Description Usage Arguments Details Value Author(s) Examples

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | ```
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)
)
``` |

`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 ( |

`ncores` |
Number of cores to use for computations. |

`x` |
The name of the dimension in |

`FUN` |
A function to use for aggregating data within each bin. |

`...` |
Additional arguments passed to |

`ignore.na` |
Logical indicating if |

`drop` |
A logical indicating if empty bin combinations should be removed
from the output. By default ( |

`empty` |
When |

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input data has been replaced by its bin number (an integer).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.

A dataframe.

Mike DeBerardine

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | ```
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, ]
``` |

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