Probability of positive log-ratio

Description

WARNING - this function is generally not expected to be used, but is intended as an internal function. It is included for backwards compatibility with the pplr package, but may be deprecated and then hidden in future. Users should generally use pumaDE instead.

This function calculates the probability of positive log-ratio (PPLR) between any two specified conditions in the input data, mean and standard deviation of gene expression level for each condition.

Usage

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pplr(e, control, experiment, sorted=TRUE)

Arguments

e

a data frame containing the mean and standard deviation of gene expression levels for each condition.

control

an integer denoting the control condition.

experiment

an integer denoting the experiment condition.

sorted

Boolean. Should PPLR values be sorted by value? If FALSE, PPLR values are returned in same order as supplied.

Details

The input of 'e' should be a data frame comprising of 2*n components, where n is the number of conditions. The first 1,2,...,n components include the mean of gene expression values for conditions 1,2,...,n, and the n+1, n+2,...,2*n components contain the standard deviation of expression levels for condition 1,2,...,n.

Value

The return is a data frame. The description of the components are below.

index

The original row number of genes.

cM

The mean expression levels under control condition.

sM

The mean expression levels under experiment condition.

cStd

The standard deviation of gene expression levels under control condition.

sStd

The standard deviation of gene expression levels under experiment condition.

LRM

The mean log-ratio between control and experiment genes.

LRStd

The standard deviation of log-ratio between control and experiment genes.

stat

A statistic value which is -mean/(sqrt(2)*standard deviation).

PPLR

Probability of positive log-ratio.

Author(s)

Xuejun Liu, Marta Milo, Neil D. Lawrence, Magnus Rattray

References

Liu,X., Milo,M., Lawrence,N.D. and Rattray,M. (2006) Probe-level variances improve accuracy in detecting differential gene expression, Bioinformatics, 22(17):2107-13.

See Also

Related methods pumaDE, bcomb and hcomb

Examples

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  data(exampleE)
  data(exampleStd)
  r<-bcomb(exampleE,exampleStd,replicates=c(1,1,1,2,2,2),method="map")
  p<-pplr(r,1,2)

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