Entering edit mode
Seyit Ali KAYIS
▴
30
@seyit-ali-kayis-5446
Last seen 10.3 years ago
Dear All,
I have microRNA microaaray expression data from Agilent platform
exported by
the Agilent Feature Extraction (AFE) image analysis software.
Initially, we
performed quantile normalisation using GeneSpring (version 12.1)
software
(demo version). Being an R user, I wanted to do rest of the analysis
by
using R. So, I repeated quantile normalisation using "AgiMicroRna"
library.
Before further analysis, I compared raw ( gTotalProbeSignal(raw) from
GeneSpring and ddTGS$TGS from AgiMicroRNA) and quantile normalised (
gTotalProbeSignal(normalized) from GeneSpring and ddNORM$TGS from
AgiMicroRNA ) output of two softwares. Raw output of two softwares are
the
same. However, there are some differences in the some of the quantile
normalised output, although majority of the output are very similar. I
suppose GeneSpring and "AgiMicroRna" are using the same algorithm and
they
should produce the same results. I was wondering whether I am doing
something wrong during the procees? Does any one faced similar
situation?
My sessionInfo() and steps I am following in AgiMicroRna are below.
Any comment, help deeply appreciated.
Kind Regards
Seyit Ali
======================================================================
======
==============
> sessionInfo()
R version 2.15.1 (2012-06-22)
Platform: i386-pc-mingw32/i386 (32-bit)
locale:
[1] LC_COLLATE=English_Australia.1252 LC_CTYPE=English_Australia.1252
LC_MONETARY=English_Australia.1252
[4] LC_NUMERIC=C LC_TIME=English_Australia.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] AgiMicroRna_2.6.0 affycoretools_1.28.0 KEGG.db_2.7.1
GO.db_2.7.1 RSQLite_0.11.1
[6] DBI_0.2-5 AnnotationDbi_1.18.1 preprocessCore_1.18.0
affy_1.34.0 limma_3.12.1
[11] Biobase_2.16.0 BiocGenerics_0.2.0
loaded via a namespace (and not attached):
[1] affyio_1.24.0 annaffy_1.28.0 annotate_1.34.1
BiocInstaller_1.4.7 biomaRt_2.12.0 Biostrings_2.24.1
[7] Category_2.22.0 gcrma_2.28.0 genefilter_1.38.0
GOstats_2.22.0 graph_1.34.0 grid_2.15.1
[13] GSEABase_1.18.0 IRanges_1.14.4 lattice_0.20-6
RBGL_1.32.1
RCurl_1.91-1.1 splines_2.15.1
[19] stats4_2.15.1 survival_2.36-14 tools_2.15.1
XML_3.9-4.1
xtable_1.7-0 zlibbioc_1.2.0
======================================================================
======
============
"AgiMicroRna" steps
library(AgiMicroRna)
sessionInfo()
AFE.TGS = TRUE
half= FALSE # ddTGS signal with 'half method'
offset=0
makePLOT=FALSE
# NORMALIZATION of ddTGS
NORMmethod="quantile"
makePLOTpre=TRUE
makePLOTpost=TRUE
# FILTERING PROBES
control = TRUE
IsGeneDetected = TRUE
wellaboveNEG = FALSE
limIsGeneDetected = 50
limNEG = 25
makePLOT = FALSE
# READING THE Target File
targets=readTargets(infile="targets.txt",verbose=TRUE)
# READING THE DATA (RGList)
dd=readMicroRnaAFE(targets,verbose=TRUE)
names(dd)
# PRE-PROCESSING # USING AFE gTotalGeneSignal & # NORMALIZATION
# tgsMicroRna: creates an uRNAList object that contains the Total
Gene
Signal computed
# by the Agilent Feature Extraction algorithms
# tgsNormalization: creates an uRNAList object containing the
Normalized
Total
# Gene Signal in log 2 scale
if(AFE.TGS) {
message('pre-processing: AFE TGS')
cat('\n')
ddTGS = tgsMicroRna(dd, half = FALSE, makePLOT = FALSE,verbose =
FALSE)
ddNORM = tgsNormalization(ddTGS, "quantile", makePLOTpre = FALSE,
makePLOTpost = FALSE, targets, verbose = TRUE)
}
# Obtaining raw data
TGSTGS<-cbind(ddTGS$genes, ddTGS$TGS)
# Obtaining quantile normalised data
NormData<-cbind(ddNORM$genes, ddNORM$TGS)
======================================================================
======
=====
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Dr. Seyit Ali KAYIS
Selcuk University, Faculty of Agriculture
Kampus/Konya, Turkey
skayis@selcuk.edu.tr, s_a_kayis@yahoo.com, s_a_kayis@hotmail.com
Tel: +90 332 223 2830 Mobile: +90 535 587 1139
Greetings from Konya, Turkey
http://www.ziraat.selcuk.edu.tr/skayis/
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