ti_merlot.Rd
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., El Kholtei, J., Mottelson, N., Horokhovskyi, Y., Treutlein, B., Soeding, J., 2018. Reconstructing complex lineage trees from scRNA-seq data using MERLoT.