Multi-Modal Similarity Matrix Factorization for Integrative Multi-Omics Data Analysis
To calculate the similarity matrix
calculate the normalized mutual information.
Calculate the cost
Calculate the agreement-based measurement
Calculate the chi-squared distance
Calculate the Euclidean distance
initialize the sub-matrix Ci into alpha*Li by SVD
Initialize from the similairty matrix list
the main part for M2SMF and clustering result
Calculate the modularity
Generate simulated data
Normalize the input matrix by column
the function to update alpha
the function to update Li, for i=1,2,...,N
A new method to implement clustering from multiple modality data of certain samples, the function M2SMF() jointly factorizes multiple similarity matrices into a shared sub-matrix and several modality private sub-matrices, which is further used for clustering. Along with this method, we also provide function to calculate the similarity matrix and function to evaluate the best cluster number from the original data.