In mdeber/BRGenomics: Tools for the Efficient Analysis of High-Resolution Genomics Data

Lists

To efficiently work with several datasets, we recommend storing the GRanges objects within a standard, named list, e.g. grl <- list(a_rep1 = gr1, b_rep1 = gr2, ...).

library(BRGenomics)
data("PROseq")
data("txs_dm6_chr4")

# make 3 datasets
ps1 <- PROseq[seq(1, length(PROseq), 3)]
ps2 <- PROseq[seq(2, length(PROseq), 3)]
ps3 <- PROseq[seq(3, length(PROseq), 3)]

# use the "=" assignment in list() to give names to the list elements
ps_list <- list(ps1 = ps1, ps2 = ps2, ps3 = ps3)
names(ps_list)
ps_list

A named list like the one above can be passed as an argument to nearly every function in BRGenomics, and many functions will automatically return dataframes, or melted dataframes that use the list names as the sample names (which can simplify plotting with ggplot2 or lattice).

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Note that BRGenomics does also support the use of GRangesList or CompressedGRangesList classes for grouping multiple datasets. However, care should be taken when using these, as many functions with methods for GRanges objects also have methods for GRangesList objects. Furthermore, GRangesList objects can be automatically coerced into CompressedGRangesList objects, which can lower memory usage but can also incur significant performance penalties.

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Example: Summarizing Signal from Multiple Samples

We can pass our list of GRanges directly to getCountsByRegions to simultaneously count reads for each dataset, and melt the result for plotting with ggplot:

getCountsByRegions(ps_list, txs_dm6_chr4[1:5], ncores = 1)

# melt, and use the optional region_names argument
txs_counts <- getCountsByRegions(ps_list, txs_dm6_chr4, melt = TRUE,
                                 region_names = txs_dm6_chr4$tx_name,
                                 ncores = 1)
head(txs_counts)

library(ggplot2)
ggplot(txs_counts, aes(x = sample, y = signal, fill = sample)) + 
  geom_violin() + 
  theme_bw()

Example: Making Browser Shots in R

By using the getCountsByPositions() on several datasets over a single region-of-interest, we can bake our own genome browser shots within R. Using the same region we plotted earlier:

cbp_maxtx <- getCountsByPositions(ps_list, txs_dm6_chr4[135], 
                                  melt = TRUE, ncores = 1)
head(cbp_maxtx)

ggplot(cbp_maxtx, aes(x = position, y = signal)) + 
    facet_wrap(~sample, ncol = 1, strip.position = "right") + 
    geom_col(size = 0.5, color = "darkgray") +
    coord_cartesian(expand = FALSE) +
    labs(title = txs_dm6_chr4$tx_name[135],
         x = "Distance from TSS", y = "PRO-seq Signal") + 
    theme_classic() + 
    theme(strip.text.y = element_text(angle = 0),
          strip.background = element_blank(),
          axis.line.x = element_blank(),
          axis.ticks.x = element_blank())

Multiplexed GRanges

There is another data structure that is widely supported with BRGenomics, called a multiplexed GRanges. A multiplexed GRanges is a single GRanges object that contains multiple metadata fields containing basepair-resolution coverage data for different datasets. We currently recommend users use lists of GRanges objects, but you might find that multiplexed GRanges objects have performance benefits for your data (notes on this below).

A multiplexed GRanges object can be made using the mergeGRangesData() with option multiplex = TRUE.

ps_multi <- mergeGRangesData(ps1, ps2, ps3, multiplex = TRUE, ncores = 1)
ps_multi

As in any GRanges object, the metadata fields are accessible as a dataframe with the mcols() function, and individual columns are accessible with the $ operator.^[The dataframe, by default, is not a base R data.frame, but rather an S4 DataFrame. The distinction isn't important for end users, and it's unlikely users will encounter any reason to coerce the class using as.data.frame.]

mcols(ps_multi)

ps_multi$ps1[1:5]

This data structure can be passed to most functions in BRGenomics. To be explicit, users should set the field argument, when it exists, to set the datasets for which calculations should be performed:

# for all datasets (all fields), get counts in the first 5 transcripts
getCountsByRegions(ps_multi, txs_dm6_chr4[1:5], 
                   field = names(mcols(ps_multi)),
                   ncores = 1)

# get counts for ps2 dataset only
getCountsByRegions(ps_multi, txs_dm6_chr4[1:5], 
                   field = "ps2", ncores = 1)

# if no field is given, most functions will default to using all fields
getCountsByRegions(ps_multi, txs_dm6_chr4[1:5], 
                   ncores = 1)

In our experience with PRO-seq data, and to our surprise, a list of GRanges objects typically outperforms multiplexed GRanges on a typical laptop, despite the theoretical benefits of multiplexing.

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In principle, the multiplexed GRanges structure is designed to reduce memory usage and increase performance, but this has not been our experience when dealing with sparse, basepair-resolution data like PRO-seq. Overlapping signal with regions of interest (e.g. in getCountsByRegions() or getCountsByPosition()) is relatively efficient for reasonably sized GRanges objects, and these calculations scale reasonably well with multicore processing. While multiplexing means that signal overlapping only has to be performed once, we've found that this is a relatively small benefit in practice that is easily offset by having to do signal calculations on large sparse vectors of signal counts. However, the relative benefits of using lists or multiplexing are likely to depend on the nature of the data being analyzed, as well as well as computer hardware.

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Saving Binary R Files

While the handling of GRanges data in BRGenomics is relatively fast, the initial importation of bigWig or bedGraph files as GRanges objects remains a noticeable bottleneck. This may be tolerable for interactive workflows, in which data is imported once before undergoing lengthy analysis, but the bottleneck is a significant detriment for users who regularly import data.

To avoid this bottleneck, users can save reusable data structures as binary R files, which effectively save the memory state of R objects. Not only are these objects rapidly reloaded into memory upon importation, but they have the added benefit of saving the user from repeating data formatting.

Any R object can be saved to storage using the saveRDS() function, and re-imported using readRDS().

# save the PRO-seq GRanges for later import
saveRDS(PROseq, file = "~/PROseq.RData") 

# save a list of GRanges
saveRDS(ps_list, file = "~/ps_list.RData")

# re-import
ps_list <- readRDS("~/ps_list.RData")

The save() and load() commands can also be used to accomplish the same thing, although they work slightly differently.^[Unlike the saveRDS()/ readRDS() commands, the read()/load() commands maintain the original name of the objects. For instance, if you save(PROseq, file = "~/ps.RData"), and in a new R session run read("~/ps.RData"), a new object called PROseq will be created in your new environment. (Note that this is the same way that RStudio saves your current working environment to disk, i.e. it saves the entire environment into an RData file, which can then be reloaded, remaking every data object).]

mdeber/BRGenomics documentation built on Aug. 3, 2024, 3:43 a.m.