mCGfinder

A novel network regularized matrix decomposition method to detect mutated cancer genes in tumour samples with inter-patient heterogeneity

======================= Instructions to mCGfinder software (version 1.0.0)

Developer: Jianing Xi xjn@mail.ustc.edu.cn from Health Informatics Lab, School of Information Science and Technology, University of Science and Technology of China

Requirement

• 4GB memory
• MATLAB R2015a or later

Gene interaction network information

The default If you want to use the default gene interaction network [iRefIndex 9] (http://irefindex.org), just ignore this step. Otherwise, if you want to use a user-defined network, replace the two files below with the files of the user-defined network:

• ./network/index_genes.txt
• ./network/edge_list.txt

The first text file index_genes.txt is a table of the connection between the node id and the related gene names. The second text file edge_list.txt is the edges table that assigns the source nodes to the target nodes.

Run mCGfinder on somatic mutation data file

We provide an example data file of somatic mutations in breast invasive carcinoma (BRCA) samples from [UCSC Cancer Genomics Browser] (https://genome-cancer.soe.ucsc.edu/proj/site/hgHeatmap/) in ./data/example_data.zip. To analyze this data, please extract the .txt file somatic_data_BRCA.txt from ./example_data.zip and put the .txt file in the ./data folder. If you want to analyze the example data file with the default configurations, please run ./demo_mCGfinder.m and then the result file will be automatically saved in the directory ./output/.

If you want to analyze a user-specific data, a .txt file of the mutation binary table (samples x genes) of the sample names and the gene symbols must be provided as the format of the example data file. Put the txt file of the user-specific data in the ./data folder and run ./demo_mCGfinder.m.

Configurations of mCGfinder

The configurations of mCGfinder can be changed in script file ./demo_mCGfinder.m, and the descriptions of these parameters are provided below:

    =================================================================================================
    | PARAMETER NAME       | DESCRIPTION                                                            |
    =================================================================================================
    |CompLeastProportion   |Least sample proportion included in each components, which represents   |
    |                      |minimum proportion of the samples in every components given by the      |
    |                      |mCGfinder. The default proportion is set to 15%.                        |
    -------------------------------------------------------------------------------------------------
    |maxCompoent           |Maximum number of components, which denotes the number of components    |
    |                      |given components given by mCGfinder at most. The default number is 5.   |
    -------------------------------------------------------------------------------------------------
    |NetConf.lambda_T      |The tuning parameter of network regularization, which is used to balance|
    |                      |the fitness of the model (first term) and the smoothness of the scores  |
    |                      |of connected genes (second term). The default number is 0.1.            |
    -------------------------------------------------------------------------------------------------

Output variables of mCGfinder

The descriptions of output variables of mCGfinder are provided below:

    =================================================================================================
    | VARIABLE NAME        | DESCRIPTION                                                            |
    =================================================================================================
    |detected_genes        |Genes detected by mCGfinder as significantly mutated cancer genes.      |
    -------------------------------------------------------------------------------------------------
    |S_sample_indicator    |The sample indicator vectors of all component, which indicates the      |
    |                      |assignment of tumour samples to the every components. The i-th          |
    |                      |coefficient being 1 represents that the i-th samples are included in the|
    |                      |component, and 0 otherwise.                                             |
    -------------------------------------------------------------------------------------------------
    |Symbol_Net            |The investigated gene list in the gene interaction network.             |
    -------------------------------------------------------------------------------------------------
    |G_gene_score          |The gene score vectors of all components, of which the coefficients are |
    |                      |related to the gene lists variable 'Symbol_Net', and a higher value of  |
    |                      |a certain coefficient presents a larger potential of the gene to be     |
    |                      |cancer gene candidate.                                                  |
    -------------------------------------------------------------------------------------------------
    |Q_values              |The q-values of all investigated genes in variable 'Symbol_Net', which  |
    |                      |are obtained by Benjamini-Hochberg false discovery rates control of the |
    |                      |p-values of the investigated genes.                                     |
    -------------------------------------------------------------------------------------------------

mCGfinder for users without MATLAB licenses

For users without MATLAB licenses, we also offer mCGfinder standalone version for Windows ./mCGfinder_standalone.zip.

• Step 1: MATLAB runtime installer: verify the MATLAB runtime is installed and ensure you have installed version 8.5 (R2015a). If the MATLAB runtime is not installed, please download the Windows 64-bit version of the MATLAB runtime for R2015a from the MathWorks Web site by navigating to

  [http://www.mathworks.com/products/compiler/mcr/index.html] (http://www.mathworks.com/products/compiler/mcr/index.html)

  Or run the installation file ./mCGfinder_standalone/MyAppInstaller_web.exe provided in the .zip file.

• Step 2: Extract the ./mCGfinder_standalone.zip file, and locate the executable file ./mCGfinder_standalone/mCGfinder.exe and the two folders ./mCGfinder_standalone/data and ./mCGfinder_standalone/network in the same directory ./mCGfinder_standalone/. Put the .txt files of mutation data in folder ./mCGfinder_standalone/data/ and network files index_genes.txt and edge_list.txt in folder ./mCGfinder_standalone/network/.

• Step 3: Run ./mCGfinder_standalone/mCGfinder.exe. Please wait until the current program is finished, and the output variables are automatically saved as .txt files in folder ./mCGfinder_standalone/output.

Contact

Please feel free to contact us if you need any help: xjn@mail.ustc.edu.cn.