How to visualize genomic data in Bioconductor?

Install Bioconductor Packages

• Install the core Bioconductor packages using BiocManager. In R, execute: if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager").

 

• To install packages like GenomicFeatures and GenomicRanges, use: BiocManager::install("GenomicFeatures") and BiocManager::install("GenomicRanges").

Load Your Data

• Example files can be loaded using read.csv() or similar functions for your specific data format.

 

• Use GenomicRanges::GRanges to transform your data into a genomic range object. Example: GRanges(seqnames = Rle(c("chr1", "chr2"), c(1, 2)), ranges = IRanges(start = c(1, 10), end = c(5, 15))).

Visualize with Gviz

• Install the Gviz package if it is not already installed using: BiocManager::install("Gviz").

 

• Initialize necessary tracks, such as DataTrack for numeric data or GeneRegionTrack for displaying genes. Use DataTrack(data = myData, start = startPositions, end = endPositions, chromosome = "chr1") to create a data track.

 

• Generate the plot using Gviz::plotTracks function. For example: plotTracks(list(geneTrack, dataTrack), from = minStart, to = maxEnd).

Explore Plot Customizations

• Modify track appearance with options like col.histogram for DataTracks or geneSymbols for GeneRegionTracks.

 

• Utilize layout parameters in plotTracks, such as background.title and col.axis, to enhance visualization aesthetics.

Save Your Visualization

• For further analysis, save the plot using basic R functions: pdf(file = "myGenomicPlot.pdf"), followed by your plotting code, and then dev.off().

 

• Ensure you've tailored your plotting parameters to the output format chosen, especially resolutions and font sizes, for increased clarity.

Explore Advanced Visualization Options

• Try integrating interactive plots using packages like plotly to convert static plots into interactive web graphics.

 

• Consider developing dashboards in R Shiny that dynamically load and visualize genomic data, offering a more robust exploration experience.