# code chunk number 15: grl-simul # set.seed( 1 ) P3 <- autoplot(ir, stat = "coverage", geom = "line", facets = sample ~. ![]() P2 <- autoplot(ir, aes(fill = pair)) + theme(legend.position = "none" ) # code chunk number 14: ir-exp # p1 <- autoplot(ir) # add meta data df <- DataFrame(value = rnorm(N, 10, 3 ), score = rnorm(N, 100, 30 ), Width = sample( 70 : 75, size = N,replace = TRUE )) Ir <- IRanges(start = sample( 1 : 300, size = N, replace = TRUE ), # code chunk number 13: ir-load # set.seed( 1 ) # code chunk number 12: seqinfo # autoplot(sq) # code chunk number 11: seqinfo-src # data(hg19Ideogram, package = "biovizBase" ) Layout_circle(gr, geom = "link", linked.to = "to.gr", radius = 6, trackWidth = 1 ) TrackWidth = 3, grid = TRUE, aes(y = score)) + Layout_circle(gr, geom = "point", color = "red", radius = 14 , Layout_circle(gr, geom = "bar", radius = 10, trackWidth = 4 , Ggplot() + layout_circle(gr, geom = "ideo", fill = "gray70", radius = 7, trackWidth = 3 ) + # code chunk number 9: gr-autoplot-circle # autoplot(gr, layout = 'circle' ) # code chunk number 8: gr-facet-strand # autoplot(gr, stat = "coverage", geom = "area" ,įacets = strand ~ seqnames, aes(fill = strand)) Tracks( 'non-group' = p1, 'lfish = TRUE' = p2, 'lfish = FALSE' = p3) # lfish = FALSE, save space p3 <- autoplot(gra, aes(fill = group, group = group), geom = "alignment", lfish = FALSE ) p2 <- autoplot(gra, aes(fill = group, group = group), geom = "alignment" ) # in this way, group labels could be shown as y axis. # default is lfish = TRUE, each group keep one row. # when use group method, gaps only computed for grouped intervals. # if you desn't specify group, then group based on stepping levels, and gaps are computed without # considering extra group method p1 <- autoplot(gra, aes(fill = group), geom = "alignment" ) # code chunk number 6: autoplot.Rnw:236-237 # autoplot(gr, geom = "arch", aes(color = value), facets = sample ~ seqnames) # use value to fill the bar p2 <- autoplot(gr.b, geom = "bar", aes(fill = value)) # code chunk number 5: bar-default # p1 <- autoplot(gr.b, geom = "bar" ) Width = sample( 4 : 9, size = 10, replace = TRUE )), Gr.b <- GRanges(seqnames = "chr1", IRanges(start = seq( 1, 100, by = 10 ), # code chunk number 4: bar-default-pre # set.seed( 123 ) # code chunk number 3: default # autoplot(gr) Idx <- sample( 1 : length (gr), size = 50 ) Sample = sample( c ( "Normal", "Tumor" ), Width = sample( 70 : 75, size = N,replace = TRUE )), Start = sample( 1 : 300, size = N, replace = TRUE ), , type = c("heatmap", "link", "pcp", "boxplot", "scatterplot.matrix"), ot = FALSE, # S3 method for class 'RangedSummarizedExperiment':Īutoplot(object. "t", rotate = FALSE, ot = FALSE, main_to_pheno "scatterplot.matrix", "pcp", "MA", "boxplot", Rownames.label = TRUE, colnames.label = TRUE,Īutoplot(object. Geom = NULL, type = c("viewSums", "viewMins", "viewMaxs", "viewMeans")) , xlab, ylab, main, nbin = 30, binwidth,įacetByRow = TRUE, stat = c("bin", "identity", "slice"), Type = c("viewSums", "viewMins", "viewMaxs", "viewMeans"))Īutoplot(object. Geom = NULL, stat = c("bin", "identity", "slice"), ![]() , xlab, ylab, main, truncate.gaps =įALSE, truncate.fun = NULL, ratio = 0.0025,Ĭ("alignment"), stat = c("identity", "reduce"),Īutoplot(object. , xlab, ylab, main, which)Īutoplot(object, which. Resize.extra = 10, space.skip = 0.1, verage =Īutoplot(object. "estimate"), coord = c("linear", "genome"), , which, xlab, ylab, main,īsgenome, geom = "line", stat = "coverage", method = c("raw", , xlab, ylab, main, which,Īutoplot(object. Geom = NULL, stat = NULL, l = "gray50",Ĭoverage.fill = l, lfish = FALSE)Īutoplot(object. ![]() , xlab, ylab, main, indName = "grl_name", Layout = c("linear", "karyogram", "circle"))Īutoplot(object. Truncate.fun = NULL, ratio = 0.0025, space.skip = 0.1,Ĭoord = c("default", "genome", "truncate_gaps"), , chr, xlab, ylab, main, truncate.gaps = FALSE, Usage # S3 method for class 'GRanges':Īutoplot(object. Simpler and easy to produce fairly complicate graphics, though you may Genomic data compare to low level ggplot method, it is much ![]() Object, it tries to give better default graphics and customizedĬhoices for each data type, quick and convenient to explore your Autoplot: Generic autoplot function Description autoplot is a generic function to visualize various data
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