The R package **HiLDA** is developed under the Bayesian framework to allow
statistically testing whether there is a change in the mutation burdens of
mutation signatures between two groups. The mutation signature is defined based
on the independent model proposed by Shiraishiâ€™s et al.

Shiraishi et al.Â A simple model-based approach to inferring and visualizing cancer mutation signatures, bioRxiv, doi: http://dx.doi.org/10.1101/019901.

**Zhi Yang**, Priyatama Pandey, Darryl Shibata, David V. Conti, Paul Marjoram, Kimberly D. Siegmund. HiLDA: a statistical approach to investigate differences in mutational signatures, bioRxiv, doi: https://doi.org/10.1101/577452

**HiLDA** requires several CRAN and Bioconductor R packages to be
installed. Dependencies are usually handled automatically, when installing the
package using the following commands:

```
install.packages("BiocManager")
BiocManager::install("HiLDA")
```

[NOTE: Ignore the first line if you already have installed the
*BiocManager*.]

You can also download the newest version from the GitHub using *devtools*:

`devtools::install_github("USCbiostats/HiLDA")`

In order to run HiLDA, one also needs to install an external program called Just Another Gibbs Sampler, JAGS, downloaded from this website http://mcmc-jags.sourceforge.net/. For more details, please follow the INSTALL file to install the program.

`HiLDA`

is a package built on some basic functions from `pmsignature`

including
how to read the input data. Here is an example from `pmsignature`

on the input
data, *mutation features* are elements used for categorizing mutations such as:

- 6 substitutions (C>A, C>G, C>T, T>A, T>C and T>G)
- 2 flanking bases (A, C, G and T)
- transcription direction.

```
sample1 chr1 100 A C
sample1 chr1 200 A T
sample1 chr2 100 G T
sample2 chr1 300 T C
sample3 chr3 400 T C
```

- The 1st column shows the name of samples
- The 2nd column shows the name of chromosome
- The 3rd column shows the coordinate in the chromosome
- The 4th column shows the reference base (A, C, G, or T).
- The 5th colum shows the alternate base (A, C, G, or T).

Here, *inputFile* is the path for the input file. *numBases* is the number of
flanking bases to consider including the central base (if you want to consider
two 5â€™ and 3â€™ bases, then set 5). Also, you can add transcription direction
information using *trDir*. *numSig* sets the number of mutation signatures
estimated from the input data. You will see a warning message on some mutations
are being removed.

`library(HiLDA)`

`## Loading required package: ggplot2`

```
inputFile <- system.file("extdata/esophageal.mp.txt.gz", package="HiLDA")
G <- hildaReadMPFile(inputFile, numBases=5, trDir=TRUE)
```

```
## Warning in hildaReadMPFile(inputFile, numBases = 5, trDir = TRUE): Out of
## 24861 mutations, we could obtaintranscription direction information for 24728
## mutation. Other mutations are removed.
```

Also, we also provided a small simulated dataset which contains 10 mutational catalogs andused it for demonstrating the key functions in HiLDA. We start with loading the sample dataset G stored as extdata/sample.rdata.

```
load(system.file("extdata/sample.rdata", package = "HiLDA"))
class(G)
```

```
## [1] "MutationFeatureData"
## attr(,"package")
## [1] "HiLDA"
```

If youâ€™d like to use the USC data in the manuscript, please download the data from the OSF home page https://osf.io/a8dzx/

`HiLDA`

After we read in the sample data G, we can run the local and the global tests
from HiLDA. Here, we specify the *inputG* as *G*, the number of mutational
signatures to be three, the indices for the reference group to be 1:4, the
number of iterations to be 1000. *localTest* being *FALSE* means that a global
test is called while it being *TRUE* means that a local test is called instead.

```
set.seed(123)
hildaGlobal <- hildaTest(inputG=G, numSig=3, localTest=FALSE,
refGroup=1:4, nIter=1000)
```

`## module glm loaded`

```
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 6370
## Unobserved stochastic nodes: 1304
## Total graph size: 14909
##
## Initializing model
```

```
hildaLocal <- hildaTest(inputG=G, numSig=3, localTest=TRUE,
refGroup=1:4, nIter=1000)
```

```
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 6370
## Unobserved stochastic nodes: 1305
## Total graph size: 14903
##
## Initializing model
```

This object is used to provide an initial values for running MCMC sampling to
reduce the running time by using the EM algorithm from *pmsignature* package
developed by Shiraishi et al.

`Param <- pmgetSignature(G, K = 3)`

```
## #trial: 1; #iteration: 64; time(s): 0.08; convergence: TRUE; loglikelihood: -8202.3184
## #trial: 2; #iteration: 27; time(s): 0.05; convergence: TRUE; loglikelihood: -8202.3184
## #trial: 3; #iteration: 39; time(s): 0.05; convergence: TRUE; loglikelihood: -8202.3200
## #trial: 4; #iteration: 21; time(s): 0.03; convergence: TRUE; loglikelihood: -8202.3187
## #trial: 5; #iteration: 63; time(s): 0.09; convergence: TRUE; loglikelihood: -8202.3185
## #trial: 6; #iteration: 12; time(s): 0.02; convergence: TRUE; loglikelihood: -8202.3184
## #trial: 7; #iteration: 22; time(s): 0.04; convergence: TRUE; loglikelihood: -8202.3334
## #trial: 8; #iteration: 22; time(s): 0.04; convergence: TRUE; loglikelihood: -8202.3184
## #trial: 9; #iteration: 12; time(s): 0.02; convergence: TRUE; loglikelihood: -8202.3184
## #trial: 10; #iteration: 20; time(s): 0.03; convergence: TRUE; loglikelihood: -8202.3187
```

In a very similar way as running the HiLDA test, one just needs to specify
*useInits* to be *Param* returned by the previous function to allow the initial
values to be used in the MCMC sampling.

```
set.seed(123)
hildaGlobal <- hildaTest(inputG=G, numSig=3, useInits = Param,
localTest=TRUE, refGroup=1:4, nIter=1000)
```

```
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 6370
## Unobserved stochastic nodes: 1305
## Total graph size: 14903
##
## Initializing model
```

```
hildaLocal <- hildaTest(inputG=G, numSig=3, useInits = Param,
localTest=TRUE, refGroup=1:4, nIter=1000)
```

```
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 6370
## Unobserved stochastic nodes: 1305
## Total graph size: 14903
##
## Initializing model
```

After the MCMC sampling finishes, we can compute the potential scale reduction
statistic to examine the convergence of two chains. Usually it is recommended
to be less than 1.10. If not, it can be done by increasing the number of
*nIter*.

`hildaRhat(hildaGlobal)`

`## [1] 1.05479`

`hildaRhat(hildaLocal)`

`## [1] 1.147583`

To allow users to compare the mutational signatures from both pmsignature and HiLDA, this function is used to plot the results from pmsignature.

```
pmPlots <- pmBarplot(G, Param, refGroup=1:4, sigOrder=c(1,3,2))
cowplot::plot_grid(pmPlots$sigPlot, pmPlots$propPlot, rel_widths = c(1,3))
```

In contrast, the following function is used to plot the results from HiLDA.

```
hildaPlots <- hildaBarplot(G, hildaLocal, refGroup=1:4, sigOrder=c(1,3,2))
cowplot::plot_grid(pmPlots$sigPlot, pmPlots$propPlot, rel_widths = c(1,3))
```

To visualize the 95% credible interval of the mean differences in exposures, the following function plots the differences along with the mutational signatures.

```
hildaDiffPlots <- hildaDiffPlot(G, hildaLocal, sigOrder=c(1,3,2))
cowplot::plot_grid(hildaDiffPlots$sigPlot, hildaDiffPlots$diffPlot,
rel_widths = c(1,3))
```