ITRPCA

To mine latent association features in multiple similarities and association data, we present an improved tensor robust principal component analysis (ITRPCA) method. First, we integrate the prior information of drug and disease to compute five indicators for drug similarity and two indicators for disease similarity. Considering that validated drug-disease associations are extremely sparse, a weighted k-nearest neighbor (WKNN) preprocessing step is employed to enrich the association matrix that aids in prediction. Then, we construct a drug tensor and a disease tensor by using multi-similarity matrices and an updated association matrix. Finally, we apply ITRPCA to isolate the low-rank tensor and noise information in these two tensors, respectively. We focus on the drug-disease association pairs in the clean low-rank tensor to infer promising indications for drugs.

Requirements

• Matlab >= 2014

Installation

ITRPCA can be downloaded by

git clone https://github.com/YangPhD84/ITRPCA

Installation has been tested in a Windows platform.

Datasets Description

• Gdataset.mat: this file contains information about the gold standard dataset;
• Gdataset_diseases_name.xls: disease IDs in the gold standard dataset;
• Gdataset_drugs_name.xls: drug IDs in the gold standard dataset;
• Gdataset_known_associations.xls：all drug-disease associations in the gold standard dataset；
• Inpeddateset.mat: this file contains the Inpeddateset information for independent testing；
• Inpeddateset_diseases_name.xls：disease IDs in the Inpeddateset;
• Inpeddateset_drugs_name.xls：drug IDs in the Inpeddateset;
• Inpeddateset_known_associations.xls：all drug-disease associations in the Inpeddateset；
• drug_ChemS: chemical structure similarity matrix;
• drug_AtcS: drug's ATC code similarity matrix;
• drug_SideS: side-effect similarity matrix;
• drug_DDIS: drug-drug interaction similarity matrix;
• drug_TargetS: drug's target profile similarity matrix;
• disease_PhS: disease phenotype similarity matrix;
• disease_DoS: disease ontology similarity matrix;
• didr: disease-drug association matrix.

Functions Description

• WKNN.m: this function can implement the WKNN preprocessing algorithm;
• fITRPCA.m: this function is the core algorithm of the ITRPCA model for implementing association prediction;
• ffindw.m: this function determines the weight vector omega;
• itrpca_tnn_lp_stop.m: this function solves the problem of tensor robust principal component analysis based on the weighted tensor Schatten p-Norm;
• prox_l1.m: this function presents M-ADMM method in l1 norm operator;
• prox_tnn.m: this function is used to update the low-rank tensor in the model;
• solve_Lp_w.m: this function can solve our ITRPCA model;
• Tprod.m: this function is capable of extracting the correlation matrix within a low-rank tensor.

Instructions

We provide detailed step-by-step instructions for running ITRPCA model.

Step 1: add datasets\functions paths

addpath('Datasets');
addpath('Functions');

Step 2: load datasets with association matirx and similarity matrices

load Gdataset
A_DR = didr;
[dn,dr] = size(A_DR);
Trr = zeros(dr,dr,1);
Trr(:,:,1) = drug_ChemS;
Trr(:,:,2) = drug_AtcS;
Trr(:,:,3) = drug_SideS;
Trr(:,:,4) = drug_DDIS;
Trr(:,:,5) = drug_TargetS;
Tdd = zeros(dn,dn,1);
Tdd(:,:,1) = disease_PhS;
Tdd(:,:,2) = disease_DoS;

Step 3: parameter Settings

The hyper-parameters are fixed.

p = 0.9; 
K = 30;
rat1 = 0.1;
rat2 = 0.2;

Step 4: run the improved tensor robust principal component analysis (ITRPCA)

A_recovery =fITRPCA(Trr,Tdd,A_DR,p,K,rat1,rat2);

A Quickstart Guide

Users can immediately start playing with ITRPCA running Demo_ITRPCA.m  in matlab.

• Demo_ITRPCA.m: it demonstrates a process of predicting drug-disease associations on the gold standard dataset (Gdataset) by ITRPCA algorithm.

Run ITRPCA on User's Own Data

We provided instructions on implementing ITRPCA model with user's own data. One could directly run ITRPCA model in  Demo_ITRPCA.m  with custom data by the following instructions.

Step 1: Prepare your own data and add the corresponding dataset files.

The required data includes drug-disease association matirx and similarity matrices, which are all saved by mat files.

Step 2: Modify four lines in Demo_ITRPCA.m

You can find Gdataset, didr, Trr, Tdd in Demo_ITRPCA.m. All you need to do is replace them with your own data.

Contact

If you have any questions or suggestions with the code, please let us know. Contact Mengyun Yang at mengyun_yang@126.com