calcLociStat {MethCP} | R Documentation

## Calculate the per-cytosine statistics for simple two-group comparisons.

### Description

`calcLociStat` calculates per-cytosine based statistics between two population groups.

### Usage

    calcLociStat(
        bs.object, group1, group2, test = c("DSS", "methylKit"),
        BPPARAM = bpparam())

### Arguments

`bs.object` | a `BSseq` object from the `bsseq` package.
`group1` | a character vector containing the sample names of the treatment group.
`group2` | a character vector containing the sample names of the control group.
`test` | a character string containing the names of the test to be performed per cytosine.
`BPPARAM` | An optional BiocParallelParam instance determining the parallel back-end to be used during evaluation, or a list of BiocParallelParam instances, to be applied in sequence for nested calls to BiocParallel functions. Default bpparam().

### Details

For each cytosine, `calcLociStat` calculates a statistics using either package `DSS` or `methylKit` to test the differences between two groups, and returns a `MethCP` object. For customized per-cytosine statistics, please use the function `methcpFromStat`. The input `bs.object` is a `BSseq` object from the `bsseq` package which contains the raw data including coverges, methylated counts and position infomation for every cytosine in the dataset.

### Value

a `MethCP` object that is not segmented.

### Examples

    library(bsseq)
    library(GenomicRanges)
    library(IRanges)

    set.seed(0286374)

    # Similate a small dataset with 11 cyotsine and 6 samples,
    # 3 in the treatment group and 3 in the control group. The
    # methylation ratio are generated using Binomial distribution
    # with probability 0.3.
    nC <- 2000
    sim_cov <- rnbinom(6*nC, 5, 0.5) + 5
    sim_M <- vapply(
        sim_cov, function(x) rbinom(1, x, 0.3),
        FUN.VALUE = numeric(1))
    sim_cov <- matrix(sim_cov, ncol = 6)
    sim_M <- matrix(sim_M, ncol = 6)
    # methylation ratios in the DMRs in the treatment group are
    # generated using Binomial(0.7)
    DMRs <- c(600:622, 1089:1103, 1698:1750)
    sim_M[DMRs, 1:3] <- vapply(
        sim_cov[DMRs, 1:3], function(x) rbinom(1, x, 0.7),
        FUN.VALUE = numeric(1))
    # sample names
    sample_names <- c(paste0("treatment", 1:3), paste0("control", 1:3))
    colnames(sim_cov) <- sample_names
    colnames(sim_M) <- sample_names

    # create a bs.object
    bs_object <- BSseq(gr = GRanges(
        seqnames = "Chr01", IRanges(
            start = (1:nC)*10, width = 1)),
        Cov = sim_cov, M = sim_M, sampleNames = sample_names)
    # methcp_obj1 <- calcLociStat(
    #     bs_object,
    #     group1 = paste0("treatment", 1:3),
    #     group2 = paste0("control", 1:3),
    #     test = "DSS")
    methcp_obj2 <- calcLociStat(
        bs_object,
        group1 = paste0("treatment", 1:3),
        group2 = paste0("control", 1:3),
        test = "methylKit")

[Package MethCP version 1.4.0 Index]