---
title: "MAFDash"
output: 
  flexdashboard::flex_dashboard:
    source_code: embed
    theme: yeti
    orientation: rows
    vertical_layout: scroll
params:
  maffile: "MAF object or path to MAF file"
  titletext: "MAF Dash"
---

```{r setup, include=FALSE}
library(plotly)
library(dplyr)
library(crosstalk)
library(flexdashboard)
library(DT)
library(htmltools)
library(bsplus)
library(knitr)
library(maftools)

maf_file <- params$maffile
title_text <- params$titletext
# maf_file <- "/Users/tandonm/Documents/my_tools/MAFdash/4/data/TCGA_THYM/mutect2/TCGA_THYM.mutect2.maf"
# title_text <- ""
# source("scripts/helper_functions/helper_functions.oncoplot.R")

```


```{r read-MAF-file, results="hide" }

maf <- read.maf(maf_file)

```

Summary {data-orientation=rows}
=======================================================================
Row {data-height=120}
-------------------------------------
###  `r title_text` 
```{r make-summary-table }
summaryInfo <- maf@summary
summaryTable <- data.frame(Genome=summaryInfo$summary[summaryInfo$ID=="NCBI_Build"],
                           Number_of_samples=summaryInfo$summary[summaryInfo$ID=="Samples"],
                           Number_of_genes=summaryInfo$summary[summaryInfo$ID=="nGenes"],
                           Mean_Variants=prettyNum(summaryInfo$Mean[summaryInfo$ID=="total"],digits=4),
                           Median_Variants=prettyNum(summaryInfo$Median[summaryInfo$ID=="total"], digits=4)
)
datatable(summaryTable, style="bootstrap4", class=c("display"),
          fillContainer=T,
          escape = F,
          selection = "none",
          rownames=F,
          colnames=tools::toTitleCase(gsub("_"," ", colnames(summaryTable))),
          options=list(dom = 't',ordering=F,autoWidth=F,
                       columnDefs = list(list(width = '80px', targets = "_all"),
                                         list(className = 'dt-center', targets = "_all")),
          initComplete = JS(
            "function(settings, json) {",
            "$(this.api().table().header()).css({'font-size': '120%', 'background-color': '#ace1fa', 'color': '#424242'});",
            "}")             
          ),
          autoHideNavigation=TRUE
          )  %>%
  DT::formatStyle(columns = 1:ncol(summaryTable), fontSize = '120%',fontWeight="bold") 

```

Row {.tabset .tabset-fade data-height=800}
-------------------------------------
### Summary Plots
```{r make-summary-maftools, fig.width=12, fig.height=8 }
plotmafSummary(maf = maf, titvRaw = F)

```

### Interactive Heatmap
```{r make-interactive-heatmap, fig.width=12, fig.height=8  }
snp_mat <- mutCountMatrix(maf = maf)
snp_mat$total <- rowSums(snp_mat)
snp_mat <- snp_mat[with(snp_mat,order(-total)),]
snp_mat <- snp_mat[1:50,]
mat1 <-subset(snp_mat, select=-(total))
mat1 <- log2(mat1 +1)
mat1<- t(mat1)


### This makes the "waterfall" effect for the gene/sample matrix
# source("scripts/helper_functions/helper_functions.oncoplot.R")
oncomat <- createOncoMatrix(maf, g=colnames(mat1))$oncoMatrix
mat1 <- mat1[match(colnames(oncomat), rownames(mat1)), match(rownames(oncomat), colnames(mat1))]

### Set up sizing parameters
minsize=200
maxsize=850
nsample=ncol(mat1)
px_per_sample=13
hm_height=paste0(
        min(c(maxsize, max(c(minsize,nsample*px_per_sample)))),
        "px")

ngenes=nrow(mat1)
px_per_gene=px_per_sample
hm_width=paste0(
        min(c(maxsize, max(c(minsize,ngenes*px_per_gene)))),
        "px")

require(canvasXpress)

### Render plot
canvasXpress(
  data = mat1,
  colorSpectrum = list("black","blue","magenta","red","gold"),
  graphType = "Heatmap",
  # samplesClustered = TRUE,
  # variablesClustered = TRUE,
  samplesClustered = FALSE,
  variablesClustered = FALSE,
  showTransition = FALSE,
  smpLabelScaleFontFactor = 0.5,
  varLabelScaleFontFactor = 0.5,
  width = hm_width,
  height = hm_height,
  # titleFont="10px Verdana",
  legendScaleFontFactor=1,
  # showLegendTitle=T#,
  titleScaleFontFactor=0.3,
  title="log2(Num Variants)"
  )



```

### Burden Plots {data-orientation=columns}
```{r make-burden-plot-dot }
# source("scripts/helper_functions/helper_functions.oncoplot.R")
burden_plot <- make_burden_plot(maf, plotType = "Dotplot")

dotplotly <- ggplotly(burden_plot,tooltip = "text", width=500, height=600) %>%
      layout(yaxis=list(autorange = T,fixedrange= F))

burden_plot <- make_burden_plot(maf, plotType = "Barplot")

barplotly <- ggplotly(burden_plot,tooltip = "text", width=800, height=600) %>%
      layout(yaxis=list(autorange = T,fixedrange= F))

### Use CSS/javascript flex-wrap to contain the two plots
div(
  style = "display: flex; flex-wrap: wrap; justify-content: center",
  div(dotplotly, style = "width: 40%; border: groove;"),
  div(barplotly, style = "width: 60%; border: groove;"),
)

```


### Oncoplot
``` {r make-oncoplot, fig.width=12, fig.height=6 }
# source("scripts/helper_functions/helper_functions.oncoplot.R")
onco <- make_oncoplot(maf)
draw(onco)
```


### Co-occurence of Mutated Genes
``` {r somatic-interactions, fig.width=12, fig.height=6 }
pval_low=0.05
pval_high=0.01

source("~/Documents/helper_functions/helper_functions.oncoplot.R")
make_single_ribbon_plot(maf,
                        pval_low = pval_low, pval_high = pval_high, 
                        gene_colors = "grey50",
                        shrink_factor=1.5)
```


Variant Table
=======================================================================
```{r make-data-table, results="hide" }
mydf <- make_variant_table(maf)
column_labels <- setNames(colnames(mydf), make.names(colnames(mydf)))
colnames(mydf) <- names(column_labels)

n_samples <- length(unique(mydf$Sample.ID))

extra_data <- mydf %>% group_by(Hugo.Symbol) %>% mutate(Gene.Altered.in.Cohort.frac=length(unique(Sample.ID))/n_samples)
extra_data <- extra_data %>% group_by(Hugo.Symbol, Protein.Change) %>%
                             mutate(Variant.in.Cohort.frac=length(unique(Sample.ID))/n_samples)

mydf <- cbind(mydf[,1:2], extra_data[,c("Gene.Altered.in.Cohort.frac","Variant.in.Cohort.frac")], mydf[,3:ncol(mydf)])



normdb_cols <- intersect(c("gnomAD.Frequency","ExAC.Frequency","X1000Genomes.Frequency"),colnames(mydf))
columns_to_round <- c("Gene.Altered.in.Cohort.frac","Variant.in.Cohort.frac", "Tumor.Alt.Frequency",#"Tumor.Depth",
                      normdb_cols)
for (colname in columns_to_round) {
  mydf[,colname] <- as.numeric(as.character(mydf[,colname]))
  mydf[,colname] <- ifelse(is.na(mydf[,colname]), 0, mydf[,colname])
  mydf[,colname] <- round(mydf[,colname],3)
}

mydf[,"Tumor.Depth"] <-  as.numeric(as.character(mydf[,"Tumor.Depth"]))

mydf <- mutate(mydf, mean.freq.normals = round(rowMeans(select(mydf, all_of(normdb_cols)), na.rm = TRUE),3))


mycounts <- mydf %>% group_by(Sample.ID) %>% summarise(num_mut=n(), .groups="drop")
mydf$Sample.ID <- factor(as.character(mydf$Sample.ID), levels=unique(as.character(mycounts$Sample.ID)[order(mycounts$num_mut,decreasing = T)]))


```
Inputs {.sidebar data-width=300}
-------------------------------------
```{r set-up-variant-filters }


# Wrap data frame in SharedData
variant_sd <- SharedData$new(mydf)

# Create a filter input
h4("Filter data by cohort values")
filter_select("sample_filter", "Sample ID", variant_sd, group=~Sample.ID)
filter_slider("fracgene_filter", "Gene mutated in cohort (frequency)", variant_sd, column=~Gene.Altered.in.Cohort.frac, step=0.01, width=250)
br()
h4("Filter data by variant values")
filter_select("gene_filter", "Gene Symbol", variant_sd, group=~Hugo.Symbol)
filter_select("protchange_filter", "Protein change", variant_sd, group=~Protein.Change)
filter_slider("fracvar_filter", "Variant found in cohort (frequency)", variant_sd, column=~Variant.in.Cohort.frac, step=0.01, width=250)
filter_slider("tumdepth_filter", "Total depth (count) at site", variant_sd, column=~Tumor.Depth, step=5, width=250)
filter_slider("tumfreq_filter", "Frequency of mutated allele at site", variant_sd, column=~Tumor.Alt.Frequency, step=0.01, width=250)
filter_select("vartype_filter", "Type of variant (SNP, INDEL)", variant_sd, group=~Variant.Type)
br()
h4("Filter common variants in normal databases")
filter_slider("normal_mean_freq_filter", "Mean frequency in gnomAD, ExAC, and 1000 Genomes", variant_sd, column=~mean.freq.normals, step=0.01, width=250)
# filter_slider("gnomad_filter", "gnomAD frequency", variant_sd, column=~gnomAD.Frequency, step=0.01, width=250)
# filter_slider("exac_filter", "ExAC frequency", variant_sd, column=~ExAC.Frequency, step=0.01, width=250)
# filter_slider("kgenomes_filter", "1000Genomes frequency", variant_sd, column=~X1000Genomes.Frequency, step=0.01, width=250)
br()

```

Row {data-height=500}
-------------------------------------
### Variant Table

```{r render-full-table }
datatable(variant_sd, extensions=c("Scroller","FixedColumns","Buttons","ColReorder"), style="bootstrap", class=c("compact","display"), width="90%",
          fillContainer=F,
          escape = F,
          selection = "none",
          rownames=F,
          colnames=tools::toTitleCase(gsub("_"," ", colnames(mydf))),
          options=list(dom = 'BSRlfrtip',
                       deferRender=TRUE,
                       scrollX=T,
                       scrollY=450,
                       scroller=T,
                       buttons =list('copy', 'print', list(
                                    extend = 'collection',
                                    buttons = c('csv', 'excel', 'pdf'),
                                    text = 'Download'
                                  )),
                       colReorder=TRUE,
                       fixedColumns = list(leftColumns = 4)
                       )
          )

```