Mutational Signature Analysis tool

This is framework for identifying specific genes through the analysis of the association study between mutational signature and gene expression. Go to the page below to see the results of the sample data.

https://altaaaaaaa.github.io/SigAnalysis-B/

How to execute code

To install the current version of this Github repo, git clone this repo or download the zip file. Unzip the contents of SigProfilerExtractor-master.zip or the zip file of a corresponding branch.

In the command line, please run the following:

$ python CODE.py --data_folder=[datafolder name] --deseq_folder=[deseq result foloder name]

[datafolder name] => yMat = mutation count(vcf file to csv), result of Sigprofiler
[deseqfolder name] => result of DESeq(tsv)

Category	Parameter	Variable Type	Parameter Description
Input Data
	input_type	String	The type of input: "vcf": used for vcf format inputs. Input data is signature analysis data obtained as a result of sigprofiler. As input data of sigprofiler, a somatic mutation dataset was used.
	output	String	The name of the output folder. The output folder will be generated in the current working directory.
	reference_genome	String	The name of the reference genome. The default reference genome is "GRCh38". This parameter is applicable only if the input_type is "vcf".
	DESeq data	String	DESeq represents the result of DEG analysis by obtaining the LogFC value of the mutation dataset, and refers to gene expression data.
	Used data	String	DESeq represents the result of DEG analysis by obtaining the LogFC value of the mutation dataset, and refers to gene expression data.
	data	String	COSMIC signature: 30 signature data already analyzed Gene-Gene link data: Gene-gen network data for signature classification
Output Data
	result of sigprofiler	String	Results of analyzing somatic mutation sample data using signature analysis tool called sigprofiler.(process and exposure data)
	heatmap	Image	Heatmap to express the similarity between the most likely signatures analyzed using sigprofiler and already studied cosmic signature data through cosine similarity.
	gene data	Table	The result of identifying the genes whose correlation and p-value between each contribution of the sample and the gene expression data satisfies a certain range.
	Results of node classification	Image and Table	Through node classification, genes identified by multiple signatures are classified as single signatures that are most similar.