Package index • lineaGT

Data

Example of the input datasets and of the fitted object.

cov.df.example: Example coverage data

vaf.df.example: Example mutation data

vaf.df.example: Example mutation data

Fit function

Main function to perform the fit of the model.

fit(): Creates an object of class mvnmm.

fit_mutations(): Fit the mutations clustering

fit_phylogenies(): Fit the phylogenetic trees

fit_growth_rates(): Infer growth rates for each clone and subclone.

filter_dataset(): Filters the input dataset.

Getter functions

Functions to extract the main elements of the fitted object.

get_lineages(): Extract the data lineages.

get_dimensions(): Extract the model dimensions.

get_timepoints(): Extract the data timepoints.

get_cov_dataframe(): Retrieve the coverage dataframe.

get_vaf_dataframe(): Retrieve the mutations dataframe.

get_labels(): Extract the observations labels.

get_unique_labels(): Extract the list of unique observations labels.

get_unique_muts_labels(): Retrieve the list of unique labels of mutation clusters.

get_mean(): Extract the estimated mean parameters.

get_sigma(): Extract the estimated variance parameters.

get_covariance_Sigma(): Extract the estimated covariance matrices.

get_covariance_Cholesky(): Extract the estimated Cholesky matrices, used to factorise the covariance matrix.

get_weights(): Extract the estimated mixing proportions.

get_z_probs(): Extract the estimated posterior probabilities.

get_ISs(): Get the number of ISs per cluster.

estimate_n_pops(): Function implemented to estimate the real number of clones in each cluster.

Visualization functions

Functions to visualize the fit results.

plot_scatter_density(): 2D scatterplot and density

plot_mixture_weights(): Barplot of the per-cluster mixture weights and number of ISs.

plot_marginal(): Histogram of the marginal distribution of each dimension

plot_mullerplot(): Muller plot

plot_growth_regression(): Visualize the regression given the infered growth rates.

plot_growth_rates(): Visualize the infered growth rates.

plot_vaf(): VAF 2D scatterplot

plot_vaf_time(): VAF over time

plot_phylogeny(): Clonal evolution trees

plot_differentiation_tree(): Visualize the number of subclones on the differentiation tree

Training

Functions to visualize losses, Information Criteria and gradient norms computed during training.

plot_losses(): Function to plot the training losses.

plot_IC(): Function to plot the Information Criteria computed during model selection.

plot_gradient_norms(): Function to plot the gradients norms.

S3 object methods

Print and plot methods for the fitted object.

print(<mvnmm>): Print method

plot(<mvnmm>): Mullerplot

Helper functions

Function to configure and install the required dependencies.

configure_environment(): Configure the reticulate environment

have_loaded_env(): Check if there is a loaded conda environment.

have_python_deps(): Check if Python packages are installed in the environment.

which_conda_env(): Retrieve the name of the currently loaded environment.

load_conda_env(): Load the input conda environment.