R: Root Mean Square Test for Methylation Analysis

R Documentation

Root Mean Square Test for Methylation Analysis

Description

Count data in MethylIT.utils pipeline is carried in GRanges objects. This function provides a shortcut to apply the parametric Bootstrap of 2x2 Contingency independence test, which is implemented in function bootstrap2x2. The goodness of fit statistic is the root-mean-square statistic (RMST) or Hellinger divergence, as proposed by Perkins et al. [1, 2]. Hellinger divergence (HD) is computed as proposed in [3].

Usage

rmstGR(LR, count.col = 1:2, control.names = NULL,
  treatment.names = NULL, stat = "rmst", pooling.stat = "sum",
  tv.cut = NULL, hdiv.cut = NULL, hdiv.col = NULL,
  num.permut = 100, pAdjustMethod = "BH", pvalCutOff = 0.05,
  saveAll = FALSE, num.cores = 1L, tasks = 0L, verbose = TRUE, ...)

Arguments

`LR`	A list of GRanges, a GRangesList, a CompressedGRangesList object. Each GRanges object from the list must have two columns: methylated (mC) and unmethylated (uC) counts. The name of each element from the list must coincide with a control or a treatment name.
`count.col`	2d-vector of integers with the indexes of the read count columns. If not given, then it is asssumed that the methylated and unmethylated read counts are located in columns 1 and 2 of each GRanges metacolumns. If object LR is the output of Methyl-IT function `estimateDivergence`, then columns 1:4 are the read count columns: columns 1 and 2 are methylated and unmethylated read counts from the reference group, while columns 3 and 4 are methylated and unmethylated read counts from the treatment group, respectively. In this case, if the requested comparison is reference versus treatment, then no specification is needed for count.col. The comparison control versus treatment can be obtained by setting count.col = 3:4 and providing control.names and treatment.names.
`control.names, treatment.names`	Names/IDs of the control samples, which must be included in the variable GR at the metacolumn. Default is NULL. If NULL, then it is assumed that each GRanges object in LR has four columns of counts. The first two columns correspond to the methylated and unmethylated counts from control/reference and the other two columns are the methylated and unmethylated counts from treatment, respectively.
`stat`	Statistic to be used in the testing: 'rmst' (root mean square test) or 'hdiv' (Hellinger divergence test).
`pooling.stat`	statistic used to estimate the methylation pool: row sum, row mean or row median of methylated and unmethylated read counts across individuals. If the number of control samples is greater than 2 and pooling.stat is not NULL, then they will pooled. The same for treatment. Otherwise, all the pairwise comparisons will be done.
`tv.cut`	A cutoff for the total variation distance (TVD; absolute value of methylation levels differences) estimated at each site/range as the difference of the group means of methylation levels. If tv.cut is provided, then sites/ranges k with abs(TV_k) < tv.cut are removed before to perform the regression analysis. Its value must be NULL or a number 0 < tv.cut < 1.
`hdiv.cut`	An optional cutoff for the Hellinger divergence (hdiv). If the LR object derives from the previous application of function `estimateDivergence`, then a column with the hdiv values is provided. If combined with tv.cut, this permits a more effective filtering of the signal from the noise. Default is NULL.
`hdiv.col`	Optional. Columns where hdiv values are located in each GRange object from LR. It must be provided if together with hdiv.cut. Default is NULL.
`num.permut`	Number of permutations.
`pAdjustMethod`	method used to adjust the results; default: BH
`pvalCutOff`	cutoff used when a p-value adjustment is performed
`saveAll`	if TRUE all the temporal results are returned
`num.cores, tasks`	Paramaters for parallele computation using package `BiocParallel-package`: the number of cores to use, i.e. at most how many child processes will be run simultaneously (see `bplapply` and the number of tasks per job (only for Linux OS).
`verbose`	if TRUE, prints the function log to stdout
`...`	Additional parameters for function `MethylIT.utils`.

Details

Samples from each group are pooled according to the statistic selected (see parameter pooling.stat) and a unique GRanges object is created with the methylated and unmethylated read counts for each group (control and treatment) in the metacolumn. So, a contingency table can be built for range from GRanges object.

Value

A GRanges object with the original sample counts, bootstrap p-value probability, total variation (difference of methylation levels), and p-value adjusment.

References

Perkins W, Tygert M, Ward R. Chi-square and Classical Exact Tests Often Wildly Misreport Significance; the Remedy Lies in Computers. [Internet]. Uploaded to ArXiv. 2011. Report No.: arXiv:1108.4126v2.
Perkins, W., Tygert, M. & Ward, R. Computing the confidence levels for a root-mean square test of goodness-of-fit. 217, 9072-9084 (2011).
Basu, A., Mandal, A. & Pardo, L. Hypothesis testing for two discrete populations based on the Hellinger distance. Stat. Probab. Lett. 80, 206-214 (2010).

Examples

#' A list of GRanges
set.seed(123)
sites = 15
data <- list(
  C1 = data.frame(chr = "chr1", start = 1:sites,
                  end = 1:sites,strand = '*',
                  mC = rnbinom(size = 8, mu = 3, n = sites),
                  uC = rnbinom(size = 50, mu = 10, n = sites)),
  C2 = data.frame(chr = "chr1", start = 1:sites,
                  end = 1:sites, strand = '*',
                  mC = rnbinom(size = 8, mu = 3, n = sites),
                  uC = rnbinom(size = 50, mu = 10, n = sites)),
  T1 = data.frame(chr = "chr1", start = 1:sites,
                  end = 1:sites,strand = '*',
                  mC = rnbinom(size = 50, mu = 10, n = sites),
                  uC = rnbinom(size = 10, mu = 10, n = sites)),
  T2 = data.frame(chr = "chr1", start = 1:sites,
                  end = 1:sites, strand = '*',
                  mC = rnbinom(size = 50, mu = 10, n = sites),
                  uC = rnbinom(size = 5, mu = 10, n = sites)))
#' Transforming the list of data frames into a list of GRanges objects
data = lapply(data,
              function(x)
                makeGRangesFromDataFrame(x, keep.extra.columns = TRUE))

rmstGR(LR = data, control.names = c("C1", "C2"),
       treatment.names = c("T1", "T2"),
       tv.cut = 0.25, num.permut = 100, pAdjustMethod="BH",
       pvalCutOff = 0.05, num.cores = 4L, verbose=TRUE)

[Package MethylIT.utils version 0.3.1 ]