Dear all,

I am analyzing data of qPCR plates in which each well correponds to a miRNA.
The system does not have replicates within the same plate.
the data consistes in 3 types of cells: I have 3 replicates for cell type
one; 2 replicates for cell type 2 and only one sampled cell type 3.
The data of each cell type and each replicate is in a separate file where
the data has 5 columns
miRNA ID  Target or Endogenous  Position on plate  Passed or Failed Ct value
miR-18a Target A1 Passed 26.99  this means that I have 6 files:
File    Treatment
sample1.txt    cell type1
sample2.txt    cell type1
sample3.txt    cell type1
sample1.txt    cell type2
sample2.txt    cell type2
sample1.txt    cell type3



For the miRNA which did not have expression in a biological replicate, I
have replaced NA by 0.

question 1 -So after normalizing using quantile, several miRNA which did not
have expression in sample 1 cell type 3 the value were normalized to
23.1191666666667. I notice that this happened only for this cell type, for
which I do not have replicates. Can someone explain me why does this happen?

question 2- the interpretation of values of Ct=0 and Ct>35. Shouldn't be the
same, that there is no expression, or levels of expression under capacity of
detection?

question 3- I notice that miRNAs which are flagged by failed are being
included on the analysis. Shouldn't these miRNAs be excluded? I thought that
these would be, and I could find a filter for miRNA classified as
undetermined but not for miRNAs flagged as failed.

thanks in advance for the help. Kind regards,
Andreia

the code that I have used is bellow:

library(HTqPCR)
source("http://www.bioconductor.org/biocLite.R")
biocLite("statmod")
library(statmod)
setwd("G:/margarida_miRNA")
path<-("G:/margarida_miRNA")
files <- read.delim(file.path(path, "Data.txt"))
raw <- readCtData(files=files$File, path=path, n.features = 96, flag = 4,
feature = 1, type = 2, position = 3, Ct = 5, header = FALSE, SDS = FALSE)
pdf("plotCtOverview_raw_data.pdf")
plotCtOverview(raw,genes=g, xlim=c(0,50), groups=files$Treatment, conf.int
=TRUE)
dev.off()
raw.cat<-setCategory(raw, groups=files$Treatment, Ct.max=38, Ct.min=8,
flag=TRUE, flag.out="Failed",verbose=TRUE, quantile=0.8)
pdf("plot_Category.pdf")
plotCtCategory(raw.cat)
plotCtCategory(raw.cat, by.feature=TRUE, cexRow=0.1)
dev.off()
q.norm<-normalizeCtData(raw.cat,norm="quantile")
library(HTqPCR)
qFilt<-filterCtData(q.norm, remove.type="Endogenous")
iqr.values<-apply(exprs(q.norm),1,IQR)
iqr.filt<-filterCtData(q.norm, remove.IQR=1.5,
remove.category="Undetermined")
pdf("severalgraphs.pdf")
plotCtCor(q.norm, main="Ct correlation")
plotCtDensity(q.norm)
plotCtDensity(iqr.filt)
plotCtBoxes(q.norm)
plotCtBoxes(iqr.filt)
plotCtScatter(q.norm, cards=c(1,2), col="type", diag=TRUE)
plotCtScatter(q.norm, cards=c(1,3), col="type", diag=TRUE)
plotCtScatter(q.norm, cards=c(2,3), col="type", diag=TRUE)
plotCtScatter(q.norm, cards=c(4,5), col="type", diag=TRUE)
plotCtScatter(q.norm, cards=c(1,6), col="type", diag=TRUE)
plotCtScatter(q.norm, cards=c(2,6), col="type", diag=TRUE)
plotCtScatter(q.norm, cards=c(3,6), col="type", diag=TRUE)
plotCtScatter(q.norm, cards=c(4,6), col="type", diag=TRUE)
plotCtScatter(q.norm, cards=c(5,6), col="type", diag=TRUE)
plotCtPairs(q.norm, col="type", diag=TRUE)
dev.off()



design<-model.matrix(~0+files$Treatment)

colnames(design)<-c("CT1","CT2","CT3")




contrasts<-makeContrasts(CT1-CT2, levels=design)
qDE.limma<-limmaCtData(iqr.filt, design=design, contrasts=contrasts,
ndups=1, spacing=1)
qDE.limma
dim(qDE.limma)
names(qDE.limma)
pdf("testingandheatmap.pdf")
plotCtRQ(qDE.limma, p.val=0.05, transform="log10", col="#9E0142")
g<-featureNames(iqr.filt)
plotCtHeatmap(iqr.filt, gene.names=g, dist="euclidean", cexRow=0.4)
cluster.list<-clusterCt(iqr.filt, type="genes", dist="euclidean",
n.cluster=8, hang=-1,cex=0.5)
dev.off()

 sessionInfo()
R version 2.11.1 (2010-05-31)
i386-pc-mingw32

locale:
[1] LC_COLLATE=Portuguese_Portugal.1252  LC_CTYPE=Portuguese_Portugal.1252

[3] LC_MONETARY=Portuguese_Portugal.1252 LC_NUMERIC=C

[5] LC_TIME=Portuguese_Portugal.1252

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
[1] statmod_1.4.6      HTqPCR_1.2.0       limma_3.4.4
RColorBrewer_1.0-2
[5] Biobase_2.8.0

loaded via a namespace (and not attached):
[1] affy_1.26.1           affyio_1.16.0         gdata_2.8.0
[4] gplots_2.8.0          gtools_2.6.2          preprocessCore_1.10.0
[7] tools_2.11.1




-- 
--------------------------------------------
Andreia J. Amaral
Unidade de Imunologia Clínica
Instituto de Medicina Molecular
Universidade de Lisboa
email: andreiaamaral@fm.ul.pt
          andreia.fonseca@gmail.com

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