psuedocode for message passing algorithm utilizing spatial, ssRNA-seq and ChIP data, via pytorch

torch_MP_pseudo.py

import torch
import torch.nn as nn
import torch.nn.functional as F

class GraphConvolution(nn.Module):
    def __init__(self, in_features, out_features):
        super(GraphConvolution, self).__init__()
        self.weight = nn.Parameter(torch.FloatTensor(in_features, out_features))
        self.bias = nn.Parameter(torch.FloatTensor(out_features))

    def forward(self, x, adj):
        support = torch.matmul(x, self.weight)
        output = torch.matmul(adj, support) + self.bias
        return output

class MessagePassingNetwork(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(MessagePassingNetwork, self).__init__()
        self.gc_chip = GraphConvolution(input_dim, hidden_dim)
        self.gc_rna = GraphConvolution(input_dim, hidden_dim)
        self.gc_spatial = GraphConvolution(input_dim, hidden_dim)
        self.fc = nn.Linear(hidden_dim * 3, output_dim)

    def forward(self, chip_data, rna_data, spatial_data, adj):
        x_chip = F.relu(self.gc_chip(chip_data, adj))
        x_rna = F.relu(self.gc_rna(rna_data, adj))
        x_spatial = F.relu(self.gc_spatial(spatial_data, adj))

        # Combine features from different data types
        x_combined = torch.cat((x_chip.unsqueeze(1), x_rna.unsqueeze(1), x_spatial.unsqueeze(1)), dim=1)
        x_combined = torch.mean(x_combined, dim=1)  # Average pooling
        
        # Fully connected layer for final prediction
        output = self.fc(x_combined)
        return output

# Define input data (ChIP-seq, ssRNAseq, and spatial data)
chip_seq_data = ...  # Shape: (num_nodes, num_features)
ssRNAseq_data = ...  # Shape: (num_nodes, num_features)
spatial_data = ...   # Shape: (num_nodes, num_features)

# Define adjacency matrix (assuming a pre-defined graph structure)
adjacency_matrix = ...  # Shape: (num_nodes, num_nodes)

# Instantiate and forward pass through the message-passing network
input_dim = chip_seq_data.shape[1]  # Assuming all data types have the same input dimension
hidden_dim = 64
output_dim = 32
mpn = MessagePassingNetwork(input_dim, hidden_dim, output_dim)
output = mpn(chip_seq_data, ssRNAseq_data, spatial_data, adjacency_matrix)