workflow_cnv_analysis.Rmd

---
title: "CNV Pathway Overlap Analysis"
author: "Ted Laderas"
date: "8/15/2019"
output: html_notebook
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## Transforming the Gistic Data

For information on how the dummy MAF file was made from the GISTIC results, please refer to `format_gistic.Rmd`.

```{r}
library(here)
library("packageDir")
library("roxygen2")
library("stringr")
library("DT")
```

## Running Overlap analysis

A number of settings options have to be set for this analysis. 

### Somatic Mutation Settings

```{r}
path_detail <- readRDS("data/path_detail.rds")

settings <- getBasicSettings()

#modify the settings object
settings$somatic_mutation_aberration_summary$interactive <- "n"

settings$somatic_mutation_aberration_summary$`Analyze pathways for individual members of the cohort? (y or n) `<- "n"

settings$somatic_mutation_aberration_summary$`Select a .maf file containing the data set to be analyzed.` <- "data/gistic_long_file.maf"

settings$somatic_mutation_aberration_summary$`Would you like to include PolyPhen analysis results in this analysis? (y/n) `<- "n"

settings$somatic_mutation_aberration_summary$`Have manual gene symbol corrections already been conducted? (y/n)` <- "y"

settings$somatic_mutation_aberration_summary$`Analyze pathways for individual members of the cohort? (y or n) ` <- "n"

settings$somatic_mutation_aberration_summary$`Have manual gene symbol corrections already been conducted? (y/n)` <- "y"

settings$somatic_mutation_aberration_summary$mutation_type <- c("CNV")

settings$somatic_mutation_aberration_summary$`Would you like to filter out hypermutators?
If yes, please enter a mutation count threshold.
If no just press enter n ` <- "n"

settings$somatic_mutation_aberration_summary$`Use special path significance analysis settings for this data type? (y/n)` <- "n"
 
```

```{r}
settings$functional_drug_screen_summary$`Please select a file with a drug screen results data set
` <- "data/Targetome_Level123_8_7_17.txt"

settings$functional_drug_screen_summary$interactive <- "n"

settings$functional_drug_screen_summary$`Have manual symbol corrections been performed yet for the current data set? (y/n)` <- "y"

settings$functional_drug_screen_summary$`Analyze pathways for individual members of the cohort? (y or n) ` <- "n"

settings$functional_drug_screen_summary$`
 To analyze drug screen panel coverage (for a panel that has or has not been run), enter p
 To process drug screen result set enter d
 To save an HTML summary of the results enter h
 To exit drug screen interface, enter q
` <- "p"

 
settings$functional_drug_screen_summary$gene_stat <- "g"
 
settings$functional_drug_screen_summary$`Enter \\"g\\" to examine coverage using a set of gene names. Enter \\"d\\" to examine coverage using drug names, along with a drug target matrix: ` <- "g"

settings$functional_drug_screen_summary$`Please select a file` <- "data/Targetome_Level123_8_7_17.txt"

settings$functional_drug_screen_summary$`Please type in the name of the column with the gene symbols: ` <-	"targets"
settings$functional_drug_screen_summary$`Have manual gene symbol corrections already been made? (y/n)` <-	"y"

```

```{r}
study = getStudyObject(study.name="hnscc_cnv_only", geneIdentifierType="HUGO", path_detail = path_detail, settings = settings)
```

## Running the Analysis

`loadBasicArms()` loads the data from the specified locations.

`autoRunFromSettings()` runs all of the mutation processing and overlap analysis code.

```{r}
study <- loadBasicArms(study)
study <- autoRunFromSettings(study)
saveStudy(study)
```

# Light Paths from CNV Analysis

```{r}
lightPaths <- read.delim("output/study_hnscc_cnv_only/results/overlap_analysis/Aberrationally enriched, containing drug targets.txt", sep="\t")
DT::datatable(lightPaths)

```

#compare to mutation only analysis

```{r}
light_paths_mutation <- read.delim(here("output/study_hnscc/results/overlap_analysis/Aberrationally enriched, containing drug targets.txt"), sep="\t")
DT::datatable(lightPaths)

```

# Common to both analyses

```{r}
intersect(as.character(light_paths_mutation$path_id),as.character(lightPaths$path_id))
```


# Light Pathways In CNV analysis, but not mutation

```{r}
setdiff(as.character(lightPaths$path_id), as.character(light_paths_mutation$path_id))
```

# Light pathways unique to mutation and not in CNV 

```{r}
setdiff(as.character(lightPaths$path_id), as.character(light_paths_mutation$path_id))
```


# CNV Dark Pathways

```{r}
darkPaths <- read.delim("output/study_hnscc_cnv_only/results/overlap_analysis/Aberration enriched, not drug targeted.txt", sep= "\t", stringsAsFactors = FALSE)
DT::datatable(darkPaths)
```

# Dark Paths Common to Both Analyses

```{r}
dark_paths_mutation <- read.delim("output/study_hnscc/results/overlap_analysis/Aberration enriched, not drug targeted.txt", sep= "\t", stringsAsFactors = FALSE)
intersect(dark_paths_mutation$path_id, darkPaths$path_id)
```

## Dark Paths unique to CNV

```{r}
dark_paths_mutation <- read.delim("output/study_hnscc/results/overlap_analysis/Aberration enriched, not drug targeted.txt", sep= "\t", stringsAsFactors = FALSE)
setdiff(darkPaths$path_id,dark_paths_mutation$path_id)
```

## Dark Paths unique to Mutation

```{r}
dark_paths_mutation <- read.delim("output/study_hnscc/results/overlap_analysis/Aberration enriched, not drug targeted.txt", sep= "\t", stringsAsFactors = FALSE)
setdiff(dark_paths_mutation$path_id,darkPaths$path_id)
```