In hms-dbmi/spacemut: Analysis of spatial mutation patterns.

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "##"
)

The vignette describes inference of spatially-varying mutational processes. The guideline starts with matrix of regional mutation rates of mutation types in genomic regions, infers mutational components, estimates their biological relevance and groups in strand-independend/strand-dependent mutational processes. Finally, the guideline provides quality estimates of reconstructed processes using bootstrap and reflection correlations.

Loading the data

library(spacemut)

Matrix of regional mutation rates is loaded from the local web server:

info <- read.table("http://pklab.med.harvard.edu/ruslan/spacemut/TOPMed_30kb.txt",header=TRUE)
dim(info)

head(info[,1:6])

knitr::kable(head(info[,1:6]))

Retain only columns of regional mutation rates:

rate <- info[,-c(1:3)]

Inference of mutational components and processes

Function extract.comp decomposes observed regional mutation rates in a small number of mutational components using independent component analysis. Below we estimate 13 mutational components:

comp.info <- extract.comp(rate,13)

We rearrange components to have their order and signs consistent with the paper:

process.order <- c(1,2,9,10,13,8,4,5,3,6,12,11,7)
sign.change <- 7
comp.info <- rearrange.components(comp.info,process.order,sign.change)

Output object comp.info consists of matrices of components spectra and intensities. Matrix of components spectra:

icM <- comp.info[[1]]
head(icM[,1:4])

knitr::kable(head(icM[,1:4]))

Matrix of components intensities:

icS <- comp.info[[2]]
head(icS[,1:4])

knitr::kable(head(icS[,1:4]))

Of note, spectra and intensities have negative values due to Z-score transformation of mutation rates in the method. Positive (negative) values in spectra and intensities indicate higher (lower) values compared to genome-wide average. Reflection matrix of correlations between spectrum and reverse complementary spectrum of each pair of components is used to separate extracted components in strand symmetric, asymmetric and noise:

plot.reflection.matrix(icM)

For example, comp. 10 is symmetic, since it has spectrum reverse complementary to itself:

reflection.scatter(10,10,icM)

On the other hand comp. 1 is not symmetrical:

reflection.scatter(1,1,icM)

However, comp. 1 is reflected to comp. 2 spectrum representing a single strand-dependent process:

reflection.scatter(1,2,icM)

Function reflection.test formally classifies components in symmetric/asymmetric, noise and combine them in mutational processes.

ref.prop <- reflection.test(icM)

Object ref.prop contains classification of components, including symmetric/asymmteric type and reflected component ref.comp for each component:

head(ref.prop[[1]])

knitr::kable(head(ref.prop[[1]]))

A symmetric mutational component corresponds to a strand-independent mutational process, while a pair of two reflected components corresponds to a single strand-dependent mutational process. Annotation of mutational processes includes components that correspond to it (comp.X comp.Y):

head(ref.prop[[2]])

knitr::kable(head(ref.prop[[2]]))

Spectrum visualization of symmetric comp. 10 with equal rates of complementary mutations:

draw.signature(icM[,10])

Spectrum visualization of asymmetric comp. 1 with imbalanced rates of complementary mutations:

draw.signature(icM[,1])

Routine plot.intensities enables exploration of spatial variation in component intensity along the genome. Intensity of maternal process, estimated as sum of intensities of comp. 4 and comp. 5, along left arm of chromosome 8 is shown below:

plot.intensities(icS[,7]+icS[,8], info, chr=8, start=0,end=4e+7,  span.wind=30)

Finally, robustness of components can be evaluated using statistical bootstrap of genomic regions or reflection correlation. Rounine spectra.bootstrap estimates Spearman correlations between a component in original and n.boot bootstrapped inferences:

boot <- spectra.bootstrap(icM,rate,n.boot=100,n.cores=20)

Visualization of summarized statistics:

visualize.bootstrap(boot,icM=icM)

Estimate number of components to extract

Number 13 of initially extracted components may seems rather arbitrary. Reflection property suggests a natural criterion to evaluate number of components to extract that maximumizes number of components having reflection property. More specifically, routine select.ncomponents extracts a range of components from n.min and n.max and evaluates optimal choice:

stat.extract <- select.ncomponents(rate,n.min=2,n.max=35,cutoff=0.7,n.cores=20)

head(stat.extract)

knitr::kable(head(stat.extract,3))

Number of components and processes having reflection property depending on number of extracted components:

show.optimal.comp(stat.extract)

hms-dbmi/spacemut documentation built on Feb. 8, 2021, 6:27 a.m.