filterData: Filter the positions of interest

In derfinder: Annotation-agnostic differential expression analysis of RNA-seq data at base-pair resolution via the DER Finder approach

Description

For a group of samples this function reads the coverage information for a specific chromosome directly from the BAM files. It then merges them into a DataFrame and removes the bases that do not pass the cutoff. This is a helper function for loadCoverage and preprocessCoverage.

Usage

filterData(
  data,
  cutoff = NULL,
  index = NULL,
  filter = "one",
  totalMapped = NULL,
  targetSize = 8e+07,
  ...
)

Arguments

data	Either a list of Rle objects or a DataFrame with the coverage information.
cutoff	The base-pair level cutoff to use. It's behavior is controlled by filter.
index	A logical Rle with the positions of the chromosome that passed the cutoff. If NULL it is assumed that this is the first time using filterData and thus no previous index exists.
filter	Has to be either 'one' (default) or 'mean'. In the first case, at least one sample has to have coverage above cutoff. In the second case, the mean coverage has to be greater than cutoff.
totalMapped	A vector with the total number of reads mapped for each sample. The vector should be in the same order as the samples in data. Providing this data adjusts the coverage to reads in targetSize library prior to filtering. See getTotalMapped for calculating this vector.
targetSize	The target library size to adjust the coverage to. Used only when totalMapped is specified. By default, it adjusts to libraries with 80 million reads.
...	Arguments passed to other methods and/or advanced arguments. Advanced arguments: verbose If TRUE basic status updates will be printed along the way. returnMean If TRUE the mean coverage is included in the result. FALSE by default. returnCoverage If TRUE, the coverage DataFrame is returned. TRUE by default.

Details

If cutoff is NULL then the data is grouped into DataFrame without applying any cutoffs. This can be useful if you want to use loadCoverage to build the coverage DataFrame without applying any cutoffs for other downstream purposes like plotting the coverage values of a given region. You can always specify the colsubset argument in preprocessCoverage to filter the data before calculating the F statistics.

Value

A list with up to three components.

coverage

is a DataFrame object where each column represents a sample. The number of rows depends on the number of base pairs that passed the cutoff and the information stored is the coverage at that given base. Included only when returnCoverage = TRUE.

position

is a logical Rle with the positions of the chromosome that passed the cutoff.

meanCoverage

is a numeric Rle with the mean coverage at each base. Included only when returnMean = TRUE.

colnames

Specifies the column names to be used for the results DataFrame. If NULL, names from data are used.

smoothMean

Whether to smooth the mean. Used only when filter = 'mean'. This option is used internally by regionMatrix.

Passed to the internal function .smootherFstats, see findRegions.

Author(s)

Leonardo Collado-Torres

See Also

loadCoverage, preprocessCoverage, getTotalMapped

Examples

## Construct some toy data
library("IRanges")
x <- Rle(round(runif(1e4, max = 10)))
y <- Rle(round(runif(1e4, max = 10)))
z <- Rle(round(runif(1e4, max = 10)))
DF <- DataFrame(x, y, z)

## Filter the data
filt1 <- filterData(DF, 5)
filt1

## Filter again but only using the first two samples
filt2 <- filterData(filt1$coverage[, 1:2], 5, index = filt1$position)
filt2

derfinder documentation built on Dec. 20, 2020, 2 a.m.