CompSRana: Computes potency estimates of single-cells using the...
In aet21/SCENT: Single Cell Entropy
CompSRana R Documentation
Computes potency estimates of single-cells
using the signaling entropy rate
Description
This is the main user function for computing signaling entropy of
single cells. It takes as input the gene expression profile of
single cells and the adjacency matrix of a connected network. These
inputs will be typically the output of the DoIntegPPI function.
Usage
CompSRana(integ.l, local = FALSE, mc.cores = 1)
Arguments
integ.l
The output from DoIntegPPI function.
local
A logical (default is FALSE). If TRUE, function returns the normalized
local signaling entropies of each gene in the network alongside the unnormalized
local entropies. If FALSE, only unnormalized local entropies are returned.
mc.cores
The number of cores to use, i.e. at most how many child processes will
be run simultaneously. The option is initialized from environment variable
MC_CORES if set. Must be at least one, and parallelization requires at
least two cores.
Value
A list with four elements:
SR
The global signaling entropy rate. It is normalized by the
maximum rate, hence a value between 0 and 1.
inv
The stationary distribution of every sample.
locS
The unnormalised local entropies of each gene in every cell.
nlocS
The normalised local entropies of each gene, so that each value is
between 0 and 1.
References
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.
aet21/SCENT documentation built on Aug. 1, 2022, 12:05 p.m.
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| CompSRana | R Documentation |
Computes potency estimates of single-cells using the signaling entropy rate
Description
This is the main user function for computing signaling entropy of
single cells. It takes as input the gene expression profile of
single cells and the adjacency matrix of a connected network. These
inputs will be typically the output of the DoIntegPPI function.
Usage
CompSRana(integ.l, local = FALSE, mc.cores = 1)
Arguments
integ.l |
The output from |
local |
A logical (default is FALSE). If TRUE, function returns the normalized local signaling entropies of each gene in the network alongside the unnormalized local entropies. If FALSE, only unnormalized local entropies are returned. |
mc.cores |
The number of cores to use, i.e. at most how many child processes will be run simultaneously. The option is initialized from environment variable MC_CORES if set. Must be at least one, and parallelization requires at least two cores. |
Value
A list with four elements:
SR |
The global signaling entropy rate. It is normalized by the maximum rate, hence a value between 0 and 1. |
inv |
The stationary distribution of every sample. |
locS |
The unnormalised local entropies of each gene in every cell. |
nlocS |
The normalised local entropies of each gene, so that each value is between 0 and 1. |
References
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.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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