InferPotency: Infer Distinct potency states from cells' SR values
In ChenWeiyan/LandSCENT: Landscape Single Cell Entropy

Description Usage Arguments Value References Examples

This function infers the discrete potency states of single cells and its distribution across the single cell population.

1	InferPotency(Integration.l, pheno.v = NULL, diffvar = TRUE, maxPS = 5)

`Integration.l`	A list object from `CompSRana` function.
`pheno.v`	A phenotype vector for the single cells, of same length and order as the columns of `Integration.l$expMC`. Function can also automatically extract phenotype information from your original sce/cds data, please store the phenotype information under the name of `phenoInfo`
`diffvar`	A logical. Default is TRUE. Specifies whether the Gaussian mixture model to be fit assumes components to have different (default) or equal variance. In the latter case, use modelNames = c("E").
`maxPS`	Maximum number of potency states, when inferring discrete potency states of single cells. Default value is 5.

Integration.l A list incorporates the input list with some new elements.

Integration.l$potencyState Inferred discrete potency states for each single cell. It is indexed so that the index increases as the signaling entropy of the state decreases

Integration.l$distPSPH If phenotype information provided, it will be a table giving the distribution of single-cells across potency states and phenotypes

Integration.l$prob Table giving the probabilities of each potency state per phenotype value

Integration.l$hetPS The normalised Shannon Index of potency per phenotype value

Teschendorff AE, 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 AE, 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.

data(Example.m)
data(net13Jun12.m)
Integration.l <- DoIntegPPI(exp.m = Example.m[, c(1:58,61:84,86:98,100)], ppiA.m = net13Jun12.m)
data(SR.v)
Integration.l$SR <- SR.v
InferPotency.o <- InferPotency(Integration.l)