preprocess_final_HTS_data {BPRMeth} | R Documentation

## (DEPRECATED) Pre-process final HTS data for downstream analysis

### Description

(DEPRECATED) `preprocess_final_HTS_data` performs a final filtering and preprocessing on the data for use in downstream analysis. These include, removing noisy gene expression data, removing or not un-expressed genes and log2-transorming of the FPKM values.

### Usage

    preprocess_final_HTS_data(methyl_region, prom_reg, rna_data,
      gene_log2_transf = TRUE, gene_outl_thresh = TRUE, gex_outlier = 300)

### Arguments

`methyl_region` | Methylation region data, which are the output of the "`create_region_object`" function.
`prom_reg` | A `GRanges` object containing corresponding annotated promoter regions for each entry of the `methyl_region` list.
`rna_data` | A `GRanges` object containing corresponding RNA-Seq data for each entry of the `methyl_region` list. This is the output of the "`read_rna_encode_caltech` function.
`gene_log2_transf` | Logical, whether or not to log2 transform the gene expression data.
`gene_outl_thresh` | Logical, whehter or not to remove outlier gene expression data.
`gex_outlier` | Numeric, denoting the threshold above of which the gene expression data (before the log2 transformation) are considered as noise.

### Value

An object which contains following information:

- `methyl_region`: The subset of promoter methylation region data after the filtering process.

- `gex`: A vectoring storing only the corresponding gene expression values for each promoter region.

- `rna_data`: The corresponding gene expression data stored as a GRanges object.

### Author(s)

C.A.Kapourani C.A.Kapourani@ed.ac.uk

### See Also

`read_rna_encode_caltech` `process_haib_caltech_wrap`

### Examples

    # Obtain the path to the BS file and then read it
    bs_file <- system.file("extdata", "rrbs.bed", package = "BPRMeth")
    bs_data <- read_bs_encode_haib(bs_file)

    # Create promoter regions
    rnaseq_file <- system.file("extdata", "rnaseq.bed", package = "BPRMeth")
    annot_data <- read_rna_encode_caltech(rnaseq_file)
    prom_region <- create_anno_region(annot_data)

    # Create methylation regions
    methyl_reg <- create_region_object(bs_data, prom_region,
      filter_empty_region = FALSE)

    # Keep only covered genomic regions
    cov_ind <- which(!is.na(methyl_reg))
    methyl_reg <- methyl_reg[cov_ind]
    prom_region <- prom_region[cov_ind, ]
    annot_data <- annot_data[cov_ind, ]

    # Finally preprocess the HTS data
    res <- preprocess_final_HTS_data(methyl_reg, prom_region, annot_data)

[Package BPRMeth version 1.6.0 Index]