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

Description Usage Arguments Value Author(s) Examples

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

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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

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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.