# Analyzing Multiple Datasets

#### 15 April 2020

#### Package

BRGenomics 0.99.31

# 1 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)

    ## [1] "ps1" "ps2" "ps3"

    ps_list

    ## $ps1
    ## GRanges object with 15794 ranges and 1 metadata column:
    ##           seqnames    ranges strand |     score
    ##              <Rle> <IRanges>  <Rle> | <integer>
    ##       [1]     chr4      1295      + |         1
    ##       [2]     chr4     42590      + |         2
    ##       [3]     chr4     42595      + |         1
    ##       [4]     chr4     42618      + |         1
    ##       [5]     chr4     42622      + |         2
    ##       ...      ...       ...    ... .       ...
    ##   [15790]     chr4   1307114      - |         1
    ##   [15791]     chr4   1307122      - |         1
    ##   [15792]     chr4   1307300      - |         1
    ##   [15793]     chr4   1316537      - |         1
    ##   [15794]     chr4   1319369      - |         1
    ##   -------
    ##   seqinfo: 7 sequences from an unspecified genome
    ##
    ## $ps2
    ## GRanges object with 15793 ranges and 1 metadata column:
    ##           seqnames    ranges strand |     score
    ##              <Rle> <IRanges>  <Rle> | <integer>
    ##       [1]     chr4     41428      + |         1
    ##       [2]     chr4     42593      + |         5
    ##       [3]     chr4     42596      + |         1
    ##       [4]     chr4     42619      + |         2
    ##       [5]     chr4     42652      + |         3
    ##       ...      ...       ...    ... .       ...
    ##   [15789]     chr4   1307032      - |         1
    ##   [15790]     chr4   1307115      - |         2
    ##   [15791]     chr4   1307126      - |         1
    ##   [15792]     chr4   1307301      - |         1
    ##   [15793]     chr4   1318960      - |         1
    ##   -------
    ##   seqinfo: 7 sequences from an unspecified genome
    ##
    ## $ps3
    ## GRanges object with 15793 ranges and 1 metadata column:
    ##           seqnames    ranges strand |     score
    ##              <Rle> <IRanges>  <Rle> | <integer>
    ##       [1]     chr4     42588      + |         1
    ##       [2]     chr4     42594      + |         2
    ##       [3]     chr4     42601      + |         1
    ##       [4]     chr4     42621      + |         1
    ##       [5]     chr4     42657      + |         1
    ##       ...      ...       ...    ... .       ...
    ##   [15789]     chr4   1307075      - |         1
    ##   [15790]     chr4   1307120      - |         1
    ##   [15791]     chr4   1307283      - |         1
    ##   [15792]     chr4   1307742      - |         1
    ##   [15793]     chr4   1319004      - |         1
    ##   -------
    ##   seqinfo: 7 sequences from an unspecified genome

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`).

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.

## 1.1 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)

    ##   ps1 ps2 ps3
    ## 1   1   0   0
    ## 2  20  22  17
    ## 3   4   4   5
    ## 4  36  47  43
    ## 5  84  90  89

    # 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)

    ##        region signal sample
    ## 1 FBtr0346692      1    ps1
    ## 2 FBtr0344900     20    ps1
    ## 3 FBtr0340499      4    ps1
    ## 4 FBtr0333704     36    ps1
    ## 5 FBtr0333705     84    ps1
    ## 6 FBtr0100246   1017    ps1

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

## 1.2 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)

    ##   region position signal sample
    ## 1      1        1      0    ps1
    ## 2      1        2      0    ps1
    ## 3      1        3      0    ps1
    ## 4      1        4      0    ps1
    ## 5      1        5      0    ps1
    ## 6      1        6      0    ps1

    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())

# 2 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

    ## GRanges object with 47380 ranges and 3 metadata columns:
    ##           seqnames    ranges strand |       ps1       ps2       ps3
    ##              <Rle> <IRanges>  <Rle> | <integer> <integer> <integer>
    ##       [1]     chr4      1295      + |         1         0         0
    ##       [2]     chr4     41428      + |         0         1         0
    ##       [3]     chr4     42588      + |         0         0         1
    ##       [4]     chr4     42590      + |         2         0         0
    ##       [5]     chr4     42593      + |         0         5         0
    ##       ...      ...       ...    ... .       ...       ...       ...
    ##   [47376]     chr4   1307742      - |         0         0         1
    ##   [47377]     chr4   1316537      - |         1         0         0
    ##   [47378]     chr4   1318960      - |         0         1         0
    ##   [47379]     chr4   1319004      - |         0         0         1
    ##   [47380]     chr4   1319369      - |         1         0         0
    ##   -------
    ##   seqinfo: 7 sequences from an unspecified genome

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.1

    mcols(ps_multi)

    ## DataFrame with 47380 rows and 3 columns
    ##             ps1       ps2       ps3
    ##       <integer> <integer> <integer>
    ## 1             1         0         0
    ## 2             0         1         0
    ## 3             0         0         1
    ## 4             2         0         0
    ## 5             0         5         0
    ## ...         ...       ...       ...
    ## 47376         0         0         1
    ## 47377         1         0         0
    ## 47378         0         1         0
    ## 47379         0         0         1
    ## 47380         1         0         0

    ps_multi$ps1[1:5]
    ## [1] 1 0 0 2 0

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)

    ##   ps1 ps2 ps3
    ## 1   1   0   0
    ## 2  20  22  17
    ## 3   4   4   5
    ## 4  36  47  43
    ## 5  84  90  89

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

    ## [1]  0 22  4 47 90

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

    ## field not found in dataset.gr; will default to using all fields in dataset.gr

    ##   ps1 ps2 ps3
    ## 1   1   0   0
    ## 2  20  22  17
    ## 3   4   4   5
    ## 4  36  47  43
    ## 5  84  90  89

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.

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.

# 3 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.2