preprocess_final_HTS_data: (DEPRECATED) Pre-process final HTS data for downstream...
In BPRMeth: Model higher-order methylation profiles

Description Usage Arguments Value Author(s) See Also Examples

View source: R/deprecated_process_functions.R

(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.

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

`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.

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.

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

read_rna_encode_caltech process_haib_caltech_wrap

# 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)