

Hi everyone, 



The original authors have replied my question. I thought i'll share it with everyone and for archival purposes. 



regards 


----- Forwarded Message ----- 
From: "HyungJun Cho" <hj4cho@korea.ac.kr> 
To: "Jae K. Lee" <jaeklee@virginia.edu> 
Cc: "Julian Lee" <julian@omniarray.com>, "HyungJun Cho" <hj4cho@korea.ac.kr> 
Sent: Thursday, January 24, 2008 7:17:00 PM (GMT+0800) Asia/Hong_Kong 
Subject: RE: Re: MiPP 



Hi Julian, 

Here are my answers. 


> 
> i) how do I elucidate the misclassified samples in the model? ie which samples in the training or testing 
> set were false positive/false negative?  


The main goal of MiPP is to provide an optimal model with some signficant genes. Because you have a training set and an indepdendent test set, refit the training set to your data with the genes in the optimal model and validate your test set. 


> ii) The most parsimonious model is 6 with genes 571,436,366,457,413 and 635. Is it possible to elucidate the 
> misclassified samples in previous orders, eg order 1 with Te.Error Rate of 0.1176? 

The model with the 6 genes is the final optimal model. By our algorithm, the 6 genes were selected in the order.  to elucidate the misclassified samples, do like the above answer (i).  


> iii) Could i use the built model to validate on other independent datasets?  


Do like the above answer (i).  


HyungJun 








From: "Jae K. Lee" [ jaeklee@virginia.edu ] 
To: Julian Lee [ julian@omniarray.com ] 
Date: Wed 23 Jan 2008 07:46:01 
Subject: Re: MiPP 

  Dr. HyungJun Cho may be able to answer these questions better than me. 

----- Original Message ----- 
From: "Julian Lee" 
To: 
Cc: 
Sent: Monday, January 21, 2008 9:24 PM 
Subject: MiPP 


> Hi all, 
> 
> I'm relatively new to Bioconductor and am still figuring out how to use MiPP for my work. 
> 
>>From the help sheet in the MiPP documentation, 
> 
> ########## 
> #Example 1: When an independent test set is available 
> 
> data(leukemia) 
> 
> #Normalize combined data 
> leukemia <- cbind(leuk1, leuk2) 
> leukemia <- mipp.preproc(leukemia, data.type="MAS4") 
> 
> #Train set 
> x.train <- leukemia[,1:38] 
> y.train <- factor(c(rep("ALL",27),rep("AML",11))) 
> 
> #Test set 
> x.test <- leukemia[,39:72] 
> y.test <- factor(c(rep("ALL",20),rep("AML",14))) 
> 
> #Compute MiPP 
> out <- mipp(x=x.train, y=y.train, x.test=x.test, y..test=y.test, probe.ID = 1:nrow(x.train), n.fold=5, 
> percent.cut=0.05, rule="lda") 
> 
> #Print candidate models 
> out$model 
> 
> Order Gene Tr.ER Tr.MiPP Tr.sMiPP Te.ER Te.MiPP Te.sMiPP Select 
> 1 1 571 0.0526 30.86 0.8122 0.1176 23.92 0.7035 
> 2 2 436 0.0000 36.89 0.9707 0.0294 30.41 0.8945 
> 3 3 366 0.0000 37.95 0.9988 0.0294 31.35 0.9222 
> 4 4 457 0.0000 38.00 0.9999 0.0294 32.14 0.9453 
> 5 5 413 0.0000 38.00 1.0000 0.0294 32.18 0.9464 
> 6 6 635 0.0000 38.00 1.0000 0.0000 33.75 0.9927 ** 
> 7 7 648 0.0000 38.00 1.0000 0.0000 33.62 0.9889 
> 8 8 181 0.0000 38.00 1.0000 0.0294 31.99 0.9409 
> 9 9 309 0.0000 38.00 1.0000 0.0000 33.46 0.9842 
> 10 10 99 0.0000 38.00 1.0000 0.0882 28.56 0.8400 
> 
> Here are some questions, 
> 
> i) how do I elucidate the misclassified samples in the model? ie which samples in the training or testing 
> set were false positive/false negative? 
> 
> ii) The most parsimonious model is 6 with genes 571,436,366,457,413 and 635. Is it possible to elucidate the 
> misclassified samples in previous orders, eg order 1 with Te.Error Rate of 0.1176? 
> 
> iii) Could i use the built model to validate on other independent datasets? 
> 
> thank you 
> 
> regards 
> 
> Julian Lee 
> Bioinformatics Specialist 
> National Cancer Center Singapore 
> 
> R version 2.5.1 (2007-06-27) 
> i386-pc-mingw32 
> 
> locale: 
> LC_COLLATE=English_United States.1252;LC_CTYPE=English_United States.1252;LC_MONETARY=English_United 
> States.1252;LC_NUMERIC=C;LC_TIME=English_United States.1252 
> 
> attached base packages: 
> [1] "tools" "stats" "graphics" "grDevices" "utils" "datasets" "methods" "base" 
> 
> other attached packages: 
> MiPP MASS e1071 class Biobase 
> "1.8.0" "7..2-34" "1.5-16" "7.2-34" "1.14.1" 
> 






-------------------------------------- 

HyungJun Cho, Ph..D., 

Assistant Professor 

Department of Statistics 

and Department of Biostatistics 

Korea University, Seoul, Korea 

Phone: +82-2-3290-2242 

E-mail:  hj4cho@korea.ac.kr 

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