Normalizedata function

Normalizing and filtering

This function allows filtering of genes and cells to be used in the downstream analysis.

Normalizedata(
  object,
  mintotal = 1000,
  minexpr = 0,
  minnumber = 0,
  maxexpr = Inf,
  downsample = FALSE,
  dsn = 1,
  rseed = NULL
)

## S4 method for signature 'DISCBIO'
Normalizedata(
  object,
  mintotal = 1000,
  minexpr = 0,
  minnumber = 0,
  maxexpr = Inf,
  downsample = FALSE,
  dsn = 1,
  rseed = NULL
)

Arguments

• object: DISCBIO class object.
• mintotal: minimum total transcript number required. Cells with less than mintotal transcripts are filtered out. Default is 1000.
• minexpr: minimum required transcript count of a gene in at least minnumber cells. All other genes are filtered out. Default is 0.
• minnumber: minimum number of cells that are expressing each gene at minexpr transcripts. Default is 0.
• maxexpr: maximum allowed transcript count of a gene in at least a single cell after normalization or downsampling. All other genes are filtered out. Default is Inf.
• downsample: A logical vector. Default is FALSE. If downsample is set to TRUE, then transcript counts are downsampled to mintotal transcripts per cell, instead of the normalization. Downsampled versions of the transcript count data are averaged across dsn samples
• dsn: A numeric value of the number of samples to be used to average the downsampled versions of the transcript count data. Default is 1 which means that sampling noise should be comparable across cells. For high numbers of dsn the data will become similar to the median normalization.
• rseed: Random integer to enforce reproducible clustering. results

Returns

The DISCBIO-class object input with the ndata and fdata slots filled.

Examples

sc <- DISCBIO(valuesG1msTest) # changes signature of data

# In this case this function is used to normalize the reads
sc_normal <- Normalizedata(
  sc,
  mintotal = 1000, minexpr = 0, minnumber = 0, maxexpr = Inf,
  downsample = FALSE, dsn = 1, rseed = 17000
)
summary(sc_normal@fdata)