| Title: | Extraction of Differential Gene Expression |
|---|---|
| Description: | The edge package implements methods for carrying out differential expression analyses of genome-wide gene expression studies. Significance testing using the optimal discovery procedure and generalized likelihood ratio tests (equivalent to F-tests and t-tests) are implemented for general study designs. Special functions are available to facilitate the analysis of common study designs, including time course experiments. Other packages such as sva and qvalue are integrated in edge to provide a wide range of tools for gene expression analysis. |
| Authors: | John D. Storey, Jeffrey T. Leek and Andrew J. Bass |
| Maintainer: | John D. Storey <[email protected]>, Andrew J. Bass <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 2.37.0 |
| Built: | 2024-09-19 06:16:36 UTC |
| Source: | https://github.com/bioc/edge |
Runs qvalue on a deSet object.
apply_qvalue(object, ...) ## S4 method for signature 'deSet' apply_qvalue(object, ...)apply_qvalue(object, ...) ## S4 method for signature 'deSet' apply_qvalue(object, ...)
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deSet object with slots updated by qvalue
calculations.
John Storey, Andrew Bass
Storey JD and Tibshirani R. (2003) Statistical significance for genome-wide studies. Proceedings of the National Academy of Sciences, 100: 9440-9445
# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # Run lrt (or odp) and apply_qvalue de_lrt <- lrt(de_obj) de_lrt <- apply_qvalue(de_lrt, fdr.level = 0.05, pi0.method = "bootstrap", adj=1.2) summary(de_lrt)# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # Run lrt (or odp) and apply_qvalue de_lrt <- lrt(de_obj) de_lrt <- apply_qvalue(de_lrt, fdr.level = 0.05, pi0.method = "bootstrap", adj=1.2) summary(de_lrt)
Runs sva on the null and full models in
deSet. See sva for additional details.
apply_sva(object, ...) ## S4 method for signature 'deSet' apply_sva(object, ...)apply_sva(object, ...) ## S4 method for signature 'deSet' apply_sva(object, ...)
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deSet object where the surrogate variables
estimated by sva are added to the full model and null model
matrices.
John Storey, Jeffrey Leek, Andrew Bass
Leek JT, Storey JD (2007) Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis. PLoS Genet 3(9): e161. doi:10.1371/journal.pgen.0030161
Leek JT and Storey JD. (2008) A general framework for multiple testing dependence. Proceedings of the National Academy of Sciences, 105: 18718- 18723.
# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # run surrogate variable analysis de_sva <- apply_sva(de_obj) # run odp/lrt with surrogate variables added de_odp <- odp(de_sva, bs.its = 30) summary(de_odp)# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # run surrogate variable analysis de_sva <- apply_sva(de_obj) # run odp/lrt with surrogate variables added de_odp <- odp(de_sva, bs.its = 30) summary(de_odp)
Access the full model fitted coefficients of a
deFit object.
betaCoef(object) ## S4 method for signature 'deFit' betaCoef(object)betaCoef(object) ## S4 method for signature 'deFit' betaCoef(object)
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betaCoef returns the regression coefficients for the full
model fit.
John Storey, Andrew Bass
# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # run fit_models to get model fits de_fit <- fit_models(de_obj) # extract beta coefficients beta <- betaCoef(de_fit)# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # run fit_models to get model fits de_fit <- fit_models(de_obj) # extract beta coefficients beta <- betaCoef(de_fit)
build_models creates a deSet object. The user inputs
the full and null models.
build_models(data, cov, full.model = NULL, null.model = NULL, ind = NULL)build_models(data, cov, full.model = NULL, null.model = NULL, ind = NULL)
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deSet object
John Storey, Andy Bass
# create ExpressionSet object from kidney dataset library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null.model <- ~sex full.model <- ~sex + ns(age, df=4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null.model, full.model = full.model)# create ExpressionSet object from kidney dataset library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null.model <- ~sex full.model <- ~sex + ns(age, df=4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null.model, full.model = full.model)
build_study generates the full and null models for users unfamiliar
with building models in R. There are two types of experimental designs:
static and time-course. For more details, refer to the vignette.
build_study(data, grp = NULL, adj.var = NULL, bio.var = NULL, tme = NULL, ind = NULL, sampling = c("static", "timecourse"), basis.df = 2, basis.type = c("ncs", "poly"))build_study(data, grp = NULL, adj.var = NULL, bio.var = NULL, tme = NULL, ind = NULL, sampling = c("static", "timecourse"), basis.df = 2, basis.type = c("ncs", "poly"))
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basis.type |
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deSet object
John Storey, Andy Bass
# create ExpressionSet object from kidney dataset library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr # create deSet object from data de_obj <- build_study(data = kidexpr, adj.var = sex, tme = age, sampling = "timecourse", basis.df = 4)# create ExpressionSet object from kidney dataset library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr # create deSet object from data de_obj <- build_study(data = kidexpr, adj.var = sex, tme = age, sampling = "timecourse", basis.df = 4)
Object returned from fit_models containing information
regarding the model fits for the experiment.
fit.fullmatrix: containing fitted values for the full model.
fit.nullmatrix: containing fitted values for the null model.
res.fullmatrix: the residuals of the full model.
res.nullmatrix: the residuals of the null model.
dH.fullvector: contains diagonal elements in the projection
matrix for the full model.
beta.coefmatrix: fitted coefficients for the full model.
stat.typestring: information on the statistic of interest.
Currently, the only options are “lrt” and “odp”.
fitNull(deFit)Access fitted data from null model.
fitFull(deFit)Access fitted data from full model.
resNull(deFit)Access residuals from null model fit.
resFull(deFit)Access residuals from full model fit.
betaCoef(deFit)Access beta coefficients in linear model.
sType(deFit)Access statistic type of model fitting utilized in function.
John Storey, Jeffrey Leek, Andrew Bass
Creates a deSet object that extends the
ExpressionSet object.
deSet(object, full.model, null.model, individual = NULL) ## S4 method for signature 'ExpressionSet' deSet(object, full.model, null.model, individual = NULL)deSet(object, full.model, null.model, individual = NULL) ## S4 method for signature 'ExpressionSet' deSet(object, full.model, null.model, individual = NULL)
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individual |
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deSet object
It is essential that the null and full models have the same variables as the ExpressionSet phenoType column names.
John Storey, Andrew Bass
# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) pDat <- as(cov, "AnnotatedDataFrame") exp_set <- ExpressionSet(assayData = kidexpr, phenoData = pDat) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- deSet(exp_set, null.model = null_model, full.model = full_model) # optionally add individuals to experiment, in this case there are 36 # individuals that were sampled twice indSamples <- as.factor(rep(1:36, each = 2)) de_obj <- deSet(exp_set, null.model = null_model, full.model = full_model, ind = indSamples) summary(de_obj)# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) pDat <- as(cov, "AnnotatedDataFrame") exp_set <- ExpressionSet(assayData = kidexpr, phenoData = pDat) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- deSet(exp_set, null.model = null_model, full.model = full_model) # optionally add individuals to experiment, in this case there are 36 # individuals that were sampled twice indSamples <- as.factor(rep(1:36, each = 2)) de_obj <- deSet(exp_set, null.model = null_model, full.model = full_model, ind = indSamples) summary(de_obj)
The deSet class extends the ExpressionSet class.
While the ExpressionSet class contains information about the
experiment, the deSet class contains both experimental information and
additional information relevant for differential expression analysis,
explained below in Slots.
null.modelformula: contains the adjustment variables in the
experiment. The null model is used for comparison when fitting the
full model.
full.modelformula: contains the adjustment variables and the
biological variables of interest.
null.matrixmatrix: the null model as a matrix.
full.matrixmatrix: the full model as a matrix.
individualfactor: contains information on which sample
is from which individual in the experiment.
qvalueObjS3 object: containing qvalue object.
See qvalue for additional details.
as(ExpressionSet, "deSet")Coerce objects of
ExpressionSet to deSet.
lrt(deSet, ...)Performs a generalized likelihood ratio test using the full and null models.
odp(deSet, ...)Performs the optimal discovery procedure, which is a new approach for optimally performing many hypothesis tests in a high-dimensional study.
kl_clust(deSet, ...)An implementation of mODP that assigns genes to modules based off of the Kullback-Leibler distance.
fit_models(deSet, ...)Fits a linear model to each gene by method of least squares.
apply_qvalue(deSet, ...)Applies qvalue
function.
apply_sva(deSet, ...)Applies surrogate variable analysis
(sva).
fullMatrix(deSet)Access and set full matrix.
nullMatrix(deSet)Access and set null matrix.
fullModel(deSet)Access and set full model.
nullModel(deSet)Access and set null model.
individual(deSet)Access and set individual slot.
qvalueObj(deSet)Access qvalue object.
See qvalue.
validObject(deSet)Check validity of deSet object.
See ExpressionSet for additional slot information.
John Storey, Jeffrey Leek, Andrew Bass
The edge package implements methods for carrying out differential expression analyses of genome-wide gene expression studies. Significance testing using the optimal discovery procedure and generalized likelihood ratio tests (equivalent to F-tests and t-tests) are implemented for general study designs. Special functions are available to facilitate the analysis of common study designs, including time course experiments. Other packages such as snm, sva, and qvalue are integrated in edge to provide a wide range of tools for gene expression analysis.
John Storey, Jeffrey Leek, Andrew Bass
## Not run: browseVignettes("edge") ## End(Not run)## Not run: browseVignettes("edge") ## End(Not run)
The data provide gene expression measurements in an endotoxin study where four subjects were given endotoxin and four subjects were given a placebo. Blood samples were collected and leukocytes were isolated from the samples before infusion and at times 2, 4, 6, 9, 24 hours.
data(endotoxin)data(endotoxin)
endoexpr: A 500 rows by 46 columns data frame containing expression values.
class: A vector of length 46 containing information about which individuals were given endotoxin.
ind: A vector of length 46 providing indexing measurements for each individual in the experiment.
time: A vector of length 46 indicating time measurements.
endotoxin dataset
The data is a random subset of 500 genes from the full dataset. To download the full data set, go to http://genomine.org/edge/.
Storey JD, Xiao W, Leek JT, Tompkins RG, and Davis RW. (2005) Significance
analysis of time course microarray experiments. PNAS, 102: 12837-12842.
http://www.pnas.org/content/100/16/9440.full
library(splines) # import data data(endotoxin) ind <- endotoxin$ind class <- endotoxin$class time <- endotoxin$time endoexpr <- endotoxin$endoexpr cov <- data.frame(individual = ind, time = time, class = class) # formulate null and full models in experiement # note: interaction term is a way of taking into account group effects mNull <- ~ns(time, df=4, intercept = FALSE) + class mFull <- ~ns(time, df=4, intercept = FALSE) + ns(time, df=4, intercept = FALSE):class + class # create deSet object de_obj <- build_models(endoexpr, cov = cov, full.model = mFull, null.model = mNull, ind = ind) # Perform ODP/lrt statistic to determine significant genes in study de_odp <- odp(de_obj, bs.its = 10) de_lrt <- lrt(de_obj, nullDistn = "bootstrap", bs.its = 10) # summarize significance results summary(de_odp)library(splines) # import data data(endotoxin) ind <- endotoxin$ind class <- endotoxin$class time <- endotoxin$time endoexpr <- endotoxin$endoexpr cov <- data.frame(individual = ind, time = time, class = class) # formulate null and full models in experiement # note: interaction term is a way of taking into account group effects mNull <- ~ns(time, df=4, intercept = FALSE) + class mFull <- ~ns(time, df=4, intercept = FALSE) + ns(time, df=4, intercept = FALSE):class + class # create deSet object de_obj <- build_models(endoexpr, cov = cov, full.model = mFull, null.model = mNull, ind = ind) # Perform ODP/lrt statistic to determine significant genes in study de_odp <- odp(de_obj, bs.its = 10) de_lrt <- lrt(de_obj, nullDistn = "bootstrap", bs.its = 10) # summarize significance results summary(de_odp)
fit_models fits a model matrix to each gene by using the least
squares method. Model fits can be either statistic type "odp" (optimal
discovery procedure) or "lrt" (likelihood ratio test).
fit_models(object, stat.type = c("lrt", "odp"), weights = NULL) ## S4 method for signature 'deSet' fit_models(object, stat.type = c("lrt", "odp"), weights = NULL)fit_models(object, stat.type = c("lrt", "odp"), weights = NULL) ## S4 method for signature 'deSet' fit_models(object, stat.type = c("lrt", "odp"), weights = NULL)
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If "odp" method is implemented then the null model is removed from the full model (see Storey 2007). Otherwise, the statistic type has no affect on the model fit.
deFit object
fit_models does not have to be called by the user to use
odp, lrt or kl_clust as it is an
optional input and is implemented in the methods. The
deFit object can be created by the user if a different
statistical implementation is required.
John Storey
Storey JD. (2007) The optimal discovery procedure: A new approach to simultaneous significance testing. Journal of the Royal Statistical Society, Series B, 69: 347-368.
Storey JD, Dai JY, and Leek JT. (2007) The optimal discovery procedure for large-scale significance testing, with applications to comparative microarray experiments. Biostatistics, 8: 414-432.
Storey JD, Xiao W, Leek JT, Tompkins RG, and Davis RW. (2005) Significance analysis of time course microarray experiments. Proceedings of the National Academy of Sciences, 102: 12837-12842.
# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # retrieve statistics from linear regression for each gene fit_lrt <- fit_models(de_obj, stat.type = "lrt") # lrt method fit_odp <- fit_models(de_obj, stat.type = "odp") # odp method # summarize object summary(fit_odp)# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # retrieve statistics from linear regression for each gene fit_lrt <- fit_models(de_obj, stat.type = "lrt") # lrt method fit_odp <- fit_models(de_obj, stat.type = "odp") # odp method # summarize object summary(fit_odp)
Access the fitted data from the full model in a
deFit object.
fitFull(object) ## S4 method for signature 'deFit' fitFull(object)fitFull(object) ## S4 method for signature 'deFit' fitFull(object)
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fitFull returns a matrix of fitted values from full model.
John Storey, Andrew Bass
# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # run fit_models to get model fits de_fit <- fit_models(de_obj) # extract fitted values for full model fitted_full <- fitFull(de_fit)# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # run fit_models to get model fits de_fit <- fit_models(de_obj) # extract fitted values for full model fitted_full <- fitFull(de_fit)
Access the fitted data from the null model in an
deFit object.
fitNull(object) ## S4 method for signature 'deFit' fitNull(object)fitNull(object) ## S4 method for signature 'deFit' fitNull(object)
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fitNull returns a matrix of fitted values from null model.
John Storey, Andrew Bass
# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # run fit_models to get model fits de_fit <- fit_models(de_obj) # extract fitted values from null model fitted_null <- fitNull(de_fit)# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # run fit_models to get model fits de_fit <- fit_models(de_obj) # extract fitted values from null model fitted_null <- fitNull(de_fit)
These generic functions access and set the full matrix for
deSet object.
fullMatrix(object) fullMatrix(object) <- value ## S4 method for signature 'deSet' fullMatrix(object) ## S4 replacement method for signature 'deSet' fullMatrix(object) <- valuefullMatrix(object) fullMatrix(object) <- value ## S4 method for signature 'deSet' fullMatrix(object) ## S4 replacement method for signature 'deSet' fullMatrix(object) <- value
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fullMatrix returns the value of the full model matrix.
Andrew Bass, John Storey
# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # extract the full model equation as a matrix mat_full <- fullMatrix(de_obj)# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # extract the full model equation as a matrix mat_full <- fullMatrix(de_obj)
These generic functions access and set the full model for
deSet object.
fullModel(object) fullModel(object) <- value ## S4 method for signature 'deSet' fullModel(object) ## S4 replacement method for signature 'deSet' fullModel(object) <- valuefullModel(object) fullModel(object) <- value ## S4 method for signature 'deSet' fullModel(object) ## S4 replacement method for signature 'deSet' fullModel(object) <- value
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the formula for the full model.
John Storey, Andrew Bass
# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # extract out the full model equation mod_full <- fullModel(de_obj) # change the full model in the experiment fullModel(de_obj) <- ~sex + ns(age, df = 2)# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # extract out the full model equation mod_full <- fullModel(de_obj) # change the full model in the experiment fullModel(de_obj) <- ~sex + ns(age, df = 2)
The data provide gene expression measurements in peripheral blood leukocyte samples from three Moroccan groups leading distinct ways of life: desert nomadic (DESERT), mountain agrarian (VILLAGE), and coastal urban (AGADIR).
data(gibson)data(gibson)
batch: Batches in experiment.
location: Environment/lifestyle of Moroccan Amazigh groups.
gender: Sex of individuals.
gibexpr: A 500 rows by 46 columns matrix of gene expression values.
gibson dataset
These data are a random subset of 500 genes from the total number of genes in the original data set. To download the full data set, go to http://genomine.org/de/.
Idaghdour Y, Storey JD, Jadallah S, and Gibson G. (2008) A genome-wide gene expression signature of lifestyle in peripheral blood of Moroccan Amazighs. PLoS Genetics, 4: e1000052.
# import data(gibson) batch <- gibson$batch gender <- gibson$gender location <- gibson$location gibexpr <- gibson$gibexpr cov <- data.frame(Batch = batch, Gender = gender, Location = location) # create deSet for experiment- static experiment mNull <- ~Gender + Batch mFull <- ~Gender + Batch + Location # create deSet object de_obj <- build_models(gibexpr, cov = cov, full.model = mFull, null.model = mNull) # Perform ODP/lrt statistic to determine significant genes in study de_odp <- odp(de_obj, bs.its = 10) de_lrt <- lrt(de_obj, nullDistn = "bootstrap", bs.its = 10) # summarize significance results summary(de_odp)# import data(gibson) batch <- gibson$batch gender <- gibson$gender location <- gibson$location gibexpr <- gibson$gibexpr cov <- data.frame(Batch = batch, Gender = gender, Location = location) # create deSet for experiment- static experiment mNull <- ~Gender + Batch mFull <- ~Gender + Batch + Location # create deSet object de_obj <- build_models(gibexpr, cov = cov, full.model = mFull, null.model = mNull) # Perform ODP/lrt statistic to determine significant genes in study de_odp <- odp(de_obj, bs.its = 10) de_lrt <- lrt(de_obj, nullDistn = "bootstrap", bs.its = 10) # summarize significance results summary(de_odp)
These generic functions access and set the individual slot in
deSet.
individual(object) individual(object) <- value ## S4 method for signature 'deSet' individual(object) ## S4 replacement method for signature 'deSet' individual(object) <- valueindividual(object) individual(object) <- value ## S4 method for signature 'deSet' individual(object) ## S4 replacement method for signature 'deSet' individual(object) <- value
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individual returns information regarding dinstinct individuals
sampled in the experiment.
John Storey, Andrew Bass
library(splines) # import data data(endotoxin) ind <- endotoxin$ind time <- endotoxin$time class <- endotoxin$class endoexpr <- endotoxin$endoexpr cov <- data.frame(individual = ind, time = time, class = class) # create ExpressionSet object pDat <- as(cov, "AnnotatedDataFrame") exp_set <- ExpressionSet(assayData = endoexpr, phenoData = pDat) # formulate null and full models in experiement # note: interaction term is a way of taking into account group effects mNull <- ~ns(time, df=4, intercept = FALSE) mFull <- ~ns(time, df=4, intercept = FALSE) + ns(time, df=4, intercept = FALSE):class + class # create deSet object de_obj <- deSet(exp_set, full.model = mFull, null.model = mNull, individual = ind) # extract out the individuals factor ind_exp <- individual(de_obj)library(splines) # import data data(endotoxin) ind <- endotoxin$ind time <- endotoxin$time class <- endotoxin$class endoexpr <- endotoxin$endoexpr cov <- data.frame(individual = ind, time = time, class = class) # create ExpressionSet object pDat <- as(cov, "AnnotatedDataFrame") exp_set <- ExpressionSet(assayData = endoexpr, phenoData = pDat) # formulate null and full models in experiement # note: interaction term is a way of taking into account group effects mNull <- ~ns(time, df=4, intercept = FALSE) mFull <- ~ns(time, df=4, intercept = FALSE) + ns(time, df=4, intercept = FALSE):class + class # create deSet object de_obj <- deSet(exp_set, full.model = mFull, null.model = mNull, individual = ind) # extract out the individuals factor ind_exp <- individual(de_obj)
Gene expression measurements from kidney samples were obtained from 72 human subjects ranging in age from 27 to 92 years. Only one array was obtained per individual, and the age and sex of each individual were recorded.
data(kidney)data(kidney)
kidcov: A 133 rows by 6 columns data frame detailing the study design.
kidexpr: A 500 rows by 133 columns matrix of gene expression values, where each row corresponds to a different probe-set and each column to a different tissue sample.
age: A vector of length 133 giving the age of each sample.
sex: A vector of length 133 giving the sex of each sample.
kidney dataset
These data are a random subset of 500 probe-sets from the total number of
probe-sets in the original data set. To download the full data set, go to
http://genomine.org/edge/. The age and sex are contained
in kidcov data frame.
Storey JD, Xiao W, Leek JT, Tompkins RG, and Davis RW. (2005) Significance
analysis of time course microarray experiments. PNAS, 102: 12837-12842.
http://www.pnas.org/content/100/16/9440.full
# import data data(kidney) sex <- kidney$sex age <- kidney$age kidexpr <- kidney$kidexpr # create model de_obj <- build_study(data = kidexpr, adj.var = sex, tme = age, sampling = "timecourse", basis.df = 4) # use the ODP/lrt method to determine significant genes de_odp <- odp(de_obj, bs.its=10) de_lrt <- lrt(de_obj, nullDistn = "bootstrap", bs.its = 10) # summarize significance results summary(de_odp)# import data data(kidney) sex <- kidney$sex age <- kidney$age kidexpr <- kidney$kidexpr # create model de_obj <- build_study(data = kidexpr, adj.var = sex, tme = age, sampling = "timecourse", basis.df = 4) # use the ODP/lrt method to determine significant genes de_odp <- odp(de_obj, bs.its=10) de_lrt <- lrt(de_obj, nullDistn = "bootstrap", bs.its = 10) # summarize significance results summary(de_odp)
kl_clust is an implementation of mODP that assigns genes to modules
based on of the Kullback-Leibler distance.
kl_clust(object, de.fit = NULL, n.mods = 50) ## S4 method for signature 'deSet,missing' kl_clust(object, de.fit = NULL, n.mods = 50) ## S4 method for signature 'deSet,deFit' kl_clust(object, de.fit = NULL, n.mods = 50)kl_clust(object, de.fit = NULL, n.mods = 50) ## S4 method for signature 'deSet,missing' kl_clust(object, de.fit = NULL, n.mods = 50) ## S4 method for signature 'deSet,deFit' kl_clust(object, de.fit = NULL, n.mods = 50)
object |
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de.fit |
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n.mods |
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mODP utilizes a k-means clustering algorithm where genes are assigned to a cluster based on the Kullback-Leiber distance. Each gene is assigned an module-average parameter to calculate the ODP score (See Woo, Leek and Storey 2010 for more details). The mODP and full ODP produce nearly exact results but mODP has the advantage of being computationally faster.
A list with the following slots:
mu.full: cluster averaged fitted values from full model.
mu.null: cluster averaged fitted values from null model.
sig.full: cluster standard deviations from full model.
sig.null: cluster standard deviations from null model.
n.per.mod: total members in each cluster.
clustMembers: cluster membership for each gene.
The results are generally insensitive to the number of modules after a certain threshold of about n.mods>=50 in our experience. It is recommended that users experiment with the number of modules. If the number of modules is equal to the number of genes then the original ODP is implemented.
John Storey, Jeffrey Leek
Storey JD. (2007) The optimal discovery procedure: A new approach to simultaneous significance testing. Journal of the Royal Statistical Society, Series B, 69: 347-368.
Storey JD, Dai JY, and Leek JT. (2007) The optimal discovery procedure for large-scale significance testing, with applications to comparative microarray experiments. Biostatistics, 8: 414-432.
Woo S, Leek JT, Storey JD (2010) A computationally efficient modular optimal discovery procedure. Bioinformatics, 27(4): 509-515.
# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # mODP method de_clust <- kl_clust(de_obj) # change the number of clusters de_clust <- kl_clust(de_obj, n.mods = 10) # input a deFit object de_fit <- fit_models(de_obj, stat.type = "odp") de_clust <- kl_clust(de_obj, de.fit = de_fit)# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # mODP method de_clust <- kl_clust(de_obj) # change the number of clusters de_clust <- kl_clust(de_obj, n.mods = 10) # input a deFit object de_fit <- fit_models(de_obj, stat.type = "odp") de_clust <- kl_clust(de_obj, de.fit = de_fit)
lrt performs a generalized likelihood ratio test using the full and
null models.
lrt(object, de.fit, nullDistn = c("normal", "bootstrap"), weights = NULL, bs.its = 100, seed = NULL, verbose = TRUE, mod.F = FALSE, ...) ## S4 method for signature 'deSet,missing' lrt(object, de.fit, nullDistn = c("normal", "bootstrap"), weights = NULL, bs.its = 100, seed = NULL, verbose = TRUE, mod.F = FALSE, ...) ## S4 method for signature 'deSet,deFit' lrt(object, de.fit, nullDistn = c("normal", "bootstrap"), weights = NULL, bs.its = 100, seed = NULL, verbose = TRUE, mod.F = FALSE, ...)lrt(object, de.fit, nullDistn = c("normal", "bootstrap"), weights = NULL, bs.its = 100, seed = NULL, verbose = TRUE, mod.F = FALSE, ...) ## S4 method for signature 'deSet,missing' lrt(object, de.fit, nullDistn = c("normal", "bootstrap"), weights = NULL, bs.its = 100, seed = NULL, verbose = TRUE, mod.F = FALSE, ...) ## S4 method for signature 'deSet,deFit' lrt(object, de.fit, nullDistn = c("normal", "bootstrap"), weights = NULL, bs.its = 100, seed = NULL, verbose = TRUE, mod.F = FALSE, ...)
object |
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de.fit |
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weights |
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seed |
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verbose |
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Additional arguments for |
lrt fits the full and null models to each gene using the
function fit_models and then performs a likelihood ratio test.
The user has the option to calculate p-values a Normal distribution
assumption or through a bootstrap algorithm. If nullDistn is
"bootstrap" then empirical p-values will be determined from the
qvalue package (see empPvals).
deSet object
John Storey, Andrew Bass
Storey JD, Xiao W, Leek JT, Tompkins RG, and Davis RW. (2005) Significance analysis of time course microarray experiments. Proceedings of the National Academy of Sciences, 102: 12837-12842.
http://en.wikipedia.org/wiki/Likelihood-ratio_test
# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # lrt method de_lrt <- lrt(de_obj, nullDistn = "normal") # to generate p-values from bootstrap de_lrt <- lrt(de_obj, nullDistn = "bootstrap", bs.its = 30) # input a deFit object directly de_fit <- fit_models(de_obj, stat.type = "lrt") de_lrt <- lrt(de_obj, de.fit = de_fit) # summarize object summary(de_lrt)# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # lrt method de_lrt <- lrt(de_obj, nullDistn = "normal") # to generate p-values from bootstrap de_lrt <- lrt(de_obj, nullDistn = "bootstrap", bs.its = 30) # input a deFit object directly de_fit <- fit_models(de_obj, stat.type = "lrt") de_lrt <- lrt(de_obj, de.fit = de_fit) # summarize object summary(de_lrt)
These generic functions access and set the null matrix for
deSet object.
nullMatrix(object) nullMatrix(object) <- value ## S4 method for signature 'deSet' nullMatrix(object) ## S4 replacement method for signature 'deSet' nullMatrix(object) <- valuenullMatrix(object) nullMatrix(object) <- value ## S4 method for signature 'deSet' nullMatrix(object) ## S4 replacement method for signature 'deSet' nullMatrix(object) <- value
object |
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value |
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nullMatrix returns the value of the null model matrix.
John Storey, Andrew Bass
deSet, fullModel and
fullModel
# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # extract the null model as a matrix mat_null <- nullMatrix(de_obj)# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # extract the null model as a matrix mat_null <- nullMatrix(de_obj)
These generic functions access and set the null model for
deSet object.
nullModel(object) nullModel(object) <- value ## S4 method for signature 'deSet' nullModel(object) ## S4 replacement method for signature 'deSet' nullModel(object) <- valuenullModel(object) nullModel(object) <- value ## S4 method for signature 'deSet' nullModel(object) ## S4 replacement method for signature 'deSet' nullModel(object) <- value
object |
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value |
|
nullModel returns the formula for the null model.
John Storey, Andrew Bass
# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # extract the null model equation mod_null <- nullModel(de_obj) # change null model in experiment but must update full model nullModel(de_obj) <- ~1 fullModel(de_obj) <- ~1 + ns(age, df=4)# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # extract the null model equation mod_null <- nullModel(de_obj) # change null model in experiment but must update full model nullModel(de_obj) <- ~1 fullModel(de_obj) <- ~1 + ns(age, df=4)
odp performs the optimal discovery procedure, which is a framework for
optimally performing many hypothesis tests in a high-dimensional study. When
testing whether a feature is significant, the optimal discovery procedure
uses information across all features when testing for significance.
odp(object, de.fit, odp.parms = NULL, weights = NULL, bs.its = 100, n.mods = 50, seed = NULL, verbose = TRUE, ...) ## S4 method for signature 'deSet,missing' odp(object, de.fit, odp.parms = NULL, weights = NULL, bs.its = 100, n.mods = 50, seed = NULL, verbose = TRUE, ...) ## S4 method for signature 'deSet,deFit' odp(object, de.fit, odp.parms = NULL, weights = NULL, bs.its = 100, n.mods = 50, seed = NULL, verbose = TRUE, ...)odp(object, de.fit, odp.parms = NULL, weights = NULL, bs.its = 100, n.mods = 50, seed = NULL, verbose = TRUE, ...) ## S4 method for signature 'deSet,missing' odp(object, de.fit, odp.parms = NULL, weights = NULL, bs.its = 100, n.mods = 50, seed = NULL, verbose = TRUE, ...) ## S4 method for signature 'deSet,deFit' odp(object, de.fit, odp.parms = NULL, weights = NULL, bs.its = 100, n.mods = 50, seed = NULL, verbose = TRUE, ...)
object |
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odp.parms |
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weights |
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bs.its |
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n.mods |
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seed |
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verbose |
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... |
The full ODP estimator computationally grows quadratically with respect to the number of genes. This becomes computationally taxing at a certain point. Therefore, an alternative method called mODP is used which has been shown to provide results that are very similar. mODP utilizes a clustering algorithm where genes are assigned to a cluster based on the Kullback-Leiber distance. Each gene is assigned an module-average parameter to calculate the ODP score and it reduces the computations time to approximately linear (see Woo, Leek and Storey 2010). If the number of clusters is equal to the number of genes then the original ODP is implemented. Depending on the number of hypothesis tests, this can take some time.
deSet object
John Storey, Jeffrey Leek, Andrew Bass
Storey JD. (2007) The optimal discovery procedure: A new approach to simultaneous significance testing. Journal of the Royal Statistical Society, Series B, 69: 347-368.
Storey JD, Dai JY, and Leek JT. (2007) The optimal discovery procedure for large-scale significance testing, with applications to comparative microarray experiments. Biostatistics, 8: 414-432.
Woo S, Leek JT, Storey JD (2010) A computationally efficient modular optimal discovery procedure. Bioinformatics, 27(4): 509-515.
kl_clust, build_models and
deSet
# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # odp method de_odp <- odp(de_obj, bs.its = 30) # input a deFit object or ODP parameters ... not necessary de_fit <- fit_models(de_obj, stat.type = "odp") de_clust <- kl_clust(de_obj, n.mods = 10) de_odp <- odp(de_obj, de.fit = de_fit, odp.parms = de_clust, bs.its = 30) # summarize object summary(de_odp)# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # odp method de_odp <- odp(de_obj, bs.its = 30) # input a deFit object or ODP parameters ... not necessary de_fit <- fit_models(de_obj, stat.type = "odp") de_clust <- kl_clust(de_obj, n.mods = 10) de_odp <- odp(de_obj, de.fit = de_fit, odp.parms = de_clust, bs.its = 30) # summarize object summary(de_odp)
These generic functions access and set the qvalue object in the
deSet object.
qvalueObj(object) qvalueObj(object) <- value ## S4 method for signature 'deSet' qvalueObj(object) ## S4 replacement method for signature 'deSet' qvalueObj(object) <- valueqvalueObj(object) qvalueObj(object) <- value ## S4 method for signature 'deSet' qvalueObj(object) ## S4 replacement method for signature 'deSet' qvalueObj(object) <- value
object |
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value |
S3 |
qvalueObj returns a qvalue object.
John Storey, Andrew Bass
# import data library(splines) library(qvalue) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # run the odp method de_odp <- odp(de_obj, bs.its = 20) # extract out significance results qval_obj <- qvalueObj(de_odp) # run qvalue and assign it to deSet slot pvals <- qval_obj$pvalues qval_new <- qvalue(pvals, pfdr = TRUE, fdr.level = 0.1) qvalueObj(de_odp) <- qval_new# import data library(splines) library(qvalue) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # run the odp method de_odp <- odp(de_obj, bs.its = 20) # extract out significance results qval_obj <- qvalueObj(de_odp) # run qvalue and assign it to deSet slot pvals <- qval_obj$pvalues qval_new <- qvalue(pvals, pfdr = TRUE, fdr.level = 0.1) qvalueObj(de_odp) <- qval_new
Access the fitted full model residuals in an deFit
object.
resFull(object) ## S4 method for signature 'deFit' resFull(object)resFull(object) ## S4 method for signature 'deFit' resFull(object)
object |
|
resFull returns a matrix of residuals from full model.
John Storey, Andrew Bass
# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # run fit_models to get model fits de_fit <- fit_models(de_obj) # extract out the full residuals from the model fit res_full <- resFull(de_fit)# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # run fit_models to get model fits de_fit <- fit_models(de_obj) # extract out the full residuals from the model fit res_full <- resFull(de_fit)
Access the fitted null model residuals in an deFit
object.
resNull(object) ## S4 method for signature 'deFit' resNull(object)resNull(object) ## S4 method for signature 'deFit' resNull(object)
object |
|
resNull returns a matrix of residuals from null model.
John Storey, Andrew Bass
# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # run fit_models to get model fits de_fit <- fit_models(de_obj) # extract out the null residuals from the model fits res_null <- resNull(de_fit)# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # run fit_models to get model fits de_fit <- fit_models(de_obj) # extract out the null residuals from the model fits res_null <- resNull(de_fit)
Show function for deFit and deSet
objects.
show(object) ## S4 method for signature 'deFit' show(object) ## S4 method for signature 'deSet' show(object)show(object) ## S4 method for signature 'deFit' show(object) ## S4 method for signature 'deSet' show(object)
object |
|
Nothing of interest
John Storey, Andrew Bass
# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # get summary summary(de_obj) # run odp and summarize de_odp <- odp(de_obj, bs.its= 20) de_odp# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # get summary summary(de_obj) # run odp and summarize de_odp <- odp(de_obj, bs.its= 20) de_odp
Access the statistic type in a deFit object. Can
either be the optimal discovery procedure (odp) or the likelihood ratio
test (lrt).
sType(object) ## S4 method for signature 'deFit' sType(object)sType(object) ## S4 method for signature 'deFit' sType(object)
object |
|
sType returns the statistic type- either "odp" or "lrt".
John Storey, Andrew Bass
fit_models, deFit and
deSet
# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # run fit_models to get model fits de_fit <- fit_models(de_obj) # extract the statistic type of model fits stat_type <- sType(de_fit)# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # run fit_models to get model fits de_fit <- fit_models(de_obj) # extract the statistic type of model fits stat_type <- sType(de_fit)
Summary of deFit and deSet objects.
summary(object, ...) ## S4 method for signature 'deFit' summary(object) ## S4 method for signature 'deSet' summary(object, ...)summary(object, ...) ## S4 method for signature 'deFit' summary(object) ## S4 method for signature 'deSet' summary(object, ...)
object |
|
... |
additional parameters |
Summary of deSet object
John Storey, Andrew Bass
# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # get summary summary(de_obj) # run odp and summarize de_odp <- odp(de_obj, bs.its= 20) summary(de_odp)# import data library(splines) data(kidney) age <- kidney$age sex <- kidney$sex kidexpr <- kidney$kidexpr cov <- data.frame(sex = sex, age = age) # create models null_model <- ~sex full_model <- ~sex + ns(age, df = 4) # create deSet object from data de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model, full.model = full_model) # get summary summary(de_obj) # run odp and summarize de_odp <- odp(de_obj, bs.its= 20) summary(de_odp)