ZipfNorm: Zipf Normalization

Description Usage Arguments Value References Examples

Description

Zipf plot based normalization.

Usage

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  Zipf.Normalize(x, y, cutoff=6,optim=FALSE, method)

Arguments

x,y

data: two vectors.

cutoff

a large enought value such that the values larger than the cutoff (approximately) follows a power law distribution.

optim

Find the optimal normalization parameters if TRUE

method

use both power transformation and scalingby default. If 'scaling' is specified, skip power transformation.

Value

x

reference profile (not normalized)

y

normalized profile

scaler

Linear rescaling normalization parameter estimate

power

power transformation parameter estimate

scaler.optim

Optimized estimate of the linear rescaling parameter

power.optim

Optimzed estimate of the power transformation parameter.

mat.optim

A matrix of the objective function values generated to find the optimal estimates.

coef

Coefficient table to display the estimates.

References

Wang, B. (2020) A Zipf-plot based normalization method for high-throughput RNA-Seq data. PLoS ONE, (in press).

Examples

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data(LCL)
names(LCL)
x <- LCL$p47
y <- LCL$p107
outx <- ZipfPlot(x)
plot(outx,type='l')
outy <- ZipfPlot(y)
lines(outy,col=2)

out2 <- Zipf.Normalize(x,y)
outy2 <- ZipfPlot(out2$y)
lines(outy2,col=4)

  

bda documentation built on Aug. 19, 2021, 9:06 a.m.

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