Genomics data often stored in a matrix like format, where each row is a feature (gene, transcript, protein etc.) and columns are variables (e.g. signal intensity of experiments such as RNA-seq, ChIP-seq, Pol-II ChIP-seq etc.). Variables are often grouped by replicates, time-points or specific experimental conditions such as wild type, deletion, control, treatment etc. In such a multidimensional data, plotting a x-y scatter plot between different groups require lots of data wrangling before it goes for final ggplot.
corplot has functions to generate heatbox and pairwise scatter plots
directly from feature matrix given in a tbl format. Let’s have a look
into required input data and resultant plots out of corpot.
if(require("devtools")){
devtools::install_github("cparsania/corplot")
} else{
install.packages("devtools")
devtools::install_github("cparsania/corplot")
}expr_mat_file <- system.file("extdata" ,"example_data_expr_mat_01.txt" , package = "corplot")
expr_mat <- readr::read_delim(expr_mat_file , delim = "\t")
expr_mat
#> # A tibble: 6,338 x 9
#> gene_name Control_Rep.A Control_Rep.B Treat1_Rep.A Treat1_Rep.B Treat2_Rep.A
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 C1_00010… 1 0 1 2.81 5.13
#> 2 C1_00020… 9.65 9.32 9.15 9.32 10.9
#> 3 C1_00030… 5.46 4.70 4.64 5.36 6.15
#> 4 C1_00040… 10.9 10.5 11.4 12.2 11.3
#> 5 C1_00050… 0 1 1 5.88 5.43
#> 6 C1_00060… 13.4 13.0 12.9 13.1 13.2
#> 7 C1_00070… 12.9 12.7 12.6 12.2 11.2
#> 8 C1_00080… 10.4 9.81 10.2 9.96 10.4
#> 9 C1_00090… 7.33 6.61 6.13 6.88 7
#> 10 C1_00100… 10.0 10.1 10.9 10.9 10.8
#> # … with 6,328 more rows, and 3 more variables: Treat2_Rep.B <dbl>,
#> # Treat3_Rep.A <dbl>, Treat3_Rep.B <dbl>
## calculate pairwise correlation
cor_tbl <- corplot::get_pairwise_cor_tbl(expr_mat , var = "gene_name" , method = "pearson")
cor_tbl
#> # A tibble: 64 x 3
#> var1 var2 corr
#> <chr> <chr> <dbl>
#> 1 Control_Rep.A Control_Rep.A 1
#> 2 Control_Rep.B Control_Rep.A 0.96
#> 3 Treat1_Rep.A Control_Rep.A 0.93
#> 4 Treat1_Rep.B Control_Rep.A 0.9
#> 5 Treat2_Rep.A Control_Rep.A 0.86
#> 6 Treat2_Rep.B Control_Rep.A 0.86
#> 7 Treat3_Rep.A Control_Rep.A 0.91
#> 8 Treat3_Rep.B Control_Rep.A 0.91
#> 9 Control_Rep.A Control_Rep.B 0.96
#> 10 Control_Rep.B Control_Rep.B 1
#> # … with 54 more rows
cp <- corplot::get_corr_heat_box(cor_tbl,var1 = var1, var2 = var2 ,value = corr)
cp + viridis::scale_fill_viridis() + ggplot2::theme(axis.text.x = ggplot2::element_text(angle=90))
All samples vs all samples correlation heatbox has redundant samples on each axis. This makes plot less readable. Alternate way to overcome this is to plot samples of replicate 1 vs samples of replicate 2.
cor_tbl2 <- cor_tbl %>% dplyr::filter(grepl("Rep.A", var1) ) %>% dplyr::filter(grepl("Rep.B", var2) )
cor_tbl2
#> # A tibble: 16 x 3
#> var1 var2 corr
#> <chr> <chr> <dbl>
#> 1 Control_Rep.A Control_Rep.B 0.96
#> 2 Treat1_Rep.A Control_Rep.B 0.95
#> 3 Treat2_Rep.A Control_Rep.B 0.86
#> 4 Treat3_Rep.A Control_Rep.B 0.91
#> 5 Control_Rep.A Treat1_Rep.B 0.9
#> 6 Treat1_Rep.A Treat1_Rep.B 0.94
#> 7 Treat2_Rep.A Treat1_Rep.B 0.93
#> 8 Treat3_Rep.A Treat1_Rep.B 0.9
#> 9 Control_Rep.A Treat2_Rep.B 0.86
#> 10 Treat1_Rep.A Treat2_Rep.B 0.9
#> 11 Treat2_Rep.A Treat2_Rep.B 0.99
#> 12 Treat3_Rep.A Treat2_Rep.B 0.9
#> 13 Control_Rep.A Treat3_Rep.B 0.91
#> 14 Treat1_Rep.A Treat3_Rep.B 0.92
#> 15 Treat2_Rep.A Treat3_Rep.B 0.9
#> 16 Treat3_Rep.A Treat3_Rep.B 0.97
corplot::get_corr_heat_box(cor_tbl2,var1 = var1, var2 = var2, value = corr) +
viridis::scale_fill_viridis() 
groups_file <- expr_mat_file <- system.file("extdata" ,"example_data_01_sample_groups.txt" , package = "corplot")
groups <- readr::read_delim(file = groups_file,delim = "\t")
groups
#> # A tibble: 8 x 3
#> samples condition repl
#> <chr> <chr> <chr>
#> 1 Control_Rep.A Control Rep.A
#> 2 Control_Rep.B Control Rep.B
#> 3 Treat1_Rep.A Treat1 Rep.A
#> 4 Treat1_Rep.B Treat1 Rep.B
#> 5 Treat2_Rep.A Treat2 Rep.A
#> 6 Treat2_Rep.B Treat2 Rep.B
#> 7 Treat3_Rep.A Treat3 Rep.A
#> 8 Treat3_Rep.B Treat3 Rep.B
csp <- corplot::get_pair_wise_scatter(dat_tbl = expr_mat, group_tbl = groups,var_plot = condition, var_plot_group = repl,dat_id = gene_name)
csp
cor_tbl2 <- cor_tbl %>% dplyr::rename(`Rep.A`=var1, `Rep.B` = var2) %>%
dplyr::filter(grepl("Rep.A" ,`Rep.A`)) %>%
dplyr::filter(grepl("Rep.B" ,`Rep.B`)) %>%
TidyWrappers::tbl_replace_string("_.*" , "")
cor_tbl2
#> # A tibble: 16 x 3
#> Rep.A Rep.B corr
#> <chr> <chr> <dbl>
#> 1 Control Control 0.96
#> 2 Treat1 Control 0.95
#> 3 Treat2 Control 0.86
#> 4 Treat3 Control 0.91
#> 5 Control Treat1 0.9
#> 6 Treat1 Treat1 0.94
#> 7 Treat2 Treat1 0.93
#> 8 Treat3 Treat1 0.9
#> 9 Control Treat2 0.86
#> 10 Treat1 Treat2 0.9
#> 11 Treat2 Treat2 0.99
#> 12 Treat3 Treat2 0.9
#> 13 Control Treat3 0.91
#> 14 Treat1 Treat3 0.92
#> 15 Treat2 Treat3 0.9
#> 16 Treat3 Treat3 0.97
csp + ggplot2::geom_text(data = cor_tbl2, x = 4, y = 18, ggplot2::aes(label = paste("r","=",corr , sep = "")) ,
fontface="italic" , col = "red",size = 5)
csp2 <- corplot::get_pair_wise_scatter(dat_tbl = expr_mat, group_tbl = groups,var_plot = condition, var_plot_group = repl,dat_id = gene_name,view_matrix = FALSE)
csp2
cor_tbl3 <- cor_tbl2 %>% dplyr::filter(`Rep.A` == `Rep.B`)
csp3 <- corplot::get_pair_wise_scatter(dat_tbl = expr_mat, group_tbl = groups,var_plot = condition, var_plot_group = repl,dat_id = gene_name,view_matrix = FALSE)
csp2 + ggplot2::geom_text(data = cor_tbl3, x = 3, y = 18, ggplot2::aes(label = paste("r","=",corr , sep = "")) ,
fontface="italic" , col = "red")