Splicing maps
This report holds all analysis and plots used to create main figure 5 A-C with and all related supplementary figures.
library(knitr)
library(grid)
library(gridGraphics)
library(gridExtra)
library(scales)
library(reshape2)
library(ggplot2)
library(rtracklayer)
library(GenomicFeatures)
library(GenomicAlignments)
library(viridis)
library(tibble)
library(dplyr)
library(tidyr)
library(ComplexHeatmap)
library(kableExtra)
library(GenomicRanges)
library(BindingSiteFinder)
library(readr)
library(circlize)
library(patchwork)
library(ggsci)
library(ggbeeswarm)
library(zoo)source("../styles.R")
source("../helper.R")# import table and format
table3As <- read_csv("~/Projects/sf3b1/03_Splicing/results202212/golong_all_2022_12_diff_betabinomial_less_strict_3AS_all.csv")
standChrs = c(paste0("chr", seq(1:22)), "chrX", "chrY")
table3As = table3As %>%
as.data.frame() %>%
select(gene, gene_id, chrom, strand, start, alternative, end, padj, all_mut_PSI, all_wt_PSI, total_PSI) %>%
rownames_to_column() %>%
subset(chrom %in% standChrs) %>%
mutate(end = end + 1) %>%
mutate(alternative = ifelse(strand == "+", alternative + 1, alternative)) %>%
mutate(deltaPSI = all_mut_PSI - all_wt_PSI) %>%
mutate(sig = ifelse(padj < 0.1, TRUE, FALSE)) %>%
mutate(distAltEnd = ifelse(strand == "+", (alternative - end)*-1, (start - alternative)*-1 ))
table3As$id = 1:nrow(table3As)
# make granges
rngEnd = GRanges(
seqnames = table3As$chrom,
ranges = IRanges(start = table3As$start,
end = table3As$end),
gene = table3As$gene,
geneID = table3As$gene_id,
strand = table3As$strand,
padj = table3As$padj,
deltaPSI = table3As$deltaPSI,
sig = table3As$sig,
distAltEnd = table3As$distAltEnd,
id = table3As$id,
wtPSI = table3As$all_wt_PSI,
mutPSI = table3As$all_mut_PSI
)
names(rngEnd) = c(1:length(rngEnd))
table3AsPlus = subset(table3As, strand == "+")
table3AsMinus = subset(table3As, strand == "-")
rngAltPlus = GRanges(
seqnames = table3AsPlus$chrom,
ranges = IRanges(start = table3AsPlus$start,
end = table3AsPlus$alternative),
gene = table3AsPlus$gene,
geneID = table3AsPlus$gene_id,
strand = table3AsPlus$strand,
padj = table3AsPlus$padj,
deltaPSI = table3AsPlus$deltaPSI,
sig = table3AsPlus$sig,
distAltEnd = table3AsPlus$distAltEnd,
id = table3AsPlus$id,
wtPSI = table3AsPlus$all_wt_PSI,
mutPSI = table3AsPlus$all_mut_PSI
)
rngAltMinus = GRanges(
seqnames = table3AsMinus$chrom,
ranges = IRanges(start = table3AsMinus$alternative,
end = table3AsMinus$end),
gene = table3AsMinus$gene,
geneID = table3AsMinus$gene_id,
strand = table3AsMinus$strand,
padj = table3AsMinus$padj,
deltaPSI = table3AsMinus$deltaPSI,
sig = table3AsMinus$sig,
distAltEnd = table3AsMinus$distAltEnd,
id = table3AsMinus$id,
wtPSI = table3AsMinus$all_wt_PSI,
mutPSI = table3AsMinus$all_mut_PSI
)
rngAlt = c(rngAltPlus, rngAltMinus)
rngAlt = rngAlt[order(rngAlt$id)]
names(rngAlt) = c(1:length(rngAlt))
saveRDS(rngEnd, file = "./data/rngEnd.rds")
saveRDS(rngAlt, file = "./data/rngAlt.rds")
export(rngEnd, con = "./data/rngEnd.bed", format = "BED")
export(rngAlt, con = "./data/rngAlt.bed", format = "BED")df = rngEnd %>%
as.data.frame() %>%
mutate(region = ifelse(distAltEnd >= 12 & distAltEnd <= 21, "Affected", "Not Affected"))
ggplot(df, aes(x = distAltEnd, y = padj, group = distAltEnd, fill = region)) +
geom_boxplot(outlier.size = 0.1, lwd = 0.5) +
xlim(0,200) +
theme_pub() +
scale_fill_npg() +
theme(legend.position = "top") +
labs(
x = "Distance between splice sites [nt]",
y = "Adjusted P value",
fill = "Region type"
)
ggsave("splicingEvents_200.pdf", width = 10, height = 4, device = "pdf")df = rngEnd %>%
as.data.frame() %>%
mutate(region = ifelse(distAltEnd >= 12 & distAltEnd <= 21, "Affected", "Not Affected"))
ggplot(df, aes(x = distAltEnd, y = padj, group = distAltEnd, fill = region)) +
geom_boxplot(outlier.size = 0.1, lwd = 0.5) +
xlim(0,100) +
theme_pub() +
scale_fill_npg() +
theme(legend.position = "top") +
labs(
x = "Distance between splice sites [nt]",
y = "Adjusted P value",
fill = "Region type"
)
ggsave("splicingEvents_100.pdf", width = 10, height = 4, device = "pdf")ggplot(df, aes(x = distAltEnd, y = padj, group = distAltEnd, fill = region)) +
geom_boxplot(outlier.size = 0.1, lwd = 0.5) +
xlim(0,50) +
theme_pub() +
scale_fill_npg() +
theme(legend.position = "top") +
labs(
x = "Distance between splice sites [nt]",
y = "Adjusted P value",
fill = "Region type"
)
ggsave("splicingEvents_50.pdf", width = 4, height = 4, device = "pdf")dfN = df %>%
filter(sig == TRUE) %>%
group_by(distAltEnd) %>%
summarise(n = n()) %>%
mutate(region = ifelse(distAltEnd >= 12 & distAltEnd <= 21, "Affected", "Not Affected"))
ggplot(dfN, aes(x = distAltEnd, y = n, group = distAltEnd, fill = region)) +
geom_col(width = 0.9) +
xlim(0,50) +
theme_pub() +
scale_fill_npg() +
theme(legend.position = "top") +
labs(
x = "Distance between splice sites [nt]",
y = "#N [Significant]",
fill = "Region type"
)
ggsave("splicingEvents_hist_50.pdf", width = 4, height = 4, device = "pdf")# Load clip data SF3B1 WT
clipFilesWt = "/Users/mirko/Projects/sf3b1/01_data_subsamp/wt/cov/replicate"
clipFiles = c(clipFilesWt)
clipFiles = list.files(clipFiles, pattern = ".bw$", full.names = TRUE)
clipFilesP = clipFiles[grep(clipFiles, pattern = "Plus")]
clipFilesM = clipFiles[grep(clipFiles, pattern = "Minus")]
# Organize clip data in dataframe
colData = data.frame(
id = 1:3,
condition = factor(c("WT", "WT", "WT")),
clPlus = clipFilesP,
clMinus = clipFilesM)
bdsSF3B1_WT = BSFDataSetFromBigWig(ranges = rngEnd, meta = colData)
# Load clip data SF3B1 MUT
clipFilesMut = "/Users/mirko/Projects/sf3b1/01_data_subsamp/mut/cov/replicate"
clipFiles = c(clipFilesMut)
clipFiles = list.files(clipFiles, pattern = ".bw$", full.names = TRUE)
clipFilesP = clipFiles[grep(clipFiles, pattern = "Plus")]
clipFilesM = clipFiles[grep(clipFiles, pattern = "Minus")]
# Organize clip data in dataframe
colData = data.frame(
id = 1:2,
condition = factor(c("MUT", "MUT")),
clPlus = clipFilesP,
clMinus = clipFilesM)
bdsSF3B1_MUT = BSFDataSetFromBigWig(ranges = rngEnd, meta = colData)
# Load clip data U2AF65 WT
clipFilesWt = "/Users/mirko/Projects/BindingSiteStrength/01_data/05_U2AF65_cellLines/K562/cov/replicates/"
clipFiles = c(clipFilesWt)
clipFiles = list.files(clipFiles, pattern = ".bw$", full.names = TRUE)
clipFilesP = clipFiles[grep(clipFiles, pattern = "plus")]
clipFilesM = clipFiles[grep(clipFiles, pattern = "minus")]
# Organize clip data in dataframe
colData = data.frame(
id = 1:4,
condition = factor(rep("WT",4)),
clPlus = clipFilesP,
clMinus = clipFilesM)
# creating objects and importing clip siganl
bdsU2AF2_WT = BSFDataSetFromBigWig(ranges = rngEnd, meta = colData)
# Load eclip data SF3B1 WT
clipFilesWt = "/Users/mirko/Projects/sf3b1/04_eCLIP/sf3b1/"
clipFiles = c(clipFilesWt)
clipFiles = list.files(clipFiles, pattern = ".bw$", full.names = TRUE)
clipFilesP = clipFiles[grep(clipFiles, pattern = "plus")]
clipFilesM = clipFiles[grep(clipFiles, pattern = "minus")]
# Organize clip data in dataframe
colData = data.frame(
id = 1:2,
condition = factor(rep("WT",2)),
clPlus = clipFilesP,
clMinus = clipFilesM)
# creating objects and importing clip siganl
bdsSF3B1_ECLIP = BSFDataSetFromBigWig(ranges = rngEnd, meta = colData)
clipData = list(bdsSF3B1_WT = bdsSF3B1_WT,
bdsSF3B1_MUT = bdsSF3B1_MUT,
bdsU2AF2_WT = bdsU2AF2_WT,
bdsSF3B1_ECLIP = bdsSF3B1_ECLIP)For meta profiles iCLIP crosslinks are aligned at the canonical and alternative splice sites, in a symmetrical 200nt wide window. From all events (#N=7,951), only those with a distance from 0-100nt are shown (#N=4,478). Note that for each plot all events with zero crosslinks are also removed. The final number of ranges used is indicated above each plot.
makeHmMatrixList <- function(clipSet, plot.topLim.size = 1){
### --------------------------------------------------------------------------
### Ranges
### --------------------------------------------------------------------------
# select ranges
# -> select within 100nt distance
rngEndCurr = rngEnd
rngAltCurr = rngAlt
rngEndCurr = rngEnd[rngEnd$distAltEnd <= 100]
rngAltCurr = rngAlt[rngAlt$distAltEnd <= 100]
# shuffle ranges around
# -> to avoid auto sorting by P value
set.seed(1234)
reorderIdx = sample(1:length(rngEndCurr))
rngEndCurr = rngEndCurr[reorderIdx,]
rngAltCurr = rngAltCurr[reorderIdx,]
### --------------------------------------------------------------------------
### Coverage
### --------------------------------------------------------------------------
# coverage 3'AS outer position
bdsSel = setRanges(clipSet, resize(rngEndCurr, fix = "end", width = 1) + 200)
cov1 = coverageOverRanges(bdsSel, returnOptions = "merge_all_replicates", method = "mean")
m1 = cov1
# coverage 3'AS inner position
bdsSel = setRanges(clipSet, resize(rngAltCurr, fix = "end", width = 1) + 200)
cov2 = coverageOverRanges(bdsSel, returnOptions = "merge_all_replicates", method = "mean")
m2 = cov2
# sorting by distance to 3'AS
distIdx = order(rngEndCurr$distAltEnd, decreasing = FALSE)
m1 = m1[distIdx,]
m2 = m2[distIdx,]
# remove rows with no signal
m1 = m1[rowSums(m1) > 0,]
m2 = m2[rowSums(m2) > 0,]
# scale values in matrix for plotting
m1 = minMaxNorm(m1)
m2 = minMaxNorm(m2)
m1 = t(apply(m1, 1, smoothing, lambda=0.05, dim=401))
m2 = t(apply(m2, 1, smoothing, lambda=0.05, dim=401))
# combine matrices
keep = intersect(rownames(m1), rownames(m2))
m1 = m1[rownames(m1) %in% keep,]
m2 = m2[rownames(m2) %in% keep,]
### --------------------------------------------------------------------------
### Annotations
### --------------------------------------------------------------------------
# set reference range for annotations
range = rngEndCurr
currM = m1
# annotate with distance between alternative and constitutive 3'ss
dfDist = data.frame(dist = range$distAltEnd[as.numeric(rownames(currM))])
dfDist$dist[dfDist$dist > 100] = 100
haDist = rowAnnotation(
dist = anno_points(dfDist, size = unit(2, "points"), gp = gpar(col = "#595959")),
width = unit(1, "cm"), annotation_name_gp = gpar(fontsize = 6)
)
# annotate with dPSI/ PSI values
df = data.frame(deltaPSI = range$deltaPSI[as.numeric(rownames(currM))])
haDeltaPSI = rowAnnotation(
deltaPSI = anno_points(df, size = unit(2, "points"), gp = gpar(col = "#4c435a", alpha = 1), ylim = c(-1,1) ),
width = unit(2, "cm"), annotation_name_gp = gpar(fontsize = 6)
)
df = data.frame(psiWT = range$wtPSI[as.numeric(rownames(currM))])
haWtPSI = rowAnnotation(
psiWT = anno_points(df, size = unit(2, "points"), gp = gpar(col = "#0086b3", alpha = 0.5)), # , ylim = c(-1,0)
width = unit(2, "cm"), annotation_name_gp = gpar(fontsize = 6)
)
df = data.frame(psiMUT = range$mutPSI[as.numeric(rownames(currM))])
haMutPSI = rowAnnotation(
psiMUT = anno_points(df, size = unit(2, "points"), gp = gpar(col = "#990000", alpha = 0.5)), # , ylim = c(-1,0)
width = unit(2, "cm"), annotation_name_gp = gpar(fontsize = 6)
)
# annotate with significant results
sig = range$sig[as.numeric(rownames(currM))]
haSig = rowAnnotation(sigRes = sig, col = list(sigRes = c("FALSE" = "white", "TRUE" = "black")),
gp = gpar(lwd = 0.1),
show_annotation_name=F, annotation_name_gp = gpar(fontsize = 6),
annotation_legend_param = list(sigRes = list(nrow = 1, direction = "horizontal"))
)
# split by range
split = factor(ifelse(dfDist$dist >= 12 & dfDist$dist <= 21, "2",
ifelse(dfDist$dist <= 12 & dfDist$dist <= 21, "1", "3")),
levels = c("1", "2", "3"))
### --------------------------------------------------------------------------
### Plotting
### --------------------------------------------------------------------------
# use raw-data for barplot on top
mm1 = cov1[rownames(cov1) %in% rownames(m1),]
mm2 = cov2[rownames(cov2) %in% rownames(m2),]
mm1 = mm1[,c(30:250)]
mm2 = mm2[,c(100:320)]
# formatting of matrix outline
rownames(m1) = NULL
colnames(m1) = -200:200
colnames(m1)[as.numeric(colnames(m1)) %% 100 != 0] = ""
rownames(m2) = NULL
colnames(m2) = -200:200
colnames(m2)[as.numeric(colnames(m2)) %% 100 != 0] = ""
# change heatmap plotting frame
m1 = m1[,c(30:250)]
m2 = m2[,c(100:320)]
# annotate with top col means profile
df1 = data.frame(sums = colMeans(mm1))
haMeans1 = HeatmapAnnotation(cov = anno_barplot(df1, gp = gpar(fill = "#595959", col = "#595959"), ylim = c(0, plot.topLim.size)),
height = unit(2, "cm"), show_annotation_name=F, annotation_name_gp = gpar(fontsize = 6))
df2 = data.frame(sums = colMeans(mm2))
haMeans2 = HeatmapAnnotation(cov = anno_barplot(df2, gp = gpar(fill = "#595959", col = "#595959"), ylim = c(0, plot.topLim.size)),
height = unit(2, "cm"), show_annotation_name=F, annotation_name_gp = gpar(fontsize = 6))
# color scale
custom.col = colorRamp2(c(0,0.05,0.1,0.15,0.2,0.25), viridis(6, option = "mako", direction = -1))
# plot heatmap
h1 = Heatmap(m1,
column_title = "3'AS outer", name = "iCLIP signal",
cluster_rows = FALSE, cluster_columns = FALSE,
show_column_names = TRUE, show_row_names = FALSE,
col = custom.col,
row_split = split, row_gap = unit(0.1, "cm"),
column_names_gp = gpar(fontsize = 8),
top_annotation = haMeans1,
right_annotation = c(haSig, haDeltaPSI, haWtPSI, haMutPSI),
border = TRUE,
use_raster = TRUE, raster_by_magick = TRUE,
raster_magick_filter = "Spline",
raster_quality = 1, raster_resize_mat = mean,
heatmap_legend_param = list(
legend_width = unit(6, "cm"),
title_position = "topleft", direction = "horizontal"
)
)
h2 = Heatmap(m2,
column_title = "3'AS inner", name = "iCLIP signal",
cluster_rows = FALSE, cluster_columns = FALSE,
show_column_names = TRUE, show_row_names = FALSE,
col = custom.col,
row_split = split, row_gap = unit(0.1, "cm"),
top_annotation = haMeans2,
left_annotation = c(haDist),
column_names_gp = gpar(fontsize = 8),
border = TRUE,
use_raster = TRUE, raster_by_magick = TRUE,
raster_magick_filter = "Spline",
raster_quality = 1, raster_resize_mat = mean,
heatmap_legend_param = list(
legend_width = unit(6, "cm"),
title_position = "topleft", direction = "horizontal"
)
)
plotList = h2 + h1
matDim = dim(m1)
l = list(plotList = plotList, matDim = matDim)
return(l)
}p = makeHmMatrixList(clipSet = clipData$bdsSF3B1_WT, plot.topLim.size = 1)
draw(p$plotList, ht_gap = unit(0.2, "cm"), heatmap_legend_side = "bottom", annotation_legend_side = "bottom",
column_title = paste0("SF3B1-WT (#N:", format(p$matDim[1], big.mark = ","), ")"))
p = makeHmMatrixList(clipSet = clipData$bdsSF3B1_MUT, plot.topLim.size = 1)
draw(p$plotList, ht_gap = unit(0.2, "cm"), heatmap_legend_side = "bottom", annotation_legend_side = "bottom",
column_title = paste0("SF3B1-MUT (#N:", format(p$matDim[1], big.mark = ","), ")"))
p = makeHmMatrixList(clipSet = clipData$bdsU2AF2_WT, plot.topLim.size = 3)
draw(p$plotList, ht_gap = unit(0.2, "cm"), heatmap_legend_side = "bottom", annotation_legend_side = "bottom",
column_title = paste0("U2AF2-WT (#N:", format(p$matDim[1], big.mark = ","), ")"))
p = makeHmMatrixList(clipSet = clipData$bdsSF3B1_ECLIP, plot.topLim.size = 0.05)
draw(p$plotList, ht_gap = unit(0.2, "cm"), heatmap_legend_side = "bottom", annotation_legend_side = "bottom",
column_title = paste0("SF3B1-eCLIP (#N:", format(p$matDim[1], big.mark = ","), ")"))
plotHeatmaps <- function(plot.topLim.size = 1){
### --------------------------------------------------------------------------
### Ranges
### --------------------------------------------------------------------------
rngEndCurr = rngEnd
# shuffle ranges around
# -> to avoid auto sorting by P value
set.seed(1234)
reorderIdx = sample(1:length(rngEndCurr))
rngEndCurr = rngEndCurr[reorderIdx,]
# cut ranges to window size
rngEndCurr = rngEndCurr[rngEndCurr$distAltEnd <= 100]
rngEndCurr = resize(rngEndCurr, fix = "end", width = 1) + 200
### --------------------------------------------------------------------------
### Coverage
### --------------------------------------------------------------------------
# make cov ewt
bds.wt = setRanges(bdsSF3B1_WT, rngEndCurr)
cov.wt = coverageOverRanges(bds.wt, returnOptions = "merge_all_replicates", method = "mean")
cov.wt = cov.wt[rowSums(cov.wt) > 0,]
# make cov mut
bds.mut = setRanges(bdsSF3B1_MUT, rngEndCurr)
cov.mut = coverageOverRanges(bds.mut, returnOptions = "merge_all_replicates", method = "mean")
cov.mut = cov.mut[rowSums(cov.mut) > 0,]
# make cov u23
bds.u2 = setRanges(bdsU2AF2_WT, rngEndCurr)
cov.u2 = coverageOverRanges(bds.u2, returnOptions = "merge_all_replicates", method = "mean")
cov.u2 = cov.u2[rowSums(cov.u2) > 0,]
# combine matrices
keep = intersect(rownames(cov.wt), rownames(cov.mut))
cov.wt = cov.wt[rownames(cov.wt) %in% keep,]
cov.mut = cov.mut[rownames(cov.mut) %in% keep,]
keep = intersect(rownames(cov.wt), rownames(cov.u2))
cov.wt = cov.wt[rownames(cov.wt) %in% keep,]
cov.mut = cov.mut[rownames(cov.mut) %in% keep,]
cov.u2 = cov.u2[rownames(cov.u2) %in% keep,]
m1 = cov.wt
m2 = cov.mut
m3 = cov.u2
# sorting by distance to 3'AS
currDist = rngEndCurr$distAltEnd[as.numeric(rownames(cov.wt))]
distIdx = order(currDist, decreasing = FALSE)
m1 = m1[distIdx,]
m2 = m2[distIdx,]
m3 = m3[distIdx,]
# scale values in matrix for plotting
m1 = minMaxNorm(m1)
m2 = minMaxNorm(m2)
m3 = minMaxNorm(m3)
m1 = t(apply(m1, 1, smoothing, lambda=0.05, dim=401))
m2 = t(apply(m2, 1, smoothing, lambda=0.05, dim=401))
m3 = t(apply(m3, 1, smoothing, lambda=0.05, dim=401))
### --------------------------------------------------------------------------
### Annotations
### --------------------------------------------------------------------------
# set reference range for annotations
range = rngEndCurr
currM = m1
# annotate with distance between alternative and constitutive 3'ss
dfDist = data.frame(dist = range$distAltEnd[as.numeric(rownames(currM))])
dfDist$dist[dfDist$dist > 100] = 100
haDist = rowAnnotation(
dist = anno_points(dfDist, size = unit(2, "points"), gp = gpar(col = "#595959")),
width = unit(1, "cm"), annotation_name_gp = gpar(fontsize = 6)
)
# annotate with dPSI/ PSI values
df = data.frame(deltaPSI = range$deltaPSI[as.numeric(rownames(currM))])
haDeltaPSI = rowAnnotation(
deltaPSI = anno_points(df, size = unit(2, "points"), gp = gpar(col = "#4c435a", alpha = 1), ylim = c(-1,1) ),
width = unit(2, "cm"), annotation_name_gp = gpar(fontsize = 6)
)
df = data.frame(psiWT = range$wtPSI[as.numeric(rownames(currM))])
haWtPSI = rowAnnotation(
psiWT = anno_points(df, size = unit(2, "points"), gp = gpar(col = "#0086b3", alpha = 0.5)), # , ylim = c(-1,0)
width = unit(2, "cm"), annotation_name_gp = gpar(fontsize = 6)
)
df = data.frame(psiMUT = range$mutPSI[as.numeric(rownames(currM))])
haMutPSI = rowAnnotation(
psiMUT = anno_points(df, size = unit(2, "points"), gp = gpar(col = "#990000", alpha = 0.5)), # , ylim = c(-1,0)
width = unit(2, "cm"), annotation_name_gp = gpar(fontsize = 6)
)
# annotate with significant results
sig = range$sig[as.numeric(rownames(currM))]
haSig = rowAnnotation(sigRes = sig, col = list(sigRes = c("FALSE" = "white", "TRUE" = "black")),
gp = gpar(lwd = 0.1),
show_annotation_name=F, annotation_name_gp = gpar(fontsize = 6),
annotation_legend_param = list(sigRes = list(nrow = 1, direction = "horizontal"))
)
# split by range
split = factor(ifelse(dfDist$dist >= 12 & dfDist$dist <= 21, "2",
ifelse(dfDist$dist <= 12 & dfDist$dist <= 21, "1", "3")),
levels = c("1", "2", "3"))
### --------------------------------------------------------------------------
### Plotting
### --------------------------------------------------------------------------
# use raw-data for barplot on top
mm1 = cov.wt[,c(30:250)]
mm2 = cov.mut[,c(30:250)]
mm3 = cov.u2[,c(30:250)]
# color scale
# formatting of matrix outline
rownames(m1) = NULL
colnames(m1) = -200:200
colnames(m1)[as.numeric(colnames(m1)) %% 50 != 0] = ""
rownames(m2) = NULL
colnames(m2) = -200:200
colnames(m2)[as.numeric(colnames(m2)) %% 50 != 0] = ""
rownames(m3) = NULL
colnames(m3) = -200:200
colnames(m3)[as.numeric(colnames(m3)) %% 50 != 0] = ""
# change heatmap plotting frame
m1 = m1[,c(30:250)]
m2 = m2[,c(30:250)]
m3 = m3[,c(30:250)]
# annotate with top col means profile
df1 = data.frame(sums = colMeans(mm1))
haMeans1 = HeatmapAnnotation(cov = anno_barplot(df1, gp = gpar(fill = "#595959", col = "#595959"), ylim = c(0, plot.topLim.size)),
height = unit(2, "cm"), show_annotation_name=F, annotation_name_gp = gpar(fontsize = 6))
df2 = data.frame(sums = colMeans(mm2))
haMeans2 = HeatmapAnnotation(cov = anno_barplot(df2, gp = gpar(fill = "#595959", col = "#595959"), ylim = c(0, plot.topLim.size)),
height = unit(2, "cm"), show_annotation_name=F, annotation_name_gp = gpar(fontsize = 6))
df3 = data.frame(sums = colMeans(mm3))
haMeans3 = HeatmapAnnotation(cov = anno_barplot(df3, gp = gpar(fill = "#595959", col = "#595959"), ylim = c(0, plot.topLim.size)),
height = unit(2, "cm"), show_annotation_name=F, annotation_name_gp = gpar(fontsize = 6))
# color scale
custom.col = colorRamp2(c(0,0.05,0.1,0.15,0.2,0.25), viridis(6, option = "mako", direction = -1))
# plot heatmap
h1 = Heatmap(m1,
column_title = "SF3B1 WT", name = "iCLIP signal",
cluster_rows = FALSE, cluster_columns = FALSE,
show_column_names = TRUE, show_row_names = FALSE,
col = custom.col,
row_split = split, row_gap = unit(0.1, "cm"),
column_names_gp = gpar(fontsize = 8),
top_annotation = haMeans1,
right_annotation = c(haSig, haDeltaPSI, haWtPSI, haMutPSI),
border = TRUE,
use_raster = TRUE, raster_by_magick = TRUE,
raster_magick_filter = "Spline",
raster_quality = 1, raster_resize_mat = mean,
heatmap_legend_param = list(
legend_width = unit(6, "cm"),
title_position = "topleft", direction = "horizontal"
)
)
h2 = Heatmap(m2,
column_title = "SF3B1 MUT", name = "iCLIP signal",
cluster_rows = FALSE, cluster_columns = FALSE,
show_column_names = TRUE, show_row_names = FALSE,
col = custom.col,
row_split = split, row_gap = unit(0.1, "cm"),
top_annotation = haMeans2,
column_names_gp = gpar(fontsize = 8),
border = TRUE,
use_raster = TRUE, raster_by_magick = TRUE,
raster_magick_filter = "Spline",
raster_quality = 1, raster_resize_mat = mean,
heatmap_legend_param = list(
legend_width = unit(6, "cm"),
title_position = "topleft", direction = "horizontal"
)
)
h3 = Heatmap(m3,
column_title = "U2AF2 WT", name = "iCLIP signal",
cluster_rows = FALSE, cluster_columns = FALSE,
show_column_names = TRUE, show_row_names = FALSE,
col = custom.col,
row_split = split, row_gap = unit(0.1, "cm"),
top_annotation = haMeans3,
column_names_gp = gpar(fontsize = 8),
border = TRUE,
use_raster = TRUE, raster_by_magick = TRUE,
raster_magick_filter = "Spline",
raster_quality = 1, raster_resize_mat = mean,
heatmap_legend_param = list(
legend_width = unit(6, "cm"),
title_position = "topleft", direction = "horizontal"
)
)
plotList = h3 + h2 + h1
matDim = dim(m1)
l = list(plotList = plotList, matDim = matDim)
return(l)
}p = plotHeatmaps(plot.topLim.size = 3)
draw(p$plotList, ht_gap = unit(0.2, "cm"), heatmap_legend_side = "bottom", annotation_legend_side = "bottom",
column_title = paste0("Combined (#N:", format(p$matDim[1], big.mark = ","), ")"))
The following plots show at which distances the canonical splice sites is preferred over the alternative and vice versa. For the zoom out version usage fraction per distance is computed as a rolling mean over 20nt. For the zoom in fractions are shown on a per nt level.
df = rngEnd %>% as.data.frame() %>%
mutate(sel = ifelse(wtPSI < 0.5, "Low", "High")) %>%
group_by(distAltEnd, sel) %>%
summarise(n = n(), .groups = "keep") %>%
pivot_wider(values_from = n, names_from = sel) %>%
mutate(distAltEnd = as.integer(distAltEnd)) %>%
mutate(High = ifelse(is.na(High), 0, High)) %>%
mutate(Low = ifelse(is.na(Low), 0, Low)) %>%
ungroup() %>%
mutate(rs_high = rollapply(data = High, width = 20, FUN = mean, align = "left", fill = NA)) %>%
mutate(rs_low = rollapply(data = Low, width = 20, FUN = mean, align = "left", fill = NA)) %>%
group_by(distAltEnd) %>%
summarise(High = sum(rs_high), Low = sum(rs_low), Sum = sum(rs_high, rs_low), .groups = "keep") %>%
mutate(FracHigh = High/Sum, FracLow = Low/Sum) %>%
select(distAltEnd, FracHigh, FracLow) %>%
pivot_longer(-distAltEnd) %>%
mutate(bin = as.numeric(distAltEnd)) %>%
drop_na()
ggplot() +
geom_line(data = df, aes(x = bin, y = value, color = name, group = name), size = 0.5) +
geom_smooth(data = df, aes(x = bin, y = value, color = name, group = name),
method = "loess", se = FALSE, size = 1, span = 0.1) +
theme_pub() +
scale_color_npg(name = "3' splice site usage", labels = c("3'SS (constitutive)", "*3'SS (alternative)")) +
labs(
x = "Splice site distance (running mean [nt])",
y = "Usage fraction",
color = "WT inclusion level"
) +
theme(legend.position = "top", aspect.ratio = 1) +
xlim(0,250) 
ggsave("psiDistance_zoom_out.pdf", width = 4, height = 4, device = "pdf")df = rngEnd %>% as.data.frame() %>%
mutate(sel = ifelse(wtPSI < 0.5, "Low", "High")) %>%
group_by(distAltEnd, sel) %>%
summarise(n = n()) %>%
pivot_wider(values_from = n, names_from = sel) %>%
mutate(distAltEnd = as.integer(distAltEnd)) %>%
mutate(High = ifelse(is.na(High), 0, High)) %>%
mutate(Low = ifelse(is.na(Low), 0, Low)) %>%
group_by(distAltEnd) %>%
summarise(High = sum(High), Low = sum(Low), Sum = sum(High, Low)) %>%
mutate(FracHigh = High/Sum, FracLow = Low/Sum) %>%
select(distAltEnd, FracHigh, FracLow) %>%
pivot_longer(-distAltEnd) %>%
mutate(bin = as.numeric(distAltEnd))
dfN = rngEnd %>% as.data.frame() %>%
select(distAltEnd, wtPSI) %>%
mutate(bin = cut_width(distAltEnd, width = 10, boundary = 0)) %>%
mutate(bin = as.numeric(bin)) %>%
select(c(bin, wtPSI))
ggplot() +
ggrastr::rasterise(geom_quasirandom(data = dfN, aes(x = bin, y = wtPSI), color = "#808080", size = 0.1, alpha = 0.5), dpi = 300) +
geom_line(data = df, aes(x = bin, y = value, color = name, group = name), size = 0.8) +
geom_smooth(data = df, aes(x = bin, y = value, color = name, group = name),
method = "loess", se = FALSE, size = 1, span = 0.3) +
theme_pub() +
scale_color_npg(name = "3' splice usage", labels = c("3'SS (canonical)", "*3'SS (alternative)")) +
labs(
x = "Splice site distance [nt]",
y = "Usage fraction",
color = "WT inclusion level"
) +
theme(legend.position = "top") +
scale_y_continuous(
breaks = c(seq(from = -0.2, to = 1.2, by = 0.2)),
name = "Usage fraction",
sec.axis = sec_axis(~., name = "WT-PSI distribution")
) +
xlim(0,50)
ggsave("psiDistance_zoom_in.pdf", width = 4, height = 4, device = "pdf")ggplot() +
ggrastr::rasterise(geom_quasirandom(data = dfN, aes(x = bin, y = wtPSI), color = "#808080", size = 0.1, alpha = 0.5), dpi = 300) +
geom_line(data = df, aes(x = bin, y = value, color = name, group = name), size = 0.8) +
geom_smooth(data = df, aes(x = bin, y = value, color = name, group = name),
method = "loess", se = FALSE, size = 1, span = 0.3) +
theme_pub() +
scale_color_npg(name = "3' splice usage", labels = c("3'SS (canonical)", "*3'SS (alternative)")) +
labs(
x = "Splice site distance [nt]",
y = "Usage fraction",
color = "WT inclusion level"
) +
theme(legend.position = "top") +
scale_y_continuous(
breaks = c(seq(from = -0.2, to = 1.2, by = 0.2)),
name = "Usage fraction",
sec.axis = sec_axis(~., name = "WT-PSI distribution")
) +
xlim(0,30)
ggsave("psiDistance_zoom_in2.pdf", width = 4, height = 4, device = "pdf")sessionInfo()R version 4.2.1 (2022-06-23)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats4 grid stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] zoo_1.8-12 ggbeeswarm_0.7.2
[3] ggsci_3.0.0 patchwork_1.1.2
[5] circlize_0.4.15 readr_2.1.4
[7] BindingSiteFinder_1.7.8 kableExtra_1.3.4
[9] ComplexHeatmap_2.14.0 tidyr_1.3.0
[11] dplyr_1.1.2 tibble_3.2.1
[13] viridis_0.6.3 viridisLite_0.4.2
[15] GenomicAlignments_1.33.1 Rsamtools_2.13.4
[17] Biostrings_2.65.6 XVector_0.37.1
[19] SummarizedExperiment_1.27.3 MatrixGenerics_1.9.1
[21] matrixStats_1.0.0 GenomicFeatures_1.49.7
[23] AnnotationDbi_1.59.1 Biobase_2.57.1
[25] rtracklayer_1.57.0 GenomicRanges_1.49.1
[27] GenomeInfoDb_1.33.10 IRanges_2.31.2
[29] S4Vectors_0.35.4 BiocGenerics_0.43.4
[31] ggplot2_3.4.2 reshape2_1.4.4
[33] scales_1.2.1 gridExtra_2.3
[35] gridGraphics_0.5-1 knitr_1.43
loaded via a namespace (and not attached):
[1] BiocFileCache_2.5.2 systemfonts_1.0.4 plyr_1.8.8
[4] splines_4.2.1 BiocParallel_1.31.13 digest_0.6.31
[7] foreach_1.5.2 htmltools_0.5.5 fansi_1.0.4
[10] magrittr_2.0.3 memoise_2.0.1 cluster_2.1.4
[13] doParallel_1.0.17 tzdb_0.4.0 vroom_1.6.3
[16] svglite_2.1.1 prettyunits_1.1.1 colorspace_2.1-0
[19] blob_1.2.4 rvest_1.0.3 rappdirs_0.3.3
[22] ggdist_3.3.0 textshaping_0.3.6 xfun_0.39
[25] crayon_1.5.2 RCurl_1.98-1.12 jsonlite_1.8.5
[28] iterators_1.0.14 glue_1.6.2 polyclip_1.10-4
[31] gtable_0.3.3 zlibbioc_1.43.0 webshot_0.5.4
[34] GetoptLong_1.0.5 DelayedArray_0.23.2 distributional_0.3.2
[37] shape_1.4.6 DBI_1.1.3 Rcpp_1.0.10
[40] progress_1.2.2 clue_0.3-64 bit_4.0.5
[43] htmlwidgets_1.6.2 httr_1.4.6 RColorBrewer_1.1-3
[46] pkgconfig_2.0.3 XML_3.99-0.14 farver_2.1.1
[49] dbplyr_2.3.2 utf8_1.2.3 tidyselect_1.2.0
[52] labeling_0.4.2 rlang_1.1.1 munsell_0.5.0
[55] tools_4.2.1 cachem_1.0.8 cli_3.6.1
[58] generics_0.1.3 RSQLite_2.3.1 evaluate_0.21
[61] stringr_1.5.0 fastmap_1.1.1 yaml_2.3.7
[64] ragg_1.2.5 bit64_4.0.5 purrr_1.0.1
[67] KEGGREST_1.37.3 nlme_3.1-162 ggrastr_1.0.2
[70] xml2_1.3.4 biomaRt_2.53.3 compiler_4.2.1
[73] rstudioapi_0.14 beeswarm_0.4.0 filelock_1.0.2
[76] curl_5.0.1 png_0.1-8 tweenr_2.0.2
[79] stringi_1.7.12 lattice_0.21-8 Matrix_1.5-4.1
[82] vctrs_0.6.3 pillar_1.9.0 lifecycle_1.0.3
[85] GlobalOptions_0.1.2 bitops_1.0-7 R6_2.5.1
[88] BiocIO_1.7.1 vipor_0.4.5 codetools_0.2-19
[91] MASS_7.3-60 rjson_0.2.21 withr_2.5.0
[94] GenomeInfoDbData_1.2.9 mgcv_1.8-42 parallel_4.2.1
[97] hms_1.1.3 rmarkdown_2.22 Cairo_1.6-0
[100] ggforce_0.4.1 restfulr_0.0.15