README.md

In markgene/yamatCN: Yet Another Methylation Array Toolkit Copy Number Analysis

Yet Another Methylation Array Toolkit (YAMAT) - Copy-Number Analysis Package

The package is part of yamat ecosystem for methylation array data analysis. It focuses on copy-number analysis.

Quick Start

Install.

```{r, install_yamatcn} if (! ("devtools" %in% installed.packages()) install.packages("devtools") devtools::install_github("markgene/yamatCN")

Prepare the data.

```{r prep}
library(yamatCN)
library(minfiData)

ref <- RGsetEx[, 1:3]
qry <- RGsetEx[, 4:6]
report_dir <- tempdir()

Conumee pipeline:

```{r conumee}

Analysis

conumee_pipe( ref = ref, qry = qry, report_dir = report_dir, norm_method = "swan", batch = NULL, batch2 = NULL ) -> conumee_result

Report

outdir <- "~/Downloads/yamatCN" conumee_report <- yamatCN::report_pipe( conumee_result, outdir, genome_plot_width = 9, genome_plot_height = 15, cn_boundary = c(1.8, 2.2) )

MethylCNV pipeline:

```{r methylcnv}
# Analysis
methylcnv_pipe(
  ref = ref,
  qry = qry,
  report_dir = report_dir,
  norm_method = "methylcnv"
) -> methylcnv_result

# Report: TBA.

Conumee without binning (CWOB) pipeline:

```{r cwob}

Analysis

cwob_pipe( ref = ref, qry = qry, report_dir = report_dir, norm_method = "yamat", batch = NULL, batch2 = NULL ) -> cwob_result

Report

cwob_report <- yamatCN::report_pipe( cwob_result, outdir, genome_plot_width = 9, genome_plot_height = 15, size = 1, cn_boundary = c(1.8, 2.2) ) ```

Known batch effect can be removed by setting batch and batch2 arguments.

Appendix: CNV Pipelines in Papers and Bioconductor

I am listing a few copy-number analysis pipelines published in research papers or archived in Bioconductor.

• Conumee. As described in its document, it "contains a set of processing and plotting methods for performing copy-number variation (CNV) analysis using Illumina 450k or EPIC methylation arrays". The parameters are supposed to be tuned as described in the vignette. However, it is unclear how the parameters have been tuned and which metrics were used for tuning. Also, it is unclear which preprocessing workflow works best with the pipeline.

• MethylCNV.

• ChAMP CNV pipeline.

• CopyNumber450k.

• CopyNumber450kCancer.

• RnBeads CNV pipeline.

A common step of CNV analyses is the segmentation. I am listing some of the segmentation methods:

• DNAcopy
• GLAD
• copynumber

markgene/yamatCN documentation built on Dec. 7, 2019, 4:36 a.m.