resamp.adj {LPE} | R Documentation |
Adjusts the fdr based on rank invariant genes
resamp.adj(x,y, q=0.01, iterations=5, min.genes.int=10)
x |
Replicated data from first experimental condition (as matrix or data-frame) |
y |
Replicated data from second experimental condition (as matrix or data-frame) |
q |
q is the quantile width; q=0.01 corresponds to 100 quantiles |
iterations |
Number of iterations to be performed to obtain critical z-statistics |
min.genes.int |
Determines the minimum number of genes in a subinterval for selecting the adaptive intervals. |
Returns the z-statistics for the null distribution, obtained from resampling the rank invariant genes within each quantile. These z-statistic values are compared with z-statiscs from the original data, and fdr is calculated.
Nitin Jainnitin.jain@pfizer.com
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.
# Loading the library and the data library(LPE) data(Ley) dim(Ley) # Gives 12488*7 # First column is ID. # Subsetting the data subset.Ley <- Ley[1:1000,] subset.Ley[,2:7] <- preprocess(subset.Ley[,2:7],data.type="MAS5") # Finding the baseline distribution of condition 1 and 2. var.1 <- baseOlig.error(subset.Ley[,2:4], q=0.01) var.2 <- baseOlig.error(subset.Ley[,5:7], q=0.01) # Applying LPE lpe.result <- lpe(subset.Ley[,2:4],subset.Ley[,5:7], var.1, var.2, probe.set.name=subset.Ley[,1]) z.stats.null <- resamp.adj(subset.Ley[,2:4], subset.Ley[,5:7], q=0.01, iterations=2,min.genes.int=10 )