Package index • lineaGT

Fit function

Main function to perform the fit of the model.

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.

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

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.

Print and plot methods for the fitted object.

Example of the input datasets and of the fitted object.