fit_linear_model(
input_matrix,
model_matrix,
- ncounts,
+ size_factors = TRUE,
group_matrix = NULL,
gene_specific_model_tensor = NULL,
kernel_input = NULL,
gene_names = NULL,
cell_names = NULL,
+ variance = "VI_Estimate",
inference_method = "SVI",
method_specific_args = list()
)Vector with length equal to the number of cells. It -contains some kind of factor relate to the library size. For example, -it might contain the total UMI of each cell (i.e. colSums(input_matrix)) -or the UMI of each cell divided by the average across all of them.
Boolean. Decides if a scaling factor for the +expression of each cell should be computed
Vector containing the names of the cells
String. Either "VI_Estimate" or "Hessian".
String. Either "SVI" or "HMC"
gamma_lr parameters to tune the decay of the learning rate using "ClippedAdam";
batch_size number of data points or observations sampled from the input matrix in
each iteration of the optimization algorithm;
threshold parameters to stop the inference earlier when convergence is reached.
+Default value is set to 0, i.e. all steps will be done;
HMC only:
num_samples number of iterations after the warmup-phase, it also indicates the
posterior samples each chain will produce;
num_chains number of chains for the optimization algorithm;
warmup_steps number of iterations of the warmup-phase;
Shared:
cuda ;
Shared:
cuda Boolean, indicates if CUDA should be used if available;
jit_compile ;
full_cov ;
theta_bounds ;