n.genes.adaptive.int {LPE} | R Documentation |
Instead of dividing the genes equally in 100 intervals, this function divides them adaptively based on three rules: a) min. number of genes (default =10), b) max. number of genes = total/100; c) based on Median + fraction(SD) from the starting gene of each interval
n.genes.adaptive.int(baseOlig.error.step1.res, min.genes.int=10, div.factor=1)
baseOlig.error.step1.res |
It is the result from baseOlig.error.step1 function. |
min.genes.int |
It is the minimum number of genes in the interval, default=10. |
div.factor |
(1/div.factor) is the fraction of Standard Deviation which we wish to include in each interval to calculate number of genes in each interval |
Returns a vector respresenting the number of genes in each interval.
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 by 7 Ley[1:3,] # Returns # ID c1 c2 c3 t1 t2 t3 # 1 AFFX-MurIL2_at 4.06 3.82 4.28 11.47 11.54 11.34 # 2 AFFX-MurIL10_at 4.56 2.79 4.83 4.25 3.72 2.94 # 3 AFFX-MurIL4_at 5.14 4.10 4.59 4.67 4.71 4.67 Ley[1:1000,2:7] <- preprocess(Ley[1:1000,2:7],data.type="MAS5") # Finding the baseline distribution of subset of the data # condition one (3 replicates) var.1 <- baseOlig.error.step1(Ley[1:1000,2:4], q=0.01) dim(var.1) # Returns a matrix of 1000 by 2 (A,M) format n.genes.subint <- n.genes.adaptive.int(var.1, min.genes.int=10, div.factor=1)