ti_merlot | R Documentation |
Will generate a trajectory using MERLoT.
This method was wrapped inside a container. The original code of this method is available here.
ti_merlot(
sigma = "local",
distance = "euclidean",
ndim = 20L,
density_norm = TRUE,
n_local = c(5L, 7L),
w_width = 0.01,
n_components_to_use = 3L,
N_yk = 100L,
lambda_0 = 8e-10,
mu_0 = 0.0025,
increaseFactor_mu = 20L,
increaseFactor_lambda = 20L,
FixEndpoints = FALSE
)
sigma |
Diffusion scale parameter of the Gaussian kernel. A larger sigma
might be necessary if the eigenvalues can not be found because of a singularity
in the matrix. Must a character vector – |
distance |
A character vector specifying which distance metric to use.
Allowed measures are the Euclidean distance (default), the cosine distance
( |
ndim |
Number of eigenvectors/dimensions to return. Domain: U(2, 20). Default: 20. Format: integer. |
density_norm |
Logical. If TRUE, use density normalisation. Default: TRUE. Format: logical. |
n_local |
If sigma == 'local', the |
w_width |
Window width to use for deciding the branch cutoff. Domain: e^U(-9.21, 0.00). Default: 0.01. Format: numeric. |
n_components_to_use |
Which components to use in downstream analysis. Domain: U(2, 20). Default: 3. Format: integer. |
N_yk |
Number of nodes for the elastic principal tree. Domain: U(2, 1000). Default: 100. Format: integer. |
lambda_0 |
Principal elastic tree energy function parameter. Domain: e^U(-27.63, -13.82). Default: 8e-10. Format: numeric. |
mu_0 |
Principal elastic tree energy function parameter. Domain: U(5e-04, 0.005). Default: 0.0025. Format: numeric. |
increaseFactor_mu |
Factor by which the mu will be increased for the embedding. Domain: U(2, 50). Default: 20. Format: numeric. |
increaseFactor_lambda |
Factor by which the mu will be increased for the embedding. Domain: U(2, 50). Default: 20. Format: numeric. |
FixEndpoints |
Documentation not provided by authors. Default: FALSE. Format: logical. |
A TI method wrapper to be used together with
infer_trajectory
Parra, R.G., Papadopoulos, N., Ahumada-Arranz, L., Kholtei, J.E., Mottelson, N., Horokhovsky, Y., Treutlein, B., Soeding, J., 2018. Reconstructing complex lineage trees from scRNA-seq data using MERLoT.
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