MarkMissing: Return Column and Row Names of Samples and Probes under the...

In TransBioInfoLab/coMethDMR: Accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies

Return Column and Row Names of Samples and Probes under the Missingness Theshold

Description

Return Column and Row Names of Samples and Probes under the Missingness Theshold

Usage

MarkMissing(dnaM_df, sampMissing_p = 0.5, probeMissing_p = 0.25)

Arguments

dnaM_df	A data frame of DNA methylation values. Samples are columns. Row names are probe IDs.
sampMissing_p	The maximum proportion of missingness allowed in a sample. Defaults to 50%.
probeMissing_p	The maximum proportion of missingness allowed in a probe. Defaults to 25%.

Details

Before calculating the missing proportion of samples, probes with missingness greater than the threshold are dropped first.

Value

A list of four entries:

• dropSamples: the column names of samples with more than sampMissing_p percent missing values

• keepSamples: the column names of samples with less than or equal to sampMissing_p percent missing values

• dropProbes: the row names of probes with more than probeMissing_p percent missing values

• keepProbes: the row names of probes with less than or equal to probeMissing_p percent missing values

Examples

  ###  Setup  ###
  values_num <- c(
    0.1, 0.1, 0.1, 0.1, 0.1,
    0.1, 0.1, 0.1, 0.1,  NA,
    0.1, 0.1, 0.1, 0.1,  NA,
    0.1, 0.1, 0.1,  NA,  NA,
    0.1, 0.1, 0.1,  NA,  NA,
    0.1, 0.1,  NA,  NA,  NA,
    0.1, 0.1,  NA,  NA,  NA,
    0.1,  NA,  NA,  NA,  NA,
     NA,  NA,  NA,  NA,  NA
  )
  values_mat <- matrix(values_num, nrow = 9, ncol = 5, byrow = TRUE)
  rownames(values_mat) <- paste0("probe_0", 1:9)
  colnames(values_mat) <- paste0("sample_0", 1:5)
  values_df <- as.data.frame(values_mat)
  
  
  ###  Simple Calculations  ###
  MarkMissing(values_df)
  MarkMissing(values_df, probeMissing_p = 0.5)
  MarkMissing(values_df, sampMissing_p = 0.25)
  
  
  ###  Using the Output  ###
  mark_ls <- MarkMissing(values_df, probeMissing_p = 0.5)
  valuesPurged_df <- values_df[ mark_ls$keepProbes, mark_ls$keepSamples ]
  valuesPurged_df
  

TransBioInfoLab/coMethDMR documentation built on Sept. 14, 2022, 7:09 p.m.