NoiseFiltering: Noise Filtering

Description

Given a matrix or data frame of count data, this function estimates the size factors as follows: Each column is divided by the geometric means of the rows. The median (or, if requested, another location estimator) of these ratios (skipping the genes with a # geometric mean of zero) is used as the size factor for this column. Source: DESeq package.

Usage

NoiseFiltering(
  object,
  percentile = 0.8,
  CV = 0.3,
  geneCol = "yellow",
  FgeneCol = "black",
  erccCol = "blue",
  Val = TRUE,
  plot = TRUE,
  export = FALSE,
  quiet = FALSE,
  filename = "Noise_filtering_genes_test"
)
# S4 method for DISCBIO
NoiseFiltering(
  object,
  percentile = 0.8,
  CV = 0.3,
  geneCol = "yellow",
  FgeneCol = "black",
  erccCol = "blue",
  Val = TRUE,
  plot = TRUE,
  export = FALSE,
  quiet = FALSE,
  filename = "Noise_filtering_genes_test"
)

Value

The DISCBIO-class object input with the noiseF slot filled.

Arguments

object: DISCBIO class object.
percentile: A numeric value of the percentile. It is used to validate the ERCC spik-ins. Default is 0.8.
CV: A numeric value of the coefficient of variation. It is used to validate the ERCC spik-ins. Default is 0.5.
geneCol: Color of the genes that did not pass the filtration.
FgeneCol: Color of the genes that passt the filtration.
erccCol: Color of the ERCC spik-ins.
Val: A logical vector that allows plotting only the validated ERCC spike-ins. Default is TRUE. If Val=FALSE will plot all the ERCC spike-ins.
plot: A logical vector that allows plotting the technical noise. Default is TRUE.
export: A logical vector that allows writing the final gene list in excel file. Default is TRUE.
quiet: if `TRUE`, suppresses printed output
filename: Name of the exported file (if `export=TRUE`)

Examples

Run this code

sc <- DISCBIO(valuesG1msTest) # changes signature of data
sd_filtered <- NoiseFiltering(sc, export = FALSE)
str(sd_filtered)

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