binNdimensions {BRGenomics} | R Documentation

## Generating and Aggregating Data Within N-dimensional Bins

### Description

Divide data along different dimensions into equally spaced bins, and summarize the datapoints that fall into any of these n-dimensional bins.

### Usage

    binNdimensions(
      dims.df,
      nbins = 10,
      use_bin_numbers = TRUE,
      ncores = getOption("mc.cores", 2L)
    )

    aggregateByNdimBins(
      x,
      dims.df,
      nbins = 10,
      FUN = mean,
      ...,
      ignore.na = TRUE,
      drop = FALSE,
      empty = NA,
      use_bin_numbers = TRUE,
      ncores = getOption("mc.cores", 2L)
    )

    densityInNdimBins(
      dims.df,
      nbins = 10,
      use_bin_numbers = TRUE,
      ncores = getOption("mc.cores", 2L)
    )

### Arguments

`dims.df` | A dataframe containing one or more columns of numerical data for which bins will be generated.
`nbins` | Either a number giving the number of bins to use for all dimensions (default = 10), or a vector containing the number of bins to use for each dimension of input data given.
`use_bin_numbers` | A logical indicating if ordinal bin numbers should be returned (`TRUE`), or if in place of the bin number, the center value of that bin should be returned. For instance, if the first bin encompasses data from 1 to 3, with `use_bin_numbers = TRUE`, a 1 is returned, but when `FALSE`, 2 is returned.
`ncores` | Number of cores to use for computations.
`x` | The name of the dimension in `dims.df` to aggregate, or a separate numerical vector or dataframe of data to be aggregated. If `x` is a numerical vector, each value in `x` corresponds to a row of `dims.df`, and so `length(x)` must be equal to `nrow(dims.df)`. Likewise, if `x` is a dataframe, `nrow(x)` must equal `nrow(dims.df)`. Supplying a dataframe for `x` has the advantage of simultaneously aggregating different sets of data, and returning a single dataframe.
`FUN` | A function to use for aggregating data within each bin.
`...` | Additional arguments passed to `FUN`.
`ignore.na` | Logical indicating if `NA` values of `x` should be ignored. Default is `TRUE`.
`drop` | A logical indicating if empty bin combinations should be removed from the output. By default (`FALSE`), all possible combinations of bins are returned, and empty bins contain a value given by `empty`.
`empty` | When `drop = FALSE`, the value returned for empty bins. By default, empty bins return `NA`. However, in many circumstances (e.g. if `FUN = sum`), the empty value should be `0`.

### Details

These functions take in data along 1 or more dimensions, and for each dimension the data is divided into evenly-sized bins from the minimum value to the maximum value. For instance, if each row of `dims.df` were a gene, the columns (the different dimensions) would be various quantitative measures of that gene, e.g. expression level, number of exons, length, etc. If plotted in cartesian coordinates, each gene would be a single datapoint, and each measurement would be a separate dimension.

`binNdimensions` returns the bin numbers themselves. The output dataframe has the same dimensions as the input `dims.df`, but each input data has been replaced by its bin number (an integer). If codeuse_bin_numbers = FALSE, the center points of the bins are returned instead of the bin numbers.

`aggregateByNdimBins` summarizes some input data `x` in each combination of bins, i.e. in each n-dimensional bin. Each row of the output dataframe is a unique combination of the input bins (i.e. each n-dimensional bin), and the output columns are identical to those in `dims.df`, with the addition of one or more columns containing the aggregated data in each n-dimensional bin. If the input `x` was a vector, the column is named "value"; if the input `x` was a dataframe, the column names from `x` are maintained.

`densityInNdimBins` returns a dataframe just like `aggregateByNdimBins`, except the "value" column contains the number of observations that fall into each n-dimensional bin.

### Value

A dataframe.

### Author(s)

Mike DeBerardine

### Examples

    data("PROseq") # import included PROseq data
    data("txs_dm6_chr4") # import included transcripts

    #--------------------------------------------------#
    # find counts in promoter, early genebody, and near CPS
    #--------------------------------------------------#

    pr <- promoters(txs_dm6_chr4, 0, 100)
    early_gb <- genebodies(txs_dm6_chr4, 500, 1000, fix.end = "start")
    cps <- genebodies(txs_dm6_chr4, -500, 500, fix.start = "end")

    df <- data.frame(counts_pr = getCountsByRegions(PROseq, pr),
                     counts_gb = getCountsByRegions(PROseq, early_gb),
                     counts_cps = getCountsByRegions(PROseq, cps))

    #--------------------------------------------------#
    # divide genes into 20 bins for each measurement
    #--------------------------------------------------#

    bin3d <- binNdimensions(df, nbins = 20, ncores = 1)

    length(txs_dm6_chr4)
    nrow(bin3d)
    bin3d[1:6, ]

    #--------------------------------------------------#
    # get number of genes in each bin
    #--------------------------------------------------#

    bin_counts <- densityInNdimBins(df, nbins = 20, ncores = 1)

    bin_counts[1:6, ]

    #--------------------------------------------------#
    # get mean cps reads in bins of promoter and genebody reads
    #--------------------------------------------------#

    bin2d_cps <- aggregateByNdimBins("counts_cps", df, nbins = 20,
                                     ncores = 1)

    bin2d_cps[1:6, ]

    subset(bin2d_cps, is.finite(counts_cps))[1:6, ]

    #--------------------------------------------------#
    # get median cps reads for those bins
    #--------------------------------------------------#

    bin2d_cps_med <- aggregateByNdimBins("counts_cps", df, nbins = 20,
                                         FUN = median, ncores = 1)

    bin2d_cps_med[1:6, ]

    subset(bin2d_cps_med, is.finite(counts_cps))[1:6, ]

[Package BRGenomics version 1.6.0 Index]