QcConfigFeatures: QcConfigFeatures
In FrietzeLabUVM/ssvQC: QC for enrichment based NGS

QcConfigFeatures-class

R Documentation

QcConfigFeatures

Description

See the example feature config file system.file(package = "ssvQC", "extdata/ssvQC_peak_config.csv").

Usage

QcConfigFeatures(
  config_df,
  run_by = "All",
  to_run = NULL,
  to_run_reference = NULL,
  color_by = "file",
  color_mapping = NULL,
  feature_load_FUN = NULL,
  n_peaks = 1000,
  balance_groups = FALSE,
  overlap_extension = 0,
  consensus_fraction = getOption("SQC_CONSENSUS_FRACTION", 0),
  consensus_n = getOption("SQC_CONSENSUS_N", 1),
  process_features = getOption("SQC_PROCESS_FEATURES", TRUE),
  is_null = FALSE
)

QcConfigFeatures.null()

QcConfigFeatures.files(
  file_paths,
  file_paths.input = character(),
  run_separately = TRUE,
  sample_names = NULL,
  sample_names.split = NULL,
  group_names = NULL,
  group_name.input = "input",
  group_colors = NULL,
  feature_load_FUN = NULL,
  n_peaks = 1000,
  balance_groups = FALSE,
  overlap_extension = 0,
  consensus_fraction = getOption("SQC_CONSENSUS_FRACTION", 0),
  consensus_n = getOption("SQC_CONSENSUS_N", 1),
  process_features = getOption("SQC_PROCESS_FEATURES", TRUE)
)

QcConfigFeatures.parse(
  feature_config_file,
  process_features = getOption("SQC_PROCESS_FEATURES", TRUE)
)

QcConfigFeatures.save_config(object, file)

Arguments

`config_df`	data.frame defining configuration parameters. At a minimum, paths to valid files in either the first column or a column named "file". Additional columns defined by color_by and run_by parameters have a big impact on the configuration.
`run_by`	character that defines the column of config_df that groups the features. The default of "All" will simply group all features into a single comparison.
`to_run`	group name to run
`to_run_reference`	group name to included in all runs
`color_by`	character that defines the column of config_df that controls color mapping. The default of "file" will assign a unique color to every feature set.
`color_mapping`	named character vector that maps values of color_by to valid R colors, i.e. "red" or "#FF0000".
`feature_load_FUN`	function that takes a vector of file paths and returns list of GRanges.
`n_peaks`	number of peaks to subset for
`balance_groups`	If TRUE, will attempt to represent imbalanced groups more equally
`overlap_extension`	bp to extends regions by before calculating overlaps
`consensus_fraction`	A numeric between 0 and 1 specifying the fraction of grs that must overlap to be considered consensus.
`consensus_n`	An integer number specifying the absloute minimum of input grs that must overlap for a site to be considered consensus.
`process_features`	if TRUE, features are loaded and overlapped to generate assement regions as part of creating QcConfigFeatures object
`is_null`	if TRUE this object will be treated as NULL
`file_paths`	character paths to files
`run_separately`	If TRUE, each item is considered a separate group. Default is TRUE.
`group_names`	vector of group names to assign from according to groups
`group_colors`	vector of colors to use per group
`feature_config_file`	A valid feature config file.
`groups`	numeric vector of group assignments. 1 is first item in group_names, 2 is second, etc. Default is seq_along(file_path)

Details

Or create a config file using QcConfigFeatures.save_config after making one using a different method.

Value

QcConfigFeatures object

a QcConfigFeatures object

A QcConfigFeatures object

Invisibly returns path to written config file.

Slots

feature_load_FUN: function.
n_peaks: numeric.
consensus_fraction: numeric.
consensus_n: numeric.

Examples

feature_config_file = system.file(package = "ssvQC", "extdata/ssvQC_peak_config.csv")
config_df = .parse_config_body(feature_config_file)
config_df$file = file.path(system.file(package = "ssvQC", "extdata"), config_df$file)
feature_conf = QcConfigFeatures(config_df, process_features = TRUE)
QcConfigFeatures.null()
np_files = dir(system.file(package = "ssvQC", "extdata"), pattern = "Peak$", full.names = TRUE)
object = QcConfigFeatures.files(np_files, balance_groups = TRUE)
object = ssvQC.prepFeatures(object)
plot(object)

object = QcConfigFeatures.files(np_files, 
  group_names = c("10A", "AT1", "CA1"), 
  sample_names = c("MCF10A_CTCF", "MCF10AT1_CTCF", "MCF10CA1a_CTCF"))
object = ssvQC.prepFeatures(object)
plot(object)
feature_config_file = system.file(package = "ssvQC", "extdata/ssvQC_peak_config.csv")
object = QcConfigFeatures.parse(feature_config_file)
plot(object)
feature_config_file = system.file(package = "ssvQC", "extdata/ssvQC_peak_config.csv")
feature_config = QcConfigFeatures.parse(feature_config_file)

FrietzeLabUVM/ssvQC documentation built on March 25, 2024, 12:24 a.m.