blocksStats {Repitools} | R Documentation

## Calculate statistics for regions in the genome

### Description

For each region of interest or TSS, this routine interrogates probes or sequence data for either a high level of absolute signal or a change in signal for some specified contrast of interest. Regions can be surroundings of TSSs, or can be user-specified regions. The function determines if the `start` and `end` coordinates of `anno` should be used as regions or as TSSs, if the up and down coordinates are `NULL` or are numbers.

### Usage

The ANY,data.frame method:
`blocksStats{ANY,data.frame}(x, anno, ...)`
The ANY,GRanges method:
`blocksStats{ANY,GRanges}(x, anno, up = NULL, down = NULL, ...)`

### Arguments

x:

  A `GRangesList`, `AffymetrixCelSet`, or a `data.frame` of data. Or a `character` vector of BAM paths to the location of the BAM files.

anno:

  Either a `data.frame` or a `GRanges` giving the gene coordinates or regions of interest. If it is a `data.frame`, then the column names are (at least) `chr`, `name`, `start`, `end`. Column `strand` is also mandatory, if `up` and `down` are `NULL`.

seq.len:

  If sequencing reads need to be extended, the fragment size to be used.

p.anno:

  A `data.frame` with (at least) columns `chr`, `position`, and `index`. This is an optional parameter of the `AffymetrixCelSet` method, because it can be automatically retrieved for such array data. The parameter is also optional, if `mapping` is not `NULL`.

mapping:

  If a mapping with `annotationLookup` or `annotationBlocksLookup` has already been done, it can be passed in, and avoids unnecessary re-conmputing of the mapping list within `blocksStats`.

chrs:

  If `p.anno` is `NULL`, and is retrieved from an ACP file, this vector gives the textual names of the chromosomes.

log2.adj:

  Whether to take $log_2$ of array intensities.

design:

  A design matrix specifying the contrast to compute (i.e. The samples to use and what differences to take.).

up:

  The number of bases upstream to consider in calculation of statistics. If not provided, the starts and ends in `anno` are used as region boundaries.

down:

  The number of bases upstream to consider in calculation of statistics. If not provided, the starts and ends in `anno` are used as region boundaries.

lib.size:

  A string that indicates whether to use the total lane count, total count within regions specified by `anno`, or normalisation to a reference lane by the negative binomial quantile-to-quantile method, as the library size for each lane. For total lane count use `"lane"`, for region sums use `"blocks"`, and for the normalisation use `"ref"`.

robust:

  Numeric. If it is 0, then a robust linear model is not fitted. If it is greater than 0, a robust linear model is used, and the number specifies the minimum number of probes a region has to have, for statistics to be reported for that region.

p.adj:

  The method used to adjust p-values for multiple testing. Possible values are listed in `p.adjust`.

Acutoff:

  If `libSize` is `"ref"`, this argument must be provided. Otherwise, it must not. This parameter is a cutoff on the "A" values to take, before calculating trimmed mean.

verbose:

  Logical; whether to output commments of the processing.

...

  Parameters described above, that are not used in the function called, but are passed further into a private function that uses them in its processing.

### Details

For array data, the statstics are either determined by a t-test, or a linear model. For sequencing data, the two groups are assumed to be from a negative binomial distribution, and an exact test is used.

### Value

A `data.frame`, with the same number of rows as there are features described by `anno`, but with additional columns for the statistics calculated at each feature.

### Author(s)

Mark Robinson

### See Also

`annotationLookup` and `annotationBlocksLookup`

### Examples

      require(GenomicRanges)
      intensities <- matrix(c(6.8, 6.5, 6.7, 6.7, 6.9,
                              8.8, 9.0, 9.1, 8.0, 8.9), ncol = 2)
      colnames(intensities) <- c("Normal", "Cancer")
      d.matrix <- matrix(c(-1, 1))
      colnames(d.matrix) <- "Cancer-Normal"
      probe.anno <- data.frame(chr = rep("chr1", 5),
                               position = c(4000, 5100, 6000, 7000, 8000),
                               index = 1:5)
      anno <- GRanges("chr1", IRanges(7500, 10000), '+', name = "Gene 1")
      blocksStats(intensities, anno, 2500, 2500, probe.anno, log2.adj = FALSE, design = d.matrix)

[Package Repitools version 1.36.0 Index]