Question: cellHTS2 variance by plate calculation?
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8.0 years ago by
Guest User12k
Guest User12k wrote:
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 > -- Sent via the guest posting facility at bioconductor.org.
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ADD COMMENTlink modified 8.0 years ago by Joseph Barry160 • written 8.0 years ago by Guest User12k
Answer: cellHTS2 variance by plate calculation?
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8.0 years ago by
Joseph Barry160
Dana-Farber Cancer Institute, Boston, USA
Joseph Barry160 wrote:
Dear Stephen, In the median absolute deviation calculation, R by default adjusts by constant scale factor of 1.4826 for asymptotically normal consistency (see ?mad for further details). Could you check to see if this explains the discrepancy in your excel result? If not, perhaps it would be helpful if you could repeat your calculations on a specific dataset that we both have access to and share your results. The KcViabSmall dataset that is distributed with cellHTS2 is one option. In this way it will be easier for others to pinpoint the source of this discrepancy. Best wishes, Joseph On Dec 8, 2011, at 2:10 AM, Stephen Baird [guest] wrote: > > 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 >> > > > -- > Sent via the guest posting facility at bioconductor.org. > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor
ADD COMMENTlink written 8.0 years ago by Joseph Barry160
Joseph, Thanks, that is the answer. I didn't see the use of that factor in cellHTS2 documentation and had not realized it was used all the time in R for Mean Absolute Deviation. Stephen On 08.12.2011 10:24 AM, Joseph Barry wrote: > Dear Stephen, > > In the median absolute deviation calculation, R by default adjusts by constant scale factor of 1.4826 for asymptotically normal consistency (see ?mad for further details). Could you check to see if this explains the discrepancy in your excel result? > > If not, perhaps it would be helpful if you could repeat your calculations on a specific dataset that we both have access to and share your results. The KcViabSmall dataset that is distributed with cellHTS2 is one option. In this way it will be easier for others to pinpoint the source of this discrepancy. > > Best wishes, > Joseph > > On Dec 8, 2011, at 2:10 AM, Stephen Baird [guest] wrote: > >> 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 >> >> -- >> Sent via the guest posting facility at bioconductor.org. >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor@r-project.org >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor [[alternative HTML version deleted]]
ADD REPLYlink written 8.0 years ago by Stephen Baird10
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