---
title: "Analyze Copy Number Signatures with sigminer"
author: "Shixiang Wang ( wangsx1@sysucc.org.cn )"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Analyze Copy Number Signatures with sigminer}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

> Exploring copy number signatures with recently developed approach have been described at [The repertoire of copy number alteration signatures in human cancer](https://xsliulab.github.io/Pan-cancer_CNA_signature/).
>
> A more general introduction please read [Extract, Analyze and Visualize Mutational Signatures with Sigminer](https://shixiangwang.github.io/sigminer-book/index.html).

```{r setup}
library(sigminer)
```

For this analysis, data with six columns are required.

- Chromosome
- Start.bp
- End.bp
- modal_cn (i.e. total copy number, integer)
- minor_cn (i.e. copy number for minor allele, integer)
- sample

## Generate allele-specific copy number profile

```{r}
load(system.file("extdata", "toy_segTab.RData",
  package = "sigminer", mustWork = TRUE
))

set.seed(1234)
segTabs$minor_cn <- sample(c(0, 1), size = nrow(segTabs), replace = TRUE)
cn <- read_copynumber(segTabs,
  seg_cols = c("chromosome", "start", "end", "segVal"),
  genome_measure = "wg", complement = TRUE, add_loh = TRUE
)
```

```{r}
cn
cn@data
```

## Classify the segments with Steele et al method

> If you want to try other type of copy number signatures,
> change the method argument.

```{r}
tally_s <- sig_tally(cn, method = "S")

str(tally_s$all_matrices, max.level = 1)
```

## Find de novo signatures

```{r}
sig_denovo = sig_auto_extract(tally_s$all_matrices$CN_48)
head(sig_denovo$Signature)
```


## Refit (19) reference signatures

This directly calculates the contribution of 19 reference signatures.

```{r}
act_refit = sig_fit(t(tally_s$all_matrices$CN_48), sig_index = "ALL", sig_db = "CNS_TCGA")
```
We can use some threshold to keep really contributed signautres.

```{r}
act_refit2 = act_refit[apply(act_refit, 1, function(x) sum(x) > 0.1),]

rownames(act_refit2)
```

## Plot signatures

For de novo signatures:

```{r, fig.width=10, fig.height=3}
show_sig_profile(sig_denovo, mode = "copynumber", method = "S", style = "cosmic")
```

Show the activity/exposure.

```{r}
show_sig_exposure(sig_denovo)
```


For reference signatures, you can just select what you want:

```{r, fig.height=8, fig.width=10}
show_sig_profile(
  get_sig_db("CNS_TCGA")$db[, rownames(act_refit2)],
  style = "cosmic", 
  mode = "copynumber", method = "S", check_sig_names = FALSE)
```

Similarly for showing activity.

```{r}
show_sig_exposure(act_refit2)
```

NOTE that this case shows relatively large difference with different approaches,
so you need to pick based on your data size/quality and double-check the results.
In general, for small-size data set, the refitting approach is recommended.

## Signature assignment

To assign the de-novo signatures to reference signatures, we use cosine similarity.

```{r}
get_sig_similarity(sig_denovo, sig_db = "CNS_TCGA")
```