examples/visualsations_example.R

In chapmandu2/CancerCellLines: Analysis of cancer cell line data

library(CancerCellLines)
library(dplyr)

#set up a connection to the full database
dbpath <- '~/BigData/CellLineData/CancerCellLines.db'
full_con <- setupSQLite(dbpath)
dplyr_con <- src_sqlite(full_con@dbname)

######
# EXAMPLE 1
#####
#specify the genes
ex1_genes <- c('BRAF', 'NRAS', 'CRAF', 'TP53')

#get the melanoma cell lines
ex1_cell_lines <- dplyr_con %>% tbl('ccle_sampleinfo') %>% dplyr::filter(Site_primary=='skin') %>%
  collect %>% as.data.frame
ex1_cell_lines <- ex1_cell_lines$CCLE_name
ex1_cell_lines[1:10]

#get BRAF and MEK inhibitors
ex1_drugs <- c('AZD6244','PLX4720','PD-0325901')

#make a tall frame
ex1_tall_df <- makeTallDataFrame(full_con, ex1_genes, ex1_cell_lines, ex1_drugs)
ex1_tall_df

#convert this into a wide data frame
ex1_wide_df <- ex1_tall_df %>% makeWideFromTallDataFrame
ex1_wide_df

#compare the drug activities
pairs(~AZD6244_resp+PLX4720_resp+`PD-0325901_resp`, ex1_wide_df)

#make a heatmap!
plotHeatmap(ex1_tall_df)
plotHeatmap(ex1_tall_df, order_feature='PLX4720_resp')

############
## Example 2: EGFR inhibitors vs EGFR mutation status or expression
############

#get all cell lines
ex2_cell_lines <- dplyr_con %>% tbl('ccle_sampleinfo') %>%
  collect %>% as.data.frame
ex2_cell_lines <- ex2_cell_lines$CCLE_name

#make a data frame for the affy analysis
df <- makeRespVsGeneticDataFrame(full_con, gene='EGFR',
                                 cell_lines=ex2_cell_lines,
                                 drug='Erlotinib',
                                 data_types = 'affy',
                                 drug_df = NULL)

#scatter plot of EGFR expression vs Erlotinib response
plotRespVsGeneticHist(df, 'affy', FALSE)

#histogram of Erlotinib response coloured by EGFR expression
plotRespVsGeneticPoint(df, 'affy', FALSE)

############
## Example 3: BRAF inhibitors vs BRAF mutation status
###########

#make a data frame for the affy analysis
df <- makeRespVsGeneticDataFrame(full_con, gene='BRAF',
                                 cell_lines=ex2_cell_lines,
                                 drug='PLX4720',
                                 data_types = 'hybcap',
                                 drug_df = NULL)

#scatter plot of EGFR expression vs Erlotinib response
plotRespVsGeneticHist(df, 'hybcap', FALSE)

#histogram of Erlotinib response coloured by EGFR expression
plotRespVsGeneticPoint(df, 'hybcap', FALSE)

########
## Example 4: Comparing SMARCA4 expression in SMARCA4 mutated cell lines to wildtype
########

#get lung cell lines
ex4_cell_lines <- dplyr_con %>% tbl('ccle_sampleinfo') %>% filter(Site_primary == 'lung') %>%
  collect %>% as.data.frame
ex4_cell_lines <- ex4_cell_lines$CCLE_name

#make the data frame
gvg.df <- makeGeneticVsGeneticDataFrame(full_con,
                                        cell_lines=ex4_cell_lines,
                                        gene1='SMARCA4',
                                        data_type1='hybcap',
                                        gene2='SMARCA4',
                                        data_type2='affy')

#view the data frame
head(gvg.df)

#do the plot
plotGeneticVsGeneticPoint(gvg.df)

#all in one go with axes swapped
makeGeneticVsGeneticDataFrame(full_con, cell_lines=ex4_cell_lines, gene1='SMARCA4', data_type1='affy',
                              gene2='SMARCA4', data_type2='hybcap') %>% plotGeneticVsGeneticPoint()

#two continuous
makeGeneticVsGeneticDataFrame(full_con, cell_lines=ex4_cell_lines, gene1='SMARCA4', data_type1='affy',
                              gene2='SMARCA4', data_type2='cn') %>% plotGeneticVsGeneticPoint()

#two discrete
makeGeneticVsGeneticDataFrame(full_con, cell_lines=ex4_cell_lines, gene1='SMARCA4', data_type1='hybcap',
                              gene2='KRAS', data_type2='hybcap') %>% plotGeneticVsGeneticPoint()

#also plot by cell line with one feature a y axis and another as fill colour
#continous + discrete
makeGeneticVsGeneticDataFrame(full_con, cell_lines=ex4_cell_lines, gene1='SMARCA4', data_type1='affy',
                              gene2='SMARCA4', data_type2='hybcap') %>% plotGeneticVsGeneticHist()

#continous + continous
makeGeneticVsGeneticDataFrame(full_con, cell_lines=ex4_cell_lines[1:25], gene1='SMARCA4', data_type1='affy',
                              gene2='SMARCA4', data_type2='cn') %>% plotGeneticVsGeneticHist(label_option = TRUE)


############
## Interactive visualisations
############
#using custom data
data(dietlein_data)
full_con <- setupSQLite('~/BigData/CellLineData/CancerCellLines.db')
shinyRespVsGeneticApp(con=full_con, drug_df=dietlein_data)

#CCLE data
shinyRespVsGeneticApp(con=full_con)

#just interested in genomic data
shinyGeneticVsGeneticApp(con=full_con)

###########
# Comparing drug response only
##########
ex5_cell_lines <- dplyr_con %>% tbl('ccle_sampleinfo') %>%
  collect %>% as.data.frame
ex5_cell_lines <- ex5_cell_lines$CCLE_name

#make a data frame
df <- makeRespVsRespDataFrame(full_con,
                              cell_lines=ex5_cell_lines,
                              drugs=c('Erlotinib', 'AZD6244'),
                              tissue_info = 'ccle')
head(df)

#makes a wide data frame
wide.df <- df %>% makeWideFromRespVsRespDataFrame()
head(wide.df)

#now do some plots
plotRespVsRespWaterfall(filter(df, grepl('Erlotinib', assayed_id)))
plotRespVsRespDensity(df)
plotRespVsRespPairs(df)

#and an app
shinyRespVsRespApp(con=full_con)