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To understand the workings of cellHTS2 I ran a couple of analyses of
one plate with either negative or median scaling normalization with
and without variance adjustment. Without variance adjustment my excel
normalization calculations are the same as cellHTS2. With variance
adjustment my numbers are always different (whether by plate or
experiment adjustment). As I understand calculating the variance
adjustment, I should divide each normalized well plate value with the
median absolute deviation value calculated on only the "sample" wells'
normalized values. Has anyone else verified the cellHTS2 output
calculations using one of the variance options? or have a suggestions
in how I can prove my calculations are wrong?
Thanks,
Stephen
-- output of sessionInfo():
> sessionInfo()
R version 2.14.0 (2011-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
locale:
[1] LC_CTYPE=en_CA.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_CA.UTF-8 LC_COLLATE=en_CA.UTF-8
[5] LC_MONETARY=en_CA.UTF-8 LC_MESSAGES=en_CA.UTF-8
[7] LC_PAPER=C LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] grid stats graphics grDevices utils datasets
methods
[8] base
other attached packages:
[1] KEGG.db_2.5.0 HTSanalyzeR_2.5.1 RankProd_2.24.0
[4] cellHTS2.alex_2.16.0 BioNet_1.10.1 RBGL_1.28.0
[7] GSEABase_1.14.0 graph_1.30.0 annotate_1.30.0
[10] igraph_0.5.5-2 GO.db_2.5.0 org.Hs.eg.db_2.5.0
[13] RSQLite_0.9-4 DBI_0.2-5 AnnotationDbi_1.14.1
[16] cellHTS2_2.16.0 locfit_1.5-6 lattice_0.20-0
[19] akima_0.5-4 hwriter_1.3 vsn_3.20.0
[22] splots_1.18.0 genefilter_1.34.0 Biobase_2.12.2
[25] RColorBrewer_1.0-5
loaded via a namespace (and not attached):
[1] affy_1.30.0 affyio_1.20.0 biomaRt_2.8.1
[4] Category_2.18.0 limma_3.8.3 MASS_7.3-14
[7] prada_1.28.0 preprocessCore_1.14.0 RCurl_1.6-9
[10] rrcov_1.3-01 splines_2.14.0 stats4_2.14.0
[13] survival_2.36-10 tools_2.14.0 XML_3.4-2
[16] xtable_1.5-6
>
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