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rough-draft_draw_scatter-plots.R
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#!/usr/bin/env Rscript
# rough-draft_draw_scatter-plots.R
# KA
# Load libraries, set options ================================================
suppressMessages(library(tidyverse))
options(scipen = 999)
options(ggrepel.max.overlaps = Inf)
# Initialize functions and themes ============================================
load_RDS_mRNA <- function(
p_RDS = "notebook/KA.2023-0608-0714.rds-data-objects_min-4-cts-all-but-1-samps",
p_mRNA = "rds_mRNA",
f_RDS
) {
readRDS(paste(p_RDS, p_mRNA, f_RDS, sep = "/"))
}
load_RDS_ncRNA <- function(
p_RDS = "notebook/KA.2023-0608-0714.rds-data-objects_min-4-cts-all-but-1-samps",
p_ncRNAcm = "rds_pa-ncRNA-collapsed-merged",
f_RDS
) {
readRDS(paste(p_RDS, p_ncRNAcm, f_RDS, sep = "/"))
}
plot_scatter <- function(
df = df_xy,
x = x,
y = y,
lm = linear_model,
r = r,
equation = lm_equation,
r_sq = lm_r_sq,
color = "#06636833",
xlab = "log2(FC) nascent",
ylab = "log2(FC) steady state",
draw_density = FALSE,
x_low = -7,
x_high = 7,
y_low = -7,
y_high = 7
) {
scatter <- ggplot(df, aes(x = x, y = y)) +
geom_abline( # x = y (example of one-to-one linear relationship)
slope = 1,
linetype = "solid",
color = "#00000050",
size = 1
) +
geom_hline( # y = 0
yintercept = 0,
linetype = "solid",
color = "#000000",
size = 1
) +
geom_vline( # x = 0
xintercept = 0,
linetype = "solid",
color = "#000000",
size = 1
) +
geom_point(size = 2.5, color = color) +
{ if(base::isTRUE(draw_density)) geom_density_2d(color = "#FFFFFF") } +
geom_abline( # linear regression (linear_model) slope and intercept
slope = coefficients(lm)[2],
intercept = coefficients(lm)[1],
linetype = "dotdash",
color = "#B40400",
alpha = 0.8,
size = 6
) +
annotate( # label the Pearson correlation
"text",
hjust = 0,
x = x_low,
y = y_high * 0.9,
size = 7,
label = paste("Pearson r =", r) #,
# fontface = "bold"
) +
annotate( # label the model equation
"text",
hjust = 0,
x = x_low,
y = y_high * 0.8,
size = 7,
label = equation #,
# fontface = "bold"
) +
annotate( # label the r-squared value
"text",
hjust = 0,
x = x_low,
y = y_high * 0.7,
size = 7,
label = bquote(r^2 ~ "=" ~ .(r_sq)) #,
# fontface = "bold"
) +
xlim(x_low, x_high) +
ylim(y_low, y_high) +
xlab(xlab) +
ylab(ylab) +
theme_AG_boxed_no_legend
return(scatter)
}
model_plot_scatter_log2FC <- function(
x, y, color, xlab, ylab, x_low, x_high, y_low, y_high
) {
# ...
#
# :param x: sample on x axis <data.frame>
# :param y: sample on y axis <data.frame>
# :param color: ... <chr>
# :param xlab: ... <chr>
# :param ylab: ... <chr>
# :param x_low: ... <int (neg)>
# :param x_high: ... <int (pos)>
# :param y_low: ... <int (neg)>
# :param y_high: ... <int (pos)>
# :return results_list: ... <list>
# Debug
debug <- FALSE
if(base::isTRUE(debug)) {
x = mRNA_N_Q_n3d$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`
y = `mRNA-AS_N_Q_n3d`$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`
color = "#3A538833"
xlab = "log2(FC) mRNA antisense"
ylab = "log2(FC) mRNA"
draw_density = TRUE
x_low = -9
x_high = 9
y_low = -9
y_high = 9
}
# Create dataframe of features present in both samples
vec_LFC_x <- x[x[["features"]] %in% y[["features"]], ][["log2FoldChange"]]
vec_LFC_y <- y[y[["features"]] %in% x[["features"]], ][["log2FoldChange"]]
df_xy <- tibble::tibble(x = vec_LFC_x, y = vec_LFC_y)
# Calculate linear model and correlation coefficients
linear_model <- lm(df_xy$y ~ df_xy$x)
lm_summary <- summary(linear_model)
lm_coef <- linear_model$coefficients
lm_m <- lm_coef[["df_xy$x"]]
lm_intercept <- lm_coef[["(Intercept)"]]
lm_equation <- paste(
"y =",
paste(
round(lm_intercept, 2),
paste(
round(lm_m, 2),
"x", # names(lm_coef[-1]),
sep = "", # sep = " * ",
collapse =" + "
),
sep = " + "
),
"+ e" # "+ ϵ"
)
lm_r_sq <- lm_summary$adj.r.squared
rho <- cor(df_xy$y, df_xy$x, method = "spearman")
r <- cor(df_xy$y, df_xy$x, method = "pearson")
# Draw scatter plot
scatter <- plot_scatter(
df = df_xy,
x = x,
y = y,
lm = linear_model,
r = round(r, 2),
equation = lm_equation,
r_sq = round(lm_r_sq, 3),
color = color,
xlab = xlab,
ylab = ylab,
draw_density = FALSE,
x_low = x_low,
x_high = x_high,
y_low = y_low,
y_high = y_high
)
# Draw scatter plot with density-line overlay
scatter_density <- plot_scatter(
df = df_xy,
x = x,
y = y,
lm = linear_model,
r = round(r, 2),
equation = lm_equation,
r_sq = round(lm_r_sq, 3),
color = color,
xlab = xlab,
ylab = ylab,
draw_density = TRUE,
x_low = x_low,
x_high = x_high,
y_low = y_low,
y_high = y_high
)
# Return results
results_list <- list()
results_list[["01_x"]] <- x
results_list[["01_y"]] <- y
results_list[["02_vec_LFC_x"]] <- vec_LFC_x
results_list[["02_vec_LFC_y"]] <- vec_LFC_y
results_list[["02_df_xy"]] <- df_xy
results_list[["03_a_linear_model"]] <- linear_model
results_list[["03_b_lm_summary"]] <- lm_summary
results_list[["03_c_lm_m"]] <- lm_m
results_list[["03_d_lm_r_sq"]] <- lm_r_sq
results_list[["03_e_lm_equation"]] <- lm_equation
results_list[["04_cor_rho"]] <- rho
results_list[["04_cor_r"]] <- r
results_list[["05_scatter"]] <- scatter
results_list[["05_scatter_density"]] <- scatter_density
return(results_list)
}
output_rds <- function(
list_obj,
outfile = paste0(
"/Users/kalavatt/Desktop", "/",
deparse(substitute(list_obj)), ".rds"
)
) {
readr::write_rds(list_obj, outfile)
}
print_scatter <- function(
dataframe,
outpath = "/Users/kalavatt/Desktop",
width = 7,
height = 7
) {
# ...
#
# :param dataframe: ...
# :param outpath: ...
# :param density: ...
# :param width: ...
# :param height: ...
debug <- FALSE
if(base::isTRUE(debug)) {
dataframe = dataframe
outpath = "/Users/kalavatt/Desktop"
width = 7
height = 7
}
part_1 <- unlist(stringr::str_split(deparse(substitute(dataframe)), "_"))[2]
part_2 <- unlist(stringr::str_split(deparse(substitute(dataframe)), "_"))[3]
part_3 <- unlist(stringr::str_split(deparse(substitute(dataframe)), "_"))[1]
part_0 <- "scatter-density"
file_prefix <- paste(part_0, part_1, part_2, part_3, sep = "_")
outfile <- paste0(
outpath, "/",
file_prefix, ".",
format(Sys.time(), format = "%F_%H.%M.%S"),
".pdf"
)
pdf(file = outfile, width = width, height = height)
print(dataframe$`05_scatter_density`)
dev.off()
part_0 <- "scatter"
file_prefix <- paste(part_0, part_1, part_2, part_3, sep = "_")
outfile <- paste0(
outpath, "/",
file_prefix, ".",
format(Sys.time(), format = "%F_%H.%M.%S"),
".pdf"
)
pdf(file = outfile, width = width, height = height)
print(dataframe$`05_scatter`)
dev.off()
}
model_plot_scatter_ORFs <- function(
df, color, plot_kb, xlab, ylab, x_low, x_high, y_low, y_high
) {
# ...
#
# :param x: sample on x axis <data.frame>
# :param y: sample on y axis <data.frame>
# :param color: ... <chr>
# :param plot_kb: ... <lgl>
# :param xlab: ... <chr>
# :param ylab: ... <chr>
# :param x_low: ... <int (neg)>
# :param x_high: ... <int (pos)>
# :param y_low: ... <int (neg)>
# :param y_high: ... <int (pos)>
# :return results_list: ... <list>
# Debug
debug <- FALSE
if(base::isTRUE(debug)) {
df = dge_mRNA_N_Q_n3d
color = "#3A538833"
plot_kb = TRUE
xlab = "ORF length (kb)"
ylab = "Nab3-AID/Parent\nlog2(FC) nascent"
x_low = 0
x_high = 15
y_low = -3.5
y_high = 8.5
}
# Create dataframe of features present in both samples
if(base::isTRUE(plot_kb)) {
vec_LFC_x <- df[["width"]] / 1000
} else {
vec_LFC_x <- df[["width"]]
}
vec_LFC_y <- df[["log2FoldChange"]]
df_xy <- tibble::tibble(x = vec_LFC_x, y = vec_LFC_y)
# Calculate linear model and correlation coefficients
linear_model <- lm(df_xy$y ~ df_xy$x)
lm_summary <- summary(linear_model)
lm_coef <- linear_model$coefficients
lm_m <- lm_coef[["df_xy$x"]]
lm_intercept <- lm_coef[["(Intercept)"]]
lm_equation <- paste(
"y =",
paste(
round(lm_intercept, 2),
paste(
round(lm_m, 2),
"x", # names(lm_coef[-1]),
sep = "", # sep = " * ",
collapse =" + "
),
sep = " + "
),
"+ e" # "+ ϵ"
)
lm_r_sq <- lm_summary$adj.r.squared
rho <- cor(df_xy$y, df_xy$x, method = "spearman")
r <- cor(df_xy$y, df_xy$x, method = "pearson")
# Draw scatter plot
scatter <- plot_scatter(
df = df_xy,
x = df_xy$x,
y = df_xy$y,
lm = linear_model,
r = round(r, 2),
equation = lm_equation,
r_sq = round(lm_r_sq, 2),
color = color,
xlab = xlab,
ylab = ylab,
draw_density = FALSE,
x_low = x_low,
x_high = x_high,
y_low = y_low,
y_high = y_high
)
# Draw scatter plot with density-line overlay
scatter_density <- plot_scatter(
df = df_xy,
x = x,
y = y,
lm = linear_model,
r = round(r, 2),
equation = lm_equation,
r_sq = round(lm_r_sq, 2),
color = color,
xlab = xlab,
ylab = ylab,
draw_density = TRUE,
x_low = x_low,
x_high = x_high,
y_low = y_low,
y_high = y_high
)
# Return results
results_list <- list()
results_list[["02_vec_LFC_x"]] <- vec_LFC_x
results_list[["02_vec_LFC_y"]] <- vec_LFC_y
results_list[["02_df_xy"]] <- df_xy
results_list[["03_a_linear_model"]] <- linear_model
results_list[["03_b_lm_summary"]] <- lm_summary
results_list[["03_c_lm_m"]] <- lm_m
results_list[["03_d_lm_r_sq"]] <- lm_r_sq
results_list[["03_e_lm_equation"]] <- lm_equation
results_list[["04_cor_rho"]] <- rho
results_list[["04_cor_r"]] <- r
results_list[["05_scatter"]] <- scatter
results_list[["05_scatter_density"]] <- scatter_density
return(results_list)
}
count_sig_up <- function(df) {
n_up <- df %>%
dplyr::filter(padj < 0.05 & log2FoldChange > 1.5) %>%
nrow()
return(n_up)
}
count_sig_down <- function(df) {
n_down <- df %>%
dplyr::filter(padj < 0.05 & log2FoldChange < -1.5) %>%
nrow()
return(n_down)
}
# Load custom ggplot2 themes -------------------------------------------------
theme_slick <- theme_classic() +
theme(
panel.grid.major = ggplot2::element_line(linewidth = 0.4),
panel.grid.minor = ggplot2::element_line(linewidth = 0.2),
axis.line = ggplot2::element_line(linewidth = 0.2),
axis.ticks = ggplot2::element_line(linewidth = 0.4),
axis.text = ggplot2::element_text(color = "black"),
axis.title.x = ggplot2::element_text(),
axis.title.y = ggplot2::element_text(),
plot.title = ggplot2::element_text(),
text = element_text(family = "")
)
theme_AG <- theme_classic() +
theme(
panel.grid.major = ggplot2::element_line(linewidth = 3),
panel.grid.minor = ggplot2::element_line(linewidth = 2),
axis.line = ggplot2::element_line(linewidth = 0.5),
axis.ticks = ggplot2::element_line(linewidth = 1.0),
axis.text = ggplot2::element_text(
color = "black", size = 20, face = "bold"
),
axis.title.x = ggplot2::element_text(size = 25, face = "bold"),
axis.title.y = ggplot2::element_text(size = 25, face = "bold"),
plot.title = ggplot2::element_text(size = 20),
text = element_text(family = "")
)
theme_AG_boxed <- theme_AG +
theme(
axis.line = ggplot2::element_line(linewidth = 0),
panel.border = element_rect(colour = "black", fill = NA, linewidth = 2)
)
theme_slick_no_legend <- theme_slick + theme(legend.position = "none")
theme_AG_no_legend <- theme_AG + theme(legend.position = "none")
theme_AG_boxed_no_legend <- theme_AG_boxed + theme(legend.position = "none")
# Get situated, load RDS files ===============================================
# Set work dir
p_base <- "/Users/kalavatt/projects-etc"
p_exp <- "2022-2023_RRP6-NAB3/results/2023-0215"
setwd(paste(p_base, p_exp, sep = "/"))
# Load RDS files
p_RDS <- "notebook/KA.2023-0608-0714.rds-data-objects_min-4-cts-all-but-1-samps"
p_mRNA <- "rds_mRNA"
p_mRNA_AS <- "rds_mRNA-AS"
p_ncRNAcm <- "rds_ncRNA-collapsed"
p_R64_etc <- "rds_coding-non-pa-ncRNA"
# mRNA features ------------------------------------------
load_mRNA_N_Q_r6n <- FALSE #ARGUMENT
if(base::isTRUE(load_mRNA_N_Q_r6n)) {
mRNA_N_Q_r6n <- readRDS(paste(
p_RDS,
p_mRNA,
"mRNA_DGE-analysis_N-Q-rrp6∆_N-Q-WT.rds",
sep = "/"
))
}
load_mRNA_SS_Q_r6n <- TRUE #ARGUMENT
if(base::isTRUE(load_mRNA_SS_Q_r6n)) {
mRNA_SS_Q_r6n <- readRDS(paste(
p_RDS,
p_mRNA,
"mRNA_DGE-analysis_SS-Q-rrp6∆_SS-Q-WT.rds",
sep = "/"
))
}
load_mRNA_SS_G1_r6n <- FALSE #ARGUMENT
if(base::isTRUE(load_mRNA_SS_G1_r6n)) {
mRNA_SS_G1_r6n <- readRDS(paste(
p_RDS,
p_mRNA,
"mRNA_DGE-analysis_SS-G1-rrp6∆_SS-G1-WT.rds",
sep = "/"
))
}
load_mRNA_N_Q_n3d <- TRUE #ARGUMENT
if(base::isTRUE(load_mRNA_N_Q_n3d)) {
mRNA_N_Q_n3d <- readRDS(paste(
p_RDS,
p_mRNA,
"mRNA_DGE-analysis_N-Q-nab3d_N-Q-parental.rds",
sep = "/"
))
}
load_mRNA_SS_Q_n3d <- FALSE #ARGUMENT
if(base::isTRUE(load_mRNA_SS_Q_n3d)) {
mRNA_SS_Q_n3d <- readRDS(paste(
p_RDS,
p_mRNA,
"mRNA_DGE-analysis_SS-Q-nab3d_SS-Q-parental.rds",
sep = "/"
))
}
# collapsed, merged pa-ncRNA features --------------------
load_ncRNAcm_N_Q_r6n <- FALSE #ARGUMENT
if(base::isTRUE(load_ncRNAcm_N_Q_r6n)) {
ncRNAcm_N_Q_r6n <- readRDS(paste(
p_RDS,
p_ncRNAcm,
"ncRNA-collapsed_DGE-analysis_N-Q-rrp6∆_N-Q-WT.rds",
sep = "/"
))
}
load_ncRNAcm_SS_Q_r6n <- TRUE #ARGUMENT
if(base::isTRUE(load_ncRNAcm_SS_Q_r6n)) {
ncRNAcm_SS_Q_r6n <- readRDS(paste(
p_RDS,
p_ncRNAcm,
"ncRNA-collapsed_DGE-analysis_SS-Q-rrp6∆_SS-Q-WT.rds",
sep = "/"
))
}
load_ncRNAcm_SS_G1_r6n <- FALSE #ARGUMENT
if(base::isTRUE(load_ncRNAcm_SS_G1_r6n)) {
ncRNAcm_SS_G1_r6n <- readRDS(paste(
p_RDS,
p_ncRNAcm,
"ncRNA-collapsed_DGE-analysis_SS-G1-rrp6∆_SS-G1-WT.rds",
sep = "/"
))
}
load_ncRNAcm_N_Q_n3d <- TRUE #ARGUMENT
if(base::isTRUE(load_ncRNAcm_N_Q_n3d)) {
ncRNAcm_N_Q_n3d <- readRDS(paste(
p_RDS,
p_ncRNAcm,
"ncRNA-collapsed_DGE-analysis_N-Q-nab3d_N-Q-parental.rds",
sep = "/"
))
}
load_ncRNAcm_SS_Q_n3d <- FALSE #ARGUMENT
if(base::isTRUE(load_ncRNAcm_SS_Q_n3d)) {
ncRNAcm_SS_Q_n3d <- readRDS(paste(
p_RDS,
p_ncRNAcm,
"ncRNA-collapsed_DGE-analysis_SS-Q-nab3d_SS-Q-parental.rds",
sep = "/"
))
}
# antisense mRNA features --------------------------------
`load_mRNA-AS_N_Q_r6n` <- FALSE #ARGUMENT
if(base::isTRUE(`load_mRNA-AS_N_Q_r6n`)) {
`mRNA-AS_N_Q_r6n` <- readRDS(paste(
p_RDS,
p_mRNA_AS,
"mRNA-AS_DGE-analysis_N-Q-rrp6∆_N-Q-WT.rds",
sep = "/"
))
}
`load_mRNA-AS_SS_Q_r6n` <- FALSE #ARGUMENT
if(base::isTRUE(`load_mRNA-AS_SS_Q_r6n`)) {
`mRNA-AS_SS_Q_r6n` <- readRDS(paste(
p_RDS,
p_mRNA_AS,
"mRNA-AS_DGE-analysis_SS-Q-rrp6∆_SS-Q-WT.rds",
sep = "/"
))
}
`load_mRNA-AS_SS_G1_r6n` <- FALSE #ARGUMENT
if(base::isTRUE(`load_mRNA-AS_SS_G1_r6n`)) {
`mRNA-AS_SS_G1_r6n` <- readRDS(paste(
p_RDS,
p_mRNA_AS,
"mRNA-AS_DGE-analysis_SS-G1-rrp6∆_SS-G1-WT.rds",
sep = "/"
))
}
`load_mRNA-AS_N_Q_n3d` <- TRUE #ARGUMENT
if(base::isTRUE(`load_mRNA-AS_N_Q_n3d`)) {
`mRNA-AS_N_Q_n3d` <- readRDS(paste(
p_RDS,
p_mRNA_AS,
"mRNA-AS_DGE-analysis_N-Q-nab3d_N-Q-parental.rds",
sep = "/"
))
}
`load_mRNA-AS_SS_Q_n3d` <- FALSE #ARGUMENT
if(base::isTRUE(`load_mRNA-AS_SS_Q_n3d`)) {
`mRNA-AS_SS_Q_n3d` <- readRDS(paste(
p_RDS,
p_mRNA_AS,
"mRNA-AS_DGE-analysis_SS-Q-nab3d_SS-Q-parental.rds",
sep = "/"
))
}
# R64 coding, noncoding, etc. features ------------------
`load_R64-etc_N_Q_r6n` <- FALSE #ARGUMENT
if(base::isTRUE(`load_R64-etc_N_Q_r6n`)) {
`R64-etc_N_Q_r6n` <- readRDS(paste(
p_RDS,
p_R64_etc,
"coding-non-pa-ncRNA_DGE-analysis_N-Q-rrp6∆_N-Q-WT.rds",
sep = "/"
))
}
`load_R64-etc_SS_Q_r6n` <- FALSE #ARGUMENT
if(base::isTRUE(`load_R64-etc_SS_Q_r6n`)) {
`R64-etc_SS_Q_r6n` <- readRDS(paste(
p_RDS,
p_R64_etc,
"coding-non-pa-ncRNA_DGE-analysis_SS-Q-rrp6∆_SS-Q-WT.rds",
sep = "/"
))
}
`load_R64-etc_SS_G1_r6n` <- FALSE #ARGUMENT
if(base::isTRUE(`load_R64-etc_SS_G1_r6n`)) {
`R64-etc_SS_G1_r6n` <- readRDS(paste(
p_RDS,
p_R64_etc,
"coding-non-pa-ncRNA_DGE-analysis_SS-G1-rrp6∆_SS-G1-WT.rds",
sep = "/"
))
}
`load_R64-etc_N_Q_n3d` <- FALSE #ARGUMENT
if(base::isTRUE(`load_R64-etc_N_Q_n3d`)) {
`R64-etc_N_Q_n3d` <- readRDS(paste(
p_RDS,
p_R64_etc,
"coding-non-pa-ncRNA_DGE-analysis_N-Q-nab3d_N-Q-parental.rds",
sep = "/"
))
}
`load_R64-etc_SS_Q_n3d` <- FALSE #ARGUMENT
if(base::isTRUE(`load_R64-etc_SS_Q_n3d`)) {
`R64-etc_SS_Q_n3d` <- readRDS(paste(
p_RDS,
p_R64_etc,
"coding-non-pa-ncRNA_DGE-analysis_SS-Q-nab3d_SS-Q-parental.rds",
sep = "/"
))
}
rm(list = ls(pattern = "p_"))
#DONE Plot/assess the following comparisons ===================================
#+ 5 - SS_Q_r6n ~ SS_Q_n3d (y ~ x):
#+ 4 - N_Q_n3d ~ N_Q_n3d_AS (y ~ x): mRNA_N_Q_n3d, `mRNA-AS_N_Q_n3d`
#+ 1 - SS_G1_r6n ~ SS_Q_r6n (y ~ x): mRNA_SS_G1_r6n, mRNA_SS_Q_r6n; ncRNAcm_SS_G1_r6n, ncRNAcm_SS_Q_r6n
#+ 2 - SS_Q_r6n ~ N_Q_r6n (y ~ x): mRNA_SS_Q_r6n, mRNA_N_Q_r6n; ncRNAcm_SS_G1_r6n, ncRNAcm_N_Q_r6n
#+ 3 - SS_Q_r6n ~ N_Q_n3d (y ~ x): mRNA_SS_Q_r6n, mRNA_N_Q_n3d; ncRNAcm_SS_G1_r6n, ncRNAcm_N_Q_n3d
# Color rules: "If all Steady State use purple 481A6C: if all Nascent, then
#+ use blue 3A5388."
# Draw scatter plots and do linear modeling ----------------------------------
# Fig. 3 -------------------------------------------------
# Fig. 3B: Look at SS_G1_r6n ~ SS_Q_r6n for mRNA
#+ i.e., mRNA_SS_G1_r6n ~ mRNA_SS_Q_r6n
run <- FALSE
if(base::isTRUE(run)) {
`mRNA_SS-G1-r6n_SS-Q-r6n` <- model_plot_scatter_log2FC(
x = mRNA_SS_Q_r6n$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`,
y = mRNA_SS_G1_r6n$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`,
color = "#481A6C33",
xlab = "Q log2(FC) steady state rrp6n/WT",
ylab = "G1 log2(FC) steady state rrp6n/WT",
x_low = -7,
x_high = 7,
y_low = -7,
y_high = 7
)
}
# Fig. 3B: Look at SS_G1_r6n ~ SS_Q_r6n for pa-ncRNA
#+ i.e., ncRNAcm_SS_G1_r6n ~ ncRNAcm_SS_Q_r6n
run <- FALSE
if(base::isTRUE(run)) {
`ncRNAcm_SS-G1-r6n_SS-Q-r6n` <- model_plot_scatter_log2FC(
x = ncRNAcm_SS_Q_r6n$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`,
y = ncRNAcm_SS_G1_r6n$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`,
color = "#481A6C33",
xlab = "Q log2(FC) steady state rrp6n/WT",
ylab = "G1 log2(FC) steady state rrp6n/WT",
x_low = -7,
x_high = 7,
y_low = -7,
y_high = 7
)
}
# Fig. 3D: Look at SS Q rrp6∆ ~ N Q rrp6∆ (SS_Q_r6n ~ N_Q_r6n) for mRNA
#+ i.e., mRNA_SS_Q_r6n ~ mRNA_N_Q_r6n
run <- FALSE
if(base::isTRUE(run)) {
`mRNA_SS-Q-r6n_N-Q-r6n` <- model_plot_scatter_log2FC(
x = mRNA_N_Q_r6n$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`,
y = mRNA_SS_Q_r6n$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`,
color = "#06636833",
xlab = "log2(FC) nascent",
ylab = "log2(FC) steady state",
x_low = -7,
x_high = 7,
y_low = -7,
y_high = 7
)
}
# Fig. 3D: Look at SS Q rrp6∆ ~ N Q rrp6∆ (SS_Q_r6n ~ N_Q_r6n) for pa-ncRNA
#+ i.e., ncRNAcm_SS_Q_r6n ~ ncRNAcm_N_Q_r6n
run <- FALSE
if(base::isTRUE(run)) {
`ncRNAcm_SS-Q-r6n_N-Q-r6n` <- model_plot_scatter_log2FC(
x = ncRNAcm_N_Q_r6n$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`,
y = ncRNAcm_SS_Q_r6n$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`,
color = "#06636833",
xlab = "log2(FC) nascent",
ylab = "log2(FC) steady state",
x_low = -7,
x_high = 7,
y_low = -7,
y_high = 7
)
}
# Fig. 4 -------------------------------------------------
# Fig. 4B: Look at SS Q rrp6∆ ~ N Q nab3d for mRNA
#+ i.e., mRNA_SS_Q_r6n ~ mRNA_N_Q_n3d
run <- FALSE
if(base::isTRUE(run)) {
`mRNA_SS-Q-r6n_N-Q-n3d` <- model_plot_scatter_log2FC(
x = mRNA_N_Q_n3d$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`,
y = mRNA_SS_Q_r6n$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`,
color = "#06636833",
xlab = "log2(FC) nascent nab3d/WT",
ylab = "log2(FC) steady state rrp6n/WT",
x_low = -9,
x_high = 9,
y_low = -9,
y_high = 9
)
}
# Supplemental table related to Fig. 4B (for revision) for mRNA
run <- TRUE
if(base::isTRUE(run)) {
x <- mRNA_N_Q_n3d$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`
y <- mRNA_SS_Q_r6n$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`
colnames(x)[6:11] <- paste("x", colnames(x)[6:11], sep = "_")
# colnames(x)
colnames(y)[6:11] <- paste("y", colnames(y)[6:11], sep = "_")
# colnames(y)
vec_cols <- c(
"genome", "seqnames", "start", "end", "width", "strand", "type",
"features", "names", "thorough"
)
df <- dplyr::full_join(x, y, by = vec_cols) %>%
dplyr::relocate(vec_cols, .before = "x_baseMean")
model <- lm(
y_log2FoldChange ~ x_log2FoldChange, data = df, na.action = na.omit
)
df$reg_line <- predict(model, newdata = df)
df <- df %>%
dplyr::mutate(
trend = dplyr::case_when(
y_log2FoldChange > reg_line ~ "y value above reg. line",
y_log2FoldChange < reg_line ~ "y value below reg. line",
TRUE ~ NA
)
)
check_trend <- TRUE
if(base::isTRUE(check_trend)) df$trend %>% table() %>% print()
readr::write_tsv(df, "~/Desktop/supp-tbl_Fig-4B_mRNA.tsv")
}
# Fig. 4B: Look at SS Q rrp6∆ ~ N Q nab3d for pa-ncRNA
#+ i.e., ncRNAcm_SS_Q_r6n ~ ncRNAcm_N_Q_n3d
run <- FALSE
if(base::isTRUE(run)) {
`ncRNAcm_SS-Q-r6n_N-Q-n3d` <- model_plot_scatter_log2FC(
x = ncRNAcm_N_Q_n3d$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`,
y = ncRNAcm_SS_Q_r6n$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`,
color = "#06636833",
xlab = "log2(FC) nascent nab3d/WT",
ylab = "log2(FC) steady state rrp6n/WT",
x_low = -9,
x_high = 9,
y_low = -9,
y_high = 9
)
}
# Supplemental table related to Fig. 4B (for revision) for pa-ncRNA
run <- TRUE
if(base::isTRUE(run)) {
x <- ncRNAcm_N_Q_n3d$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`
y <- ncRNAcm_SS_Q_r6n$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`
colnames(x)[6:11] <- paste("x", colnames(x)[6:11], sep = "_")
# colnames(x)
colnames(y)[6:11] <- paste("y", colnames(y)[6:11], sep = "_")
# colnames(y)
vec_cols <- c(
"genome", "seqnames", "start", "end", "width", "strand", "type",
"features", "names", "thorough"
)
df <- dplyr::full_join(x, y, by = vec_cols) %>%
dplyr::relocate(vec_cols, .before = "x_baseMean")
model <- lm(
y_log2FoldChange ~ x_log2FoldChange, data = df, na.action = na.omit
)
df$reg_line <- predict(model, newdata = df)
df <- df %>%
dplyr::mutate(
trend = dplyr::case_when(
y_log2FoldChange > reg_line ~ "y value above reg. line",
y_log2FoldChange < reg_line ~ "y value below reg. line",
TRUE ~ NA
)
)
check_trend <- TRUE
if(base::isTRUE(check_trend)) df$trend %>% table() %>% print()
readr::write_tsv(df, "~/Desktop/supp-tbl_Fig-4B_pancRNA.tsv")
}
# Fig. 4_: Look at SS Q rrp6∆ ~ SS Q nab3d for mRNA
#+ i.e., mRNA_SS_Q_r6n ~ mRNA_SS_Q_n3d
run <- FALSE
if(base::isTRUE(run)) {
`mRNA_SS-Q-r6n_SS-Q-n3d` <- model_plot_scatter_log2FC(
x = mRNA_SS_Q_n3d$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`,
y = mRNA_SS_Q_r6n$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`,
color = "#06636833",
xlab = "log2(FC) nascent nab3d/WT",
ylab = "log2(FC) steady state rrp6n/WT",
x_low = -9,
x_high = 9,
y_low = -9,
y_high = 9
)
}
# Fig. 4_: Look at SS Q rrp6∆ ~ SS Q nab3d for pa-ncRNA
#+ i.e., ncRNAcm_SS_Q_r6n ~ ncRNAcm_SS_Q_n3d
run <- FALSE
if(base::isTRUE(run)) {
`ncRNAcm_SS-Q-r6n_SS-Q-n3d` <- model_plot_scatter_log2FC(
x = ncRNAcm_SS_Q_n3d$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`,
y = ncRNAcm_SS_Q_r6n$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`,
color = "#06636833",
xlab = "log2(FC) nascent nab3d/WT",
ylab = "log2(FC) steady state rrp6n/WT",
x_low = -10,
x_high = 10,
y_low = -10,
y_high = 10
)
}
# Fig. 4C: Look at N Q Nab3-AID mRNA ~ N Q Nab3-AID mRNA antisense
#+ i.e., mRNA_N_Q_n3d ~ `mRNA-AS_N_Q_n3d`
run <- FALSE
if(base::isTRUE(run)) {
`mRNA-mRNA-AS_N-Q-n3d` <- model_plot_scatter_log2FC(
x = `mRNA-AS_N_Q_n3d`$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`,
y = mRNA_N_Q_n3d$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`,
color = "#3A538833",
xlab = "log2(FC) mRNA antisense",
ylab = "log2(FC) mRNA",
x_low = -9,
x_high = 9,
y_low = -9,
y_high = 9
)
}
# Fig. 4_: Look at N Q Rrp6∆ mRNA ~ N Q Rrp6∆ mRNA antisense
#+ i.e., mRNA_N_Q_r6n ~ `mRNA-AS_N_Q_r6n`
run <- TRUE
if(base::isTRUE(run)) {
`mRNA-mRNA-AS_N-Q-r6n` <- model_plot_scatter_log2FC(
x = `mRNA-AS_N_Q_r6n`$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`,
y = mRNA_N_Q_r6n$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`,
color = "#3A538833",
xlab = "log2(FC) mRNA antisense",
ylab = "log2(FC) mRNA",
x_low = -9,
x_high = 9,
y_low = -9,
y_high = 9
)
}
# Fig. 4_: Look at SS Q Nab3-AID ~ N Q Nab3-AID for mRNA
#+ i.e., mRNA_SS_Q_n3d ~ mRNA_N_Q_n3d
run <- FALSE
if(base::isTRUE(run)) {
`mRNA_SS-Q-n3d_N-Q-n3d` <- model_plot_scatter_log2FC(
x = mRNA_N_Q_n3d$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`,
y = mRNA_SS_Q_n3d$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`,
color = "#06636833",
xlab = "log2(FC) nascent n3d/parent",
ylab = "log2(FC) steady state n3d/parent",
x_low = -9,
x_high = 9,
y_low = -9,
y_high = 9
)
}
# Fig. 4_: Look at SS Q Nab3-AID ~ N Q Nab3-AID for pa-ncRNA
#+ i.e., ncRNAcm_SS_Q_n3d ~ ncRNAcm_N_Q_n3d
run <- FALSE
if(base::isTRUE(run)) {
`ncRNAcm_SS-Q-n3d_N-Q-n3d` <- model_plot_scatter_log2FC(
x = ncRNAcm_N_Q_n3d$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`,
y = ncRNAcm_SS_Q_n3d$`09_lfc_0.58_fc_1.5`$`04_t_DGE_unshrunken`,
color = "#06636833",
xlab = "log2(FC) nascent n3d/parent",
ylab = "log2(FC) steady state n3d/parent",
x_low = -10,
x_high = 10,
y_low = -10,
y_high = 10