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DiffR_Analysis.Rmd
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DiffR_Analysis.Rmd
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---
title: "Venn diagram analysis overestimates the extent of circadian rhythm reprogramming"
author: "Bharath Ananthasubramaniam"
date: "12/05/2021"
output: html_document
---
```{r packages, echo=FALSE}
library(compareRhythms)
suppressPackageStartupMessages(library(SummarizedExperiment))
suppressPackageStartupMessages(library(rain))
suppressPackageStartupMessages(library(genefilter))
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(DODR))
suppressPackageStartupMessages(library(patchwork))
library(ggthemes)
suppressPackageStartupMessages(library(gridExtra))
suppressPackageStartupMessages(library(clusterProfiler))
library(msigdbr)
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(org.Mm.eg.db))
library(openxlsx)
library(precrec)
library(ggforce)
library(HarmonicRegression)
library(gtable)
library(grid)
```
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, include = TRUE, warning = FALSE, fig.height = 3, fig.path='figures/',
dev = c('pdf'))
amp_choices <- c(0, 0.5)
set.seed(1234)
m_df <- msigdbr(species = "Mus musculus", category = "H")
m_t2g_hallmark <- m_df %>% dplyr::select(gs_name, gene_symbol) %>% as.data.frame()
m_df <- msigdbr(species = "Mus musculus", category = "C3", subcategory = "TFT:GTRD")
m_t2g_tft <- m_df %>% dplyr::select(gs_name, gene_symbol) %>% as.data.frame()
m_df <- msigdbr(species = "Mus musculus", category = "C2", subcategory = "CP:REACTOME")
m_t2g_reactome <- m_df %>% dplyr::select(gs_name, gene_symbol) %>% as.data.frame()
```
```{r graphics}
theme_custom <- function(base_size = 11, base_family = "Helvetica") {
theme_foundation(base_size = base_size, base_family = base_family) +
theme(
line = element_line(size = base_size/11, color = "grey20"),
rect = element_rect(fill = "white", size = base_size/15),
text = element_text(size = base_size),
plot.margin = margin(0.1, 0.1, 0.1, 0.1, unit = "lines"),
plot.background = element_rect(linetype = 0),
panel.border = element_blank(),
panel.background = element_blank(),
axis.line = element_line(size = rel(0.6)),
axis.text = element_text(size = rel(0.95)),
axis.text.x.bottom = element_text(vjust = 0.25),
axis.text.y.right = element_text(hjust = 0.25),
axis.ticks = element_line(size = rel(0.6)),
axis.ticks.length = unit(0.4, "lines"),
axis.title = element_text(size = rel(0.95)),
axis.title.x.bottom = element_text(vjust = 0),
axis.title.y.left = element_text(vjust = 1),
legend.background = element_rect(),
legend.position = "top",
legend.direction = "horizontal",
legend.box = "horizontal",
legend.title = element_text(size = rel(0.95)),
legend.text = element_text(size = rel(0.95)),
legend.box.spacing = unit(0.25, "lines"),
legend.key = element_rect(color = NA),
legend.key.size = unit(1.5, "lines"),
legend.box.background = element_blank(),
legend.margin = margin(t = 0, unit = "lines"),
panel.grid = element_line(size = rel(0.33)),
panel.grid.major = element_line(color = "grey80"),
panel.grid.minor = element_blank(),
strip.text = element_text(size = rel(0.95)),
strip.background = element_blank(),
panel.spacing = unit(1, "lines"),
plot.tag = element_text(face = "bold"),
plot.caption = element_blank(),
plot.title = element_blank(),
plot.subtitle = element_blank(),
aspect.ratio = 0.75
)
}
theme_set(theme_custom(base_size = 9))
update_geom_defaults("line", list(size = 0.8))
theme_custom_void <- function(base_size = 11, base_family = "Helvetica") {
theme_void(base_size = base_size, base_family = base_family) +
theme(
text = element_text(size = base_size),
legend.background = element_rect(),
legend.position = "top",
legend.direction = "horizontal",
legend.box = "horizontal",
legend.title = element_text(size = rel(0.95)),
legend.text = element_text(size = rel(0.95)),
legend.box.spacing = unit(0.25, "lines"),
legend.key = element_rect(color = NA),
legend.key.size = unit(1.5, "lines"),
legend.box.background = element_blank(),
legend.margin = margin(t = 0, unit = "lines"),
plot.tag = element_text(face = "bold", size = base_size),
plot.title = element_text(size = 9/11*base_size),
plot.margin = margin(t=0.1,r=0.1,b=0.1,l=0.1, unit = "lines"),
aspect.ratio = 0.75
)
}
df_venn <- data.frame(x= c(0, 1), y = c(0, 0), labels=c('even', 'odd'))
f_venn <- ggplot(df_venn) +
geom_circle(aes(x0 = x, y0 = y, r=1, fill=labels), alpha=0.7, size = 1, color=NA) +
geom_circle(aes(x0 = x, y0 = y, r=1, fill=labels), fill=NA, color="grey40", size=0.5) +
coord_fixed(expand = FALSE, clip = "off") + theme_custom_void() +
scale_fill_tableau(name="", palette = "Color Blind") +
annotate("text", x=0.5, y=-1.25, label="diffR transcripts \ncalled", size=5/14*8) +
annotate("curve", x=-0.25, y=-1.25, xend=-0.5, yend=-0.75, arrow=arrow(type="closed", length = unit(6, "pt")),
curvature=-0.5) +
annotate("curve", x=1.25, y=-1.25, xend=1.5, yend=-0.75, arrow=arrow(type="closed", length = unit(6, "pt")),
curvature=0.5) + theme(legend.position = "none", axis.title.x.top = element_text(size = 8))
tt <- ttheme_minimal(colhead=list(fg_params=list(fontface="bold"),
bg_params=list(fill=tableau_color_pal(palette="Superfishel Stone")(2)[1]),
padding=unit(c(1,3),"mm")),
base_size = 7, padding=unit(c(1,3),"mm"),
rowhead=list(fg_params=list(fontface="bold"),
bg_params=list(fill=tableau_color_pal(palette="Superfishel Stone")(2)[1]),
padding=unit(c(1,3),"mm")))
label_one <- function(labels) return(list(labels$id))
```
```{r functions}
venn_diagram_analysis <- function(y, exp_design, period = 24) {
exp_design <- base::cbind(exp_design,
col_number = base::seq(base::nrow(exp_design)))
group_id <- base::unique(exp_design$group)
assertthat::are_equal(length(group_id), 2)
exp_design_A <- exp_design[exp_design$group == group_id[1], ]
exp_design_A <- exp_design_A[base::order(exp_design_A$time), ]
exp_design_B <- exp_design[exp_design$group == group_id[2], ]
exp_design_B <- exp_design_B[base::order(exp_design_B$time), ]
deltat_A <- min(diff(base::unique(exp_design_A$time)))
time_A <- base::seq(min(exp_design_A$time),
max(exp_design_A$time),
by = deltat_A)
unique_times_A <- table(exp_design_A$time)
measure_sequence_A <- vapply(time_A,
function(t) {
ifelse(any(names(unique_times_A) == t),
unique_times_A[names(unique_times_A) == t],
0)
},
integer(1))
deltat_B <- min(diff(unique(exp_design_B$time)))
time_B <- seq(min(exp_design_B$time),
max(exp_design_B$time),
by = deltat_B)
unique_times_B <- table(exp_design_B$time)
measure_sequence_B <- vapply(time_B,
function(t) {
ifelse(any(names(unique_times_B) == t),
unique_times_B[names(unique_times_B) == t],
0)
},
integer(1))
y_A <- y[, exp_design_A$col_number]
y_B <- y[, exp_design_B$col_number]
assertthat::assert_that((sum(measure_sequence_A) >= 12) && (sum(measure_sequence_B) >= 12),
msg = "Not enough samples to run RAIN rhythmicity analysis confidently.")
rain_A <- mcparallel(rain(t(y_A), deltat_A, period,
measure.sequence = measure_sequence_A), "A")
rain_B <- mcparallel(rain(t(y_B), deltat_B, period,
measure.sequence = measure_sequence_B), "B")
rain_analysis <- mccollect(list(rain_A, rain_B))
circ_params_A <- compute_circ_params(y_A, exp_design_A$time, period = period)
circ_params_B <- compute_circ_params(y_B, exp_design_B$time, period = period)
results <- data.frame(adj_p_val_A = p.adjust(rain_analysis[[1]][, "pVal"], method = "BH"),
adj_p_val_B = p.adjust(rain_analysis[[2]][, "pVal"], method = "BH"),
A_amp = circ_params_A[, 1],
B_amp = circ_params_B[, 1],
A_phase = circ_params_A[, 2],
B_phase = circ_params_B[, 2])
rownames(results) <- rownames(y)
return(results)
}
venn_diagram_analysis_limma <- function(data, exp_design, period = 24, rnaseq=FALSE){
group_id <- base::levels(exp_design$group)
exp_design <- base::cbind(exp_design,
inphase = cos(2 * pi * exp_design$time / period),
outphase = sin(2 * pi * exp_design$time / period))
if ("batch" %in% colnames(exp_design)) {
design <- stats::model.matrix(~0 + group + group:inphase + group:outphase + batch,
data = exp_design)
} else {
design <- stats::model.matrix(~0 + group + group:inphase + group:outphase,
data = exp_design)
}
colnames(design) <- gsub("group", "", colnames(design))
colnames(design) <- gsub(":", "_", colnames(design))
if (rnaseq){
v <- edgeR::DGEList(data)
v <- edgeR::calcNormFactors(v)
y <- limma::voomWithQualityWeights(v, design)
} else {
y <- data
}
fit <- limma::lmFit(y, design)
fit <- limma::eBayes(fit, robust = TRUE, trend = !rnaseq)
rhythmic_in_either <- limma::topTable(fit,
coef = grep("phase", colnames(design)),
number = Inf,
sort.by = "none")
adj_p_val_A_or_B <- rhythmic_in_either$adj.P.Val
rhythmic_in_A <- limma::topTable(fit,
coef = intersect(grep(group_id[1], colnames(design)), grep("phase", colnames(design))),
number = Inf,
sort.by = "none")
rhythmic_in_B <- limma::topTable(fit,
coef = intersect(grep(group_id[2], colnames(design)), grep("phase", colnames(design))),
number = Inf,
sort.by = "none")
results <- data.frame(adj_p_val_A_or_B, adj_p_val_A = rhythmic_in_A$adj.P.Val, adj_p_val_B = rhythmic_in_B$adj.P.Val,
id = rownames(rhythmic_in_either))
return(results)
}
venn_diagram_analysis_deseq2 <- function(counts, exp_design, lengths, period = 24){
exp_design_aug <- base::cbind(exp_design,
inphase = cos(2 * pi * exp_design$time / period),
outphase = sin(2 * pi * exp_design$time / period))
if ("batch" %in% colnames(exp_design)) {
design <- stats::model.matrix(~group + group:inphase + group:outphase + batch,
data = exp_design_aug)
} else {
design <- stats::model.matrix(~group + group:inphase + group:outphase,
data = exp_design_aug)
}
colnames(design) <- gsub("group", "", colnames(design))
colnames(design) <- gsub(":", "_", colnames(design))
mode(counts) <- "integer"
dds <- DESeq2::DESeqDataSetFromMatrix(countData = counts,
colData = exp_design_aug,
design = design)
if (!is.null(lengths)) {
SummarizedExperiment::assays(dds)[["avgTxLength"]] <- lengths
}
dds <- DESeq2::DESeq(dds, test = "LRT",
reduced = design[, !grepl("phase", colnames(design))],
quiet = TRUE)
group_id <- base::levels(exp_design$group)
rhythmic_in_either <- DESeq2::results(dds, independentFiltering = FALSE,
cooksCutoff = FALSE)
adj_p_val_A_or_B <- rhythmic_in_either$padj
dds <- DESeq2::DESeq(dds, test = "LRT",
reduced = design[, !(grepl("phase", colnames(design)) & grepl(group_id[2], colnames(design)))],
quiet = TRUE)
rhythmic_in_A <- DESeq2::results(dds, independentFiltering = FALSE,
cooksCutoff = FALSE)
dds <- DESeq2::DESeq(dds, test = "LRT",
reduced = design[, !(grepl("phase", colnames(design)) & grepl(group_id[1], colnames(design)))],
quiet = TRUE)
rhythmic_in_B <- DESeq2::results(dds, independentFiltering = FALSE,
cooksCutoff = FALSE)
results <- data.frame(adj_p_val_A_or_B, adj_p_val_A = rhythmic_in_A$padj, adj_p_val_B = rhythmic_in_B$padj)
return(results)
}
compute_circ_params <- function(y, t, period) {
inphase <- cos(2 * pi * t / period)
outphase <- sin(2 * pi * t / period)
X <- model.matrix(~inphase + outphase)
fit <- lm.fit(X, t(y))
amps <- 2 * sqrt(base::colSums(fit$coefficients[-1, ]^2))
phases <- 12/pi*atan2(fit$coefficients[3,], fit$coefficients[2,])
return(cbind(amps,phases))
}
filter_genes <- function(y, group, min.count=10, min.expr=4, type="ma", n.prop=0.7) {
stopifnot(type %in% c("ma", "rnaseq"))
yA <- y[, group == levels(group)[1]]
yB <- y[, group == levels(group)[2]]
if (type == "ma") {
keep <- pmin(rowMeans(yA>min.expr), rowMeans(yB>min.expr)) > n.prop
} else {
medianLibSize <- median(colSums(y))
cpm.cutoff <- min.count/medianLibSize*1e6
keep <- pmin(rowMeans(edgeR::cpm(yA)>cpm.cutoff),
rowMeans(edgeR::cpm(yB)>cpm.cutoff)) > n.prop
}
return(keep)
}
shifter <- function(x, n = 1) {
if (n == 0) x else c(tail(x, -n), head(x, n))
}
prc_interpolate <- function(TP, FP, maxTrue, maxFeatures) {
x <- seq(maxTrue-TP)
return(data.frame("TP" = TP + x, "FP" = FP + (maxFeatures - maxTrue - FP)*x/(maxTrue - TP), "FN" = maxTrue-TP-x))
}
```
## Figure 1
```{r scenario-1-dataset, include=FALSE}
liver_hughes <- readRDS("data/GSE11923.RDS")
f1 <- kOverA(48, 5)
sel <- genefilter(assay(liver_hughes), f1)
liver_hughes <- liver_hughes[sel, ]
## ----------create artificial group of odd and even time points -------##
exp_design <- as.data.frame(colData(liver_hughes))
exp_design$time <- as.numeric(do.call(rbind, strsplit(as.character(liver_hughes$title), " ", fixed = TRUE))[,3])
exp_design$group <- factor(ifelse(exp_design$time %% 2, "O", "E"))
```
```{r schema}
df <- data.frame(x = c(seq(18, 21), 24, 25),
label=paste0("CT", c(18, 19, 20, 21, 64, 65)))
df$fill <- ifelse(df$x %% 2, "odd", "even")
df$y <- ifelse(df$x %% 2, -2, 0)
f1_1 <- ggplot(df, aes(x,y)) +
geom_line(aes(y=0), color="grey80") +
annotate("line", x=19, y=c(-2,0), color="grey80") +
annotate("line", x=21, y=c(-2,0), color="grey80") +
annotate("line", x=25, y=c(-2,0), color="grey80") +
geom_point(aes(fill=fill, color=fill), shape=22, size = 4) +
geom_text(aes(y = 1.25, label=label), size=8*5/14) +
annotate("line", y=0, x=c(21.25, 23.75), color="white", linetype="dashed") +
annotate("text", y=0, x=16.5, label="even", hjust=0, size=8*5/14) +
annotate("text", y=-2, x=16.5, label="odd", hjust=0, size=8*5/14) +
annotate("text", y=3.5, x=21, label="First scenario", fontface="bold", size=9*5/14) +
annotate("text", y=3.5, x=28, label="Second scenario", fontface="bold", size=9*5/14) +
annotate("segment", x=25.5, xend = 26.75, y=-2, yend=-2,
arrow=grid::arrow(length = unit(6, "pt"), type="closed")) +
annotate("text", y=-2, x=27.25, hjust=0,
label="scale and shift time labels \nof 500 selected transcripts", size=8*5/14) +
annotate("segment", x=25.5, xend = 26.75, y=0, yend=0,
arrow=grid::arrow(length = unit(6, "pt"), type="closed")) +
annotate("text", y=0.25, x=27.25, hjust=0,
label="unchanged", size=8*5/14) +
coord_cartesian(ylim = c(-3,4), xlim=c(17, 30)) + scale_fill_tableau(name="Dataset", palette = "Color Blind") +
scale_color_tableau(name="Dataset") + theme_void(base_size = 9) +
theme(legend.position = "none", plot.margin = margin(t=5,b=5,r=5,l=5),
panel.border = element_rect(fill=NA, colour = NA),
plot.tag = element_text(face = "bold"))
```
```{r scenario-1-analysis, include=FALSE}
VDA_scenario_1 <- venn_diagram_analysis(assay(liver_hughes), exp_design)
df1_2_1 <- data.frame(x=c(-0.5, 0.5, 1.5), y = 0,
label=c("rhythmic\n only in even", "rhythmic\n in both", "rhythmic\n only in odd"))
df1_2_1$counts <- c(sum(VDA_scenario_1$adj_p_val_A<0.05 & VDA_scenario_1$adj_p_val_B>0.05),
sum(VDA_scenario_1$adj_p_val_A<0.05 & VDA_scenario_1$adj_p_val_B<0.05),
sum(VDA_scenario_1$adj_p_val_A>0.05 & VDA_scenario_1$adj_p_val_B<0.05))
f1_2_1 <- f_venn +
geom_text(aes(x=x,y=y,label=paste0(label, " \n", counts)),
data=df1_2_1, color="white", fontface="bold.italic", size=5/14*6) +
labs(title = "FDR < 0.05")
df1_2_2 <- data.frame(x=c(-0.5, 0.5, 1.5), y = 0,
label=c("rhythmic\n only in even", "rhythmic\n in both", "rhythmic\n only in odd"))
df1_2_2$counts <- c(sum(VDA_scenario_1$adj_p_val_A<0.001 & VDA_scenario_1$adj_p_val_B>0.001),
sum(VDA_scenario_1$adj_p_val_A<0.001 & VDA_scenario_1$adj_p_val_B<0.001),
sum(VDA_scenario_1$adj_p_val_A>0.001 & VDA_scenario_1$adj_p_val_B<0.001))
f1_2_2 <- f_venn +
geom_text(aes(x=x,y=y,label=paste0(label, " \n", counts)),
data=df1_2_2, color="white", fontface="bold.italic", size=5/14*6) +
labs(title = "FDR < 0.001")
df1_3 <- data.frame(fdr_cutoff = 10^seq(-3.5, 0, by = 0.2))
df1_3$no_of_DR <- vapply(df1_3$fdr_cutoff,
function(t) {
return(sum(xor(VDA_scenario_1$adj_p_val_A < t,
VDA_scenario_1$adj_p_val_B < t))/sum(VDA_scenario_1$adj_p_val_A<t | VDA_scenario_1$adj_p_val_B<t))
}, FUN.VALUE = numeric(1))
f1_3 <- ggplot(df1_3) +
geom_line(aes_(x=~fdr_cutoff, y=~no_of_DR)) +
xlab("FDR threshold for rhythms") +
ylab("rhythmic transcripts called DiffR") + coord_cartesian(xlim = c(0.0005,0.2)) +
scale_x_continuous(trans = "log10", breaks = c(0.001, 0.01, 0.05, 0.2),
labels = c(0.001, 0.01, 0.05, 0.2)) +
scale_y_continuous(labels=scales::percent_format(accuracy = 1))
```
```{r scenario-2-dataset, include=FALSE}
##--------create artificial DR genes by exchanging labels of genes-------##
N <- 500
hughes_harm_reg <- harmonic.regression(t(assay(liver_hughes)), seq(18, 65), normalize = FALSE)
amp_cutoff <- median(hughes_harm_reg$pars$amp[hughes_harm_reg$qvals<0.05])
large_amp_genes <- hughes_harm_reg$pars[(hughes_harm_reg$pars$amp > amp_cutoff &
hughes_harm_reg$qvals<0.05), ]
rhythmic_in_A <- rownames(large_amp_genes)[sample.int(nrow(large_amp_genes))][seq(min(N, nrow(large_amp_genes)))]
new_expr <- assay(liver_hughes)
for (gene in rhythmic_in_A) {
temp <- new_expr[gene, ]
scale_shift <- runif(1)*(temp[exp_design$group == "O"] - mean(temp[exp_design$group == "O"])) + mean(temp[exp_design$group == "O"]) + hughes_harm_reg$pars[gene, "amp"]/4*rnorm(length(temp)/2)
temp[exp_design$group == "O"] <- shifter(scale_shift, sample(length(scale_shift), 1))
new_expr[gene, ] <- temp
}
```
```{r scenario-2-analysis, include=FALSE}
VDA_scenario_2 <- venn_diagram_analysis(new_expr, exp_design)
rhythmic_in_A <- VDA_scenario_2 %>%
magrittr::extract(rhythmic_in_A,) %>%
filter(adj_p_val_A<0.05 | adj_p_val_B<0.05) %>%
rownames()
rhythmic_in_A_large_amp <- rhythmic_in_A[hughes_harm_reg$pars[rhythmic_in_A, "amp"]*2 > amp_choices[2]] # amp in compareRhythms equals 2 * amp in harmonic regression
true_dr_genes <- (rownames(VDA_scenario_2) %in% rhythmic_in_A)
df1_4 <- data.frame(x=c(-0.5, 0.5, 1.5), y = 0,
label=c("rhythmic\n only in even", "rhythmic\n in both", "rhythmic\n only in odd"))
df1_4$counts <- c(sum(VDA_scenario_2$adj_p_val_A<0.05 & VDA_scenario_2$adj_p_val_B>0.05),
sum(VDA_scenario_2$adj_p_val_A<0.05 & VDA_scenario_2$adj_p_val_B<0.05),
sum(VDA_scenario_2$adj_p_val_A>0.05 & VDA_scenario_2$adj_p_val_B<0.05))
df1_4$TP <- c(sum(VDA_scenario_2$adj_p_val_A<0.05 &
VDA_scenario_2$adj_p_val_B>0.05 &
true_dr_genes), 0,
sum(VDA_scenario_2$adj_p_val_A>0.05 &
VDA_scenario_2$adj_p_val_B<0.05 &
true_dr_genes))
df1_4$TP <- paste0("[", df1_4$TP, "]")
df1_4$TP[2] <- ""
f1_4 <- f_venn +
geom_text(aes(x=x,y=y,label=paste0(label, " \n", counts, "\n", TP)),
data=df1_4, color="white", fontface="bold.italic", size=5/14*6) +
labs(title = "FDR < 0.05")
df1_5 <- vapply(10^seq(-9, log10(0.999), by=0.1),
function(t) {
est_dr_genes <- xor(VDA_scenario_2$adj_p_val_A < t,
VDA_scenario_2$adj_p_val_B < t)
tp <- sum(true_dr_genes & est_dr_genes)
fn <- sum(true_dr_genes & !est_dr_genes)
fp <- sum(!true_dr_genes & est_dr_genes)
tn <- sum(!true_dr_genes & !est_dr_genes)
return(c(fdr_cutoff = t, TP = tp, FP = fp, FN = fn, TN=tn))
}, FUN.VALUE = numeric(5)) %>%
t() %>% as.data.frame
point_1 <- with(df1_5, which.min((TP/(TP+FP)-0.5)^2 + (TP/(TP+FN)-0.5)^2))
point_2 <- with(df1_5, which.min(abs(fdr_cutoff - 0.05)))
f1_5 <- ggplot(df1_5, aes_(x=~TP/(TP+FN), y=~TP/(FP+TP))) +
geom_hline(aes_(yintercept=~N/(N + nrow(VDA_scenario_2))), color="grey50", linetype="longdash") +
geom_path(color="black") +
geom_point(aes_(x=~TP/(TP+FN), y=~TP/(FP+TP)), shape=21, size=2, stroke=0.75, fill="white", data = df1_5[point_1,]) +
geom_point(aes_(x=~TP/(TP+FN), y=~TP/(FP+TP)), shape=21, size=2, stroke=0.75, fill="grey50", data = df1_5[point_2,]) +
xlab("true DiffR transcripts recovered") +
annotate("curve", x=0.37, y=0.22, xend=0.47, yend=0.17, arrow=arrow(type="closed", length = unit(4, "pt")),
curvature=-0.25, color="grey30") + ylab("true DiffR transcripts in DiffR calls") +
annotate("text", x=0.29, y = 0.3, color="grey30", label="increasing \nFDR threshold", hjust=0, size=5/14*6.5) +
coord_cartesian(xlim=c(0,0.5), ylim=c(0,0.4)) + scale_x_continuous(labels = scales::percent_format(accuracy = 1)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1))
df1_6 <- data.frame(x=c(-0.5, 0.5, 1.5), y = 0,
label=c("rhythmic\n only in even", "rhythmic\n in both", "rhythmic\n only in odd"))
df1_6$counts <- c(sum(VDA_scenario_2$adj_p_val_A<df1_5[point_1, "fdr_cutoff"] &
VDA_scenario_2$adj_p_val_B>df1_5[point_1, "fdr_cutoff"]),
sum(VDA_scenario_2$adj_p_val_A<df1_5[point_1, "fdr_cutoff"] &
VDA_scenario_2$adj_p_val_B<df1_5[point_1, "fdr_cutoff"]),
sum(VDA_scenario_2$adj_p_val_A>df1_5[point_1, "fdr_cutoff"] &
VDA_scenario_2$adj_p_val_B<df1_5[point_1, "fdr_cutoff"]))
df1_6$TP <- c(sum(VDA_scenario_2$adj_p_val_A<df1_5[point_1, "fdr_cutoff"] &
VDA_scenario_2$adj_p_val_B>df1_5[point_1, "fdr_cutoff"] &
true_dr_genes), 0,
sum(VDA_scenario_2$adj_p_val_A>df1_5[point_1, "fdr_cutoff"] &
VDA_scenario_2$adj_p_val_B<df1_5[point_1, "fdr_cutoff"] &
true_dr_genes))
df1_6$TP <- paste0("[", df1_6$TP, "]")
df1_6$TP[2] <- ""
f1_6 <- f_venn +
geom_text(aes(x=x,y=y,label=paste0(label, " \n", counts, "\n", TP)),
data=df1_6, color="white", fontface="bold.italic", size=5/14*6) +
labs(title = paste0("FDR < ", scales::scientific(df1_5[point_1, "fdr_cutoff"], digits=2)))
```
```{r Figure1, echo=FALSE, fig.height=5.75, fig.width=6.5}
layout <- c(area(1,1,1,3), area(2,1), area(2,2), area(2,3), area(3,1), area(3,2), area(3,3))
f1_1 + f1_2_1 + f1_2_2 + f1_3 + f1_4 + f1_5 + f1_6 + plot_layout(design = layout, heights = c(1,2,2)) +
plot_annotation(tag_levels = 'A')
```
## Figure S1
```{r scenario-1-supp, include=FALSE}
dfS1_1_1 <- data.frame(x=c(-0.5, 0.5, 1.5), y = 0,
label=c("rhythmic\n only in even", "rhythmic\n in both", "rhythmic\n only in odd"))
dfS1_1_1$counts <- c(sum(VDA_scenario_1$adj_p_val_A<0.05 & VDA_scenario_1$A_amp > amp_choices[2] &
(VDA_scenario_1$adj_p_val_B>0.05 | VDA_scenario_1$B_amp < amp_choices[2])),
sum(VDA_scenario_1$adj_p_val_A<0.05 & VDA_scenario_1$A_amp > amp_choices[2] &
VDA_scenario_1$adj_p_val_B<0.05 & VDA_scenario_1$B_amp > amp_choices[2]),
sum((VDA_scenario_1$adj_p_val_A>0.05 | VDA_scenario_1$A_amp > amp_choices[2]) &
VDA_scenario_1$adj_p_val_B<0.05 & VDA_scenario_1$B_amp > amp_choices[2]))
fS1_1_1 <- f_venn +
geom_text(aes(x=x,y=y,label=paste0(label, " \n", counts)),
data=dfS1_1_1, color="white", fontface="bold.italic", size=5/14*7) +
labs(title = "FDR < 0.05")
dfS1_1_2 <- data.frame(x=c(-0.5, 0.5, 1.5), y = 0,
label=c("rhythmic\n only in even", "rhythmic\n in both", "rhythmic\n only in odd"))
dfS1_1_2$counts <- c(sum(VDA_scenario_1$adj_p_val_A<0.001 & VDA_scenario_1$A_amp > amp_choices[2] &
(VDA_scenario_1$adj_p_val_B>0.001 | VDA_scenario_1$B_amp < amp_choices[2])),
sum(VDA_scenario_1$adj_p_val_A<0.001 & VDA_scenario_1$A_amp > amp_choices[2] &
VDA_scenario_1$adj_p_val_B<0.001 & VDA_scenario_1$B_amp > amp_choices[2]),
sum((VDA_scenario_1$adj_p_val_A>0.001 | VDA_scenario_1$A_amp > amp_choices[2]) &
VDA_scenario_1$adj_p_val_B<0.001 & VDA_scenario_1$B_amp > amp_choices[2]))
fS1_1_2 <- f_venn +
geom_text(aes(x=x,y=y,label=paste0(label, " \n", counts)),
data=dfS1_1_2, color="white", fontface="bold.italic", size=5/14*7) +
labs(title = "FDR < 0.001")
dfS1_2 <- data.frame(fdr_cutoff = 10^seq(-3.5, 0, by = 0.2))
dfS1_2$frac_DR <- vapply(df1_3$fdr_cutoff,
function(t) {
return(sum(xor(VDA_scenario_1$adj_p_val_A < t & VDA_scenario_1$A_amp > amp_choices[2],
VDA_scenario_1$adj_p_val_B < t &
VDA_scenario_1$B_amp > amp_choices[2]))/sum((VDA_scenario_1$adj_p_val_A<t &
VDA_scenario_1$A_amp > amp_choices[2]) |
(VDA_scenario_1$adj_p_val_B<t &
VDA_scenario_1$B_amp > amp_choices[2])))
}, FUN.VALUE = numeric(1))
fS1_2 <- ggplot(dfS1_2) +
geom_line(aes_(x=~fdr_cutoff, y=~frac_DR)) +
xlab("FDR threshold for rhythms") +
ylab("rhythmic transcripts called DiffR") + coord_cartesian(xlim = c(0.0005,0.2)) +
scale_x_continuous(trans = "log10", breaks = c(0.001, 0.01, 0.05, 0.2),
labels = c(0.001, 0.01, 0.05, 0.2)) +
scale_y_continuous(labels=scales::percent_format(accuracy = 1))
mod_sel_scenario_1 <- compareRhythms(assay(liver_hughes), exp_design = exp_design, method = "mod_sel")
dodr_scenario_1 <- compareRhythms(assay(liver_hughes), exp_design = exp_design, method = "dodr")
limma_scenario_1 <- compareRhythms(assay(liver_hughes), exp_design = exp_design, method = "limma")
```
```{r scenario-2-supp}
dfS1_3 <- data.frame(x=c(-0.5, 0.5, 1.5), y = 0,
label=c("rhythmic\n only in even", "rhythmic\n in both", "rhythmic\n only in odd"))
dfS1_3$counts <- c(sum(VDA_scenario_2$adj_p_val_A<0.05 &
VDA_scenario_2$A_amp>amp_choices[2] &
(VDA_scenario_2$adj_p_val_B>0.05 |
VDA_scenario_2$B_amp<amp_choices[2])),
sum(VDA_scenario_2$adj_p_val_A<0.05 &
VDA_scenario_2$A_amp>amp_choices[2] &
VDA_scenario_2$adj_p_val_B<0.05 &
VDA_scenario_2$B_amp>amp_choices[2]),
sum((VDA_scenario_2$adj_p_val_A>0.05 |
VDA_scenario_2$A_amp<amp_choices[2]) &
VDA_scenario_2$adj_p_val_B<0.05 &
VDA_scenario_2$B_amp>amp_choices[2]))
dfS1_3$TP <- c(sum(VDA_scenario_2$adj_p_val_A<0.05 &
VDA_scenario_2$A_amp>amp_choices[2] &
(VDA_scenario_2$adj_p_val_B>0.05 |
VDA_scenario_2$B_amp<amp_choices[2]) &
true_dr_genes), 0,
sum((VDA_scenario_2$adj_p_val_A>0.05 |
VDA_scenario_2$A_amp<amp_choices[2]) &
VDA_scenario_2$adj_p_val_B<0.05 &
VDA_scenario_2$B_amp>amp_choices[2] &
true_dr_genes))
dfS1_3$TP <- paste0("[", dfS1_3$TP, "]")
dfS1_3$TP[2] <- ""
fS1_3 <- f_venn +
geom_text(aes(x=x,y=y,label=paste0(label, " \n", counts, "\n", TP)),
data=dfS1_3, color="white", fontface="bold.italic", size=5/14*7) +
labs(title = "FDR < 0.05")
dfS1_4 <- vapply(10^seq(-9, log10(0.999), by=0.1),
function(t) {
est_dr_genes <- xor(VDA_scenario_2$adj_p_val_A < t & VDA_scenario_2$A_amp>amp_choices[2],
VDA_scenario_2$adj_p_val_B < t & VDA_scenario_2$B_amp>amp_choices[2])
tp <- sum(true_dr_genes & est_dr_genes)
fn <- sum(true_dr_genes & !est_dr_genes)
fp <- sum(!true_dr_genes & est_dr_genes)
tn <- sum(!true_dr_genes & !est_dr_genes)
return(c(fdr_cutoff = t, TP = tp, FP = fp, FN = fn, TN=tn))
}, FUN.VALUE = numeric(5)) %>%
t() %>% as.data.frame
point_2 <- with(dfS1_4, which.min(abs(fdr_cutoff - 0.05)))
fS1_4 <- ggplot(dfS1_4, aes_(x=~TP/(TP+FN), y=~TP/(FP+TP))) +
geom_hline(aes_(yintercept=~N/(N + nrow(VDA_scenario_2))), color="grey50", linetype="longdash") +
geom_path(color="black") +
geom_point(aes_(x=~TP/(TP+FN), y=~TP/(FP+TP)), shape=21, size=2, stroke=0.75,
fill="grey50", data = dfS1_4[point_2,]) +
labs(x = "true DiffR transcripts recovered", y = "true DiffR transcripts in DiffR calls") +
annotate("curve", x=0.05, y=0.3, xend=0.15, yend=0.3, arrow=arrow(type="closed", length = unit(4, "pt")),
curvature=0, color="grey30") +
annotate("text", x=0.075, y = 0.35, color="grey20", label="increasing \nFDR threshold", hjust=0, size=5/14*7) +
coord_cartesian(xlim=c(0,0.4), ylim=c(0,0.4)) + scale_x_continuous(labels = scales::percent_format(accuracy = 1)) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1))
dfS1_5 <- VDA_scenario_2 %>%
mutate(p_min = pmin(adj_p_val_A, adj_p_val_B),
len1 = nrow(VDA_scenario_2),
len2 = nrow(subset(VDA_scenario_2, pmax(A_amp, B_amp)>amp_choices[2])))
fS1_5 <- ggplot(dfS1_5) +
stat_ecdf(aes(x=p_min, len=len1, y=..y..*len), linetype="solid") +
stat_ecdf(aes(x=p_min, len=len2, y=..y..*len), linetype="longdash",
data=filter(dfS1_5, pmax(A_amp, B_amp)>amp_choices[2])) +
coord_cartesian(xlim=c(0,0.5)) + ylab("number of rhythmic transcripts") +
annotate("text", x=0.35, y=9000, label="no ampl. threshold", size=8*5/14, color="grey20") +
annotate("text", x=0.35, y=2000, label="with ampl. threshold", size=8*5/14, color="grey20") +
xlab("FDR threshold for rhythms") + scale_y_continuous(labels = scales::label_number_si(accuracy = 1))
rhythmic_in_both <- VDA_scenario_2 %>%
magrittr::extract(rhythmic_in_A,) %>%
filter(adj_p_val_A<0.05 & adj_p_val_B<0.05) %>%
rownames()
eg_DiffR_transcript <- VDA_scenario_2 %>%
magrittr::extract(rhythmic_in_both,) %>%
arrange(desc(A_amp)) %>%
slice_head(n=1) %>% rownames()
dfS1_6 <- exp_design %>%
dplyr::select(time, group) %>%
cbind(orig=assay(liver_hughes)[eg_DiffR_transcript,],
scen_2=new_expr[eg_DiffR_transcript,]) %>%
pivot_longer(c(orig, scen_2), names_to = "type")
fS1_6 <- ggplot(dfS1_6) +
geom_line(aes(x=time, y=value, group=group), color="grey50", alpha=0.5) +
geom_point(aes(x=time, y=value, color=group), size=1) +
facet_wrap(~type, nrow=1, labeller = as_labeller(c("orig" = "First scenario", "scen_2" = "Second scenario"))) +
scale_color_tableau(name=eg_DiffR_transcript, palette = "Color Blind", labels=c("even","odd"))
fS1_7 <- ggplot(VDA_scenario_2[rhythmic_in_both,]) +
geom_point(aes(x = A_amp/B_amp, y = (A_phase - B_phase) %% 24), size=0.5) +
labs(x="Ratio of amplitude of odd to even",
y="Phase diff. between odd and even") +
scale_y_continuous(breaks = seq(0,24,6))
```
```{r FigureS1, echo=FALSE, fig.height=6.5, fig.width=7}
layout <- c(area(1,1,1,2), area(1,3), area(2,1), area(2,2), area(2,3), area(3,1), area(3,2), area(3,3))
fS1_6 + fS1_7 + fS1_1_1 + fS1_1_2 + fS1_2 + fS1_3 + fS1_4 + fS1_5 + plot_layout(design = layout, widths = c(1,1,1.05)) +
plot_annotation(tag_levels = 'A')
```
## Figure 3
```{r scenario-2-compareRhythms, include=FALSE}
all_genes <- data.frame(id = rownames(new_expr))
limma_scenario_2_1 <- compareRhythms(new_expr, exp_design = exp_design, method = "limma",
compare_fdr = 1.0, amp_cutoff = amp_choices[1], just_classify = FALSE) %>%
right_join(all_genes, by = "id") %>% arrange(id)
limma_scenario_2_2 <- compareRhythms(new_expr, exp_design = exp_design, method = "limma",
compare_fdr = 1.0, amp_cutoff = amp_choices[2], just_classify = FALSE) %>%
right_join(all_genes, by = "id") %>% arrange(id)
df3_1 <- evalmod(scores = join_scores(-log10(limma_scenario_2_1$adj_p_val_DR),
-log10(limma_scenario_2_2$adj_p_val_DR)),
labels = limma_scenario_2_1$id %in% rhythmic_in_A,
modnames = as.character(amp_choices)) %>%
fortify(reduce_points=TRUE) %>% filter(curvetype == "PRC") %>%
mutate(method = "hypothesis testing", dset = "low", amp_cutoff = as.numeric(levels(modname))[modname])
df3_2 <- evalmod(scores = join_scores(-log10(limma_scenario_2_1$adj_p_val_DR),
-log10(limma_scenario_2_2$adj_p_val_DR)),
labels = limma_scenario_2_1$id %in% rhythmic_in_A_large_amp,
modnames = as.character(amp_choices)) %>%
fortify(reduce_points=TRUE) %>% filter(curvetype == "PRC") %>%
mutate(method = "hypothesis testing", dset = "high", amp_cutoff = as.numeric(levels(modname))[modname])
mod_sel_scenario_2_1 <- compareRhythms(new_expr, exp_design = exp_design, method = "mod_sel",
schwarz_wt_cutoff = 0.0, amp_cutoff = amp_choices[1], just_classify = FALSE) %>%
right_join(all_genes, by = "id") %>% arrange(id)
mod_sel_scenario_2_2 <- compareRhythms(new_expr, exp_design = exp_design, method = "mod_sel",
schwarz_wt_cutoff = 0.0, amp_cutoff = amp_choices[2], just_classify = FALSE) %>%
right_join(all_genes, by = "id") %>% arrange(id)
grid_points <- sort(mod_sel_scenario_2_1$weights)
grid_points <- grid_points[seq(1, length(grid_points), 20)]
df3_3 <- expand.grid(schwarz_wt_cutoff = grid_points,
amp_cutoff = amp_choices)
roc <- vapply(seq(nrow(df3_3)),
function(i) {
if (df3_3[i, "amp_cutoff"] == amp_choices[1]) {
est_dr_genes <- filter(mod_sel_scenario_2_1, weights > df3_3[i, "schwarz_wt_cutoff"] & category %in% c("loss", "gain", "change"))
} else {
est_dr_genes <- filter(mod_sel_scenario_2_2, weights > df3_3[i, "schwarz_wt_cutoff"] & category %in% c("loss", "gain", "change"))
}
tp <- length(intersect(est_dr_genes$id, rhythmic_in_A))
fn <- length(setdiff(rhythmic_in_A, est_dr_genes$id))
fp <- length(setdiff(est_dr_genes$id, rhythmic_in_A))
return(c(TP = tp, FP = fp, FN = fn))
}, FUN.VALUE = numeric(3))
df3_3 <- cbind(df3_3, t(roc))
df3_3ext <- df3_3 %>% group_by(amp_cutoff) %>%
arrange(desc(TP)) %>%
filter(row_number()==1) %>%
summarize(extn = prc_interpolate(TP, FP, 500, nrow(all_genes)), .groups="rowwise") %>%
mutate(schwarz_wt_cutoff = 0, .before=amp_cutoff) %>%
as.matrix()
colnames(df3_3ext) <- gsub("extn.", "", colnames(df3_3ext))
df3_3 <- bind_rows(df3_3, data.frame(df3_3ext)) %>%
group_by(amp_cutoff) %>% arrange(desc(TP)) %>%
mutate(method = "model selection",
dset = "low",
x = TP/(TP + FN),
y = TP/(TP + FP))
df3_4 <- expand.grid(schwarz_wt_cutoff = grid_points,
amp_cutoff = amp_choices)
roc <- vapply(seq(nrow(df3_4)),
function(i) {
if (df3_4[i, "amp_cutoff"] == amp_choices[1]) {
est_dr_genes <- filter(mod_sel_scenario_2_1, weights > df3_4[i, "schwarz_wt_cutoff"] & category %in% c("loss", "gain", "change"))
} else {
est_dr_genes <- filter(mod_sel_scenario_2_2, weights > df3_4[i, "schwarz_wt_cutoff"] & category %in% c("loss", "gain", "change"))
}
tp <- length(intersect(est_dr_genes$id, rhythmic_in_A_large_amp))
fn <- length(setdiff(rhythmic_in_A_large_amp, est_dr_genes$id))
fp <- length(setdiff(est_dr_genes$id, rhythmic_in_A_large_amp))
return(c(TP = tp, FP = fp, FN = fn))
}, FUN.VALUE = numeric(3))
df3_4 <- cbind(df3_4, t(roc))
df3_4ext <- df3_4 %>% group_by(amp_cutoff) %>%
arrange(desc(TP)) %>%
filter(row_number()==1) %>%
summarize(extn = prc_interpolate(TP, FP, length(rhythmic_in_A_large_amp), nrow(all_genes)), .groups="rowwise") %>%
mutate(schwarz_wt_cutoff = 0, .before=amp_cutoff) %>%
as.matrix()
colnames(df3_4ext) <- gsub("extn.", "", colnames(df3_4ext))
df3_4 <- bind_rows(df3_4, data.frame(df3_4ext)) %>%
group_by(amp_cutoff) %>% arrange(desc(TP)) %>%
mutate(method = "model selection",
dset = "high",
x = TP/(TP + FN),
y = TP/(TP + FP))
df3 <- bind_rows(df3_1, df3_2, df3_3, df3_4) %>%
dplyr::select(c("x", "y", "amp_cutoff", "method", "dset")) %>%
dplyr::filter((amp_cutoff == 0 & dset=="low")|(amp_cutoff==0.5 & dset=="high"))
op_points <- lapply(list(limma_scenario_2_2, mod_sel_scenario_2_2),
function(d) {
if ("weights" %in% colnames(d)) {
est_dr_genes <- filter(d, weights>0.6 & category %in% c("gain", "loss", "change"))
method <- "model selection"
} else {
est_dr_genes <- filter(d, adj_p_val_DR<0.05)
method <- "hypothesis testing"
}
tp <- c(length(intersect(rhythmic_in_A, est_dr_genes$id)),
length(intersect(rhythmic_in_A_large_amp, est_dr_genes$id)))
fn <- c(length(setdiff(rhythmic_in_A, est_dr_genes$id)),
length(setdiff(rhythmic_in_A_large_amp, est_dr_genes$id)))
fp <- c(length(setdiff(est_dr_genes$id, rhythmic_in_A)),
length(setdiff(est_dr_genes$id, rhythmic_in_A_large_amp)))
x <- tp/(tp + fn)
y <- tp/(tp + fp)
return(data.frame(x=x, y=y, amp_cutoff=0.5, method = method, dset=c("low","high")))
}) %>%
bind_rows
df3_5 <- rbind(cbind(dfS1_4, dset="high", amp_cutoff=0.5), cbind(df1_5, dset="low", amp_cutoff=0)) %>%
mutate(x=TP/(TP+FN), y=TP/(FP+TP))
```
```{r Figure3, fig.height=3, fig.width=6.5}
ggplot(df3, aes(x=x, y = y, color=method, linetype=factor(amp_cutoff))) + geom_path(size=0.5) +
geom_point(shape=21, fill="white", data=filter(op_points, dset=="high"), stroke=0.75, show.legend = FALSE) +
facet_grid(cols = vars(factor(dset, levels = c("low", "high"))),
labeller = as_labeller(c(low = sprintf("Target: all %d true DiffR transcripts", length(rhythmic_in_A)),
high = sprintf("Target: %s large ampl. true DiffR transcripts", length(rhythmic_in_A_large_amp))))) +
geom_path(aes(x=x,y=y,linetype=factor(amp_cutoff)), size=0.5, color="grey50", data=df3_5) +
scale_linetype_manual(name=parse(text = 'A[cutoff]'), labels=amp_choices,
values = c("longdash", "solid")) +
scale_color_colorblind() + theme(legend.title.align = 0, legend.justification = 0,
legend.margin = margin(r=1.5, unit = "lines")) +
labs(x = "true DiffR transcripts recovered", y = "true DiffR transcripts in DiffR calls", color="") +
scale_y_continuous(labels=scales::percent_format(accuracy = 1)) + scale_x_continuous(labels=scales::percent_format(accuracy = 1))
```
## FigureS2
```{r FigureS2, echo=FALSE, fig.height=3, fig.width=5.75}
dodr_scenario_2_1 <- compareRhythms(new_expr, exp_design = exp_design, method = "dodr",
compare_fdr = 1.0, amp_cutoff = amp_choices[1], just_classify = FALSE) %>%
right_join(all_genes, by = "id") %>% arrange(id)
dodr_scenario_2_2 <- compareRhythms(new_expr, exp_design = exp_design, method = "dodr",
compare_fdr = 1.0, amp_cutoff = amp_choices[2], just_classify = FALSE) %>%
right_join(all_genes, by = "id") %>% arrange(id)
dfS2_1 <- evalmod(scores = join_scores(-log10(dodr_scenario_2_1$adj_p_val_dodr),
-log10(dodr_scenario_2_2$adj_p_val_dodr)),
labels = dodr_scenario_2_1$id %in% rhythmic_in_A,
modnames = as.character(amp_choices)) %>%
fortify(reduce_points=TRUE) %>% filter(curvetype == "PRC") %>%
mutate(method = "DODR", dset = "low", amp_cutoff = as.numeric(levels(modname))[modname])
dfS2_2 <- evalmod(scores = join_scores(-log10(dodr_scenario_2_1$adj_p_val_dodr),
-log10(dodr_scenario_2_2$adj_p_val_dodr)),
labels = dodr_scenario_2_1$id %in% rhythmic_in_A_large_amp,
modnames = as.character(amp_choices)) %>%
fortify(reduce_points=TRUE) %>% filter(curvetype == "PRC")%>%
mutate(method = "DODR", dset = "high", amp_cutoff = as.numeric(levels(modname))[modname])
dfS2_3 <- bind_rows(df3_1, df3_2, dfS2_1, dfS2_2) %>%
dplyr::select(c("x", "y", "amp_cutoff", "method", "dset")) %>%
mutate(method = ifelse(method == "hypothesis testing", "limma", method)) %>%
dplyr::filter((amp_cutoff == 0 & dset=="low")|(amp_cutoff==0.5 & dset=="high"))
op_points <- lapply(list(limma_scenario_2_2, dodr_scenario_2_2),
function(d) {
if ("adj_p_val_DR" %in% colnames(d)) {
est_dr_genes <- filter(d, adj_p_val_DR<0.05)
method <- "limma"
} else {
est_dr_genes <- filter(d, adj_p_val_dodr<0.05)
method <- "DODR"
}
tp <- c(length(intersect(rhythmic_in_A, est_dr_genes$id)),
length(intersect(rhythmic_in_A_large_amp, est_dr_genes$id)))
fn <- c(length(setdiff(rhythmic_in_A, est_dr_genes$id)),
length(setdiff(rhythmic_in_A_large_amp, est_dr_genes$id)))
fp <- c(length(setdiff(est_dr_genes$id, rhythmic_in_A)),
length(setdiff(est_dr_genes$id, rhythmic_in_A_large_amp)))
x <- tp/(tp + fn)
y <- tp/(tp + fp)
return(data.frame(x=x, y=y, amp_cutoff=0.5, method = method, dset=c("low","high")))
}) %>%
bind_rows
ggplot(dfS2_3, aes(x=x, y = y, color=method, linetype=factor(amp_cutoff))) + geom_line(size=0.5) +
geom_point(shape=21, fill="white", data=filter(op_points, dset=="high"), stroke=0.75, show.legend = FALSE) +
facet_grid(cols = vars(factor(dset, levels = c("low", "high"))),
labeller = as_labeller(c(low = sprintf("Target: all %d true DiffR transcripts", length(rhythmic_in_A)),
high = sprintf("Target: %s large ampl. true DiffR transcripts", length(rhythmic_in_A_large_amp))))) +
scale_linetype_manual(name=parse(text = 'A[cutoff]'), labels=amp_choices,
values = c("longdash", "solid")) +
scale_color_tableau(name="", palette = "Classic Green-Orange 6") +
theme(legend.title.align = 0, legend.justification = 0.35,
legend.margin = margin(r=1.5, unit = "lines")) +
guides(color= guide_legend(order = 1), linetype=guide_legend(order = 2)) +
labs(x = "true DiffR transcripts recovered", y = "true DiffR transcripts in DiffR calls") +
scale_y_continuous(labels=scales::percent_format(accuracy = 1)) + scale_x_continuous(labels=scales::percent_format(accuracy = 1))
```
## Figure 4
```{r HFD-eckel-mahan}
se <- readRDS("data/GSE52333.RDS")
exp_des_nutr_1 <- colData(se) %>% as.data.frame %>% tibble() %>%
dplyr::select(!!c("harvest.timepoint.ch1", "diet.ch1")) %>%
mutate(time = as.numeric(sub("ZT", "", harvest.timepoint.ch1)),
group = fct_recode(diet.ch1, HFD="high fat diet", control="normal chow"),
group = fct_relevel(group, "control"))
y_nutr_1 <- assay(se, "expr")
group_ids <- levels(exp_des_nutr_1$group)
keep <- filter_genes(y_nutr_1, exp_des_nutr_1$group, min.expr = 5)
y_nutr_1 <- y_nutr_1[keep, ]
results_emahan <- compareRhythms(y_nutr_1, exp_des_nutr_1, method = "limma",
just_classify = FALSE) %>%
inner_join(as.data.frame(rowData(se))[c(1,2,3)],
by=c("id"="ensembl_gene_id"))
results_emahan_modsel <- compareRhythms(y_nutr_1, exp_des_nutr_1, method = "mod_sel",
just_classify = FALSE) %>%
inner_join(as.data.frame(rowData(se))[c(1,2,3)],
by=c("id"="ensembl_gene_id"))
compare_results_nutr_1 <- full_join(results_emahan_modsel[c("id", "category")],
results_emahan[c("id", "category")], by="id",
suffix = c(".modsel",".hyptest")
) %>%
mutate(across(.fns = as.character)) %>%
replace_na(list(category.modsel="uncl", category.hyptest="arrhy"))
original_results_nutr <- readxl::read_xlsx("data/Eckel-Mahan_Results.xlsx", sheet = 1, skip = 1) %>%
dplyr::select(!!c("circadian_0.01", "Gene Symbol")) %>%
dplyr::filter(circadian_0.01 %in% c("WT-alone", "HF-alone", "BOTH")) %>%
group_by(`Gene Symbol`) %>%
dplyr::filter(row_number()==1) %>%
rename(mgi_symbol="Gene Symbol", category="circadian_0.01")
corrected_results_nutr <- readxl::read_xlsx("data/Eckel-Mahan_Results.xlsx", sheet = 1, skip = 1) %>%