GEN242: Data Analysis in Genome Biology
2026-05-21
plot: generic x-y plottingbarplot: bar plotsboxplot: box-and-whisker plothist: histogramspie: pie chartsdotchart: cleveland dot plotsimage, heatmap, contour, persp: functions to generate image-like plotsqqnorm, qqline, qqplot: distribution comparison plotspairs, coplot: display of multivariant data?myfct?plot?par[ Scroll down to continue ]
Sample data set for subsequent plots
A B C
a 0.26904539 0.47439030 0.4427788756
b 0.53178658 0.31128960 0.3233293493
c 0.93379571 0.04576263 0.0004628517
d 0.14314802 0.12066723 0.4104402000
e 0.57627063 0.83251909 0.9884746270
f 0.49001235 0.38298651 0.8235850153
g 0.66562596 0.70857731 0.7490944304
h 0.50089252 0.24772695 0.2117313873
i 0.57033245 0.06044799 0.8776291364
j 0.04087422 0.85814118 0.1061618729
plot(y[,1], y[,2], pch=20, col="red", main="Symbols and Labels")
text(y[,1]+0.03, y[,2], rownames(y))
Print instead of symbols the row names
Usage of important plotting parameters
Important arguments} - mar: specifies the margin sizes around the plotting area in order: c(bottom, left, top, right) - col: color of symbols - pch: type of symbols, samples: example(points) - lwd: size of symbols - cex.*: control font sizes - For details see ?par
Add a regression line to a plot
Call:
lm(formula = y[, 2] ~ y[, 1])
Residuals:
Min 1Q Median 3Q Max
-0.40357 -0.17912 -0.04299 0.22147 0.46623
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.5764 0.2110 2.732 0.0258 *
y[, 1] -0.3647 0.3959 -0.921 0.3839
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3095 on 8 degrees of freedom
Multiple R-squared: 0.09589, Adjusted R-squared: -0.01712
F-statistic: 0.8485 on 1 and 8 DF, p-value: 0.3839Same plot as above, but on log scale
Add a mathematical expression to a plot
iris data frame and color dots by its Species column.xlim/ylim arguments to set limits on the x- and y-axes so that all data points are restricted to the left bottom quadrant of the plot.Structure of iris data set:
[1] "data.frame" Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
setosa versicolor virginica
50 50 50 Plots line graph for all columns in data frame y. The split.screen function is used in this example in a for loop to overlay several line graphs in the same plot.
[1] 1plot(y[,1], ylim=c(0,1), xlab="Measurement", ylab="Intensity", type="l", lwd=2, col=1)
for(i in 2:length(y[1,])) {
screen(1, new=FALSE)
plot(y[,i], ylim=c(0,1), type="l", lwd=2, col=i, xaxt="n", yaxt="n", ylab="",
xlab="", main="", bty="n")
}
barplot(y[1:4,], ylim=c(0, max(y[1:4,])+0.3), beside=TRUE,
legend=letters[1:4])
text(labels=round(as.vector(as.matrix(y[1:4,])),2), x=seq(1.5, 13, by=1)
+sort(rep(c(0,1,2), 4)), y=as.vector(as.matrix(y[1:4,]))+0.04) 
bar <- barplot(m <- rowMeans(y) * 10, ylim=c(0, 10))
stdev <- sd(t(y))
arrows(bar, m, bar, m + stdev, length=0.15, angle = 90)
df <- data.frame(group = rep(c("Above", "Below"), each=10), x = rep(1:10, 2), y = c(runif(10, 0, 1), runif(10, -1, 0)))
plot(c(0,12), range(df$y), type = "n")
barplot(height = df$y[df$group == "Above"], add = TRUE, axes = FALSE)
barplot(height = df$y[df$group == "Below"], add = TRUE, axes = FALSE)
pie(y[,1], col=rainbow(length(y[,1]), start=0.1, end=0.8), clockwise=TRUE)
legend("topright", legend=row.names(y), cex=1.3, bty="n", pch=15, pt.cex=1.8,
col=rainbow(length(y[,1]), start=0.1, end=0.8), ncol=1) 
Default color palette and how to change it
[1] "black" "#DF536B" "#61D04F" "#2297E6" "#28E2E5" "#CD0BBC" "#F5C710" "gray62" [1] "#FF9900" "#FFBF00" "#FFE600" "#F2FF00" "#CCFF00"The gray function allows to select any type of gray shades by providing values from 0 to 1
Color gradients with colorpanel function from gplots library
Much more on colors in R see Earl Glynn’s color chart
With par(mfrow=c(nrow, ncol)) one can define how several plots are arranged next to each other.
The layout function allows to divide the plotting device into variable numbers of rows and columns with the column-widths and the row-heights specified in the respective arguments.
nf <- layout(matrix(c(1,2,3,3), 2, 2, byrow=TRUE), c(3,7), c(5,5),
respect=TRUE)
# layout.show(nf)
for(i in 1:3) barplot(1:10) 
After the pdf() command all graphs are redirected to file test.pdf. Works for all common formats similarly: jpeg, png, ps, tiff, …
Generates Scalable Vector Graphics (SVG) files that can be edited in vector graphics programs, such as InkScape.
Bar plots
Species components of the first four columns in the iris data set. Organize the results in a matrix where the row names are the unique values from the iris Species column and the column names are the same as in the first four iris columns.Structure of iris data set:
lattice?
Open a list of all functions available in the lattice package
[ Scroll down to continue ]
Accessing and changing global parameters:
ggplot2Plotting formalized and implemented by the grammar of graphics by Leland Wilkinson and Hadley Wickham (Wickham 2010, 2009; Wilkinson 2012). The plotting process in ggplot2 is devided into layers including:
qplot FunctionThe syntax of qplot is similar as R’s basic plot function
x: x-coordinates (e.g. col1)y: y-coordinates (e.g. col2)data: data.frame or tibble with corresponding column namesxlim, ylim: e.g. xlim=c(0,10)log: e.g. log="x" or log="xy"main: main title; see ?plotmath for mathematical formulaxlab, ylab: labels for the x- and y-axescolor, shape, size...: many arguments accepted by plot function[ Scroll down to continue ]
qplot: scatter plot basicsCreate sample data, here 3 vectors: x, y and cat
Simple scatter plot
Prints dots with different sizes and colors
Drops legend
Plot different shapes
p <- qplot(x, y, geom="point", size=x, color=cat,
main="Dot Size and Color Relative to Some Values") +
theme(legend.position = "none")
print(p)
set.seed(1410)
dsmall <- diamonds[sample(nrow(diamonds), 1000), ]
p <- qplot(carat, price, data = dsmall) +
geom_smooth(method="lm")
print(p)
ggplot Functionqplot to access full functionality of ggplot2data.frame or tibbleaes functionggplot syntax
ggplot(data, aes(...)) + geom() + ... + stat() + ...geom(mapping, data, ..., geom, position)stat(mapping, data, ..., stat, position)scalescoordinatesfacet[ Scroll down to continue ]
aes() mappings can be passed on to all components (ggplot, geom, etc.). Effects are global when passed on to ggplot() and local for other components.
x, ycolor: grouping vector (factor)group: grouping vector (factor)ggplottheme_get()theme()Example how to change background color to white
ggplot SpecificationsPlots and layers can be stored in variables
Returns information about data and aesthetic mappings followed by each layer
Print dots with different sizes and colors
Syntax to pass on other data sets
Saves plot stored in variable p to file
Standard R export functons for graphics work as well (see here).
ggplot: scatter plotsset.seed(1410)
dsmall <- as.data.frame(diamonds[sample(nrow(diamonds), 1000), ])
p <- ggplot(dsmall, aes(carat, price, color=color)) +
geom_point(size=4)
print(p) 
Interactive version of above plot can be generated with the ggplotly function from the plotly package.
p <- ggplot(dsmall, aes(carat, price)) + geom_point() +
geom_smooth(method="lm", se=FALSE) +
theme(panel.background=element_rect(fill = "white", colour = "black"))
print(p) 
p <- ggplot(dsmall, aes(carat, price, group=color)) +
geom_point(aes(color=color), size=2) +
geom_smooth(aes(color=color), method = "lm", se=FALSE)
print(p) 
p <- ggplot(dsmall, aes(carat, price)) + geom_point() + geom_smooth()
print(p) # Setting se=FALSE removes error shade
ggplot: line plotp <- ggplot(iris, aes(Petal.Length, Petal.Width, group=Species,
color=Species)) + geom_line()
print(p) 
p <- ggplot(iris, aes(Sepal.Length, Sepal.Width)) +
geom_line(aes(color=Species), size=1) +
facet_wrap(~Species, ncol=1)
print(p) 
Scatter plots with ggplot2
iris data frame and color dots by its Species column.xlim and ylim arguments to set limits on the x- and y-axes so that all data points are restricted to the left bottom quadrant of the plot.Structure of iris data set
[1] "data.frame" Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
setosa versicolor virginica
50 50 50 Sample Set: the following transforms the iris data set into a ggplot2-friendly format.
Calculate mean values for aggregates given by Species column in iris data set
Calculate standard deviations for aggregates given by Species column in iris data set
Reformat iris_mean with melt from wide to long form as expected by ggplot2. Newer alternatives for restructuring data.frames and tibbles from wide into long form use the gather and pivot_longer functions defined by the tidyr package. Their usage is shown below as well. The functions pivot_longer and pivot_wider are expected to provide the most flexible long-term solution, but may not work in older R versions.
library(reshape2) # Defines melt function
df_mean <- melt(iris_mean, id.vars=c("Species"), variable.name = "Samples", value.name="Values")
df_mean2 <- tidyr::gather(iris_mean, !Species, key = "Samples", value = "Values")
df_mean3 <- tidyr::pivot_longer(iris_mean, !Species, names_to="Samples", values_to="Values") Reformat iris_sd with melt
Define standard deviation limits
p <- ggplot(df_mean, aes(Samples, Values, fill = Species)) +
geom_bar(position="dodge", stat="identity")
print(p) 
To enforce that the bars are plotted in the order specified in the input data, one can instruct ggplot to do so by turning the corresponding column (here Species) into an ordered factor as follows.
In the above example this is not necessary since ggplot uses this order already.
p <- ggplot(df_mean, aes(Samples, Values, fill = Species)) +
geom_bar(position="dodge", stat="identity") + coord_flip() +
theme(axis.text.y=element_text(angle=0, hjust=1))
print(p) 
p <- ggplot(df_mean, aes(Samples, Values, fill = Species)) +
geom_bar(position="dodge", stat="identity") +
geom_errorbar(limits, position="dodge")
print(p) 
df <- data.frame(group = rep(c("Above", "Below"), each=10), x = rep(1:10, 2), y = c(runif(10, 0, 1), runif(10, -1, 0)))
p <- ggplot(df, aes(x=x, y=y, fill=group)) +
geom_col()
print(p)
library(RColorBrewer)
# display.brewer.all()
p <- ggplot(df_mean, aes(Samples, Values, fill=Species, color=Species)) +
geom_bar(position="dodge", stat="identity") + geom_errorbar(limits, position="dodge") +
scale_fill_brewer(palette="Blues") + scale_color_brewer(palette = "Greys")
print(p) 
p <- ggplot(df_mean, aes(Samples, Values, fill=Species, color=Species)) +
geom_bar(position="dodge", stat="identity") + geom_errorbar(limits, position="dodge") +
scale_fill_manual(values=c("red", "green3", "blue")) +
scale_color_manual(values=c("red", "green3", "blue"))
print(p) 
Bar plots
Species components of the first four columns in the iris data set. Use the melt function from the reshape2 package to bring the data into the expected format for ggplot.Structure of iris data set
[1] "data.frame" Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
setosa versicolor virginica
50 50 50 Here for line plot
y <- matrix(rnorm(500), 100, 5, dimnames=list(paste("g", 1:100, sep=""), paste("Sample", 1:5, sep="")))
y <- data.frame(Position=1:length(y[,1]), y)
y[1:4, ] # First rows of input format expected by melt() Position Sample1 Sample2 Sample3 Sample4 Sample5
g1 1 1.5336975 -1.0365027 -2.0276195 -0.4580396 -0.06460952
g2 2 -2.0960304 2.1878704 0.7260334 0.8274617 0.24192162
g3 3 -0.8233125 0.4250477 0.6526331 -0.4509962 -1.06778801
g4 4 1.0961555 0.8101104 -0.3403762 -0.7222191 -0.72737741df <- melt(y, id.vars=c("Position"), variable.name = "Samples", value.name="Values")
p <- ggplot(df, aes(Position, Values)) + geom_line(aes(color=Samples)) + facet_wrap(~Samples, ncol=1)
print(p)
Same data can be represented in box plot as follows
p <- ggplot(dsmall, aes(color, price/carat)) +
geom_jitter(alpha = I(1 / 2), aes(color=color))
print(p) 
Same violin plot as interactive plot generated with ggplotly, where the actual data points are shown as well by including geom_jitter().
p <- ggplot(dsmall, aes(color, price/carat, fill=color)) + geom_violin() + geom_jitter(aes(color=color))
ggplotly(p) 
p <- ggplot(iris, aes(x=Sepal.Width)) +
geom_histogram(aes(y = ..density.., fill = ..count..), binwidth=0.2) +
geom_density()
print(p) 
df <- data.frame(variable=rep(c("cat", "mouse", "dog", "bird", "fly")),
value=c(1,3,3,4,2))
p <- ggplot(df, aes(x = "", y = value, fill = variable)) +
geom_bar(width = 1, stat="identity") +
coord_polar("y", start=pi / 3) + ggtitle("Pie Chart")
print(p) 
p <- ggplot(df, aes(x = variable, y = value, fill = variable)) +
geom_bar(width = 1, stat="identity") +
coord_polar("y", start=pi / 3) +
ggtitle("Pie Chart")
print(p) 
Using grid package
library(grid)
a <- ggplot(dsmall, aes(color, price/carat)) + geom_jitter(size=4, alpha = I(1 / 1.5), aes(color=color))
b <- ggplot(dsmall, aes(color, price/carat, color=color)) + geom_boxplot()
c <- ggplot(dsmall, aes(color, price/carat, fill=color)) + geom_boxplot() + theme(legend.position = "none")
grid.newpage() # Open a new page on grid device
pushViewport(viewport(layout = grid.layout(2, 2))) # Assign to device viewport with 2 by 2 grid layout
print(a, vp = viewport(layout.pos.row = 1, layout.pos.col = 1:2))
print(b, vp = viewport(layout.pos.row = 2, layout.pos.col = 1))
print(c, vp = viewport(layout.pos.row = 2, layout.pos.col = 2, width=0.3, height=0.3, x=0.8, y=0.8))
Using gridExtra package
Also see patchwork in ggplot2 book here.
Spatial expression data can be visualized with the spatialHeatmap package.
[ Scroll down to continue ]
library(systemPipeR)
setlist5 <- list(A=sample(letters, 18), B=sample(letters, 16), C=sample(letters, 20), D=sample(letters, 22), E=sample(letters, 18))
OLlist5 <- overLapper(setlist=setlist5, sep="_", type="vennsets")
vennPlot(OLlist5, mymain="", mysub="default", colmode=2, ccol=c("blue", "red"))
Plots depictions of small molecules with ChemmineR package
There are many packages for plotting heatmaps in R. The following uses pheatmap.
library(pheatmap); library("RColorBrewer")
y <- matrix(rnorm(50), 10, 5, dimnames=list(paste("g", 1:10, sep=""), paste("t", 1:5, sep="")))
pheatmap(y, color=brewer.pal(9,"Blues"))
A variety of libraries are available for plotting receiver operating characteristic (ROC) curves in R:
Most commonly, in an ROC we plot the true positive rate (y-axis) against the false positive rate (x-axis) at decreasing thresholds. An illustrative example is provided in the ROCR package where one wants to inspect the content of the ROCR.simple object defining the structure of the input data in two vectors.
$predictions
[1] 0.612547843 0.364270971 0.432136142 0.140291078 0.384895941 0.244415489 0.970641299 0.890172812 0.781781371 0.868751832 0.716680598 0.360168796 0.547983407 0.385240464 0.423739359 0.101699993 0.628095575
[18] 0.744769966 0.657732644 0.490119891 0.072369921 0.172741714 0.105722115 0.890078186 0.945548941 0.984667270 0.360180429 0.448687336 0.014823599 0.543533783 0.292368449 0.701561487 0.715459280 0.714985914
[35] 0.120604738 0.319672178 0.911723615 0.757325590 0.090988280 0.529402244 0.257402979 0.589909284 0.708412104 0.326672910 0.086546283 0.879459891 0.362693564 0.230157183 0.779771989 0.876086217 0.353281048
[52] 0.212014560 0.703293499 0.689075677 0.627012496 0.240911145 0.402801992 0.134794140 0.120473353 0.665444679 0.536339509 0.623494622 0.885179651 0.353777439 0.408939895 0.265686095 0.932159806 0.248500489
[69] 0.858876675 0.491735594 0.151350957 0.694457482 0.496513160 0.123504905 0.499788081 0.310718619 0.907651100 0.340078180 0.195097957 0.371936985 0.517308606 0.419560072 0.865639036 0.018527600 0.539086009
[86] 0.005422562 0.772728821 0.703885141 0.348213542 0.277656869 0.458674210 0.059045866 0.133257805 0.083685883 0.531958184 0.429650397 0.717845453 0.537091350 0.212404891 0.930846938 0.083048377 0.468610247
[103] 0.393378108 0.663367560 0.349540913 0.194398425 0.844415442 0.959417835 0.211378771 0.943432189 0.598162949 0.834803976 0.576836208 0.380396459 0.161874325 0.912325837 0.642933593 0.392173971 0.122284044
[120] 0.586857799 0.180631658 0.085993218 0.700501359 0.060413627 0.531464015 0.084254795 0.448484671 0.938583020 0.531006532 0.785213140 0.905121019 0.748438143 0.605235403 0.842974300 0.835981859 0.364288579
[137] 0.492596896 0.488179708 0.259278968 0.991096434 0.757364019 0.288258273 0.773336236 0.040906997 0.110241034 0.760726142 0.984599159 0.253271061 0.697235328 0.620501132 0.814586047 0.300973098 0.378092079
[154] 0.016694412 0.698826511 0.658692553 0.470206008 0.501489336 0.239143340 0.050999138 0.088450984 0.107031842 0.746588080 0.480100183 0.336592126 0.579511087 0.118555284 0.233160827 0.461150807 0.370549294
[171] 0.770178504 0.537336015 0.463227453 0.790240205 0.883431431 0.745110673 0.007746305 0.012653524 0.868331219 0.439399995 0.540221346 0.567043171 0.035815400 0.806543942 0.248707470 0.696702150 0.081439129
[188] 0.336315317 0.126480399 0.636728451 0.030235062 0.268138293 0.983494405 0.728536415 0.739554341 0.522384507 0.858970526 0.383807972 0.606960209 0.138387070
$labels
[1] 1 1 0 0 0 1 1 1 1 0 1 0 1 0 0 0 1 1 1 0 0 0 0 1 0 1 0 0 1 1 0 1 1 1 0 0 1 1 0 1 0 1 0 1 0 1 0 1 0 1 1 0 1 0 1 0 0 0 0 1 1 1 1 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 0 0 1 0 0 1 0 1 0 1 1 0 1 0 0 0 1 0 0 1
[108] 0 0 1 1 1 0 0 0 1 1 0 0 1 0 0 1 0 1 0 0 1 1 1 1 1 0 1 1 0 0 0 0 1 1 0 1 0 1 0 1 1 1 1 1 0 0 0 1 1 0 1 0 0 0 0 1 0 0 1 0 0 0 0 1 1 0 1 1 1 0 1 1 0 1 1 0 1 0 0 0 1 0 0 0 1 0 1 1 0 1 0 1 0pred <- prediction(ROCR.simple$predictions, ROCR.simple$labels)
perf <- performance( pred, "tpr", "fpr" )
plot(perf)
Obtain area under the curve (AUC)
The ape package provides many useful utilities for phylogenetic analysis and tree plotting. Another useful package for plotting trees is ggtree. The following example plots two trees face to face with links to identical leaf labels.
ggbioggbio?
ggbio package (Yin, Cook, and Lawrence 2012) facilitates plotting of complex genome data objects, such as read alignments (SAM/BAM), genomic context/annotation information (gff/txdb), variant calls (VCF/BCF), and more. To easily compare these data sets, it extends the faceting facility of ggplot2 to genome browser-like tracks.GRanges, GAlignments, VCF, etc. For more details, see Table 1.1 of the ggbio vignette here.ggbio’s convenience plotting function is autoplot. For more customizable plots, one can use the generic ggplot function.ggplot2 plotting components, ggbio defines serval new components useful for genomic data visualization. A detailed list is given in Table 1.2 of the vignette here.[ Scroll down to continue ]
library(ggbio)
df1 <- data.frame(time = 1:100, score = sin((1:100)/20)*10)
p1 <- qplot(data = df1, x = time, y = score, geom = "line")
df2 <- data.frame(time = 30:120, score = sin((30:120)/20)*10, value = rnorm(120-30 +1))
p2 <- ggplot(data = df2, aes(x = time, y = score)) + geom_line() + geom_point(size = 2, aes(color = value))
tracks(time1 = p1, time2 = p2) + xlim(1, 40) + theme_tracks_sunset()
GRanges objects are essential for storing alignment or annotation ranges in R/Bioconductor. The following creates a sample GRanges object and plots its content.
library(GenomicRanges)
set.seed(1); N <- 100; gr <- GRanges(seqnames = sample(c("chr1", "chr2", "chr3"), size = N, replace = TRUE), IRanges(start = sample(1:300, size = N, replace = TRUE), width = sample(70:75, size = N,replace = TRUE)), strand = sample(c("+", "-"), size = N, replace = TRUE), value = rnorm(N, 10, 3), score = rnorm(N, 100, 30), sample = sample(c("Normal", "Tumor"), size = N, replace = TRUE), pair = sample(letters, size = N, replace = TRUE))
autoplot(gr, aes(color = strand, fill = strand), facets = strand ~ seqnames)
pos <- sapply(coverage(gr[strand(gr)=="+"]), as.numeric)
pos <- data.frame(Chr=rep(names(pos), sapply(pos, length)), Strand=rep("+", length(unlist(pos))), Position=unlist(sapply(pos, function(x) 1:length(x))), Coverage=as.numeric(unlist(pos)))
neg <- sapply(coverage(gr[strand(gr)=="-"]), as.numeric)
neg <- data.frame(Chr=rep(names(neg), sapply(neg, length)), Strand=rep("-", length(unlist(neg))), Position=unlist(sapply(neg, function(x) 1:length(x))), Coverage=-as.numeric(unlist(neg)))
covdf <- rbind(pos, neg)
p <- ggplot(covdf, aes(Position, Coverage, fill=Strand)) +
geom_col() +
facet_wrap(~Chr)
p
More complex circular example
seqlengths(gr) <- c(400, 500, 700)
values(gr)$to.gr <- gr[sample(1:length(gr), size = length(gr))]
idx <- sample(1:length(gr), size = 50)
gr <- gr[idx]
ggplot() +
layout_circle(gr, geom = "ideo", fill = "gray70", radius = 7, trackWidth = 3) +
layout_circle(gr, geom = "bar", radius = 10, trackWidth = 4, aes(fill = score, y = score)) +
layout_circle(gr, geom = "point", color = "red", radius = 14, trackWidth = 3, grid = TRUE, aes(y = score)) +
layout_circle(gr, geom = "link", linked.to = "to.gr", radius = 6, trackWidth = 1)
To make the following example work, please download and unpack this data archive containing GFF, BAM and VCF sample files.
library(rtracklayer); library(txdbmaker); library(Rsamtools); library(GenomicAlignments); library(VariantAnnotation)
ga <- readGAlignments("./data/SRR064167.fastq.bam", use.names=TRUE, param=ScanBamParam(which=GRanges("Chr5", IRanges(4000, 8000))))
p1 <- autoplot(ga, geom = "rect")
p2 <- autoplot(ga, geom = "line", stat = "coverage")
vcf <- readVcf(file="data/varianttools_gnsap.vcf", genome="ATH1")
p3 <- autoplot(vcf[seqnames(vcf)=="Chr5"], type = "fixed") + xlim(4000, 8000) + theme(legend.position = "none", axis.text.y = element_blank(), axis.ticks.y=element_blank())
txdb <- makeTxDbFromGFF(file="./data/TAIR10_GFF3_trunc.gff", format="gff3")
p4 <- autoplot(txdb, which=GRanges("Chr5", IRanges(4000, 8000)), names.expr = "gene_id")
tracks(Reads=p1, Coverage=p2, Variant=p3, Transcripts=p4, heights = c(0.3, 0.2, 0.1, 0.35)) + ylab("")
See autoplot demo here
View genome data in IGV
File -> Load from URL...https://cluster.hpcc.ucr.edu/~tgirke/Teaching/GEN242/bam_samples/SRR064154.fastq.bam
https://cluster.hpcc.ucr.edu/~tgirke/Teaching/GEN242/bam_samples/SRR064155.fastq.bam
https://cluster.hpcc.ucr.edu/~tgirke/Teaching/GEN242/bam_samples/SRR064166.fastq.bam
https://cluster.hpcc.ucr.edu/~tgirke/Teaching/GEN242/bam_samples/SRR064167.fastq.bamChr1:49,457-51,457 in position menu on top.
For viewing BAM files in IGV as part of systemPipeR workflows.
systemPipeR: utilities for building NGS analysis pipelines.Open IGV before running the following routine. Alternatively, open IGV from within R with startIGV("lm") . Note this may not work on all systems.
