exomePeakCalling-methods {exomePeak2} | R Documentation

## Method exomePeakCalling

### Description

`exomePeakCalling` call peaks of RNA modification from a MeRIP-seq data set.

### Usage

    exomePeakCalling(
      merip_bams = NULL,
      txdb = NULL,
      bsgenome = NULL,
      genome = NA,
      mod_annot = NULL,
      glm_type = c("DESeq2", "NB", "Poisson"),
      background_method = c("all", "Gaussian_mixture", "m6Aseq_prior", "manual"),
      manual_background = NULL,
      correct_GC_bg = TRUE,
      qtnorm = FALSE,
      gff_dir = NULL,
      fragment_length = 100,
      binding_length = 25,
      step_length = binding_length,
      min_peak_width = fragment_length/2,
      max_peak_width = fragment_length * 100,
      pc_count_cutoff = 5,
      bg_count_cutoff = 50,
      p_cutoff = 1e-05,
      p_adj_cutoff = NULL,
      log2FC_cutoff = 0,
      parallel = 3,
      bp_param = NULL
    )

    ## S4 method for signature 'MeripBamFileList'
    exomePeakCalling(
      merip_bams = NULL,
      txdb = NULL,
      bsgenome = NULL,
      genome = NA,
      mod_annot = NULL,
      glm_type = c("DESeq2", "NB", "Poisson"),
      background_method = c("all", "Gaussian_mixture", "m6Aseq_prior", "manual"),
      manual_background = NULL,
      correct_GC_bg = TRUE,
      qtnorm = FALSE,
      gff_dir = NULL,
      fragment_length = 100,
      binding_length = 25,
      step_length = binding_length,
      min_peak_width = fragment_length/2,
      max_peak_width = fragment_length * 100,
      pc_count_cutoff = 5,
      bg_count_cutoff = 50,
      p_cutoff = 1e-05,
      p_adj_cutoff = NULL,
      log2FC_cutoff = 0,
      parallel = 1,
      bp_param = NULL
    )

### Arguments

`merip_bams` | a `MeripBamFileList` object returned by `scanMeripBAM`.
`txdb` | a `TxDb` object for the transcript annotation. If the `TxDb` object is not available, it could be a `character` string of the UCSC genome name which is acceptable by `makeTxDbFromUCSC`. For example: `"hg19"`.
`bsgenome` | a `BSgenome` object for the genome sequence information. If the `BSgenome` object is not available, it could be a `character` string of the UCSC genome name which is acceptable by `getBSgenome`. For example: `"hg19"`.
`genome` | a `character` string of the UCSC genome name which is acceptable by `getBSgenome` or/and `makeTxDbFromUCSC`. For example: `"hg19"`. By default, the argument = NA, it should be provided when the `BSgenome` or/and the `TxDb` object are not available.
`mod_annot` | a `GRanges` or `GRangesList` object for user provided single based RNA modification annotation, the widths of the ranged object should be all equal to 1. If user provides the single based RNA modification annotation, exomePeak2 will perform reads count and (differential) modification quantification on the provided annotation. The single base annotation will be flanked by length = floor(fragment_length - binding_length/2) to account for the fragment length of the sequencing library.
`glm_type` | a `character` speciefies the type of Generalized Linear Model (GLM) fitted for the purpose of statistical inference during peak calling, which can be one of the `c("DESeq2", "NB", "Poisson")`. `DESeq2` Fit the GLM defined in function `DESeq`, which is the NB GLM with regulated estimation of the overdispersion parameters. `NB` Fit the Negative Binomial (NB) GLM. `Poisson` Fit the Poisson GLM. By default, the DESeq2 GLMs are fitted on the data set with > 1 biological replicates for both the IP and input samples, the Poisson GLM will be fitted otherwise.
`background_method` | a `character` specifies the method for the background finding, i.e. to identify the windows without modification signal. It could be one of the "Gaussian_mixture", "m6Aseq_prior", "manual", and "all"; default `= "all"`. In order to accurately account for the technical variations, it is often neccessary to estimate the GC content linear effects on windows without modification signals (background). The following methods are supported in `ExomePeak2` to differentiate the no modification background windows from the modification containig windows. `Gaussian_mixture` The background is identified by Multivariate Gaussian Mixture Model (MGMM) with 2 mixing components on the vectors containing methylation level estimates and GC content, the background regions are predicted by the Bayes Classifier on the learned GMM. `m6Aseq_prior` The background is identified by the prior knowledge of m6A topology, the windows that are not overlapped with long exons (exon length >= 400bp) and 5'UTR are treated as the background windows. This type of background should not be used if the MeRIP-seq data is not using anti-m6A antibody. `manual` The background regions are defined by user manually at the argument `manual_background`. `all` Use all windows as the background. This is equivalent to not differentiating background and signal. It can lead to biases during the sequencing depth and the GC content correction factors estimation.
`manual_background` | a `GRanges` object for the user provided unmodified background; default `= NULL`.
`correct_GC_bg` | a `logical` value of whether to estimate the GC content linear effect on background regions; default `= TRUE`. If `= TRUE`, it could lead to a more accurate estimation of GC content bias for the RNA modifications that are highly biologically related to GC content.
`qtnorm` | a `logical` of whether to perform subset quantile normalization after the GC content linear effect correction; default `= FASLE`. If `qtnorm = TRUE`, subset quantile normalization will be applied within the IP and input samples seperately to account for the inherent differences between the marginal distributions of IP and input samples.
`gff_dir` | optional, a `character` which specifies the directory toward a gene annotation GFF/GTF file, it is applied when the `TxDb` object is not available, default `= NULL`.
`fragment_length` | a positive integer number for the expected fragment length in nucleotides; default `= 100`.
`binding_length` | a positive integer number for the expected binding length of the anti-modification antibody in IP samples; default `= 25`.
`step_length` | a positive integer number for the shift distances of the sliding window; default `= binding_length`.
`min_peak_width` | a `numeric` value for the maximum width of the merged peaks; default `= fragment_length*100` .
`pc_count_cutoff` | a `numeric` value for the cutoff on average window's reads count in peak calling; default `= 5`.
`bg_count_cutoff` | a `numeric` value for the cutoff on average window's reads count in background identification; default `= 50`.
`p_cutoff` | a `numeric` value for the cutoff on p values in peak calling; default `= 1e-05`.
`p_adj_cutoff` | a `numeric` value for the cutoff on Benjamini Hochberg adjusted p values in peak calling; default `= NULL`.
`log2FC_cutoff` | a `numeric` value for the cutoff on log2 IP over input fold changes in peak calling; default `= 0`.
`parallel` | a `numeric` value specifying the number of cores used for parallel computing; default `= 3`.
`bp_param` | optional, a `BiocParallelParam` object that stores the configuration parameters for the parallel execution.

### Details

`exomePeakCalling` perform peak calling from the MeRIP-seq BAM files on exon regions defined by the user provided transcript annotations. If the `BSgenome` object is provided, the peak calling will be conducted with the GC content bias correction.

Under the default setting, for each window, exomePeak2 will fit a GLM of Negative Binomial (NB) with regulated estimation of the overdispersion parameters developed in `DESeq`. Wald tests with H0 of IP/input Log2 Fold Change (LFC) <= 0 are performed on each of the sliding windows. The significantly modified peaks are selected using the cutoff of p value < 0.0001.

### Value

a `SummarizedExomePeak` object.

### See Also

`exomePeak2`, `glmM`, `glmDM`, `normalizeGC`, `exportResults`

### Examples

    ### Define File Directories

    GENE_ANNO_GTF = system.file("extdata", "example.gtf", package="exomePeak2")

    f1 = system.file("extdata", "IP1.bam", package="exomePeak2")
    f2 = system.file("extdata", "IP2.bam", package="exomePeak2")
    f3 = system.file("extdata", "IP3.bam", package="exomePeak2")
    f4 = system.file("extdata", "IP4.bam", package="exomePeak2")
    IP_BAM = c(f1,f2,f3,f4)
    f1 = system.file("extdata", "Input1.bam", package="exomePeak2")
    f2 = system.file("extdata", "Input2.bam", package="exomePeak2")
    f3 = system.file("extdata", "Input3.bam", package="exomePeak2")
    INPUT_BAM = c(f1,f2,f3)

    f1 = system.file("extdata", "treated_IP1.bam", package="exomePeak2")
    TREATED_IP_BAM = c(f1)
    f1 = system.file("extdata", "treated_Input1.bam", package="exomePeak2")
    TREATED_INPUT_BAM = c(f1)

    ### Peak Calling

    MeRIP_Seq_Alignment <- scanMeripBAM(
                             bam_ip = IP_BAM,
                             bam_input = INPUT_BAM,
                             paired_end = FALSE
                            )

    sep <- exomePeakCalling(
                merip_bams = MeRIP_Seq_Alignment,
                gff_dir = GENE_ANNO_GTF,
                genome = "hg19"
                )

    sep <- normalizeGC(sep)

    sep <- glmM(sep)

    exportResults(sep)

    ### Differential Modification Analysis (Comparison of Two Conditions)

    MeRIP_Seq_Alignment <- scanMeripBAM(
                             bam_ip = IP_BAM,
                             bam_input = INPUT_BAM,
                             bam_treated_ip = TREATED_IP_BAM,
                             bam_treated_input = TREATED_INPUT_BAM,
                             paired_end = FALSE
                            )

    sep <- exomePeakCalling(
                merip_bams = MeRIP_Seq_Alignment,
                gff_dir = GENE_ANNO_GTF,
                genome = "hg19"
                )

    sep <- normalizeGC(sep)

    sep <- glmDM(sep)

    exportResults(sep)

[Package exomePeak2 version 1.6.1 Index]