Hi Sir,

For plotMutationSpectrum function,I want to know the exact process of calculating contribution.

Regarding second question I have run the same code 10 times,please have a look at the link for the code https://www.dropbox.com/s/sug1i8v1zoqm1ce/Mutation_signature_package_data.R?dl=0

Please have a look at this link https://www.dropbox.com/s/p30pdl0a7ifdv7a/ss1_ss2.png?dl=0 where I have attached an image of two plots which are slightly different to each other. I have used this function

plotSignatures(sigs_nmf) + ggtitle("Somatic Signatures: NMF - Barchart") and run this 10 times on the dataset supplied with the package,the plot on the right side occurred 8 times whereas the plot on the left side occurred 2 times.

We have also run this for our dataset but the dissimilarity is more.

Another question is that for your dataset supplied with the package,what is the index column meant for?

Also,what do to if I have to run this package for chr X,chr Y,chr MT.Is this package supports this?

Thanks

Swati

1. "Also,what do to if I have to run this package for chr X,chr Y,chr MT.Is this package supports this?"

All chromosomes are supported, as long as you have a reference genome that contains them.

2. "plotMutationSpectrum function,I want to know the exact process of calculating contribution."

As I outlined in Regarding somatic signature package, the function visualises the observed mutational spectrum 'M'. The details are described in detail in the vignette and in the referenced publications. With normalize = FALSE, the observed counts are plotted. With normalize set to TRUE, the counts for each motif are divided by the total number of counts in the samples.

3. NMF rerun multiple times

The NMF uses numerical methods to decompose the mutational spectrum, and some of these methods are based on a random initialisation of the matrices. The fact that several runs produce slightly different results is therefore not surprising. If you want to avoid this behaviour, you can

- Select a NMF algorithm that uses a deterministic seeding (see the vignette of the NMF package for some nice details and choices)

- Use a different decomposition method (such as PCA, k-means, ...)

- Set the random seed before running the computation. This way, rerun will give you consistent results.

4. index

The index column refers to the row of this entry in the original MAF file. It is generally not relevant for analyses and is kept to find entries in the original data set.