| Title: | Misclassification Penalized Posterior Classification |
|---|---|
| Description: | This package finds optimal sets of genes that seperate samples into two or more classes. |
| Authors: | HyungJun Cho <[email protected]>, Sukwoo Kim <[email protected]>, Mat Soukup <[email protected]>, and Jae K. Lee <[email protected]> |
| Maintainer: | Sukwoo Kim <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.77.0 |
| Built: | 2024-07-03 05:32:24 UTC |
| Source: | https://github.com/bioc/MiPP |
This data set consists of gene expression of colon cancer study.
data(colon)data(colon)
A matrix containing 2000 probe sets and 2 classes (T, F)
Alon, U., Barkai, N., Notterman, D.A., Gish, K., Ybarra, S., Mack, D., Levine, A.J. (1999). Broad Patterns of Gene Expression Revealed by Clustering Analysis of Tumor and Normal Colon Tissues probed by Oligonucleotide Arrays, PNAS, 96(12), 6745–6750.
This data set consists of gene expression of leukemia study.
data(leukemia)data(leukemia)
A matrix containing 6817 probe sets and 38 samples (2 classes: AML, ALL)
Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, P., Coller, H., Loh, M.L., Downing, J.R., Caliguri, M.A., Bloomfield, C.D., and Lander, E.S. (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 286, 531-537.
This data set consists of gene expression of leukemia study.
data(leukemia)data(leukemia)
A matrix containing 6817 probe sets and 34 samples (2 classes: AML, ALL)
Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, P., Coller, H., Loh, M.L., Downing, J.R., Caliguri, M.A., Bloomfield, C.D., and Lander, E.S. (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 286, 531-537.
This data set consists of gene expression of leukemia study.
data(leukemia)data(leukemia)
A matrix containing 6817 probe sets and 2 classes (AML, ALL)
Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, P., Coller, H., Loh, M.L., Downing, J.R., Caliguri, M.A., Bloomfield, C.D., and Lander, E.S. (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 286, 531-537.
Finds optimal sets of genes for classification
mipp(x, y, x.test = NULL, y.test = NULL, probe.ID = NULL, rule = "lda", method.cut = "t.test", percent.cut = 0.01, model.sMiPP.margin = 0.01, min.sMiPP = 0.85, n.drops = 2, n.fold = 5, p.test = 1/3, n.split = 20, n.split.eval = 100)mipp(x, y, x.test = NULL, y.test = NULL, probe.ID = NULL, rule = "lda", method.cut = "t.test", percent.cut = 0.01, model.sMiPP.margin = 0.01, min.sMiPP = 0.85, n.drops = 2, n.fold = 5, p.test = 1/3, n.split = 20, n.split.eval = 100)
x |
data matrix |
y |
class vector |
x.test |
test data matrix if available |
y.test |
test class vector if available |
probe.ID |
probe set IDs; if NULL, row numbers are assigned. |
rule |
classification rule: "lda","qda","logistic","svmlin","svmrbf"; the default is "lda". |
method.cut |
method for pre-selection; t-test is available. |
percent.cut |
proportion of pre-selected genes; the default is 0.01. |
model.sMiPP.margin |
smallest set of genes s.t. sMiPP <= (max sMiPP-model.sMiPP.margin); the default is 0.01. |
min.sMiPP |
Adding genes stops if max sMiPP is at least min.sMiPP; the default is 0.85. |
n.drops |
Adding genes stops if sMiPP decreases (n.drops) times, in addition to min.sMiPP criterion.; the default is 2. |
n.fold |
number of folds; default is 5. |
p.test |
partition percent of train and test samples when test samples are not available; the default is 1/3 for test set. |
n.split |
number of splits; the default is 20. |
n.split.eval |
numbr of splits for evalutation; the default is 100. |
model |
candiadate genes (for each split if no indep set is available |
model.eval |
Optimal sets of genes for each split when no indep set is available |
Soukup M, Cho H, and Lee JK
Soukup M, Cho H, and Lee JK (2005). Robust classification modeling on microarray data using misclassification penalized posterior, Bioinformatics, 21 (Suppl): i423-i430.
Soukup M and Lee JK (2004). Developing optimal prediction models for cancer classification using gene expression data, Journal of Bioinformatics and Computational Biology, 1(4) 681-694
########## #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$model ########## #Example 2: When an independent test set is not available data(colon) #Normalize data x <- mipp.preproc(colon) y <- factor(c("T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "N", "T", "T", "N", "N", "T", "T", "T", "T", "N", "T", "N", "N", "T", "T", "N", "N", "T", "T", "T", "T", "N", "T", "N")) #Deleting comtaminated chips x <- x[,-c(51,55,45,49,56)] y <- y[ -c(51,55,45,49,56)] #Compute MiPP out <- mipp(x=x, y=y, probe.ID = 1:nrow(x), n.fold=5, p.test=1/3, n.split=5, n.split.eval=100, percent.cut= 0.1, rule="lda") #Print candidate models for each split out$model #Print optimal models and independent evaluation for each split out$model.eval########## #Example 1: When an independent test set is available data(leukemia) #Normalize combined data leukemia <- cbind(leuk1, leuk2) leukemia <- mipp.preproc(leukemia, data.type="MAS4") #Train set x.train <- leukemia[,1:38] y.train <- factor(c(rep("ALL",27),rep("AML",11))) #Test set x.test <- leukemia[,39:72] y.test <- factor(c(rep("ALL",20),rep("AML",14))) #Compute MiPP out <- mipp(x=x.train, y=y.train, x.test=x.test, y.test=y.test, probe.ID = 1:nrow(x.train), n.fold=5, percent.cut=0.05, rule="lda") #Print candidate models out$model ########## #Example 2: When an independent test set is not available data(colon) #Normalize data x <- mipp.preproc(colon) y <- factor(c("T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "N", "T", "T", "N", "N", "T", "T", "T", "T", "N", "T", "N", "N", "T", "T", "N", "N", "T", "T", "T", "T", "N", "T", "N")) #Deleting comtaminated chips x <- x[,-c(51,55,45,49,56)] y <- y[ -c(51,55,45,49,56)] #Compute MiPP out <- mipp(x=x, y=y, probe.ID = 1:nrow(x), n.fold=5, p.test=1/3, n.split=5, n.split.eval=100, percent.cut= 0.1, rule="lda") #Print candidate models for each split out$model #Print optimal models and independent evaluation for each split out$model.eval
Performs IQR normalization, thesholding, and log2-transformation
mipp.preproc(x, data.type = "MAS5")mipp.preproc(x, data.type = "MAS5")
x |
data |
data.type |
data type is MAS5, MAS4, or dChip |
library(MiPP) data(colon) colon.nor <- mipp.preproc(colon)library(MiPP) data(colon) colon.nor <- mipp.preproc(colon)
sequentially finds optimal sets of genes for classification
mipp.seq(x, y, x.test = NULL, y.test = NULL, probe.ID = NULL, rule = "lda", method.cut = "t.test", percent.cut = 0.01, model.sMiPP.margin = 0.01, min.sMiPP = 0.85, n.drops = 2, n.fold = 5, p.test = 1/3, n.split = 20, n.split.eval = 100, n.seq=3, cutoff.sMiPP=0.7, remove.gene.each.model="all")mipp.seq(x, y, x.test = NULL, y.test = NULL, probe.ID = NULL, rule = "lda", method.cut = "t.test", percent.cut = 0.01, model.sMiPP.margin = 0.01, min.sMiPP = 0.85, n.drops = 2, n.fold = 5, p.test = 1/3, n.split = 20, n.split.eval = 100, n.seq=3, cutoff.sMiPP=0.7, remove.gene.each.model="all")
x |
data matrix |
y |
class vector |
x.test |
test data matrix if available |
y.test |
test class vector if available |
probe.ID |
probe set IDs; if NULL, row numbers are assigned. |
rule |
classification rule: "lda","qda","logistic","svmlin","svmrbf"; the default is "lda". |
method.cut |
method for pre-selection; t-test is available. |
percent.cut |
proportion of pre-selected genes; the default is 0.01. |
model.sMiPP.margin |
smallest set of genes s.t. sMiPP <= (max sMiPP-model.sMiPP.margin); the default is 0.01. |
min.sMiPP |
Adding genes stops if max sMiPP is at least min.sMiPP; the default is 0.85. |
n.drops |
Adding genes stops if sMiPP decreases (n.drops) times, in addition to min.sMiPP criterion.; the default is 2. |
n.fold |
number of folds; default is 5. |
p.test |
partition percent of train and test samples when test samples are not available; the default is 1/3 for test set. |
n.split |
number of splits; the default is 20. |
n.split.eval |
numbr of splits for evalutation; the default is 100. |
n.seq |
Number of sequential gene model selection; the default is 3. |
cutoff.sMiPP |
Cutoff point of 5 percent sMiPP to select gene models |
remove.gene.each.model |
Re-run after removing all genes in the selected models if "all" and the first gene for each of the selected models if "first" |
model |
candiadate genes (for each split if no indep set is available |
model.eval |
Optimal sets of genes for each split when no indep set is available |
genes.selected |
a list of genes selected by sequential selection |
Soukup M, Cho H, and Lee JK
Soukup M, Cho H, and Lee JK (2005). Robust classification modeling on microarray data using misclassification penalized posterior, Bioinformatics, 21 (Suppl): i423-i430.
Soukup M and Lee JK (2004). Developing optimal prediction models for cancer classification using gene expression data, Journal of Bioinformatics and Computational Biology, 1(4) 681-694
########## #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.seq(x=x.train, y=y.train, x.test=x.test, y.test=y.test, n.fold=5, percent.cut=0.01, rule="lda", n.seq=3) #Print candidate models out$model #Print the genes selected out$genes.selected ########## #Example 2: When an independent test set is not available data(colon) #Normalize data x <- mipp.preproc(colon) y <- factor(c("T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "N", "T", "T", "N", "N", "T", "T", "T", "T", "N", "T", "N", "N", "T", "T", "N", "N", "T", "T", "T", "T", "N", "T", "N")) #Deleting comtaminated chips x <- x[,-c(51,55,45,49,56)] y <- y[ -c(51,55,45,49,56)] #Compute MiPP out <- mipp.seq(x=x, y=y, n.fold=5, p.test=1/3, n.split=5, n.split.eval=100, percent.cut= 0.05, rule="lda", n.seq=2) #Print candidate models for each split out$model #Print optimal models and independent evaluation for each split out$model.eval #Print the genes selected out$genes.selected########## #Example 1: When an independent test set is available data(leukemia) #Normalize combined data leukemia <- cbind(leuk1, leuk2) leukemia <- mipp.preproc(leukemia, data.type="MAS4") #Train set x.train <- leukemia[,1:38] y.train <- factor(c(rep("ALL",27),rep("AML",11))) #Test set x.test <- leukemia[,39:72] y.test <- factor(c(rep("ALL",20),rep("AML",14))) #Compute MiPP out <- mipp.seq(x=x.train, y=y.train, x.test=x.test, y.test=y.test, n.fold=5, percent.cut=0.01, rule="lda", n.seq=3) #Print candidate models out$model #Print the genes selected out$genes.selected ########## #Example 2: When an independent test set is not available data(colon) #Normalize data x <- mipp.preproc(colon) y <- factor(c("T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "N", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "N", "T", "T", "N", "N", "T", "T", "T", "T", "N", "T", "N", "N", "T", "T", "N", "N", "T", "T", "T", "T", "N", "T", "N")) #Deleting comtaminated chips x <- x[,-c(51,55,45,49,56)] y <- y[ -c(51,55,45,49,56)] #Compute MiPP out <- mipp.seq(x=x, y=y, n.fold=5, p.test=1/3, n.split=5, n.split.eval=100, percent.cut= 0.05, rule="lda", n.seq=2) #Print candidate models for each split out$model #Print optimal models and independent evaluation for each split out$model.eval #Print the genes selected out$genes.selected