-
Notifications
You must be signed in to change notification settings - Fork 2
/
manhattan_gene.R
180 lines (165 loc) · 7.48 KB
/
manhattan_gene.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
library(dplyr)
library(tidyr)
library(ggplot2)
library(grid)
library(MASS)
# Manhattan plot of SNVs with their corresonding (-log) p-values
# associated with gene expression.
# results, snps, chooseGene, gtf, width
plot_manhattan_gene <- function(res, snps, whichGene, gtf, w = 2){
if(whichGene==''){
whichGene = res$gene_name[1]
}
manh = res[res$gene_name==whichGene, ]
manh = manh %>% left_join(snps, by='snpid')
manh = manh [,c("chr", "pos", "pValue", "snpid")]
geneForPlot = gtf[gtf$gene_name==whichGene,
c('gene_name','seqid','start','end')] %>%
group_by(gene_name) %>%
summarise(start = min(start),
end = max(end),
chr = dplyr::first(seqid))
w = (geneForPlot$end-geneForPlot$start) * w # Plot width
# Take gene neighboors (w form start/end)
g = gtf %>% filter(seqid == geneForPlot$chr) %>%
dplyr::select(gene_name, gene_id, type, seqid, start, end, strand) %>%
filter(end > max(0,(geneForPlot$start - w))) %>%
filter(start < (geneForPlot$end + w))
exons = g[g$type == 'exon',]
g = g[g$type == 'gene',]
# Find overlapping genes and sort them using count
gg <- GRanges(seqnames = g$seqid,
ranges = IRanges(start = g$start, end=g$end),
strand = g$strand,
gene_name = g$gene_name)
overlapps = as.data.frame(findOverlaps(gg,gg,ignore.strand=T))
gg$count = overlapps %>%
filter(queryHits >= subjectHits) %>%
group_by(queryHits) %>%
summarise(count=n()) %>%
dplyr::select(count)
g = g %>% left_join(data.frame(gene_name = gg$gene_name,
count = gg$count),
by='gene_name') # add count to genes
g = exons %>%
right_join(g[,c('gene_name','count')], by='gene_name') %>%
dplyr::union(g) # add gene count to exon and then make union of genes and exons
maxP = max(-log10(manh$pValue)) # size of Y axis
gls = as.integer(maxP * 0.03) + 1 # size of genes, exons below the points
igv = data.frame(gene_name = g$gene_name,
type = g$type,
xmin = g$start,
xmax = g$end,
strand = g$strand,
count = as.integer(g$count))
igv = igv[order(igv$type),] # to plot exons over
igv$where <- 1
igv$ymax = - gls*igv$where - gls * igv$count
igv$ymin = - gls*(igv$where+1) - gls * igv$count
igv$xmin = pmax(igv$xmin, geneForPlot$start - w)
igv$xmax = pmin(igv$xmax, geneForPlot$end + w)
igvText = data.frame(gene_name = igv$gene_name,
xpos = (igv$xmin+igv$xmax)/2,
ymin=igv$ymin,
ymax=igv$ymax,
count = igv$count,
type=igv$type)
igvText = igvText %>%
filter(type=='gene') %>%
group_by(count) %>%
mutate(sync = (cumsum(ymin)/dplyr::first(ymin)) %% 4 + 1)
igvText$ypos = (igvText$ymin * (igvText$sync) +
igvText$ymax * (7 - igvText$sync))/7
#### Create plots
p1 <- ggplot(manh, aes(x=pos, y=-log10(pValue))) +
ggtitle(paste(whichGene, "associated SNVs")) +
geom_point(color="steelblue", fill="steelblue", size=3) +
geom_point(data = manh[which.min(manh$pValue),],
aes(x=pos, y=-log10(pValue)),
color="orange",
size=5) +
scale_y_continuous(breaks = seq(from=0,
to=as.integer(maxP)+1,
by = as.integer(maxP/5)),
limits = c(0, as.integer(maxP)+5)) +
scale_x_continuous(limits = c(max(0,geneForPlot$start - w),
geneForPlot$end + w)) +
ylab("-log(pValue)") +
theme_classic() +
theme(plot.title = element_text(size=20, hjust = 0.5),
text=element_text(size=17),
axis.text=element_text(size=12),
axis.title.x=element_blank(),
axis.title.y=element_text(vjust=2.5, hjust = 0.5, size=17),
axis.text.y=element_text(size=12),
plot.margin=unit(c(5.5,5.5,0,5.5), "pt"))
p2 <- ggplot() + geom_rect(data = igv[igv$type=='gene',],
aes(xmax = xmax,
xmin = xmin,
ymax = ymax,
ymin = ymin,
fill = type),
inherit.aes = FALSE,
show.legend = TRUE) +
geom_rect(data = igv[igv$type=='exon',],
aes(xmax = xmax,
xmin = xmin,
ymax = ymax,
ymin = ymin,
fill = type),
inherit.aes = FALSE,
show.legend = TRUE) +
geom_text(data = igvText,
aes(x=xpos, y=ypos, label=gene_name),
size = 3,
inherit.aes = TRUE,
check_overlap = TRUE) +
geom_segment(data = filter(igv, type=='gene', strand=='+'),
mapping = aes(x = xmin,
y = (ymax+6*ymin)/7,
xend = xmax,
yend = (ymax+6*ymin)/7),
arrow = arrow(length = unit(0.2, "cm")),
size = 0.5,
color = "blue",
inherit.aes = T) +
geom_segment(data = filter(igv, type=='gene', strand=='-'),
mapping = aes(xend = xmin,
yend = (ymax+6*ymin)/7,
x = xmax,
y = (ymax+6*ymin)/7),
arrow = arrow(length = unit(0.2, "cm")),
size = 0.5,
color = "blue",
inherit.aes = T) +
scale_x_continuous(limits = c(max(0,geneForPlot$start - w),
geneForPlot$end + w)) +
xlab("Genomic position (bp)") +
theme(panel.border = element_rect(colour = "black", fill=NA),
panel.background = element_rect(fill = "white",
colour = "white",
size = 0.5, linetype = "solid"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.title.y=element_text(vjust = 2,
hjust = 0.7,
size = 9,
color = 'white'),
axis.text.y=element_text(size = 9,
color = 'white'),
axis.ticks.y=element_blank(),
legend.position="bottom",
plot.margin=unit(c(5.5,5.5,5.5,5.5), "pt"),
axis.title.x = element_text(vjust = 0,
hjust = 0.7,
size = 17),
axis.text.x = element_text(size = 12),
legend.title=element_text(size = 12),
legend.text=element_text(size = 12))
maxOverlaps = min(max(igv$count),6)
pp <- rbind(ggplotGrob(p1), ggplotGrob(p2), size = "last")
idpanels <- unique(pp$layout[grepl("panel",pp$layout$name), "t"])
pp$heights[idpanels][1] = unit(x=6,units = 'null')
pp$heights[idpanels][2] = unit(x=maxOverlaps,units = 'null')
grid.draw(pp)
}