kersplatSimPaths: Simulate Kersplat paths
In Oshlack/splatter: Simple Simulation of Single-cell RNA Sequencing Data
View source: R/kersplat-simulate.R
kersplatSimPaths R Documentation
Simulate Kersplat paths
Description
Simulate gene means for each step along each path of a Kersplat simulation
Usage
kersplatSimPaths(params, verbose)
Arguments
params
KersplatParams object containing simulation parameters.
verbose
logical. Whether to print progress messages
Details
The method of simulating paths is inspired by the method used in the PROSSTT
simulation. Changes in expression are controlled by paths.nPrograms
regulatory programs. Each of the regulatory genes in the gene network has
some association with each program. This is analogous to there being changes
in the environment (the programs) which are sensed by receptors (regulatory
genes) and cause changes in expression downstream. For each path a random
walk is generated for each program and the changes passed on to the
regulatory genes. At each step the changes propagate through the network
according to the weights on edges between genes. This algorithm is fairly
simple but should result in correlation relationships between genes. However
it is likely to be improved and adjusted in the future.
The path structure itself is specified by the paths.design parameter.
This is a data.frame with three columns: "Path", "From", and "Steps".
The Path field is an ID for each path while the Steps field controls the
length of each path. Increasing the number of steps will increase the
difference in expression between the ends of the paths. The From field sets
the originating point of each path. For example a From of 0, 0, 0
would indicate three paths from the origin while a From of 0, 1, 1
would give a branching structure with Path 1 beginning at the origin and
Path 2 and Path 3 beginning at the end of Path 1.
Value
KersplatParams object with path means
References
Papadopoulos N, Parra RG, Söding J. PROSSTT: probabilistic simulation of
single-cell RNA-seq data for complex differentiation processes.
Bioinformatics (2019). https://doi.org/10.1093/bioinformatics/btz078.
Oshlack/splatter documentation built on Dec. 10, 2024, 3:48 p.m.
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View source: R/kersplat-simulate.R
| kersplatSimPaths | R Documentation |
Simulate Kersplat paths
Description
Simulate gene means for each step along each path of a Kersplat simulation
Usage
kersplatSimPaths(params, verbose)
Arguments
params |
KersplatParams object containing simulation parameters. |
verbose |
logical. Whether to print progress messages |
Details
The method of simulating paths is inspired by the method used in the PROSSTT
simulation. Changes in expression are controlled by paths.nPrograms
regulatory programs. Each of the regulatory genes in the gene network has
some association with each program. This is analogous to there being changes
in the environment (the programs) which are sensed by receptors (regulatory
genes) and cause changes in expression downstream. For each path a random
walk is generated for each program and the changes passed on to the
regulatory genes. At each step the changes propagate through the network
according to the weights on edges between genes. This algorithm is fairly
simple but should result in correlation relationships between genes. However
it is likely to be improved and adjusted in the future.
The path structure itself is specified by the paths.design parameter.
This is a data.frame with three columns: "Path", "From", and "Steps".
The Path field is an ID for each path while the Steps field controls the
length of each path. Increasing the number of steps will increase the
difference in expression between the ends of the paths. The From field sets
the originating point of each path. For example a From of 0, 0, 0
would indicate three paths from the origin while a From of 0, 1, 1
would give a branching structure with Path 1 beginning at the origin and
Path 2 and Path 3 beginning at the end of Path 1.
Value
KersplatParams object with path means
References
Papadopoulos N, Parra RG, Söding J. PROSSTT: probabilistic simulation of single-cell RNA-seq data for complex differentiation processes. Bioinformatics (2019). https://doi.org/10.1093/bioinformatics/btz078.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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