[BioC] cellHTS2 variance by plate calculation?

[Stephen Baird [guest]]

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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