baseOlig.error.step2 {LPE} | R Documentation |
Similar to baseOlig.error.step1 function, except that now the number of bins are chosen adaptively instead of fixed 100.
baseOlig.error.step2(y,baseOlig.error.step1.res, df=10, stats=median, min.genes.int=10, div.factor=1)
y |
y is a preprocessed matrix or data frame of expression intensities in which columns are expression intensities for a particular experimental condition and rows are genes. |
baseOlig.error.step1.res |
It is the result obtained from baseOlig.error.step1 function, in which number of bins are fixed=100 |
df |
df stands for degrees of freedom. It is used in smooth.spline function to interpolate the variances of all genes. Default value is 10. |
stats |
It determines whether mean or median is to be used for the replicates |
min.genes.int |
Determines the minimum number of genes in a subinterval for selecting the adaptive intervals. |
div.factor |
Determines the factor by which sigma needs to be divided for selecting adaptive intervals. |
Returns object of class baseOlig comprising a data frame with 2 columns: A and var M, and rows for each quantile specified. The A column contains the median values of A for each quantile/bin and the M columns contains the pooled variance of the replicate chips for genes within each quantile/bin.
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, df=10) dim(var.1) var.11 <- baseOlig.error.step2(Ley[1:1000,2:4], var.1, df=10) # Returns a matrix of 1000 by 2 (A,M) format