Explore impact of Non Synonymous Filters

Summary of Findings:

This was an exploration of how TMB is affected by using nonsynonymous filters. It addresses part 1 of this OpenPBTA issue As far as the TMB comparisons go, it doesn’t matter if we use Variant_Classification filters or not. They are highly correlated no matter what and the same general TCGA to PBTA differences seem to persist.

Usage

If both run_caller_consensus_analysis-tcga.sh and run_caller_consensus_analysis-pbta.sh have been run, you can run this command. This will prepare the files needed for this notebook and run this notebook:

# bash run_explorations.sh

Table of Contents generated with DocToc

Read in the TMB files
- Read in PBTA data
- Read in TCGA data
Does the filter change a participant’s TMB?
- Plot PBTA data
- Plot TCGA data
Are some histologies affected more than others?
Does the filter affect the TCGA-PBTA comparison change?
Plot the ratio of the nonfilter/filter tmb
Plot the TMB plot with no filter data
- No filter TMB Plot
- With filter TMB Plot
Session Info

Setup

# Magrittr pipe
`%>%` <- dplyr::`%>%`

source(file.path("..", "..", "tmb-compare", "util", "cdf-plot-function.R"))

dir.create("plots", showWarnings = FALSE)

Read in the TMB files

Read in PBTA data

Import the tmb calculations that are not filtered by nonsynonymous classifications.

tmb_pbta_no_filter <- data.table::fread(file.path(
  "..",
  "results",
  "no_filter",
  "pbta-snv-mutation-tmb-coding.tsv"
)) %>%
  dplyr::filter(experimental_strategy != "Panel") %>%
  dplyr::mutate(filter = "no filter")

Repeat same steps for original tmb calculations (with filter).

tmb_pbta_with_filter <- data.table::fread(file.path(
  "..",
  "results",
  "consensus",
  "pbta-snv-mutation-tmb-coding.tsv"
)) %>%
  dplyr::filter(experimental_strategy != "Panel") %>%
  dplyr::mutate(filter = "filter")

Combine the unfilter and filtered data sets into one.

tmb_pbta <- dplyr::inner_join(tmb_pbta_no_filter,
  dplyr::select(tmb_pbta_with_filter, Tumor_Sample_Barcode, tmb),
  by = "Tumor_Sample_Barcode",
  suffix = c("_no_filter", "_filter")
)

Read in TCGA data

Repeat the same steps for the TCGA data.

tmb_tcga_no_filter <- data.table::fread(file.path(
  "..",
  "results",
  "no_filter",
  "tcga-snv-mutation-tmb-coding.tsv"
)) %>%
  dplyr::mutate(filter = "no filter")

tmb_tcga_with_filter <- data.table::fread(file.path(
  "..",
  "results",
  "consensus",
  "tcga-snv-mutation-tmb-coding.tsv"
)) %>%
  dplyr::mutate(filter = "filter")

Join these together.

tmb_tcga <- dplyr::inner_join(tmb_tcga_no_filter,
  dplyr::select(tmb_tcga_with_filter, Tumor_Sample_Barcode, tmb),
  by = "Tumor_Sample_Barcode",
  suffix = c("_no_filter", "_filter")
)

Does the filter change a participant’s TMB?

Plot PBTA data

Correlate first with Pearson’s.

cor.test(tmb_pbta$tmb_filter, tmb_pbta$tmb_no_filter)


    Pearson's product-moment correlation

data:  tmb_pbta$tmb_filter and tmb_pbta$tmb_no_filter
t = 2488.9, df = 934, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.9999143 0.9999337
sample estimates:
      cor 
0.9999246

pbta_cor_plot <- ggplot2::ggplot(
  tmb_pbta,
  ggplot2::aes(x = tmb_no_filter, y = tmb_filter, color = short_histology)
) +
  ggplot2::geom_point() +
  ggplot2::theme_classic() +
  ggplot2::theme(legend.position = "none")

pbta_cor_plot

This data is very skewed, let’s try a nonparametric correlation.

cor.test(tmb_pbta$tmb_filter, tmb_pbta$tmb_no_filter, method = "spearman")

Cannot compute exact p-value with ties


    Spearman's rank correlation rho

data:  tmb_pbta$tmb_filter and tmb_pbta$tmb_no_filter
S = 3031423, p-value < 2.2e-16
alternative hypothesis: true rho is not equal to 0
sample estimates:
      rho 
0.9778195

Still pretty good.

Plot TCGA data

cor.test(tmb_tcga$tmb_filter, tmb_tcga$tmb_no_filter)


    Pearson's product-moment correlation

data:  tmb_tcga$tmb_filter and tmb_tcga$tmb_no_filter
t = 4296.2, df = 316, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.9999893 0.9999931
sample estimates:
      cor 
0.9999914

tcga_cor_plot <- ggplot2::ggplot(
  tmb_tcga,
  ggplot2::aes(x = tmb_no_filter, y = tmb_filter, color = short_histology)
) +
  ggplot2::geom_point() +
  ggplot2::xlim(0, 5) +
  ggplot2::ylim(0, 5) +
  ggplot2::theme_classic() +
  ggplot2::theme(legend.position = "none")

tcga_cor_plot

Correlate with nonparametric correlation since we did that for PBTA.

cor.test(tmb_tcga$tmb_filter, tmb_tcga$tmb_no_filter, method = "spearman")

Cannot compute exact p-value with ties


    Spearman's rank correlation rho

data:  tmb_tcga$tmb_filter and tmb_tcga$tmb_no_filter
S = 60671, p-value < 2.2e-16
alternative hypothesis: true rho is not equal to 0
sample estimates:
      rho 
0.9886798

Are some histologies affected more than others?

Let’s make the same plots but by short_histology.

Plotting PBTA data, but setting the scales to “free” since the histologies have very different tmb ranges.

pbta_cor_plot + 
  ggplot2::facet_wrap(~short_histology, scales = "free")

Make the same plot for TCGA.

tcga_cor_plot + 
  ggplot2::facet_wrap(~short_histology)

Does the filter affect the TCGA-PBTA comparison change?

Put PBTA and TCGA data into one long format data frame.

all_data <- dplyr::bind_rows(list(tcga = tmb_tcga, pbta = tmb_pbta), .id = "dataset") %>%
  dplyr::select(tmb_filter, tmb_no_filter, dataset, Tumor_Sample_Barcode, short_histology) %>%
  tidyr::gather("filter", "tmb", -Tumor_Sample_Barcode, -dataset, -short_histology)

Plot the data overall.

ggplot2::ggplot(all_data, ggplot2::aes(x = dataset, y = tmb)) +
  ggforce::geom_sina() +
  ggplot2::theme_classic() +
  ggplot2::ylim(0, 10) +
  ggplot2::theme(legend.position = "none") +
  ggplot2::facet_wrap(~"filter")

Plot the ratio of the nonfilter/filter tmb

We’ll plot this by short_histology.

all_data %>% 
  tidyr::spread("filter","tmb") %>% 
  dplyr::mutate(ratio = tmb_filter/tmb_no_filter) %>% 
ggplot2::ggplot(ggplot2::aes(x = short_histology, y = ratio, color = short_histology)) + 
  ggplot2::geom_violin() + 
  ggplot2::theme_classic() + 
  ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1), 
                 legend.position = "none") + 
  ggplot2::xlab("") + 
  ggplot2::ylab("tmb_filter/tmb_no_filter")

Plot the TMB plot with no filter data

This code is directly copied from tmb-compare-tcga/tmb-compare-tcga.Rmd.

pbta_plot <- cdf_plot(
  df = tmb_pbta_no_filter,
  plot_title = "PBTA",
  num_col = "tmb",
  group_col = "short_histology",
  color = "#3BC8A2",
  n_group = 5,
  x_lim = c(-1.2, 1.2),
  y_lim = c(0, 400),
  x_lab = "",
  y_lab = "Coding Mutations per Mb", 
  breaks = c(0, 3, 10, 30, 100, 300)
) +
  ggplot2::theme(
    strip.text.x = ggplot2::element_text(size = 12), 
    plot.margin = grid::unit(c(0.5, 0, 0.6, 0.5), "cm")
  )

the condition has length > 1 and only the first element will be used

tcga_plot <- cdf_plot(
  df = tmb_tcga_no_filter,
  plot_title = "TCGA (Adult)",
  num_col = "tmb",
  group_col = "short_histology",
  color = "#630882",
  n_group = 5,
  x_lim = c(-1.2, 1.2),
  y_lim = c(0, 400),
  x_lab = "",
  y_lab = "Coding Mutations per Mb",
  breaks = c()
) +
  ggplot2::theme(
    axis.title.y = ggplot2::element_blank(),
    axis.text.y = ggplot2::element_blank(),
    axis.ticks.y = ggplot2::element_blank(),
    strip.text.x = ggplot2::element_text(size = 9), 
    plot.margin = grid::unit(c(0.5, 1, 0.1, 0), "cm")
  )

the condition has length > 1 and only the first element will be used

# Put the plots together
tmb_plot <- cowplot::plot_grid(pbta_plot, tcga_plot,
  align = "v",
  axis = "left",
  rel_widths = c(2.5, 1),
  label_size = 12
)

Removed 3 rows containing missing values (geom_point).Removed 1 rows containing missing values (geom_point).

# Save the plot to a png
cowplot::save_plot(file.path("plots", "no_filter_tmb-cdf-pbta-tcga.png"),
  plot = tmb_plot, base_width = 35, base_height = 20, unit = "cm"
)

Print from png

No filter TMB Plot

With filter TMB Plot

## Session Info

sessionInfo()

R version 3.6.0 (2019-04-26)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Debian GNU/Linux 9 (stretch)

Matrix products: default
BLAS/LAPACK: /usr/lib/libopenblasp-r0.2.19.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8       
 [4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=C             
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
[10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.1         pillar_1.4.2       compiler_3.6.0     RColorBrewer_1.1-2 plyr_1.8.4        
 [6] R.methodsS3_1.7.1  R.utils_2.9.0      base64enc_0.1-3    tools_3.6.0        digest_0.6.20     
[11] jsonlite_1.6       evaluate_0.14      tibble_2.1.3       gtable_0.3.0       pkgconfig_2.0.2   
[16] rlang_0.4.0        GGally_1.4.0       rstudioapi_0.10    yaml_2.2.0         xfun_0.8          
[21] colorblindr_0.1.0  dplyr_0.8.3        stringr_1.4.0      knitr_1.23         cowplot_0.9.4     
[26] grid_3.6.0         tidyselect_0.2.5   reshape_0.8.8      glue_1.3.1         data.table_1.12.2 
[31] R6_2.4.0           rmarkdown_1.13     polyclip_1.10-0    farver_1.1.0       tweenr_1.0.1      
[36] tidyr_0.8.3        ggplot2_3.2.0      purrr_0.3.2        reshape2_1.4.3     magrittr_1.5      
[41] MASS_7.3-51.4      scales_1.0.0       htmltools_0.3.6    assertthat_0.2.1   ggforce_0.2.2     
[46] colorspace_1.4-1   labeling_0.3       stringi_1.4.3      lazyeval_0.2.2     munsell_0.5.0     
[51] crayon_1.3.4       R.oo_1.22.0

---
title: "Explore impact of Non Synonymous Filters"
output: 
  html_notebook:
    toc: TRUE
    toc_float: TRUE
author: C. Savonen for ALSF CCDL
date: 2020
---

### Summary of Findings:

This was an exploration of how TMB is affected by using nonsynonymous filters. 
It addresses part 1 of [this OpenPBTA issue](https://github.com/AlexsLemonade/OpenPBTA-analysis/issues/729)
As far as the TMB comparisons go, it doesn't matter if we use `Variant_Classification` filters or not. 
They are highly correlated no matter what and the same general TCGA to PBTA differences seem to persist. 

### Usage

If both `run_caller_consensus_analysis-tcga.sh` and `run_caller_consensus_analysis-pbta.sh` have been run, you can run this command. 
This will prepare the files needed for this notebook and run this notebook:

```
# bash run_explorations.sh
```

<!-- START doctoc generated TOC please keep comment here to allow auto update -->
<!-- DON'T EDIT THIS SECTION, INSTEAD RE-RUN doctoc TO UPDATE -->
**Table of Contents**  *generated with [DocToc](https://github.com/thlorenz/doctoc)*

- [Read in the TMB files](#read-in-the-tmb-files)
  - [Read in PBTA data](#read-in-pbta-data)
  - [Read in TCGA data](#read-in-tcga-data)
- [Does the filter change a participant's TMB?](#does-the-filter-change-a-participants-tmb)
  - [Plot PBTA data](#plot-pbta-data)
  - [Plot TCGA data](#plot-tcga-data)
- [Are some histologies affected more than others?](#are-some-histologies-affected-more-than-others)
- [Does the filter affect the TCGA-PBTA comparison change?](#does-the-filter-affect-the-tcga-pbta-comparison-change)
- [Plot the ratio of the nonfilter/filter tmb](#plot-the-ratio-of-the-nonfilterfilter-tmb)
- [Plot the TMB plot with no filter data](#plot-the-tmb-plot-with-no-filter-data)
  - [No filter TMB Plot](#no-filter-tmb-plot)
  - [With filter TMB Plot](#with-filter-tmb-plot)
- [Session Info](#session-info)

<!-- END doctoc generated TOC please keep comment here to allow auto update -->

## Setup

```{r}
# Magrittr pipe
`%>%` <- dplyr::`%>%`
```

```{r}
source(file.path("..", "..", "tmb-compare", "util", "cdf-plot-function.R"))
```

```{r}
dir.create("plots", showWarnings = FALSE)
```

## Read in the TMB files

### Read in PBTA data

Import the tmb calculations that are not filtered by nonsynonymous classifications.

```{r}
tmb_pbta_no_filter <- data.table::fread(file.path(
  "..",
  "results",
  "no_filter",
  "pbta-snv-mutation-tmb-coding.tsv"
)) %>%
  dplyr::filter(experimental_strategy != "Panel") %>%
  dplyr::mutate(filter = "no filter")
```

Repeat same steps for original tmb calculations (with filter).

```{r}
tmb_pbta_with_filter <- data.table::fread(file.path(
  "..",
  "results",
  "consensus",
  "pbta-snv-mutation-tmb-coding.tsv"
)) %>%
  dplyr::filter(experimental_strategy != "Panel") %>%
  dplyr::mutate(filter = "filter")
```

Combine the unfilter and filtered data sets into one. 

```{r}
tmb_pbta <- dplyr::inner_join(tmb_pbta_no_filter,
  dplyr::select(tmb_pbta_with_filter, Tumor_Sample_Barcode, tmb),
  by = "Tumor_Sample_Barcode",
  suffix = c("_no_filter", "_filter")
)
```

### Read in TCGA data

Repeat the same steps for the TCGA data. 

```{r}
tmb_tcga_no_filter <- data.table::fread(file.path(
  "..",
  "results",
  "no_filter",
  "tcga-snv-mutation-tmb-coding.tsv"
)) %>%
  dplyr::mutate(filter = "no filter")
```

```{r}
tmb_tcga_with_filter <- data.table::fread(file.path(
  "..",
  "results",
  "consensus",
  "tcga-snv-mutation-tmb-coding.tsv"
)) %>%
  dplyr::mutate(filter = "filter")
```

Join these together. 

```{r}
tmb_tcga <- dplyr::inner_join(tmb_tcga_no_filter,
  dplyr::select(tmb_tcga_with_filter, Tumor_Sample_Barcode, tmb),
  by = "Tumor_Sample_Barcode",
  suffix = c("_no_filter", "_filter")
)
```

## Does the filter change a participant's TMB?

### Plot PBTA data

Correlate first with Pearson's. 

```{r}
cor.test(tmb_pbta$tmb_filter, tmb_pbta$tmb_no_filter)
```

```{r}
pbta_cor_plot <- ggplot2::ggplot(
  tmb_pbta,
  ggplot2::aes(x = tmb_no_filter, y = tmb_filter, color = short_histology)
) +
  ggplot2::geom_point() +
  ggplot2::theme_classic() +
  ggplot2::theme(legend.position = "none")

pbta_cor_plot
```

This data is very skewed, let's try a nonparametric correlation. 

```{r}
cor.test(tmb_pbta$tmb_filter, tmb_pbta$tmb_no_filter, method = "spearman")
```

Still pretty good. 

### Plot TCGA data

```{r}
cor.test(tmb_tcga$tmb_filter, tmb_tcga$tmb_no_filter)
```

```{r}
tcga_cor_plot <- ggplot2::ggplot(
  tmb_tcga,
  ggplot2::aes(x = tmb_no_filter, y = tmb_filter, color = short_histology)
) +
  ggplot2::geom_point() +
  ggplot2::xlim(0, 5) +
  ggplot2::ylim(0, 5) +
  ggplot2::theme_classic() +
  ggplot2::theme(legend.position = "none")

tcga_cor_plot
```

Correlate with nonparametric correlation since we did that for PBTA. 

```{r}
cor.test(tmb_tcga$tmb_filter, tmb_tcga$tmb_no_filter, method = "spearman")
```

## Are some histologies affected more than others? 

Let's make the same plots but by  `short_histology`. 

Plotting PBTA data, but setting the scales to "free" since the histologies have very different tmb ranges. 

```{r}
pbta_cor_plot + 
  ggplot2::facet_wrap(~short_histology, scales = "free")
```

Make the same plot for TCGA. 

```{r}
tcga_cor_plot + 
  ggplot2::facet_wrap(~short_histology)
```

## Does the filter affect the TCGA-PBTA comparison change?

Put PBTA and TCGA data into one long format data frame. 

```{r}
all_data <- dplyr::bind_rows(list(tcga = tmb_tcga, pbta = tmb_pbta), .id = "dataset") %>%
  dplyr::select(tmb_filter, tmb_no_filter, dataset, Tumor_Sample_Barcode, short_histology) %>%
  tidyr::gather("filter", "tmb", -Tumor_Sample_Barcode, -dataset, -short_histology)
```

Plot the data overall. 

```{r}
ggplot2::ggplot(all_data, ggplot2::aes(x = dataset, y = tmb)) +
  ggforce::geom_sina() +
  ggplot2::theme_classic() +
  ggplot2::ylim(0, 10) +
  ggplot2::theme(legend.position = "none") +
  ggplot2::facet_wrap(~"filter") 
```

## Plot the ratio of the nonfilter/filter tmb

We'll plot this by `short_histology`. 

```{r}
all_data %>% 
  tidyr::spread("filter","tmb") %>% 
  dplyr::mutate(ratio = tmb_filter/tmb_no_filter) %>% 
ggplot2::ggplot(ggplot2::aes(x = short_histology, y = ratio, color = short_histology)) + 
  ggplot2::geom_violin() + 
  ggplot2::theme_classic() + 
  ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1), 
                 legend.position = "none") + 
  ggplot2::xlab("") + 
  ggplot2::ylab("tmb_filter/tmb_no_filter")
```


## Plot the TMB plot with no filter data

This code is directly copied from `tmb-compare-tcga/tmb-compare-tcga.Rmd`.

```{r}
pbta_plot <- cdf_plot(
  df = tmb_pbta_no_filter,
  plot_title = "PBTA",
  num_col = "tmb",
  group_col = "short_histology",
  color = "#3BC8A2",
  n_group = 5,
  x_lim = c(-1.2, 1.2),
  y_lim = c(0, 400),
  x_lab = "",
  y_lab = "Coding Mutations per Mb", 
  breaks = c(0, 3, 10, 30, 100, 300)
) +
  ggplot2::theme(
    strip.text.x = ggplot2::element_text(size = 12), 
    plot.margin = grid::unit(c(0.5, 0, 0.6, 0.5), "cm")
  )
```

```{r}
tcga_plot <- cdf_plot(
  df = tmb_tcga_no_filter,
  plot_title = "TCGA (Adult)",
  num_col = "tmb",
  group_col = "short_histology",
  color = "#630882",
  n_group = 5,
  x_lim = c(-1.2, 1.2),
  y_lim = c(0, 400),
  x_lab = "",
  y_lab = "Coding Mutations per Mb",
  breaks = c()
) +
  ggplot2::theme(
    axis.title.y = ggplot2::element_blank(),
    axis.text.y = ggplot2::element_blank(),
    axis.ticks.y = ggplot2::element_blank(),
    strip.text.x = ggplot2::element_text(size = 9), 
    plot.margin = grid::unit(c(0.5, 1, 0.1, 0), "cm")
  )
```

```{r}
# Put the plots together
tmb_plot <- cowplot::plot_grid(pbta_plot, tcga_plot,
  align = "v",
  axis = "left",
  rel_widths = c(2.5, 1),
  label_size = 12
)
```

```{r}
# Save the plot to a png
cowplot::save_plot(file.path("plots", "no_filter_tmb-cdf-pbta-tcga.png"),
  plot = tmb_plot, base_width = 35, base_height = 20, unit = "cm"
)
```

Print from png

### No filter TMB Plot
![](./plots/no_filter_tmb-cdf-pbta-tcga.png)

### With filter TMB Plot
![](../../tmb-compare/plots/tmb-cdf-pbta-tcga.png)
## Session Info

```{r}
sessionInfo()
```
