鏈接來源
一切版權(quán)來源參考鏈接：此處只是用來記錄和不能出去的情況。
- Tutorial: Normalization of BigWig files using TMM from edgeR
- Tutorial: Using EnrichedHeatmap for visualization of NGS experiments
- 繪圖示例數(shù)據(jù)鏈接
涉及腳本
#### A minimal example on how to use EnrichedHeatmap together with rtracklayer:
require(EnrichedHeatmap)
require(rtracklayer)
require(circlize)
require(data.table)
## We start from a BED file with coordinates and load as GRanges:
tmp.targets <- makeGRangesFromDataFrame(
df = fread("~/your.bed", header = F),
seqnames.field = "V1", start.field = "V2", end.field = "V3")
## Say we want to take the peak center and extend it by 5kb in each direction:
tmp.extension <- 5000
## Extend center of the peaks by tmp.extension in each direction:
tmp.targets_extended <- resize(tmp.targets, fix = "center", width = tmp.extension*2)
## Now load the content of the bigwig limited to the regions we are interested in.
## This is much quicker than loading the entire bigwig and does not consume so much memory:
tmp.bigwig <- rtracklayer::import("~/your.bigwig" ,
format = "BigWig",
selection = BigWigSelection(tmp.targets_extended))
## create the normalizedMatrix that EnrichedHeatmap accepts as input.
## We use the tmp.targets center (width=1) because from what I understand normalizeMatrix
## does not allow to turn off its extend= option. Therefore we trick it by simply
## providing the peak centers and then let the function extend it by our predefined window size.
normMatrix <- normalizeToMatrix(signal = tmp.bigwig,
target = resize(tmp.targets, fix = "center", width = 1),
background = 0,
keep = c(0, 0.99), ## minimal value to the 99th percentile
target_ratio = 0,
mean_mode = "w0", ## see ?EnrichedHeatmap on other options
value_column = "score", ## = the name of the 4th column of the bigwig
extend = tmp.extension)
## a color gradient that I personally find visually appealing, which will cover
## the range from the lowest value of normMatrix to the 99th percentile
## (99th perc. avoids extreme values skewing the heatmap):
col_fun = circlize::colorRamp2(quantile(normMatrix, c(0, .99)), c("darkblue", "darkgoldenrod1"))
## heatmap function:
enrHtmp <- EnrichedHeatmap( mat = normMatrix,
pos_line = FALSE, ## no dashed lines around the start
border = FALSE, ## no box around heatmap
col = col_fun, ## color gradients from above
column_title = "Nice Heatmap", ## column title
column_title_gp = gpar(fontsize = 15, fontfamily = "sans"),
## these three options produce a high-quality pdf
## while keeping the file size small so that it easily fits
## nto any powerpoint presentation without crashing it
use_raster = TRUE, raster_quality = 10, raster_device = "png",
## turn off background colors
rect_gp = gpar(col = "transparent"),
## legend:
heatmap_legend_param = list(
legend_direction = "horizontal", ## legend horizontal
title = "legend_title"),
## options for the profile plot on top
top_annotation = HeatmapAnnotation(
enriched = anno_enriched(
gp = gpar(col = "black", lty = 1, lwd=2),
col="black")
)
) ## end of EnrichedHeatmap function
## Instead of plotting to the Rstudio device save as pdf,
## with width-2 and height-6 I personally find the heatmap visually most appealing,
## it looks good while not being too "fat":
pdf("~/EnrichedHeatmap.pdf", width = 2, height = 6)
## Plot it:
draw(enrHtmp, ## plot the heatmap from above
heatmap_legend_side = "bottom", ## we want the legend below the heatmap
annotation_legend_side = "bottom", ##
padding = unit(c(4, 4, 4, 4), "mm") ## some padding to avoid labels beyond plot borders
)
dev.off() ## close the pdf
https://twitter.com/ATpoint90/status/1162065802826342407
