HoloNet.predicting.mgc_training_for_multiple_targets#
- HoloNet.predicting.mgc_training_for_multiple_targets(X, adj, target_all_gene_expr, repeat_num=5, device='cpu', **kwargs)#
Using cell-type tensor and normalized adjancency matrix as the inputs, repeated training GNN to generate the expression of multiple target genes. Also generate the expression only using cell-type information.
- Parameters
- X
Tensor A tensor (cell_num * cell_type_num) with cell-type information. derived from ‘get_continuous_cell_type_tensor’ or ‘get_one_hot_cell_type_tensor’ function.
- adj
Tensor A normalized adjancency matrix derived from ‘adj_normalize’ function.
- target_all_gene_expr
Tensor The scaled expression tensor of target genes (cell_num * target_gene_num), from ‘get_gene_expr’ function.
- repeat_num
int(default:5) The number of repeated training, defaultly as 50.
- use_gpu
If true, model will be trained in GPU when GPU is available.
- kwargs
Other training hyperparameter in ‘mgc_repeat_training’ function.
- X
- Return type
- Returns
: Two list of multiple (repeated training) trained MGC model for generating the expression of target genes, one using MGC with cell-type information and CE tensor, one only with cell-type information.