In chapmandu2/CollateralVulnerability2016: Collateral Vulnerability Pipeline

library(CollateralVulnerability2016)
library(dplyr)

Introduction

Step 1: Set up SQLite database and directories

work_dir <- '~/BigData/CollateralVulnerability2016/paad/'
download_dir <- '~/BigData/RTCGA_downloads/'
mydb <- 'paad_output.db'
mydb_path <- paste0(work_dir, mydb)
my_con <- setupSQLite ( mydb_path ) 

Step 2: Get human geneset to work with

In this case we want all human genes using the most recent ENSEMBL annotation:

all_genes <- getAllHumanGenes(my_con)
head(all_genes)

Gene data also written to SQLite database and can be viewed with dplyr:

src_sqlite(my_con@dbname) %>% tbl('human_genes')

Step 3: Import TCGA RNAseq and mutation data

Want to import the TCGA RNAseq and mutation data so that it is ready to use for further analyses

rnaseq_dat <- getTCGARNAseqData(my_con, cancerTypes='PAAD', releaseDate = '2015-11-01', sampletag=c('01A', '06A'))
dim(rnaseq_dat)

mutation_dat <- getTCGAMutationData(my_con, cancerTypes='PAAD', releaseDate = '2015-11-01', sampletag=c('01A', '06A'))
dim(mutation_dat)

DBI::dbListTables(my_con)

Step 4: Run the low expressors analysis on RNAseq data

Use just 1000 randomly selected genes for this analysis rather than the whole set.
Analysis takes several minutes to run even with parallel processing

bisep_output <- doBISEPAnalysis(my_con, genes_n=100000)

Step 5: View the results of the low expressors analysis

bisep_results <- src_sqlite(my_con@dbname) %>%
    dplyr::tbl('bisep_results') %>%
    dplyr::filter(bisep_pval < 0.1 & pi_value <0.2) %>% 
    dplyr::collect() %>% 
    inner_join(all_genes, by=c('gene_name'='gene_id')) %>%
    dplyr::select(gene_name, gene_name.y, everything()) %>%
    dplyr::arrange(gene_name.y)
bisep_results

Step 6: Generate a plot for a given gene

doRNAseqPlot(my_con, 'ENSG00000176024')

Step 7: Do the human paralogue analysis

human_paralog_res <- countHumanParalogs(my_con, bisep_results$gene_name)

Step 8: Do the FlyMine analysis

``` {r eval = FALSE} flymine_res <- doFlyMineAnalysis(my_con, bisep_results$gene_name)

## Step 9: Do the WormMine analysis
``` {r eval = FALSE}
wormmine_res <- doWormMineAnalysis(my_con, bisep_results$gene_name)

Step 10: Do the mutation analysis

``` {r eval = FALSE} mut_res <- doMutationAnalysis(my_con, bisep_results$gene_name)

## Step 11: Combine the results
``` {r eval=FALSE}
combo_res <- combineResults(my_con, bisep_results$gene_name)

Step 12: Filter the results

``` {r eval=FALSE} filtered_results <- src_sqlite(my_con@dbname) %>% dplyr::tbl('combined_results') %>% dplyr::filter(count_paralogs > 0 & count_paralogs <= 5 & (lethal_pct_fly >= 20 | lethal_pct_worm >= 20) ) %>% dplyr::collect()

## Step 13: Show results in shiny app
``` {r eval=FALSE}
    shinyVisApp(my_con)

chapmandu2/CollateralVulnerability2016 documentation built on May 13, 2019, 3:27 p.m.