calcLociStatTimeCourse {MethCP} | R Documentation

## Calculate the per-cytosine statistics for time-course data.

### Description

For each cytosine, `calcLociStatTimeCourse` fits a linear model on the arcsin-tranformed methylation ratios, and test the differences of the slope between the treatment and the control group.

### Usage

    calcLociStatTimeCourse(
        bs.object, meta, force.slope = FALSE,
        BPPARAM = bpparam())

### Arguments

`bs.object` | a `BSseq` object from the `bsseq` package.
`meta` | a `data.frame`. See details.
`force.slope` | if `TRUE`, we force the slope in the linear model to be the same between two conditions. Otherwise, the slopes are fitted separately but not tested.
`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

`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. `meta` must contain columns named `Condition`, `Time` and `SampleName` in the dataframe. They are used to fit the linear model.

### Value

A `MethCP` object that is not segmented.

### Examples

    library(bsseq)
    # Simulate a small dataset with 2000 cyotsine and 10 samples,
    # 5 in the treatment group and 5 in the control group. The
    # methylation ratio are generated using Binomial distribution
    # with probability 0.3, 0.4, 0.5, 0.6 and 0.7 for 5 time points.
    nC <- 2000
    nsamples <- 5
    sim_cov <- rnbinom(10*nC, 5, 0.5) + 5
    sim_cov <- matrix(sim_cov, ncol = 10)
    time_point <- rep(1:nsamples, 2)
    ratios <- time_point/10 + 0.2
    sim_M <- sapply(1:(2*nsamples), function(i){
        sapply(sim_cov[, i], function(j) rbinom(1, j, ratios[i]))
    })
    sim_M <- matrix(sim_M, ncol = 2*nsamples)
    # methylation ratios in the DMRs in the treatment group are
    # generated using Binomial(0.3)
    DMRs <- c(600:622, 1089:1103, 1698:1750)
    sim_M[DMRs, 1:5] <- sapply(
        sim_cov[DMRs, 1:5], function(x) rbinom(1, x, 0.3))
    # sample names
    sample_names <- c(paste0("treatment", 1:nsamples),
    paste0("control", 1:nsamples))
    colnames(sim_cov) <- sample_names
    colnames(sim_M) <- sample_names

    # create a bs.object
    bs_object_ts <- BSseq(gr = GRanges(
        seqnames = "Chr01", IRanges(
            start = (1:nC)*10, width = 1)),
        Cov = sim_cov, M = sim_M, sampleNames = sample_names)
    DMRs_pos_ts <- DMRs*10
    meta <- data.frame(
        Condition = rep(
            c("treatment", "control"),
            each = nsamples),
        SampleName = sample_names,
        Time = time_point)
    obj_ts <- calcLociStatTimeCourse(bs_object_ts, meta)
    obj_ts

[Package MethCP version 1.2.0 Index]