spikein.normalization: function to estimate normalization parameters based on...

In carinademel/RNAlife: Quantifying kinetic parameters to determine the RNA life cycle

Description

function to estimate normalization parameters based on spike-in counts with GLM using offset length, seq depth per sample and cross-contamination control AND a spike-specific parameter to control for e.g.sequence bias

Usage

spikein.normalization(counts, spikeins = rownames(counts), spikein.lengths,
  spikein.labeling, samples = colnames(counts), conditions.labeling,
  paired = TRUE, debug = FALSE)

Arguments

counts	matrix with counts for each spike (rows) and each sample (columns)
spikeins	names of spike-ins, in same order as rownames of counts
spikein.lengths	length of spike-ins (vector ordered accoring to "spikes")
spikein.labeling	labeling status of spike-ins (vector ordered accoring to "spikes" consisting of "L" and "U")
samples	individual name for each sample, e.g. colnames of count table
conditions.labeling	labeling status of sample (vector consisting of "L" and "T")
paired	is there for every labeled sample also a total sample and vice versa? only then sequencing depths can be estimated. Else they will be set to 1.
debug	should debugging modus be used?

Value

list consisting of fitting-results in a data.frame and vector of fitted.counts

Author(s)

Carina Demel

Examples

data(spikein.counts)
spikeins = rownames(spikein.counts)
data(spikein.labeling)
data(spikein.lengths)
data(samples)
norm.res = spikein.normalization(spikein.counts, spikeins, spikein.lengths, spikein.labeling, 
	samples=colnames(spikein.counts), samples$conditions.labeling)
norm.res$sequencing.depth
norm.res$cross.contamination

carinademel/RNAlife documentation built on May 13, 2019, 12:43 p.m.