mfcnv

A method for detecting CNV using neural networks

Table of Contents

Folder information

Usage of mfcnv

Output

Platform

1.Folder information

There are six directories. 1.program This directory contains three files that have been coded, including code for training the model, for detecting simulation data, for detecting real data, for extracting eigenvalue form simulation data, for extracting eigenvalue form real data and for transforming .txt to .mat. 2.test This directory contains two subdirectories including RealData_mat and SimulationData_mat, OriginalData_bam and Reference. RealData_mat stores the eigenvalue of real data, and SimulationData_mat stores the eigenvalue of simulation data, and OriginalData_bam stores a sample of the bam format, and Reference stores a reference file in fa format. 3.trains This directory contains three sets of data, you can train the model with any set of data from them and then detect other data using this model. 4.result This directory contains a table with all statistical results and two sub-directories, which are real data results and simulation data results.

2.Usage of mfcnv

The program must be run in matlab 2016a. Step 1: Run the main_simulation.py, modify the path of the bam variable on line 107 and the path of the reference variable on line 114, and the path of the myin variable on line 143 and the path on line 178. We use mapq function to extract the quality of the comparison and calculate the correlation between neighboring bins in this file, and we get a eigenvalue file in txt format at last. Step 2: Run shengchengmat.m in matlab 2016a, modify the path of the .txt file and result file, and we get the corresponding mat format files.We have uploaded a part of the data in mat format in test/SimulationData_mat and test/RealData_mat. Step 3: Open the bpcnv_train file, changing the load (' ') in 1 to the user's file address,and run. Step 4: (1) test simulation data: Open the bpcnv_test_simulation file and change the load (' ') in 1 to the user's file address. Change the load (' ') in 3.1.1 to the user's file address. Change the mkdir (' ') and fopen (' ') in 4.5 to the user's file address,and run. (2) test real data: Open the bpcnv_test_real file and change the load (' ') in 1 to the user's file address. Change the load (' ') in 3 to the user's file address. Change fopen (' ') in 5.2 to the user's file address,and run.

3.Output

The end of the program will generate result files in the root directory and show the precision, sensitivity and F1-score in the matlab window. These result files have a total of three volumns. (1): the position where the mutation occurred (2): variation type including gain, hemi_loss and homo_loss (3): this volumn is 1, 2 or 3 corresponding to the second volumn.

4.Platform

The program must run in matlab on windows system.