Contents

In this vignette we will reproduce Figure 1 of the paper, which includes applications of IHW to RNASeq, proteomics and hQTL data.

library("IHW")
library("dplyr")
library("ggplot2")
library("grid")
library("tidyr")
library("cowplot")
library("RColorBrewer")
library("scales")
library("IHWpaper")

Define colors and names for methods we will be using throughout this vignette.

pretty_colors <- scales::hue_pal(h = c(0, 360) + 15, c = 100, l = 65, h.start = 0,direction = 1)(5)
pretty_names <- c("IHW", "BH", "Indep. Filt. \n 10 kb", "Indep. Filt. \n 200 kb", "Indep. Filt. \n 1 Mb")
rnaseq_file <- system.file("real_data_examples/result_files", "RNAseq_benchmark.Rds", package = "IHWpaper")
rnaseq_data <- readRDS(file=rnaseq_file)
panel_a_data <- group_by(rnaseq_data$alpha_df, alpha) %>% summarize(BH = max(bh_rejections), IHW=max(rejections)) %>% 
                     gather(method, rejections, BH, IHW) %>%
                     mutate(method = factor(as.character(method), levels=pretty_names))
## `summarise()` ungrouping output (override with `.groups` argument)

#RNAseq example ## Panel a)

last_vals_a <- group_by(panel_a_data, method) %>% 
               summarize(last_vals = max(rejections)) %>%
               mutate(label = method,
                   colour = pretty_colors[match(label, pretty_names)])
## `summarise()` ungrouping output (override with `.groups` argument)
panel_a <- ggplot(panel_a_data, aes(x=alpha,y=rejections,col=method)) +  
                geom_line(size=1.2) +
                xlab(expression(bold(paste("Nominal ",alpha)))) +
                ylab("Discoveries") +
                scale_color_manual(values=pretty_colors)+
                scale_x_continuous(expand=c(0,0)) +
                theme(plot.margin = unit(c(1, 7.5, 2, 1), "lines"))+
                theme(axis.title = element_text(face="bold" ))


panel_a <- pretty_legend(panel_a, last_vals_a, 0.102)
panel_a

0.1 Panel b)

breaks <- rnaseq_data$breaks
break_min <- rnaseq_data$break_min

breaks_left <- c(break_min,breaks[-length(breaks)])

step_df <- mutate(rnaseq_data$alpha_df, baseMean_left = breaks_left[stratum], baseMean_right = breaks[stratum],
                 baseMean_ratio = baseMean_right/baseMean_left , 
                 baseMean_left = baseMean_left * baseMean_ratio^.2,
                 baseMean_right = baseMean_right *baseMean_ratio^(-.2))

stratum_fun <- function(df){
  stratum <- df$stratum
  weight <- df$weight
  stratum_left <- stratum[stratum != length(stratum)]
  weight_left  <- weight[stratum_left]
  baseMean_left <- df$baseMean_right[stratum_left]
  stratum_right <- stratum[stratum != 1]
  weight_right <- weight[stratum_right]
  baseMean_right <- df$baseMean_left[stratum_right]
  data.frame(stratum_left= stratum_left, weight_left= weight_left, 
             stratum_right = stratum_right, weight_right = weight_right,
             baseMean_left = baseMean_left, baseMean_right = baseMean_right)
}

connect_df <- filter(step_df, alpha==0.1) %>% group_by(fold) %>% 
                  do(stratum_fun(.)) %>%
                  mutate(dashed = factor(ifelse(abs(weight_left - weight_right) > 10^(-4) , TRUE, FALSE),
                         levels=c(FALSE,TRUE)))


panel_b <- ggplot(filter(step_df, alpha==0.1), 
                       aes(x=baseMean_left, 
                           xend=baseMean_right, 
                           y=weight, yend=weight, col=fold)) +
                geom_segment(size=0.8)+ 
                geom_segment(data= connect_df, aes(x=baseMean_left, xend=baseMean_right, 
                                                y=weight_left, yend=weight_right, 
                                                linetype=dashed),
                             size=0.8)+
                scale_x_log10(breaks=c(1,10,100,1000,10000))+
                xlab("Mean of normalized counts")+
                ylab("Weight")+
                theme(legend.position=c(0.8,0.4)) +
                theme(plot.margin = unit(c(1, 1, 2, 1), "lines")) +
                guides(linetype=FALSE)+
                theme(axis.title = element_text(face="bold" ))


panel_b

1 Proteomics Example

proteomics_file <- system.file("real_data_examples/result_files", "proteomics_benchmark.Rds", package = "IHWpaper")
proteomics_data <- readRDS(file=proteomics_file)
panel_c_data <- group_by(proteomics_data$alpha_df, alpha) %>% summarize(BH = max(bh_rejections), IHW=max(rejections)) %>% 
                     gather(method, rejections, BH, IHW) %>%
                     mutate(method = factor(as.character(method), levels=pretty_names))
## `summarise()` ungrouping output (override with `.groups` argument)

1.1 panel c)

last_vals_c <- group_by(panel_c_data, method) %>% 
               summarize(last_vals = max(rejections)) %>%
               mutate(label = method,
                   colour = pretty_colors[match(label, pretty_names)])
## `summarise()` ungrouping output (override with `.groups` argument)
panel_c <- ggplot(panel_c_data, aes(x=alpha,y=rejections,col=method)) +  
                geom_line(size=1.2) +
                xlab(expression(bold(paste("Nominal ",alpha)))) +
                ylab("Discoveries") +
                scale_color_manual(values=pretty_colors)+
                scale_x_continuous(expand=c(0,0))+
                theme(plot.margin = unit(c(1, 7.5, 2, 1), "lines"))+
                theme(axis.title = element_text(face="bold" ))


panel_c <- pretty_legend(panel_c, last_vals_c, 0.102)
panel_c

1.2 Panel d)

breaks <- proteomics_data$breaks
break_min <- proteomics_data$break_min

breaks_left <- c(break_min,breaks[-length(breaks)])

step_df <- mutate(filter(proteomics_data$alpha_df , alpha==0.1), break_left = breaks_left[stratum],
                 break_right = breaks[stratum],
                 break_ratio = break_right/break_left , 
                 break_left =break_left * break_ratio^.1,
                 break_right = break_right *break_ratio^(-.1))

stratum_fun <- function(df){
  stratum <- df$stratum
  weight <- df$weight
  stratum_left <- stratum[stratum != length(stratum)]
  weight_left  <- weight[stratum_left]
  break_left <- df$break_right[stratum_left]
  stratum_right <- stratum[stratum != 1]
  weight_right <- weight[stratum_right]
  break_right <- df$break_left[stratum_right]
  data.frame(stratum_left= stratum_left, weight_left= weight_left, 
             stratum_right = stratum_right, weight_right = weight_right,
             break_left = break_left, break_right = break_right)
}

connecting_df <- step_df %>% group_by(fold) %>% 
                  do(stratum_fun(.)) %>%
                  mutate(dashed = factor(ifelse(abs(weight_left - weight_right) > 10^(-5) , TRUE, FALSE),
                         levels=c(FALSE,TRUE)))

panel_d <- ggplot(step_df, aes(x=break_left, xend=break_right,y=weight, yend=weight, col=fold)) +
                geom_segment(size=0.8)+                
                geom_segment(data= connecting_df, aes(x=break_left, xend=break_right, 
                                                y=weight_left, yend=weight_right, 
                                                linetype=dashed),
                             size=0.8)+
                scale_x_log10(breaks=c(1, 10,100,400)) +
                xlab("Number of peptides quantified")+
                ylab("Weight")+
                theme(legend.position=c(0.8,0.4)) +
                theme(plot.margin = unit(c(1, 1, 2, 1), "lines"))+
                guides(linetype=FALSE)+
                theme(axis.title = element_text(face="bold" ))


panel_d

2 hQTL Example

2.1 Load in data

hqtl_file <- system.file("real_data_examples/result_files", "hQTL_benchmark.Rds", package = "IHWpaper")
hqtl_data <- readRDS(file=hqtl_file)
hqtl_summary <- group_by(hqtl_data$alpha_df, alpha) %>%  gather(method, rejections, 6:10) %>%
                select(alpha, method, rejections)

# will use below later to convert to nicer names for our plot
# see http://stackoverflow.com/questions/7547597/dictionary-style-replace-multiple-items-in-r
map <- setNames(pretty_names,
               c("rejections","bh_rejections","threshold:10000","threshold:2e+05", "threshold:1e+06"))

2.2 Panel e)

hqtl_summary <- mutate(hqtl_summary, method = map[method], 
                       method= factor(method, levels = pretty_names))

last_vals_e <- group_by(hqtl_summary, method) %>% 
               summarize(last_vals = max(rejections))  %>%
               mutate(last_vals = last_vals + c(0,0, 250, 0, -350), # offset to make it look nice
                      label = method,
                      colour = pretty_colors[match(label, pretty_names)])
## `summarise()` ungrouping output (override with `.groups` argument)
panel_e <- ggplot(hqtl_summary, aes(x=alpha,y=rejections,col=method)) +  
                geom_line(size=1.2) +
                xlab(expression(bold(paste("Nominal ",alpha)))) +
                ylab("Discoveries") +
                scale_x_continuous(expand=c(0,0))+
                scale_color_manual(values=pretty_colors)+
                theme(plot.margin = unit(c(1, 7.5, 2, 1), "lines"))+
                theme(axis.title = element_text(face="bold" ))



panel_e <- pretty_legend(panel_e, last_vals_e, 0.102)
panel_e

2.3 Panel f)

breaks <-   hqtl_data$breaks
breaks <- breaks[-1]
break_min <- hqtl_data$break_min
breaks_left <- c(break_min,breaks[-length(breaks)])
step_df <- mutate(filter(hqtl_data$alpha_df,alpha==0.1), break_left = breaks_left[stratum],
                 break_right = breaks[stratum],
                 break_ratio = break_right/break_left , 
                 break_left =break_left * break_ratio^.2,
                 break_right = break_right *break_ratio^(-.2))

stratum_fun <- function(df){
  stratum <- df$stratum
  weight <- df$weight
  stratum_left <- stratum[stratum != length(stratum)]
  weight_left  <- weight[stratum_left]
  break_left <- df$break_right[stratum_left]
  stratum_right <- stratum[stratum != 1]
  weight_right <- weight[stratum_right]
  break_right <- df$break_left[stratum_right]
  data.frame(stratum_left= stratum_left, weight_left= weight_left, 
             stratum_right = stratum_right, weight_right = weight_right,
             break_left = break_left, break_right = break_right)
}

connecting_df <- step_df %>% group_by(fold) %>% 
                  do(stratum_fun(.)) %>%
                  mutate(dashed = factor(ifelse(abs(weight_left - weight_right) > 2 , TRUE, FALSE),
                         levels=c(FALSE,TRUE)))

panel_f <- ggplot(step_df, aes(x=break_left, xend=break_right,y=weight, yend=weight, col=fold)) +
                geom_segment(size=0.8)+                
                geom_segment(data= connecting_df, aes(x=break_left, xend=break_right, 
                                                y=weight_left, yend=weight_right, 
                                                linetype=dashed),
                             size=0.8)+
                scale_x_log10(breaks=c(10^4, 10^5,10^6,10^7), 
                              labels = trans_format("log10", math_format(10^.x))) +
                xlab("Genomic distance (bp)")+
                ylab("Weight")+
                theme(legend.position=c(0.8,0.4)) +
                theme(plot.margin = unit(c(1, 1, 2, 1), "lines"))+
                theme(axis.title = element_text(face="bold" ))+
                guides(linetype=FALSE)

panel_f

3 Put all panels together for main fig!

full_fig <- plot_grid(panel_a, 
                      panel_c, 
                      panel_e, panel_f,
                      labels= c("a)", "b)", "c)",
                              "d)"),
                      nrow=2)
full_fig

ggsave(plot=full_fig, file="data_examples.pdf", width=12, height=10)

4 Put all panels together for suppl fig.

suppl_fig <- plot_grid(panel_b,
                       panel_d,
                       labels= c("a)", "b)"),
                       nrow=1)
suppl_fig

ggsave(plot=suppl_fig, file="data_examples_suppl.pdf", width=12, height=5)