The purpose of this notebook is to clean copy number data related to C19MC amplifications in non-MB, non-ATRT embryonal tumors (AlexsLemonade/OpenPBTA-analysis#251).

Specifically, we are trying to identify Embryonal tumor with multilayered rosettes (ETMR), C19MC-altered tumors.

Usage

This notebook can be run via the command line from the root directory of the repository with:

Rscript -e "rmarkdown::render('analyses/molecular-subtyping-embryonal/03-clean-c19mc-data.Rmd', clean = TRUE)"

Set up

Libraries and functions

suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(ggbio))

We’re going to adapt code from the cnv-chrom-plot module to plot chromosome 19.

plot_chr19 <- function(cn_df, biospecimen_id) {
 # This function takes a seg data.frame and a biospecimen identifier and 
 # returns a plot of the chromosome 19 segment mean for that biospecimen.
 # It fills missing seg.mean values with zeroes.
 # 
 # Args:
 #   cn_df: data.frame in SEG format
 #   biospecimen_id: A Kids First Biospecimen ID used to filter cn_df
 # 
 # Returns: essentially a barplot of the non-neutral segment means
  
  bsid_data <- cn_df %>%  
    # For visualization purposes fill the missing seg.mean values with zeroes
    tidyr::replace_na(list(seg.mean = 0)) %>%
    # Reformat the chromosome variable to drop the "chr"
    dplyr::mutate(chrom = factor(gsub("chr", "", chrom), 
                                 levels = c(1:22, "X", "Y"))) %>%
    # Only look at chr19 in the relevant sample
    filter(ID == biospecimen_id,
           chrom == 19) %>%
    # Make Del/Amp variable
    dplyr::mutate(Type = dplyr::case_when(
      seg.mean < 0 ~ "Del",
      seg.mean > 0 ~ "Amp",
      seg.mean == 0 ~ "Neutral"
    ))
  
  # Turn into a GRanges for easier mapping
  bsid_ranges <- GenomicRanges::GRanges(
    seqnames = bsid_data$chrom,
    ranges = IRanges::IRanges(
      start = bsid_data$loc.start,
      end = bsid_data$loc.end
    ),
    score = bsid_data$seg.mean,
    mcols = bsid_data$Type
  )
  
  # Map this on a plot
  bsid_plot <- 
    ggbio::autoplot(bsid_ranges,
                    ggplot2::aes(y = score, fill = mcols),
                    geom = "bar") +
    ggplot2::theme_bw() +
    ggplot2::ylim(c(-2, 2)) +
    colorblindr::scale_fill_OkabeIto(name = "Type") +
    ggplot2::labs(
      title = paste(biospecimen_id, "chr19"),
      y = "segment mean"
    )
  
  return(bsid_plot)
}

Directories

results_dir <- "results"
data_dir <- file.path("..", "..", "data")

Read in Files

# full clinical file
histologies_file <- file.path(data_dir, "pbta-histologies.tsv")
histologies_df <- read_tsv(histologies_file,
                           col_types = cols(
                             molecular_subtype = col_character()
                           ))

# copy number consensus file (analysis file)
# TODO: get this from data download
cn_consensus_seg <- file.path("..", "copy_number_consensus_call", "results",
                              "pbta-cnv-consensus.seg.gz")
# file that contains the biospecimen IDs that met criteria for subtyping
biospecimen_file <- file.path(results_dir, 
                              "biospecimen_ids_embryonal_subtyping.tsv")

# get biospecimen ids as a vector rather than data frame
biospecimen_ids <- read_tsv(biospecimen_file) %>%
  pull(Kids_First_Biospecimen_ID)

# we'll filter to only the relevant biospecimens for the copy number alteration
# data that is committed to the repository
cn_consensus <- read_tsv(cn_consensus_seg) %>%
  filter(ID %in% biospecimen_ids)

Output file

output_file <- file.path(results_dir, "cleaned_chr19_cn.tsv")

Look at copy number alterations on chr 19

We’re interested in focal amplification of C19MC (miRNA cluster on chr19) for the ETMR, C19MC-altered subtype.

chr19_cn_df <- cn_consensus %>% 
  filter(chrom == "chr19",
         seg.mean > 0)

chr19_cn_df

These are not within the ranges from @sjspielman provided here:

## From UCSC Genome Brower, these are positions corresponding to 19q13.41 (C19MC region)
c19mc_region <- "19q13.41"
c19mc_start  <- 51400001
c19mc_end    <- 53600000

But they do overlap with coordinates from other papers. These are from Jo Lynne Rokita in this comment.

From Bentwich et al. Nature Genetics. 2005.:

Cluster on chromosome 19 located at positions 58,861,745–58,961,404 (HG17) and comprising 54 microRNA genes grouped into four families on the basis of hairpin sequence similarity.

version chr start end
hg17 chr19 58861745 58961404
hg19 chr19 54169933 54269592
hg38 chr19 53666679 53766338

and from Bortolin-Cavaillé et al. NAR. 2009.:

Schematic representation of the ∼100-kb long C19MC (HG18: 58 860 000–58 962 300) mapping at human chromosome 19q13.41

version chr start end
hg18 chr19 58860000 58962300
hg38 chr19 53664934 53767234

Visualization

For visualization purposes, we are filling in the missing seg.mean values with zeroes.

plot_chr19(cn_df = cn_consensus,
           biospecimen_id = "BS_69VS8PS1")

plot_chr19(cn_df = cn_consensus,
           biospecimen_id = "BS_K07KNTFY")

plot_chr19(cn_df = cn_consensus,
           biospecimen_id = "BS_TE8QFF7T")

Generate cleaned table for joining to fusion and expression data

The copy numbers in all of the above cases are high - we’ll simplify the column name to chr19_amplification.

# get a data.frame of the relevant WGS biospecimen_ids
wgs_ids <- intersect(histologies_df %>%
                       filter(experimental_strategy == "WGS") %>%
                       pull(Kids_First_Biospecimen_ID),
                     biospecimen_ids) %>%
  as.data.frame(stringsAsFactors = FALSE) %>%
  rename(Kids_First_Biospecimen_ID = ".")
          
chr19_cn_df <- chr19_cn_df %>%
  mutate(chr19_amplification = "Yes") %>%
  select(ID, chr19_amplification) %>%
  right_join(wgs_ids, by = c("ID" = "Kids_First_Biospecimen_ID")) %>%
  replace(is.na(.), "No")

Write to file.

chr19_cn_df %>% 
  rename(Kids_First_Biospecimen_ID = ID) %>%
  write_tsv(output_file)
---
title: "Molecularly Subtyping Embryonal Tumors - C19MC amplifications"
output: 
  html_notebook:
    toc: TRUE
    toc_float: TRUE
author: Jo Lynne Rokita, Stephanie J. Spielman, and Jaclyn N. Taroni
date: 2020
---

The purpose of this notebook is to clean copy number data related to C19MC amplifications in non-MB, non-ATRT embryonal tumors ([`AlexsLemonade/OpenPBTA-analysis#251`](https://github.com/AlexsLemonade/OpenPBTA-analysis/issues/251)).

Specifically, we are trying to identify Embryonal tumor with multilayered rosettes (ETMR), C19MC-altered tumors.

## Usage

This notebook can be run via the command line from the root directory of the repository with:

```
Rscript -e "rmarkdown::render('analyses/molecular-subtyping-embryonal/03-clean-c19mc-data.Rmd', clean = TRUE)"
```

## Set up

### Libraries and functions

```{r}
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(ggbio))
```

We're going to adapt code from the [`cnv-chrom-plot`](https://github.com/AlexsLemonade/OpenPBTA-analysis/tree/eadb78d9ef02d35ae06a88104b29b4d0325499a6/analyses/cnv-chrom-plot) module to plot chromosome 19.

```{r}
plot_chr19 <- function(cn_df, biospecimen_id) {
 # This function takes a seg data.frame and a biospecimen identifier and 
 # returns a plot of the chromosome 19 segment mean for that biospecimen.
 # It fills missing seg.mean values with zeroes.
 # 
 # Args:
 #   cn_df: data.frame in SEG format
 #   biospecimen_id: A Kids First Biospecimen ID used to filter cn_df
 # 
 # Returns: essentially a barplot of the non-neutral segment means
  
  bsid_data <- cn_df %>%  
    # For visualization purposes fill the missing seg.mean values with zeroes
    tidyr::replace_na(list(seg.mean = 0)) %>%
    # Reformat the chromosome variable to drop the "chr"
    dplyr::mutate(chrom = factor(gsub("chr", "", chrom), 
                                 levels = c(1:22, "X", "Y"))) %>%
    # Only look at chr19 in the relevant sample
    filter(ID == biospecimen_id,
           chrom == 19) %>%
    # Make Del/Amp variable
    dplyr::mutate(Type = dplyr::case_when(
      seg.mean < 0 ~ "Del",
      seg.mean > 0 ~ "Amp",
      seg.mean == 0 ~ "Neutral"
    ))
  
  # Turn into a GRanges for easier mapping
  bsid_ranges <- GenomicRanges::GRanges(
    seqnames = bsid_data$chrom,
    ranges = IRanges::IRanges(
      start = bsid_data$loc.start,
      end = bsid_data$loc.end
    ),
    score = bsid_data$seg.mean,
    mcols = bsid_data$Type
  )
  
  # Map this on a plot
  bsid_plot <- 
    ggbio::autoplot(bsid_ranges,
                    ggplot2::aes(y = score, fill = mcols),
                    geom = "bar") +
    ggplot2::theme_bw() +
    ggplot2::ylim(c(-2, 2)) +
    colorblindr::scale_fill_OkabeIto(name = "Type") +
    ggplot2::labs(
      title = paste(biospecimen_id, "chr19"),
      y = "segment mean"
    )
  
  return(bsid_plot)
}
```

### Directories

```{r}
results_dir <- "results"
data_dir <- file.path("..", "..", "data")
```

### Read in Files

```{r message=FALSE}
# full clinical file
histologies_file <- file.path(data_dir, "pbta-histologies.tsv")
histologies_df <- read_tsv(histologies_file,
                           col_types = cols(
                             molecular_subtype = col_character()
                           ))

# copy number consensus file (analysis file)
# TODO: get this from data download
cn_consensus_seg <- file.path("..", "copy_number_consensus_call", "results",
                              "pbta-cnv-consensus.seg.gz")
# file that contains the biospecimen IDs that met criteria for subtyping
biospecimen_file <- file.path(results_dir, 
                              "biospecimen_ids_embryonal_subtyping.tsv")

# get biospecimen ids as a vector rather than data frame
biospecimen_ids <- read_tsv(biospecimen_file) %>%
  pull(Kids_First_Biospecimen_ID)

# we'll filter to only the relevant biospecimens for the copy number alteration
# data that is committed to the repository
cn_consensus <- read_tsv(cn_consensus_seg) %>%
  filter(ID %in% biospecimen_ids)
```

### Output file

```{r}
output_file <- file.path(results_dir, "cleaned_chr19_cn.tsv")
```

## Look at copy number alterations on chr 19

We're interested in focal amplification of C19MC (miRNA cluster on chr19) for the **ETMR, C19MC-altered** subtype.

```{r}
chr19_cn_df <- cn_consensus %>% 
  filter(chrom == "chr19",
         seg.mean > 0)

chr19_cn_df
```

These are not within the ranges from @sjspielman provided [here](https://github.com/AlexsLemonade/OpenPBTA-analysis/pull/401/files#diff-77e96177f89b99ad2b1f0fe51ec5c22eR110):

```
## From UCSC Genome Brower, these are positions corresponding to 19q13.41 (C19MC region)
c19mc_region <- "19q13.41"
c19mc_start  <- 51400001
c19mc_end    <- 53600000
```

But they do overlap with coordinates from other papers.
These are from Jo Lynne Rokita in [this comment](https://github.com/AlexsLemonade/OpenPBTA-analysis/pull/458#issuecomment-581050051).

From [Bentwich et al. _Nature Genetics._ 2005.](https://doi.org/10.1038/ng1590):

> Cluster on chromosome 19 located at positions 58,861,745–58,961,404 (HG17) and comprising 54 microRNA genes grouped into four families on the basis of hairpin sequence similarity.

| version | chr | start | end |
|---------|-------|----------|----------|
| hg17 | chr19 | 58861745 | 58961404 |
| hg19 | chr19 | 54169933 | 54269592 |
| hg38 | chr19 | 53666679 | 53766338 |

and from [Bortolin-Cavaillé et al. _NAR._ 2009.](https://dx.doi.org/10.1093%2Fnar%2Fgkp205):

> Schematic representation of the ∼100-kb long C19MC (HG18: 58 860 000–58 962 300) mapping at human chromosome 19q13.41

| version | chr | start | end |
|---------|-------|----------|----------|
| hg18 | chr19 | 58860000 | 58962300 |
| hg38 | chr19 | 53664934 | 53767234 |

### Visualization

For visualization purposes, we are filling in the missing `seg.mean` values with zeroes.

```{r}
plot_chr19(cn_df = cn_consensus,
           biospecimen_id = "BS_69VS8PS1")
```


```{r}
plot_chr19(cn_df = cn_consensus,
           biospecimen_id = "BS_K07KNTFY")
```

```{r}
plot_chr19(cn_df = cn_consensus,
           biospecimen_id = "BS_TE8QFF7T")
```

### Generate cleaned table for joining to fusion and expression data

The copy numbers in all of the above cases are high - we'll simplify the column name to `chr19_amplification`.

```{r}
# get a data.frame of the relevant WGS biospecimen_ids
wgs_ids <- intersect(histologies_df %>%
                       filter(experimental_strategy == "WGS") %>%
                       pull(Kids_First_Biospecimen_ID),
                     biospecimen_ids) %>%
  as.data.frame(stringsAsFactors = FALSE) %>%
  rename(Kids_First_Biospecimen_ID = ".")
          
chr19_cn_df <- chr19_cn_df %>%
  mutate(chr19_amplification = "Yes") %>%
  select(ID, chr19_amplification) %>%
  right_join(wgs_ids, by = c("ID" = "Kids_First_Biospecimen_ID")) %>%
  replace(is.na(.), "No")
```

Write to file.

```{r}
chr19_cn_df %>% 
  rename(Kids_First_Biospecimen_ID = ID) %>%
  write_tsv(output_file)
```

