LPE (version 1.46.0)

lpeAdj: High level lpeAdj function that executes the adjusted local pooled error significance test. If more control over parameters is needed then see documentation for calculateLpeAdj.

Description

Applies the LPE algorithm with two additional options. The first is that the original LPE method sets all variances below the max variance in the ordered distribution of variances to the maximum variance. in LPEadj this option is turned off by default. The second option is to use a variance adjustment based on sample size rather than pi/2. By default the LPEadj uses the sample size based variance adjustment. It is recommended to keep both of these options to the default.

Usage

lpeAdj(dat, labels=NULL, doMax=FALSE, doAdj=TRUE, q=.01)

Arguments

dat

Replicated data of experiment containing two groups (as matrix or data-frame)

labels

vector of group labels that correspond to the columns of dat. eg. labels=c(0,0,0,1,1,1) describes two groups with three replicates each

doMax

boolean: if T then all variances below the max variance in the ordered distribution of variances are set to the maximum variance. It is recommended to use the default value of False.

doAdj

If T then run LPE with using variance adjustment value based on number of replicates (hardcoded in adjValues) rather than pi/2.

q

is the quantile width; q=0.01 corresponds to 100 quantiles i.e. percentiles. Bins/quantiles have equal number of genes and are split according to the average intensity A.

Value

• Data frame including x, median of x, y, median of y, median difference of (x,y), pooled standard deviation of difference, LPE p-value, outlier flag, probability of an outlier within x or y, .

Details

The LPE test statistic numerator is the difference in medians between the two experimental conditions. The test statistic denominator is the combined pooled standard error for the two experimental conditions obtained by looking up the var.M from each baseOlig.error variance function. The conversion to p-values is based on the Gaussian distribution for difference if order statistics (medians).

References

J.K. Lee and M.O.Connell(2003). An S-Plus library for the analysis of differential expression. In The Analysis of Gene Expression Data: Methods and Software. Edited by G. Parmigiani, ES Garrett, RA Irizarry ad SL Zegar. Springer, NewYork.

Jain et. al. (2003) Local pooled error test for identifying differentially expressed genes with a small number of replicated microarrays, Bioinformatics, 1945-1951.

Jain et. al. (2005) Rank-invariant resampling based estimation of false discovery rate for analysis of small sample microarray data, BMC Bioinformatics, Vol 6, 187.

Examples

# Creating a null dataset (two groups with three
 # replicates each)
 dat <- matrix(rnorm(6000), ncol=6)
 
 # Applying LPE
 lpe.result <- lpeAdj(dat, labels=c(0,0,0,1,1,1))