212 lines
5.8 KiB
R
212 lines
5.8 KiB
R
getwd()
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setwd("~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/plotting") # thinkpad
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getwd()
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########################################################################
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# Installing and loading required packages #
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########################################################################
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source("../Header_TT.R")
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#source("barplot_colour_function.R")
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#require(data.table)
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########################################################################
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# Read file: call script for combining df for PS #
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########################################################################
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source("../combining_two_df.R")
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#---------------------- PAY ATTENTION
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# the above changes the working dir
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#[1] "git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts"
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#---------------------- PAY ATTENTION
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#==========================
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# This will return:
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# df with NA:
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# merged_df2
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# merged_df3
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# df without NA:
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# merged_df2_comp
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# merged_df3_comp
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#===========================
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###########################
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# Data for plots
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# you need merged_df2 or merged_df2_comp
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# since this is one-many relationship
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# i.e the same SNP can belong to multiple lineages
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###########################
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# uncomment as necessary
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#<<<<<<<<<<<<<<<<<<<<<<<<<
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# REASSIGNMENT
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my_df = merged_df2
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#my_df = merged_df2_comp
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#<<<<<<<<<<<<<<<<<<<<<<<<<
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# delete variables not required
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rm(merged_df2, merged_df2_comp, merged_df3, merged_df3_comp)
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# quick checks
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colnames(my_df)
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str(my_df)
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# Ensure correct data type in columns to plot: need to be factor
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is.factor(my_df$lineage)
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my_df$lineage = as.factor(my_df$lineage)
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is.factor(my_df$lineage)
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table(my_df$mutation_info)
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########################################################################
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# end of data extraction and cleaning for plots #
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########################################################################
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#==========================
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# Plot: Lineage Distribution
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# x = mcsm_values, y = dist
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# fill = stability
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#============================
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#===================
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# Data for plots
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#===================
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# subset only lineages1-4
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sel_lineages = c("lineage1"
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, "lineage2"
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, "lineage3"
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, "lineage4")
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# uncomment as necessary
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df_lin = subset(my_df, subset = lineage %in% sel_lineages )
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# refactor
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df_lin$lineage = factor(df_lin$lineage)
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table(df_lin$lineage) #{RESULT: No of samples within lineage}
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#lineage1 lineage2 lineage3 lineage4
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#104 1293 264 1311
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# when merged_df2_comp is used
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#lineage1 lineage2 lineage3 lineage4
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#99 1275 263 1255
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length(unique(df_lin$Mutationinformation))
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#{Result: No. of unique mutations the 4 lineages contribute to}
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# sanity checks
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r1 = 2:5 # when merged_df2 used: because there is missing lineages
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if(sum(table(my_df$lineage)[r1]) == nrow(df_lin)) {
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print ("sanity check passed: numbers match")
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} else{
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print("Error!: check your numbers")
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}
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#<<<<<<<<<<<<<<<<<<<<<<<<<
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# REASSIGNMENT
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df <- df_lin
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#<<<<<<<<<<<<<<<<<<<<<<<<<
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rm(df_lin)
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#******************
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# generate distribution plot of lineages
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#******************
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# basic: could improve this!
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library(plotly)
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library(ggridges)
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g <- ggplot(df, aes(x = ratioDUET)) +
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geom_density(aes(fill = DUET_outcome)
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, alpha = 0.5) + facet_wrap(~ lineage,
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scales = "free") +
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ggtitle("Kernel Density estimates of Protein stability by lineage")
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ggplotly(g)
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# 2 : ggridges (good!)
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my_ats = 15 # axis text size
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my_als = 20 # axis label size
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fooNames=c('Lineage 1', 'Lineage 2', 'Lineage 3', 'Lineage 4')
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names(fooNames)=c('lineage1', 'lineage2', 'lineage3', 'lineage4')
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# set output dir for plots
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getwd()
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setwd("~/git/Data/pyrazinamide/output/plots")
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getwd()
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svg('lineage_dist_PS.svg')
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printFile = ggplot( df, aes(x = ratioDUET
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, y = DUET_outcome) )+
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#printFile=geom_density_ridges_gradient(
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geom_density_ridges_gradient( aes(fill = ..x..)
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, scale = 3
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, size = 0.3 ) +
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facet_wrap( ~lineage
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, scales = "free"
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# , switch = 'x'
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, labeller = labeller(lineage = fooNames) ) +
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coord_cartesian( xlim = c(-1, 1)
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# , ylim = c(0, 6)
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# , clip = "off"
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) +
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scale_fill_gradientn( colours = c("#f8766d", "white", "#00bfc4")
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, name = "DUET" ) +
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theme( axis.text.x = element_text( size = my_ats
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, angle = 90
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, hjust = 1
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, vjust = 0.4)
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# , axis.text.y = element_text( size = my_ats
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# , angle = 0
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# , hjust = 1
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# , vjust = 0)
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, axis.text.y = element_blank()
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, axis.title.x = element_blank()
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, axis.title.y = element_blank()
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, axis.ticks.y = element_blank()
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, plot.title = element_blank()
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, strip.text = element_text(size=my_als)
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, legend.text = element_text(size=10)
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, legend.title = element_text(size=my_als)
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# , legend.position = c(0.3, 0.8)
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# , legend.key.height = unit(1, 'mm')
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)
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print(printFile)
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dev.off()
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#=!=!=!=!=!=!
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# COMMENT: When you look at all mutations, the lineage differences disappear...
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# The pattern we are interested in is possibly only for dr_mutations
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#=!=!=!=!=!=!
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#===================================================
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# COMPARING DISTRIBUTIONS
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head(df$lineage)
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df$lineage = as.character(df$lineage)
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lin1 = df[df$lineage == "lineage1",]$ratioDUET
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lin2 = df[df$lineage == "lineage2",]$ratioDUET
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lin3 = df[df$lineage == "lineage3",]$ratioDUET
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lin4 = df[df$lineage == "lineage4",]$ratioDUET
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# ks test
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ks.test(lin1,lin2)
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ks.test(lin1,lin3)
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ks.test(lin1,lin4)
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ks.test(lin2,lin3)
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ks.test(lin2,lin4)
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ks.test(lin3,lin4)
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