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

knitr::opts_chunk$set(echo = FALSE,
                      message = FALSE,
                      warning = FALSE,
                      knitr.kable.NA = '')

library(dplyr)
library(readr)

Sample information

Gender: r params$gender

Sample prep: r params$sample_prep

Conumee plot

knitr::include_graphics("genome_plot_conumee.png")

Genome plot

knitr::include_graphics(params$genome_plot)

Overall calls

• Cutoff for gain: +0.15 segment mean at 5mb
• Cutoff for loss: -0.20 at 10 mb

cn_boundary <- params$cn_boundary
df <- readr::read_delim(file = params$segment_table, delim = "\t")
if (params$gender == "F") {
  df <- dplyr::filter(df, chrom != "chrY")
}
df %>%
  dplyr::mutate(segment_size = loc.end - loc.start + 1) %>%
  dplyr::filter((seg.mean.shifted < cn_boundary[1] & 
                   segment_size > cn_boundary[3]) | 
                  (seg.mean.shifted > cn_boundary[2] & 
                     segment_size > cn_boundary[4])) %>%
  dplyr::select(chrom, loc.start, loc.end, seg.mean.shifted, cytoband, detail_region) %>%
  dplyr::rename(Chr = chrom, Start = loc.start, End = loc.end, Mean = seg.mean.shifted, Cytoband = cytoband, Gene = detail_region) %>%
  dplyr::mutate(Gene = ifelse(is.na(Gene), "", Gene)) %>%
  knitr::kable()

Cancer gene calls

Calls contain the following selected genes: CCND1, CDK4, CDK6, CDKN2A/B, EGFR, ERBB2, GLI2, MDM2, MET, MYC, MYCN, NF1, PTCH1, PTEN, RB1, TP53, PDGFRA, C19MC, NF2, CCND2, TERT, MDM4, BRAF, MYB, MYBL1, PPM1D, SMARCB1.

• Cutoff for gain: +0.10 at 500kb
• Cutoff for loss: -0.15 at 500kb

cn_boundary <- params$cn_boundary_focal
df <- readr::read_delim(file = params$segment_table, delim = "\t")
if (params$gender == "F") {
  df <- dplyr::filter(df, chrom != "chrY")
}
df %>%
  dplyr::filter(!is.na(detail_region)) %>%
  dplyr::mutate(segment_size = loc.end - loc.start + 1) %>%
  dplyr::filter((seg.mean.shifted < cn_boundary[1] & 
                   segment_size > cn_boundary[3]) | 
                  (seg.mean.shifted > cn_boundary[2] & 
                     segment_size > cn_boundary[4])) %>%
  dplyr::select(chrom, loc.start, loc.end, seg.mean.shifted, cytoband, detail_region) %>%
  dplyr::rename(Chr = chrom, Start = loc.start, End = loc.end, Mean = seg.mean.shifted, Cytoband = cytoband, Gene = detail_region) %>%
  knitr::kable()

QC

Detection p-value

A detection p-value is returned for every genomic position in every sample. Small p-values indicate a good position. Positions with non-significant p-values (typically >0.01) should not be trusted.

The m+u method compares the total DNA signal (Methylated + Unmethylated) for each position to the background signal level. The background is estimated using negative control positions, assuming a normal distribution. Calculations are performed on the original (non-log) scale. - from the manual page of minfi::detectionP().

knitr::include_graphics("detection_p_mean.png")

Density plot of beta values

A good sample will have a bimodal distribution with two peaks of beta values at 0 and 1, corresponding to methylation and unmethylation respectively.

knitr::include_graphics("qc_density_plot.png")

knitr::include_graphics("qc_density_bean.png")

Probes

knitr::include_graphics("qc_probes.png")

Mean of methylation and unmethylation signal

We have not decided how to interpret the plot in the context of QC.

knitr::include_graphics("mean_unmeth_mean.png")

Note

The analysis is carried out R package yamatCN version r params$version.

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