R/CNVgears-package.R

In SinomeM/CNVgears: A Framework to Combine, Analize and Interpret CNVs Calling Results

#' CNVgears: A package to analyze CNVs calling/segmentation results
#'
#' \code{CNvgears} provides several functions to analyze the results of CNVs
#' calling and/or segmentation on SNPs arrays or NGS data.
#'
#' The CNVgears package provides several functions useful in order to perform a
#' series of analysis the result of CNVs calling or segmentation pipelines or
#' algorithms, on both Ilummina SNP array (e.g. PennCNV, iPattern or
#' EnsembleCNV) and NGS data (e.g. ModSeg and gCNV pipelines from GATK), in an
#' integrated framework. To do so all the data is imported in a standardized
#' manner, allowing the user to perform analysis and data manipulation regardless
#' of the initial raw data type, from (among the others) CNVRs creation and
#' exclusion of immunoglobulin regions, to de novo CNVs discovery and genic
#' content annotation.
#'
#' It has been originally developed for the CNVs characterization of the Italian
#' Autism Network (ITAN) collection (DOI: 10.1186/s12888-018-1937-y).
#'
#' @section Analysis pipelines examples:
#' Here are briefly illustrated some workflow examples that can be done either
#' interactively on sequentially. See the vignettes for further details.
#'
#' Staring from the results of gCNV and ModSeg pipelines on WES data in a cohort
#' of families:
#' \enumerate{
#'   \item load the intervals list (using \code{\link{read_NGS_intervals}});
#'   \item load samples table with minimal metadata (sample ID, sex, role, family ID);
#'   \item load the segmentation results of all the samples in the cohort, for each
#'   pipeline separately;
#'   \item merge adjacent segments (with equal CN);
#'   \item filter out CNVs in immunoglobulin (IG) regions;
#'   \item find eventual oversegmented samples (can be marked or excluded from the
#'   analysis);
#'   \item find replicated segments in the pipelines and merge the results into a single
#'   \code{data.table};
#'   \item create the CNVRs;
#'   \item exclude common CNVs based on the CNVRs frequency;
#'   \item annotate genic contents of the CNVs
#'   \item find the inheritance pattern of a selected subset of events (or the whole
#'   dataset) in the offspring, based on the segments of the parents;
#'   \item fine-screen putative de novo calls using the per-interval raw data (copy
#'   ratio or LRR like) of the trio;
#'   \item visualize the good de novo candidate per point raw data in the family to
#'   visually confirm the inheritance pattern.
#' }
#'
#' @section CNVgears functions:
#' The CNVgears functions are organized in groups:
#' \itemize{
#'   \item input/output
#'   \item filtering
#'   \item CNVRs
#'   \item inter results comparison and merging
#'   \item de novo discovery/inheritance pattern detection
#'   \item plotting
#' }
#'
#' @docType package
#' @name CNVgears
#'
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