FisherTest {MethylIT}  R Documentation 
Given a GRanges object with the methylated and unmethylated read counts for control and treatment in its metacolumn, Fisher's exact test is performed for each cytosine site.
FisherTest(LR, count.col = c(1, 2), control.names = NULL, treatment.names = NULL, pooling.stat = "sum", tv.cut = NULL, hdiv.cut = NULL, hdiv.col = NULL, pAdjustMethod = "BH", pvalCutOff = 0.05, saveAll = FALSE, num.cores = 1L, tasks = 0L, verbose = FALSE, ...)
LR 
A list of GRanges, a GRangesList, a CompressedGRangesList object, or an object from MethylIT downstream analyses: 'InfDiv' or "pDMP" 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 
2dvector 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 MethylIT function

control.names, treatment.names 
Names/IDs of control and treatment samples, which must be included in the variable GR at the metacolumn. Default NULL. If provided the Fisher's exact test control versus trearment is performed. 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. 
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 performing 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

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. 
pAdjustMethod 
method used to adjust the results; default: BH 
pvalCutOff 
cutoff used then a pvalue adjustment is performed 
saveAll 
if TRUE all the temporal results are returned 
num.cores 
The number of cores to use, i.e. at most how many child processes will be run simultaneously (see bpapply function from BiocParallel). 
tasks 
integer(1). The number of tasks per job. value must be a scalar integer >= 0L. In this documentation a job is defined as a single call to a function, such as bplapply, bpmapply etc. A task is the division of the X argument into chunks. When tasks == 0 (default), X is divided as evenly as possible over the number of workers (see MulticoreParam from BiocParallel package). 
verbose 
if TRUE, prints the function log to stdout 
... 
Additional parameters for function

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.
The input GRanges object with the columns of Fisher's exact test pvalue, total variation (difference of methylation levels), and pvalue adjusment.
rmstGR
## Get a dataset of Hellinger divergency of methylation levels ## from the package data(HD) ## Only the first four cytosine sites from each sample are tested hd < lapply(HD, function(hd) hd[1:4]) FisherTest(LR = hd, pooling.stat = "sum", treatment.names = c("T1", "T2"), tv.cut = NULL, pAdjustMethod="BH", pvalCutOff = 0.05, num.cores = 1L, verbose=FALSE)