Step 1: find_latent_representation

usage: gsmap run_find_latent_representations [-h] --workdir WORKDIR
                                             --sample_name SAMPLE_NAME
                                             --input_hdf5_path INPUT_HDF5_PATH
                                             --data_layer DATA_LAYER
                                             [--annotation ANNOTATION]
                                             [--epochs EPOCHS]
                                             [--feat_hidden1 FEAT_HIDDEN1]
                                             [--feat_hidden2 FEAT_HIDDEN2]
                                             [--gat_hidden1 GAT_HIDDEN1]
                                             [--gat_hidden2 GAT_HIDDEN2]
                                             [--p_drop P_DROP]
                                             [--gat_lr GAT_LR]
                                             [--n_neighbors N_NEIGHBORS]
                                             [--n_comps N_COMPS]
                                             [--weighted_adj]
                                             [--convergence_threshold CONVERGENCE_THRESHOLD]
                                             [--hierarchically]
                                             [--pearson_residuals]

Named Arguments

--workdir

Path to the working directory.

--sample_name

Name of the sample.

--input_hdf5_path

Path to the input HDF5 file.

--data_layer

Data layer for gene expression (e.g., “count”, “counts”, “log1p”).

Default: 'counts'

--annotation

Name of the annotation in adata.obs to use.

--epochs

Number of training epochs.

Default: 300

--feat_hidden1

Neurons in the first hidden layer.

Default: 256

--feat_hidden2

Neurons in the second hidden layer.

Default: 128

--gat_hidden1

Units in the first GAT hidden layer.

Default: 64

--gat_hidden2

Units in the second GAT hidden layer.

Default: 30

--p_drop

Dropout rate.

Default: 0.1

--gat_lr

Learning rate for the GAT.

Default: 0.001

--n_neighbors

Number of neighbors for GAT.

Default: 11

--n_comps

Number of principal components for PCA.

Default: 300

--weighted_adj

Use weighted adjacency in GAT.

Default: False

--convergence_threshold

Threshold for convergence.

Default: 0.0001

--hierarchically

Enable hierarchical latent representation finding.

Default: False

--pearson_residuals

Using the pearson residuals.

Default: False