qtotalNormalized {ABSSeq} | R Documentation

## Estimating size factors from the reads count table via ranking

### Description

Function of qtotal for esitmating size factors

### Usage

    qtotalNormalized(ma, qper = 0.95, qst = 0.1, qend = 0.95, qstep = 0.01,
      qbound = 0.05, mcut = 4, qcl = 1.5)

### Arguments

`ma` | a count matrix
`qper` | quantile for assessing dispersion of data, default is 0.95, which serves to avoid outliers, should in (0,1]
`qst` | start of quantile for estimating cv ratio, should be in [0,1], default is 0.1
`qend` | end of quantile for estimating cv ratio, should be in [qbound,1-qbound], default is .95
`qstep` | step of quantile for estimating cv ratio (sliding window), should be in (0,1], default is 0.01
`qbound` | window size for estimating cv and shifted size factor, default is 0.05, a smaller window size is suitable if number of genes is large
`mcut` | cutoff of mean from sliding window to avoid abnormal cv, should >=0, default is 4
`qcl` | scale for outlier detection, should >=0, default is 1.5

### Details

Given a matrix of count data, this function esitmates the size factors by qtotal method, which is based on assessing DE (CV) and ranking. The CV is estimated via sliding window.

### Value

a vector with the estimates size factors, one element per column

### Examples

    data(simuN5)
    counts <- simuN5$counts
    qtotalNormalized(counts)

[Package ABSSeq version 1.44.0 Index]