parallelMode = FALSE,
  mcores = 1,
  subsamplesize = 1000
)

`Integration.l`	A list object from `DoIntegPPI` function.
`data.m`	A scRNA-Seq data matrix with rows labeling genes and columns labeling single cells. And it can be either a log-transformed data matrix with minimal value around 0.1 (recommended), or an nonlog-transformed data matrix with minimal value 0.
`ppiA.m`	The adjacency matrix of a user-given PPI network with rownames and colnames labeling genes (same gene identifier as in `exp.m`)
`log_trans`	A logical. Whether to do log-transformation on the input data matrix or not. Default is FALSE
`parallelMode`	A logical. Indicating whether or not to run CCAT in parallel. It will be disable if datasets are < 5,000 cells. Parallel mode uses a subsampling approach to reduce runtime. Default is FALSE
`mcores`	A integer. Indicating the number of cores to use when parallelMode = TRUE
`subsamplesize`	A integer. Indicating the number of cells to subsample when parallelMode = TRUE

A list incorporates the input list and CCAT velues or CCAT values itself, depending on the input object(s):

CCAT The estimated signaling entropy rate using Pearson correlation coefficient

Chen, Weiyan, et al. Single-cell landscape in mammary epithelium reveals bipotent-like cells associated with breast cancer risk and outcome. Communications Biology 2 (2019): 306. doi: 10.1038/s42003-019-0554-8.

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.

Teschendorff Andrew E., Banerji CR, Severini S, Kuehn R, Sollich P. Increased signaling entropy in cancer requires the scale-free property of protein interaction networks. Scientific reports 5 (2015): 9646. doi: 10.1038/srep09646.

Banerji, Christopher RS, et al. Intra-tumour signalling entropy determines clinical outcome in breast and lung cancer. PLoS computational biology 11.3 (2015): e1004115. doi: 10.1371/journal.pcbi.1004115.

Teschendorff, Andrew E., Peter Sollich, and Reimer Kuehn. Signalling entropy: A novel network-theoretical framework for systems analysis and interpretation of functional omic data. Methods 67.3 (2014): 282-293. doi: 10.1016/j.ymeth.2014.03.013.

Banerji, Christopher RS, et al. Cellular network entropy as the energy potential in Waddington's differentiation landscape. Scientific reports 3 (2013): 3039. doi: 10.1038/srep03039.

### load example data & network matrix
data(Example.m)
data(net13Jun12.m)

### integrate expr matrix and PPI network
Integration.l <- DoIntegPPI(exp.m = Example.m, ppiA.m = net13Jun12.m)

### estimate SR with PCC
### get it with the integration list
Integration.l <- CCAT(Integration.l)

### or get CCAT directly from data matrix
CCAT.v <- CCAT(data.m = Example.m, ppiA.m = net13Jun12.m)