pplr {pplr} | R Documentation |
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.
pplr(e, control, experiment)
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. |
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.
The return is a data frame. The description of the components are below.
index |
The original low 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-ration between control and experiment genes. |
stat |
A statistic value which is -mean/(sqrt(2)*standard deviation). |
PPLR |
Probability of positive log-ratio. |
Xuejun Liu, Marta Milo, Neil D. Lawrence, Magnus Rattray
Liu,X., Milo,M., Lawrence,N.D. and Rattray,M. (2005) Probe-level variances improve accuracy in detecting differential gene expression, technical report available upon request.
Related method bcomb
data(exampleE) data(exampleStd) r<-bcomb(exampleE,exampleStd,replicates=c(1,1,1,2,2,2),method="sha") p<-pplr(r,1,2)