library(VariantAnnotation)
library(openxlsx)
library(tidyverse)
library(RColorBrewer)
library(ggrepel)
library(cowplot)
wd <- here::here()

extractFUNCOTATION <-
  function(vcf,
           fields = c(
             "Gencode_43_hugoSymbol",
             "Gencode_43_variantClassification",
             "Gencode_43_variantType"
           )) {
    annotation_colnames <-
      info(vcf@metadata$header)["FUNCOTATION", "Description"] %>%
      stringr::str_remove("Functional annotation from the Funcotator tool.  Funcotation fields are: ") %>%
      stringr::str_split(pattern = "\\|") %>%
      unlist()
    
    as.data.frame(info(vcf)[["FUNCOTATION"]]) %>%
      dplyr::select("FUNCOTATION" = value) %>%
      dplyr::mutate(
        FUNCOTATION = word(FUNCOTATION, 1, sep = "\\]"),
        FUNCOTATION = str_remove_all(FUNCOTATION, "[\\[\\]]")
      ) %>%
      tidyr::separate(FUNCOTATION, into = annotation_colnames,
                      sep = "\\|", fill = "right") %>%
      dplyr::select(all_of(fields))
  }

Load sample

vcf.list <- list()
vcf.filenames <- c("GATK" = "LL.annotated.vcf.gz", "PB" = "pb-LL.vcf")
for(i in seq_along(vcf.filenames)){
  vcf.dir <- file.path(wd, "VCF")
  vcf.filepath <- file.path(vcf.dir, vcf.filenames[[i]])
  
  vcf.list[[names(vcf.filenames)[[i]]]] <- readVcf(vcf.filepath, "hg38")
}
Warning: duplicate keys in header will be forced to unique rownames
  
vcf.list <- vcf.list[1]

Subsetting only mutations that passed the initial upstream quality filter and were not unmapped.

vcf.list.qc <-
  lapply(vcf.list, function(x)
    x[rowRanges(x)$FILTER == "PASS"])
vcf.list.qc <-
  lapply(vcf.list.qc, function(x)
    x[!(grepl(names(x), pattern = "chrUn_")),])

lapply(vcf.list.qc, dim)
$GATK
[1] 2605    2
vcf.list.anno <- lapply(vcf.list.qc, function(x)
  extractFUNCOTATION(
    x,
    fields = c(
      "Gencode_43_hugoSymbol",
      "Gencode_43_variantClassification",
      "Gencode_43_variantType"
    )
  ))

Construct a data frame with useful values for easier plotting:

snv.list <- list()
for(sample.name in names(vcf.list.qc)) {
  vcf <- vcf.list.qc[[sample.name]]
  df.anno <- vcf.list.anno[[sample.name]]
  snv.list[[sample.name]] <- as.data.frame(geno(vcf)[["AF"]]) %>%
    setNames(., c("AF_CTRL", "AF_PDLC")) %>%
    mutate_all( ~ unlist(.)) %>%
    cbind(., df.anno) %>%
    mutate(
      DP = info(vcf)[["DP"]],
      GERMQ = info(vcf)[["GERMQ"]],
      NALOD = info(vcf)[["NALOD"]] %>% unlist(),
      NLOD = info(vcf)[["NLOD"]] %>% unlist(),
      TLOD = info(vcf)[["TLOD"]] %>% unlist()
    ) %>%
    rownames_to_column("Variant") %>%
    mutate(Position_hg38 = word(Variant, 1, sep = "_"),
           .before = "Variant") %>%
    mutate(Variant = word(Variant, 2, sep = "_"))
}
snv.list
$GATK
NA

Visualizations

snv.df <- bind_rows(snv.list, .id = "sample")

ggplot(snv.df,
       aes(y = AF_PDLC,
           x = AF_CTRL)) +
  geom_jitter(aes(color = Gencode_43_variantClassification), size = 2) +
  ylim(0, 1) + xlim(0, 1) +
  theme_bw()

snv.df %>% 
  filter(Gencode_43_hugoSymbol == "TP53")

Variant origin filters

DP is an approximate read depth

GERMQ The phred-scaled posterior probability that the alternate allele(s) are NOT germline variants

NALOD (Normal Allele Log Odds) is negative log 10 odds of artifact in normal with same allele fraction as tumor. 5% chance is NALOD ~ 1.3

NLOD (Normal Log Odds) is log odds that the variant is not present in the normal sample

TLOD (Tumor Log Odds) is a log odds ratio that represents the likelihood that the observed tumor allele depth (coverage) is due to a real mutation.

filters = list(
  "DP" = 30,
  "GERMQ" = 90,
  "NALOD" = -log10(0.05),
  "NLOD" = 6,
  "TLOD" = 30,
  "AF_CTRL" = 0.025,
  "AF_TUMOR" = 0.1
)


snv.df %>% 
  ggplot(.,
       aes(y = AF_CTRL, x = DP)) +
  geom_point(aes(color = GERMQ),
             shape = 1, alpha = 0.75,
             size = 1.5, stroke = 1
             ) +
  scale_colour_steps2(
    high = "green",
    midpoint = 50,
    mid = "blue",
    low = "blue",
    breaks = filters[["GERMQ"]]
  ) +
  scale_x_continuous(trans = "log10") +
  scale_y_continuous(labels = scales::percent) +
  geom_hline(yintercept = filters[["AF_CTRL"]], linetype = "dashed") +
  geom_vline(xintercept = filters[["DP"]], linetype = "dashed") +
  labs(y = "CTRL AF, %")


snv.df %>% 
  dplyr::filter(
    GERMQ >= filters[["GERMQ"]],
    DP >= filters[["DP"]]
    ) %>% 
  ggplot(.,
       aes(x = TLOD,
           y = NLOD)) +
  geom_density_2d(color = "black") +
  geom_point(
    aes(color = NALOD,
        shape = GERMQ
        ), size = 2, stroke = 1) +
  scale_y_continuous(trans = "log10") +
  scale_x_continuous(trans = "log10") +
  scale_colour_steps2(
    high = "green",
    midpoint = filters[["NALOD"]] / 2,
    mid = "blue",
    low = "blue",
    breaks = filters[["NALOD"]]
  ) +
  scale_shape_binned(breaks = c(filters[["GERMQ"]]), solid = FALSE) +
  geom_hline(yintercept = filters[["NLOD"]], linetype = 2) +
  geom_vline(xintercept = filters[["TLOD"]], linetype = 2)

pull(snv.df, Gencode_43_variantClassification) %>% unique() %>%
  length() -> n.SNV.types.unfiltered

c(
  brewer.pal(n = 12, name = "Paired"),
  brewer.pal(n = n.SNV.types.unfiltered - 12, name = "Dark2")
) -> pal.colors

filter(snv.df, sample != "P069_PT_REL_BM") %>% 
  dplyr::filter(
    GERMQ >= filters[["GERMQ"]],
    DP >= filters[["DP"]]
    ) %>% 
  ggplot(.,
         aes(x = TLOD,
             y = NLOD)) +
  geom_hex(aes(fill = Gencode_43_variantClassification), alpha = 0.75) +
  scale_y_continuous(trans = "log10") +
  scale_x_continuous(trans = "log10") +
  geom_hline(yintercept = filters[["NLOD"]], linetype = 2) +
  geom_vline(xintercept = filters[["TLOD"]], linetype = 2) +
  scale_fill_manual(values = pal.colors)

Subsetting the VCF object

Constructing a filter vector for vcf_qc object:

colnames(snv.df)
 [1] "sample"                           "Position_hg38"                    "Variant"                          "AF_CTRL"                         
 [5] "AF_PDLC"                          "Gencode_43_hugoSymbol"            "Gencode_43_variantClassification" "Gencode_43_variantType"          
 [9] "DP"                               "GERMQ"                            "NALOD"                            "NLOD"                            
[13] "TLOD"                            
snv.filter.df <- snv.df %>%
  mutate(
    second_filter = case_when(
      DP <= filters[["DP"]] ~ "low_DP",
      GERMQ <= filters[["GERMQ"]] ~ "low_GERMQ",
      AF_CTRL > filters[["AF_CTRL"]] ~ "high_CTRL_AF",
      AF_PDLC < filters[["AF_TUMOR"]] ~ "low_Tumor_AF",
      NALOD < filters[["NALOD"]] ~   "low_NALOD",
      NLOD < filters[["NLOD"]] ~     "low_NLOD",
      TLOD < filters[["TLOD"]] ~     "low_TLOD",
      TRUE ~ "PASS"
    )
  )

allowed.positions <- snv.filter.df %>%
  filter(sample != "P069_PT_REL_BM" &
           second_filter == "PASS") %>% pull(Position_hg38)

snv.filter.df <- snv.filter.df %>%
  mutate(
    second_filter = case_when(
      sample == "P069_PT_REL_BM" &
        Position_hg38 %in% allowed.positions ~ "PASS",
      TRUE ~ second_filter
    )
  )

snv.filter.df %>% 
  filter(second_filter == "PASS")
snv.filter.df %>% 
  filter(Gencode_43_hugoSymbol == "TP53")
c(
  "low_DP" = "red",
  "low_GERMQ" = "orange",
  "low_NALOD" = "purple",
  "low_NLOD" = "violet",
  "low_TLOD" = "firebrick",
  "high_CTRL_AF" = "coral2",
  "low_Tumor_AF" = "blueviolet",
  "PASS" = "green"
) -> pal.colors

snv.filter.df %>%
  pivot_longer(cols = contains("AF"), values_to = "AF") %>%
  ggplot(aes(x = name, y = AF)) +
  ggbeeswarm::geom_quasirandom(aes(color = second_filter)) +
  geom_hline(yintercept = filters[["AF_CTRL"]], linetype = "dashed") +
  theme_bw() + labs(x = NULL) +
  scale_color_manual(values = pal.colors) +
  scale_y_continuous(labels = scales::percent)

snv.filter.df %>%
  filter(second_filter == "PASS") %>% 
  pivot_longer(cols = contains("AF"), values_to = "AF") %>%
  ggplot(aes(x = name, y = AF)) +
  ggbeeswarm::geom_quasirandom(aes(color = second_filter)) +
  geom_hline(yintercept = filters[["AF_CTRL"]], linetype = "dashed") +
  theme_bw() + labs(x = NULL) +
  scale_color_manual(values = pal.colors) +
  scale_y_continuous(labels = scales::percent)

Same plot but without the SNV which did not pass the filters.

Data frame of all filtered SNVs:

snv.filter.df.wide <- snv.filter.df %>%
  filter(second_filter == "PASS") %>% 
  dplyr::select(sample, Position_hg38, Variant, AF_PDLC,
         Gencode_43_hugoSymbol, Gencode_43_variantClassification) %>% 
  pivot_wider(names_from = "sample", values_from = "AF_PDLC") %>% 
  mutate(across(everything(), ~replace_na(., 0)))
snv.filter.df.wide

Data frame of missense filtered SNVs:

snv.filter.df %>%
  filter(second_filter == "PASS") %>% 
  filter(Gencode_43_variantClassification == "MISSENSE") %>% 
  dplyr::select(sample, Position_hg38, Variant, AF_PDLC,
         Gencode_43_hugoSymbol) %>% 
  pivot_wider(names_from = "sample", values_from = "AF_PDLC")
dot_position = position_jitter(height = 0,
                               width = 0.25,
                               seed = 413)

snv.filter.df %>%
  filter(second_filter == "PASS") %>% 
  
  ggplot() +
  lemon::geom_pointline(
    aes(
      x = sample,
      y = AF_PDLC,
      group = Position_hg38,
      fill = Gencode_43_variantClassification
    ),
    linecolor = "gray30",
    linetype = 3,
    position = dot_position,
    shape = 21,
    size = 2
  ) +
  geom_point(
    aes(
      x = sample,
      y = AF_PDLC,
      group = Position_hg38,
      fill = Gencode_43_variantClassification
    ),
    position = dot_position,
    shape = 21,
    size = 2
  ) +
  geom_hline(yintercept = 0.05, linetype = "dashed") +
  scale_y_continuous(labels = scales::percent, limits = c(0, 1)) +
  theme_bw() +
  labs(x = NULL, y = "Allelic frequency",
       fill = "SNV type")

dot_position = position_jitter(height = 0,
                               width = 0.25,
                               seed = 413)

snv.filter.df %>%
  filter(second_filter == "PASS") %>%
  filter(Gencode_43_variantClassification == "MISSENSE") %>%
  ggplot() +
  lemon::geom_pointline(
    aes(x = sample,
        y = AF_PDLC,
        group = Position_hg38),
    linecolor = "gray30",
    linetype = 3,
    position = dot_position,
    shape = 21,
    size = 2,
    fill = "gray90"
  ) +
  
  geom_hline(yintercept = 0.05, linetype = "dashed") +
  scale_y_continuous(labels = scales::percent, limits = c(0, 1)) +
  theme_bw() +
  labs(x = NULL, y = "Allelic frequency",
       fill = "SNV type")

TODO: add the second sample after fixing the filters.

Save the variant table

output_file_xlsx <- paste0("FilteredSNV.xlsx")
output_path_xlsx <- file.path(output_dir, output_file_xlsx)

list(
  "Total SNV" = snv_filter.df,
  "Filtered SNV" = snv_filter.df %>% 
    filter(second_filter == "PASS") %>% 
    dplyr::select(-second_filter)
) %>% 
  write.xlsx(., output_path_xlsx, overwrite = T)
sessionInfo()

R version 4.3.1 (2023-06-16)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS

Matrix products: default
BLAS/LAPACK: /home/bench-user/.apps/conda/lib/libopenblasp-r0.3.25.so;  LAPACK version 3.11.0

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

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ggrepel_0.9.4               VariantAnnotation_1.48.1    Rsamtools_2.18.0            Biostrings_2.70.1           XVector_0.42.0             
 [6] SummarizedExperiment_1.32.0 Biobase_2.62.0              GenomicRanges_1.54.1        GenomeInfoDb_1.38.1         IRanges_2.36.0             
[11] S4Vectors_0.40.2            MatrixGenerics_1.14.0       matrixStats_1.2.0           BiocGenerics_0.48.1         cowplot_1.1.2              
[16] RColorBrewer_1.1-3          openxlsx_4.2.5.2            lubridate_1.9.3             forcats_1.0.0               stringr_1.5.1              
[21] dplyr_1.1.4                 purrr_1.0.2                 readr_2.1.4                 tidyr_1.3.0                 tibble_3.2.1               
[26] ggplot2_3.4.4               tidyverse_2.0.0            

loaded via a namespace (and not attached):
 [1] DBI_1.2.0                bitops_1.0-7             gridExtra_2.3            biomaRt_2.58.0           rlang_1.1.2              magrittr_2.0.3          
 [7] compiler_4.3.1           RSQLite_2.3.4            GenomicFeatures_1.54.1   png_0.1-8                vctrs_0.6.5              pkgconfig_2.0.3         
[13] crayon_1.5.2             fastmap_1.1.1            dbplyr_2.4.0             lemon_0.4.7              labeling_0.4.3           utf8_1.2.4              
[19] rmarkdown_2.25           tzdb_0.4.0               ggbeeswarm_0.7.2         bit_4.0.5                xfun_0.41                zlibbioc_1.48.0         
[25] cachem_1.0.8             progress_1.2.3           blob_1.2.4               DelayedArray_0.28.0      BiocParallel_1.36.0      parallel_4.3.1          
[31] prettyunits_1.2.0        R6_2.5.1                 stringi_1.8.3            rtracklayer_1.62.0       Rcpp_1.0.11              knitr_1.45              
[37] Matrix_1.6-4             timechange_0.2.0         tidyselect_1.2.0         rstudioapi_0.15.0        abind_1.4-5              yaml_2.3.8              
[43] codetools_0.2-19         curl_5.1.0               lattice_0.22-5           plyr_1.8.9               withr_2.5.2              KEGGREST_1.42.0         
[49] evaluate_0.23            isoband_0.2.7            BiocFileCache_2.10.1     xml2_1.3.6               zip_2.3.0                filelock_1.0.3          
[55] pillar_1.9.0             BiocManager_1.30.22      generics_0.1.3           rprojroot_2.0.4          RCurl_1.98-1.13          hms_1.1.3               
[61] munsell_0.5.0            scales_1.3.0             glue_1.6.2               tools_4.3.1              hexbin_1.28.3            BiocIO_1.12.0           
[67] BSgenome_1.70.1          GenomicAlignments_1.38.0 XML_3.99-0.16            grid_4.3.1               AnnotationDbi_1.64.1     colorspace_2.1-0        
[73] GenomeInfoDbData_1.2.11  beeswarm_0.4.0           restfulr_0.0.15          vipor_0.4.7              cli_3.6.2                rappdirs_0.3.3          
[79] fansi_1.0.6              S4Arrays_1.2.0           gtable_0.3.4             digest_0.6.33            SparseArray_1.2.2        farver_2.1.1            
[85] rjson_0.2.21             memoise_2.0.1            htmltools_0.5.7          lifecycle_1.0.4          httr_1.4.7               here_1.0.1              
[91] MASS_7.3-60              bit64_4.0.5
---
title: "QC and filtering of annotated VCF files"
output:
  html_notebook:
    toc: true
    toc_float: true
    toc_depth: 3
    code_folding: show
    theme: united
---

```{r setup}
library(VariantAnnotation)
library(openxlsx)
library(tidyverse)
library(RColorBrewer)
library(ggrepel)
library(cowplot)
wd <- here::here()

extractFUNCOTATION <-
  function(vcf,
           fields = c(
             "Gencode_43_hugoSymbol",
             "Gencode_43_variantClassification",
             "Gencode_43_variantType"
           )) {
    annotation_colnames <-
      info(vcf@metadata$header)["FUNCOTATION", "Description"] %>%
      stringr::str_remove("Functional annotation from the Funcotator tool.  Funcotation fields are: ") %>%
      stringr::str_split(pattern = "\\|") %>%
      unlist()
    
    as.data.frame(info(vcf)[["FUNCOTATION"]]) %>%
      dplyr::select("FUNCOTATION" = value) %>%
      dplyr::mutate(
        FUNCOTATION = word(FUNCOTATION, 1, sep = "\\]"),
        FUNCOTATION = str_remove_all(FUNCOTATION, "[\\[\\]]")
      ) %>%
      tidyr::separate(FUNCOTATION, into = annotation_colnames,
                      sep = "\\|", fill = "right") %>%
      dplyr::select(all_of(fields))
  }
```

# Load sample

```{r}
vcf.list <- list()
vcf.filenames <- c("GATK" = "LL.annotated.vcf.gz", "PB" = "pb-LL.vcf")
for(i in seq_along(vcf.filenames)){
  vcf.dir <- file.path(wd, "VCF")
  vcf.filepath <- file.path(vcf.dir, vcf.filenames[[i]])
  
  vcf.list[[names(vcf.filenames)[[i]]]] <- readVcf(vcf.filepath, "hg38")
}
  
vcf.list <- vcf.list[1]
```

Subsetting only mutations that passed the initial upstream quality filter and were not unmapped.

```{r}
vcf.list.qc <-
  lapply(vcf.list, function(x)
    x[rowRanges(x)$FILTER == "PASS"])
vcf.list.qc <-
  lapply(vcf.list.qc, function(x)
    x[!(grepl(names(x), pattern = "chrUn_")),])

lapply(vcf.list.qc, dim)
```

```{r}
vcf.list.anno <- lapply(vcf.list.qc, function(x)
  extractFUNCOTATION(
    x,
    fields = c(
      "Gencode_43_hugoSymbol",
      "Gencode_43_variantClassification",
      "Gencode_43_variantType"
    )
  ))
```

Construct a data frame with useful values for easier plotting:

```{r, fig.width=8, fig.height=5}
snv.list <- list()
for(sample.name in names(vcf.list.qc)) {
  vcf <- vcf.list.qc[[sample.name]]
  df.anno <- vcf.list.anno[[sample.name]]
  snv.list[[sample.name]] <- as.data.frame(geno(vcf)[["AF"]]) %>%
    setNames(., c("AF_CTRL", "AF_PDLC")) %>%
    mutate_all( ~ unlist(.)) %>%
    cbind(., df.anno) %>%
    mutate(
      DP = info(vcf)[["DP"]],
      GERMQ = info(vcf)[["GERMQ"]],
      NALOD = info(vcf)[["NALOD"]] %>% unlist(),
      NLOD = info(vcf)[["NLOD"]] %>% unlist(),
      TLOD = info(vcf)[["TLOD"]] %>% unlist()
    ) %>%
    rownames_to_column("Variant") %>%
    mutate(Position_hg38 = word(Variant, 1, sep = "_"),
           .before = "Variant") %>%
    mutate(Variant = word(Variant, 2, sep = "_"))
}
snv.list
```

# Visualizations

```{r, fig.width=8, fig.height=6}
snv.df <- bind_rows(snv.list, .id = "sample")

ggplot(snv.df,
       aes(y = AF_PDLC,
           x = AF_CTRL)) +
  geom_jitter(aes(color = Gencode_43_variantClassification), size = 2) +
  ylim(0, 1) + xlim(0, 1) +
  theme_bw()
```

```{r}
snv.df %>% 
  filter(Gencode_43_hugoSymbol == "TP53")
```


## Variant origin filters

DP is an approximate read depth

GERMQ The phred-scaled posterior probability that the alternate allele(s) are NOT germline variants

NALOD (Normal Allele Log Odds) is negative log 10 odds of artifact in normal with same allele fraction as tumor.
5% chance is NALOD ~ 1.3

NLOD (Normal Log Odds) is log odds that the variant is not present in the normal sample

TLOD (Tumor Log Odds) is a log odds ratio that represents the likelihood that the observed tumor allele depth (coverage) is due to a real mutation.

```{r, fig.width=6, fig.height=5}
filters = list(
  "DP" = 30,
  "GERMQ" = 90,
  "NALOD" = -log10(0.05),
  "NLOD" = 6,
  "TLOD" = 30,
  "AF_CTRL" = 0.025,
  "AF_TUMOR" = 0.1
)


snv.df %>% 
  ggplot(.,
       aes(y = AF_CTRL, x = DP)) +
  geom_point(aes(color = GERMQ),
             shape = 1, alpha = 0.75,
             size = 1.5, stroke = 1
             ) +
  scale_colour_steps2(
    high = "green",
    midpoint = 50,
    mid = "blue",
    low = "blue",
    breaks = filters[["GERMQ"]]
  ) +
  scale_x_continuous(trans = "log10") +
  scale_y_continuous(labels = scales::percent) +
  geom_hline(yintercept = filters[["AF_CTRL"]], linetype = "dashed") +
  geom_vline(xintercept = filters[["DP"]], linetype = "dashed") +
  labs(y = "CTRL AF, %")

snv.df %>% 
  dplyr::filter(
    GERMQ >= filters[["GERMQ"]],
    DP >= filters[["DP"]]
    ) %>% 
  ggplot(.,
       aes(x = TLOD,
           y = NLOD)) +
  geom_density_2d(color = "black") +
  geom_point(
    aes(color = NALOD,
        shape = GERMQ
        ), size = 2, stroke = 1) +
  scale_y_continuous(trans = "log10") +
  scale_x_continuous(trans = "log10") +
  scale_colour_steps2(
    high = "green",
    midpoint = filters[["NALOD"]] / 2,
    mid = "blue",
    low = "blue",
    breaks = filters[["NALOD"]]
  ) +
  scale_shape_binned(breaks = c(filters[["GERMQ"]]), solid = FALSE) +
  geom_hline(yintercept = filters[["NLOD"]], linetype = 2) +
  geom_vline(xintercept = filters[["TLOD"]], linetype = 2)
```

```{r warning=FALSE}
pull(snv.df, Gencode_43_variantClassification) %>% unique() %>%
  length() -> n.SNV.types.unfiltered

c(
  brewer.pal(n = 12, name = "Paired"),
  brewer.pal(n = n.SNV.types.unfiltered - 12, name = "Dark2")
) -> pal.colors

filter(snv.df, sample != "P069_PT_REL_BM") %>% 
  dplyr::filter(
    GERMQ >= filters[["GERMQ"]],
    DP >= filters[["DP"]]
    ) %>% 
  ggplot(.,
         aes(x = TLOD,
             y = NLOD)) +
  geom_hex(aes(fill = Gencode_43_variantClassification), alpha = 0.75) +
  scale_y_continuous(trans = "log10") +
  scale_x_continuous(trans = "log10") +
  geom_hline(yintercept = filters[["NLOD"]], linetype = 2) +
  geom_vline(xintercept = filters[["TLOD"]], linetype = 2) +
  scale_fill_manual(values = pal.colors)
```

# Subsetting the VCF object

Constructing a filter vector for `vcf_qc` object:

```{r}
colnames(snv.df)
```

```{r}
snv.filter.df <- snv.df %>%
  mutate(
    second_filter = case_when(
      DP <= filters[["DP"]] ~ "low_DP",
      GERMQ <= filters[["GERMQ"]] ~ "low_GERMQ",
      AF_CTRL > filters[["AF_CTRL"]] ~ "high_CTRL_AF",
      AF_PDLC < filters[["AF_TUMOR"]] ~ "low_Tumor_AF",
      NALOD < filters[["NALOD"]] ~   "low_NALOD",
      NLOD < filters[["NLOD"]] ~     "low_NLOD",
      TLOD < filters[["TLOD"]] ~     "low_TLOD",
      TRUE ~ "PASS"
    )
  )

allowed.positions <- snv.filter.df %>%
  filter(sample != "P069_PT_REL_BM" &
           second_filter == "PASS") %>% pull(Position_hg38)

snv.filter.df <- snv.filter.df %>%
  mutate(
    second_filter = case_when(
      sample == "P069_PT_REL_BM" &
        Position_hg38 %in% allowed.positions ~ "PASS",
      TRUE ~ second_filter
    )
  )

snv.filter.df %>% 
  filter(second_filter == "PASS")
```

```{r}
snv.filter.df %>% 
  filter(Gencode_43_hugoSymbol == "TP53")
```


```{r, fig.width=9, fig.height=5}
c(
  "low_DP" = "red",
  "low_GERMQ" = "orange",
  "low_NALOD" = "purple",
  "low_NLOD" = "violet",
  "low_TLOD" = "firebrick",
  "high_CTRL_AF" = "coral2",
  "low_Tumor_AF" = "blueviolet",
  "PASS" = "green"
) -> pal.colors

snv.filter.df %>%
  pivot_longer(cols = contains("AF"), values_to = "AF") %>%
  ggplot(aes(x = name, y = AF)) +
  ggbeeswarm::geom_quasirandom(aes(color = second_filter)) +
  geom_hline(yintercept = filters[["AF_CTRL"]], linetype = "dashed") +
  theme_bw() + labs(x = NULL) +
  scale_color_manual(values = pal.colors) +
  scale_y_continuous(labels = scales::percent)
```

```{r, fig.width=9, fig.height=5}
snv.filter.df %>%
  filter(second_filter == "PASS") %>% 
  pivot_longer(cols = contains("AF"), values_to = "AF") %>%
  ggplot(aes(x = name, y = AF)) +
  ggbeeswarm::geom_quasirandom(aes(color = second_filter)) +
  geom_hline(yintercept = filters[["AF_CTRL"]], linetype = "dashed") +
  theme_bw() + labs(x = NULL) +
  scale_color_manual(values = pal.colors) +
  scale_y_continuous(labels = scales::percent)
```

Same plot but without the SNV which did not pass the filters.

Data frame of all filtered SNVs:

```{r}
snv.filter.df.wide <- snv.filter.df %>%
  filter(second_filter == "PASS") %>% 
  dplyr::select(sample, Position_hg38, Variant, AF_PDLC,
         Gencode_43_hugoSymbol, Gencode_43_variantClassification) %>% 
  pivot_wider(names_from = "sample", values_from = "AF_PDLC") %>% 
  mutate(across(everything(), ~replace_na(., 0)))
snv.filter.df.wide
```

Data frame of missense filtered SNVs:

```{r}
snv.filter.df %>%
  filter(second_filter == "PASS") %>% 
  filter(Gencode_43_variantClassification == "MISSENSE") %>% 
  dplyr::select(sample, Position_hg38, Variant, AF_PDLC,
         Gencode_43_hugoSymbol) %>% 
  pivot_wider(names_from = "sample", values_from = "AF_PDLC")
```

```{r, fig.width=10, fig.height=5}
dot_position = position_jitter(height = 0,
                               width = 0.25,
                               seed = 413)

snv.filter.df %>%
  filter(second_filter == "PASS") %>% 
  
  ggplot() +
  lemon::geom_pointline(
    aes(
      x = sample,
      y = AF_PDLC,
      group = Position_hg38,
      fill = Gencode_43_variantClassification
    ),
    linecolor = "gray30",
    linetype = 3,
    position = dot_position,
    shape = 21,
    size = 2
  ) +
  geom_point(
    aes(
      x = sample,
      y = AF_PDLC,
      group = Position_hg38,
      fill = Gencode_43_variantClassification
    ),
    position = dot_position,
    shape = 21,
    size = 2
  ) +
  geom_hline(yintercept = 0.05, linetype = "dashed") +
  scale_y_continuous(labels = scales::percent, limits = c(0, 1)) +
  theme_bw() +
  labs(x = NULL, y = "Allelic frequency",
       fill = "SNV type")
```

```{r, fig.width=5, fig.height=5}
dot_position = position_jitter(height = 0,
                               width = 0.25,
                               seed = 413)

snv.filter.df %>%
  filter(second_filter == "PASS") %>%
  filter(Gencode_43_variantClassification == "MISSENSE") %>%
  ggplot() +
  lemon::geom_pointline(
    aes(x = sample,
        y = AF_PDLC,
        group = Position_hg38),
    linecolor = "gray30",
    linetype = 3,
    position = dot_position,
    shape = 21,
    size = 2,
    fill = "gray90"
  ) +
  
  geom_hline(yintercept = 0.05, linetype = "dashed") +
  scale_y_continuous(labels = scales::percent, limits = c(0, 1)) +
  theme_bw() +
  labs(x = NULL, y = "Allelic frequency",
       fill = "SNV type")
```

TODO: add the second sample after fixing the filters.

# Save the variant table

```{r}
output_file_xlsx <- paste0("FilteredSNV.xlsx")
output_path_xlsx <- file.path(output_dir, output_file_xlsx)

list(
  "Total SNV" = snv_filter.df,
  "Filtered SNV" = snv_filter.df %>% 
    filter(second_filter == "PASS") %>% 
    dplyr::select(-second_filter)
) %>% 
  write.xlsx(., output_path_xlsx, overwrite = T)
```

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

