| Title: | Hierarchical Dependence in Meta-Analysis |
|---|---|
| Description: | Tools for meta-analysis in the presence of hierarchical (and/or sampling) dependence, including with gene expression studies |
| Authors: | John R. Stevens, Gabriel Nicholas |
| Maintainer: | John R. Stevens <[email protected]> |
| License: | GPL-3 |
| Version: | 1.63.0 |
| Built: | 2024-07-03 05:32:25 UTC |
| Source: | https://github.com/bioc/metahdep |
This is a class representation for the effect size estimates and other summary information from a single gene expression study, usually constructed in preparation for meta-analysis.
Objects can be created using the functions getPLM.es or new.
gn:Object of class character representing the probeset IDs of the genes in the study.
ES.mat:Object of class matrix representing the effect size estimates for each gene in the study.
Rows correspond to probesets and columns correspond to different comparisons or tests of differential expression.
If a test of differential expression was performed for different covariate levels, then there will be more than
one column, so that each row in this matrix represents a vector of effect size estimates for the corresponding probeset
in the gn slot.
Cov.mat:Object of class matrix, with each row representing the upper triangle of the variance / covariance
matrix of the vector of effect size estimates (row in the ES.mat slot) for the corresponding probeset in the gn slot.
Within each row, the order is the same as the result of a call to the upperTriangle(matrix,diag=T) function in the gdata package.
chip:Object of class character representing the chip or array version used in the study.
covariates:Object of class data.frame representing covariate differences among the columns of the matrix in the ES.mat slot.
This object has a row for each column of the matrix in the ES.mat slot, and a column for each covariate to be considered in the
meta-analysis, regardless of whether the covariate takes on multiple values in the study represented in this ES.obj object.
For best interpretability, columns of the data.frame in this covariates slot should be coded as 0/1.
dep.grpObject of class integer representing the dependence group number assigned to the study.
Studies from the same research team may be considered hierarchically dependendent and share the same dep.grp value.
replace the slot entries
Stevens J.R. and Nicholas G. (2009), metahdep: Meta-analysis of hierarchically dependent gene expression studies, Bioinformatics, 25(19):2619-2620.
See also the metahdep package vignette.
### ### See the metahdep package vignette for a full example ### data(HGU.DifExp.list) ES <- HGU.DifExp.list[[1]] slotNames(ES) head(ES@gn) head([email protected]) head([email protected]) ES@chip ES@covariates [email protected]### ### See the metahdep package vignette for a full example ### data(HGU.DifExp.list) ES <- HGU.DifExp.list[[1]] slotNames(ES) head(ES@gn) head(ES@ES.mat) head(ES@Cov.mat) ES@chip ES@covariates ES@dep.grp
Calculates effect size estimates for a single study, based on a probe-level model, in preparation for a meta-analysis. It returns an ES.obj object containing the result.
getPLM.es(abatch, trt1, trt2, covariates=NULL, dep.grp=NULL, sub.gn=NULL, bg.norm=TRUE)getPLM.es(abatch, trt1, trt2, covariates=NULL, dep.grp=NULL, sub.gn=NULL, bg.norm=TRUE)
abatch |
An |
trt1 |
A vector (or list of vectors) of array indices for treatment level 1 (control). If more than one test of differential expression is to be performed (for multiple covariate levels, for example), this should be a list of vectors; each |
trt2 |
A vector (or list of vectors) of array indices for treatment level 2 (treatment). If more than one test of differential expression is to be performed (for multiple covariate levels, for example), this should be a list of vectors; each |
covariates |
(optional) A |
dep.grp |
(optional) A single numeric value representing the dependence group number assigned to the study. Studies from the same research team may be considered hierarchically dependendent and share the same value. |
sub.gn |
(optional) A vector of geneNames (probe set ID's); the probe-level model will only be fit for these probesets. If NULL (default), all probesets are used. |
bg.norm |
(optional) A logical value specifying whether or not to perform background correction and normalization before fitting the probe-level model. |
For some subset of probesets in a gene expression study, this function calculates the effect size estimates based on Bolstad's probe-level model (Bolstad 2004), as described in Hu et al. (2006). Only two-group comparisons (treatment vs. control, for example) are supported. This is done in preparation for a meta-analysis of multiple gene expression studies.
An object of class ES.obj
John R. Stevens, Gabriel Nicholas
Bolstad B. M. (2004), Low-level Analysis of High-density Oligonucleotide Array Data: Background, Normalization and Summarization, PhD dissertation, U.C. Berkeley.
Hu P., Greenwood C.M.T., and Beyene J. (2006), Integrative Analysis of Gene Expression Data Including an Assessment of Pathway Enrichment for Predicting Prostate Cancer, Cancer Informatics 2006:2 289-300.
Stevens J.R. and Nicholas G. (2009), metahdep: Meta-analysis of hierarchically dependent gene expression studies, Bioinformatics, 25(19):2619-2620.
See also the metahdep package vignette.
### ### See the metahdep package vignette for a full example ###### ### See the metahdep package vignette for a full example ###
This includes the following four objects:
gloss.Table1 |
a data.frame containing the contents of Table 1 in the paper |
| (sample sizes, sample means, sample SDs, and covariate information from each study) | |
gloss.X |
a matrix representing the design matrix X for the meta-analysis in the paper |
gloss.theta |
a vector, representing the effect size estimates as summarized in Table 3 of the paper |
gloss.V |
a matrix, representing the covariance matrix of effect size estimates, including |
| sampling dependence in off-diagonal elements, as summarized in Table 3 of paper | |
data(gloss)data(gloss)
This object contains the four objects described above.
This data set summarizes 13 experiments with 18 study reports, all involving the effect of native-language (L1) vocabulary aids on second language (L2) reading comprehension. Some experiments produced multiple study reports, creating a dependence structure among the resulting effect size estimates.
The intended use for these data is to demonstrate the methods coded in the metahdep package.
Stevens J.R. and Taylor A.M. (2009), Hierarchical Dependence in Meta-Analysis, Journal of Educational and Behavioral Statistics, 34(1):46-73.
See also the metahdep package vignette.
data(gloss) # Look at Table 1 gloss.Table1data(gloss) # Look at Table 1 gloss.Table1
An illustrative example of a list summarizing several studies of gene expression. This is used as an example in the meta-analyis of hierarchically dependent gene expression studies.
data(HGU.DifExp.list)data(HGU.DifExp.list)
This object is a list containing 4 ES.obj objects.
Each ES.obj object represents the results from a separate gene expression study.
This object has been assembled from existing data as an artificial example; see the vignette for details on its construction. In this example, four studies were conducted, and can be summarized as follows:
| Study | Lab | Tissue | Chip |
| 1 | 1 | 0 | hgu133a |
| 1 | 1 | 1 | hgu133a |
| 2 | 1 | 0 | hgu95a |
| 3 | 2 | 0 | hgu95av2 |
| 4 | 3 | 1 | hgu133b |
Notice that study 1 involved two tissue types. The vignette shows how this example supposes that sampling dependence was
introduced in study 1 by fitting a gene-specific model with both tissue types simultaneously.
Hierarchical dependence is also present in these data because studies 1 and 2 were conducted by the same lab.
Each element of HGU.DifExp.list is an ES.obj object in the same format as returned by the getPLM.es() function.
Look at the elements of the list (and the vignette) to get an idea of how the data should be laid out.
The intended use for these data is to demonstrate a meta-analysis procedure that accounts for hierarchical dependence between studies.
The idea is that results from different studies from the same lab might be dependent.
This is an example object that is to be passed as an argument to the metahdep.format() function.
Stevens J.R. and Nicholas G. (2009), metahdep: Meta-analysis of hierarchically dependent gene expression studies, Bioinformatics, 25(19):2619-2620.
See also the metahdep package vignette.
data(HGU.DifExp.list) head(HGU.DifExp.list[[1]]@ES.mat) HGU.DifExp.list[[1]]@covariates ## etc.data(HGU.DifExp.list) head(HGU.DifExp.list[[1]]@ES.mat) HGU.DifExp.list[[1]]@covariates ## etc.
An illustrative example of a data.frame with 3 columns.
The first column is named "chip", the second "old.name", and the third "new.name".
The rows each hold the name of a chip type, a chip-specific probeset name, and a common name used to match probesets across different chip versions.
data(HGU.newnames)data(HGU.newnames)
A data.frame with observations on the following 3 variables for a subset of probesets on different chip types.
chipa character vector specifying the chip type
old.namea character vector specifying the probeset name on the chip type
new.namea character vector specifying the common identifier, such as an Entrez Gene ID, for the probeset on the chip type
This is an example of a newnames argument that is required by the metahdep.format function.
When paired with a list of ES.obj class objects (see HGU.DifExp.list) this allows the metahdep.format()
function to assemble all of the information from all of the studies for a specific gene.
The new.name is a 'common' identifier, e.g., an Entrez Gene ID. Different studies may use different chip types, or different versions of chips, where information for a gene with a particular Entrez Gene ID may have a different probeset name on each chip type.
This newnames argument is meant to facilitate the matching of gene information across different chip types.
See the metahdep package vignette for more details on the construction of this object.
Stevens J.R. and Nicholas G. (2009), metahdep: Meta-analysis of hierarchically dependent gene expression studies, Bioinformatics, 25(19):2619-2620.
See also the metahdep package vignette.
data(HGU.newnames) head(HGU.newnames)data(HGU.newnames) head(HGU.newnames)
An illustrative example of a list, where each element is a metaprep class object with data for a particular gene.
It comes from the study data held within the HGU.DifExp.list object.
data(HGU.prep.list)data(HGU.prep.list)
This object is a list of metaprep objects.
Each element of HGU.prep.list is a gene-specific object of class metaprep.
HGU.prep.list is an example of an object created by the metahdep.format() function; see the help file for the metahdep.format() function and the metahdep package vignette for details on its construction.
HGU.prep.list is an example object used as an argument to the metahdep() function.
The individual elements of this object can be extracted and passed as arguments to the more general meta-analysis functions, metahdep.HBLM(), metahdep.REMA(), and metahdep.FEMA().
Stevens J.R. and Nicholas G. (2009), metahdep: Meta-analysis of hierarchically dependent gene expression studies, Bioinformatics, 25(19):2619-2620.
See also the metahdep package vignette.
data(HGU.prep.list) HGU.prep.list[[7]] ## etc...data(HGU.prep.list) HGU.prep.list[[7]] ## etc...
Takes a list of metaprep objects and performs the specified meta-analysis on each element. Intended mainly for meta-analyzing the results of multiple gene expression studies.
metahdep(prep.list, genelist = NULL, method = "HBLM", n = 10, m = 10, center.X = FALSE, delta.split = FALSE, return.list = FALSE, two.sided = TRUE)metahdep(prep.list, genelist = NULL, method = "HBLM", n = 10, m = 10, center.X = FALSE, delta.split = FALSE, return.list = FALSE, two.sided = TRUE)
prep.list |
A list of |
genelist |
(optional) A subsetting parameter. A vector of gene/probeset names on which to perform the meta-analyses. |
method |
(optional) One of: "FEMA" - fixed effects meta-analysis, "REMA" - random effects meta-analysis, or "HBLM" - hierarchical Bayes linear model. This defaults to "HBLM". |
n |
(optional) An even integer specifying the number of steps to take over each quartile in the numerical integration over tau when doing HBLM. See |
m |
(optional) An even integer specifying the number of steps to take in the numerical integration over varsigma (given tau) when doing HBLM. See |
center.X |
(optional) A logical value specifying whether or not to center the columns of the covariate matrices. If |
delta.split |
(optional) A logical value specifying whether or not to account for hierarchical dependence via delta-splitting. Only used in methods "REMA" and "HBLM". If |
return.list |
(optional) A logical value specifying whether to return the results as a list of lists rather than as a |
two.sided |
(optional) A logical value specifying whether to transform the posterior probabilities from the HBLM method. The default |
Returns a data.frame by default. The exact contents of the data.frame will vary depending on the method argument. The row names of the data.frame will be the gene names from the prep.list argument. For all method options, the first several columns of the resulting data.frame will be the model parameter estimates (beta hats). The next group of columns will be the elements of the variance/covariance matrix for the beta hats. The next group of columns will be the p-values for the parameter estimates. The remaining columns will change depending on the method.
For FEMA (and REMA), the remaining columns are the Q statistic and its p-value – testing for model homogeneity.
For HBLM, the remaining columns are the posterior mean and variance of tau, the posterior mean and variance of varsigma, and the posterior covariance of tau and varsigma.
All columns in the data.frame have meaningful names to aid their interpretation.
John R. Stevens, Gabriel Nicholas
Stevens J.R. and Doerge R.W. (2005), A Bayesian and Covariate Approach to Combine Results from Multiple Microarray Studies, Proceedings of Conference on Applied Statistics in Agriculture, pp. 133-147.
Stevens J.R. and Nicholas G. (2009), metahdep: Meta-analysis of hierarchically dependent gene expression studies, Bioinformatics, 25(19):2619-2620.
Stevens J.R. and Taylor A.M. (2009), Hierarchical Dependence in Meta-Analysis, Journal of Educational and Behavioral Statistics, 34(1):46-73.
See also the metahdep package vignette.
data(HGU.prep.list) ## do FEMA and REMA, and view the results FEMA.results <- metahdep(HGU.prep.list, method="FEMA", center.X=TRUE) head(FEMA.results) REMA.results <- metahdep(HGU.prep.list, method="REMA", center.X=TRUE) head(REMA.results) ## get a small subset of genes ## some of these may not be suitable for all methods ## (there may not be enough data for that gene) data(HGU.newnames) set.seed(123) gene.subset <- sample(HGU.newnames$new.name, 50) ## view results from REMA and HBLM with delta splitting on subset of genes REMA.dsplit.results <- metahdep(HGU.prep.list, method="REMA", genelist=gene.subset, delta.split=TRUE, center.X=TRUE) head(REMA.dsplit.results) HBLM.dsplit.results <- metahdep(HGU.prep.list, method="HBLM", genelist=gene.subset, delta.split=TRUE, center.X=TRUE) head(HBLM.dsplit.results)data(HGU.prep.list) ## do FEMA and REMA, and view the results FEMA.results <- metahdep(HGU.prep.list, method="FEMA", center.X=TRUE) head(FEMA.results) REMA.results <- metahdep(HGU.prep.list, method="REMA", center.X=TRUE) head(REMA.results) ## get a small subset of genes ## some of these may not be suitable for all methods ## (there may not be enough data for that gene) data(HGU.newnames) set.seed(123) gene.subset <- sample(HGU.newnames$new.name, 50) ## view results from REMA and HBLM with delta splitting on subset of genes REMA.dsplit.results <- metahdep(HGU.prep.list, method="REMA", genelist=gene.subset, delta.split=TRUE, center.X=TRUE) head(REMA.dsplit.results) HBLM.dsplit.results <- metahdep(HGU.prep.list, method="HBLM", genelist=gene.subset, delta.split=TRUE, center.X=TRUE) head(HBLM.dsplit.results)
Performs a fixed effects linear model meta-analysis. It returns a list containing the results.
metahdep.FEMA(theta, V, X, meta.name = "meta-analysis", center.X = FALSE)metahdep.FEMA(theta, V, X, meta.name = "meta-analysis", center.X = FALSE)
theta |
A vector of effect size estimates from multiple studies. |
V |
The variance/covariance matrix for |
X |
A matrix of covariates for |
meta.name |
(optional) A name field for bookkeeping. This can be any character string. |
center.X |
(optional) A logical value specifying whether or not to center the columns of |
Takes a vector of effect size estimates, a variance/covariance matrix, and a covariate matrix, and fits a fixed effects linear model meta-analysis.
When a meta-analysis is to be performed for gene expression data (on a per-gene basis), the metahdep() function calls this function for each gene separately.
A list with the following named components:
beta.hats |
A vector of model estimates for the covariates given by |
cov.matrix |
The variance/covariance matrix for the |
beta.hat.p.values |
The [two-sided] p-value(s) for the |
Q |
The statistic used to test for model homogeneity / model mis-specification |
Q.p.value |
The p-value for |
name |
An optional name field |
John R. Stevens, Gabriel Nicholas
Hedges L. V. and Olkin I (1985), Statistical methods for meta-analysis, San Diego, CA: Academic Press.
Stevens J.R. and Doerge R.W. (2005), Combining Affymetrix Microarray Results, BMC Bioinformatics 6:57.
Stevens J.R. and Taylor A.M. (2009), Hierarchical Dependence in Meta-Analysis, Journal of Educational and Behavioral Statistics, 34(1):46-73.
See also the metahdep package vignette.
### ### Example 1: gene expression data ### - this uses one gene from the HGU.prep.list object # load data and extract components for meta-analysis (for one gene) data(HGU.prep.list) gene.data <- HGU.prep.list[[7]] theta <- gene.data@theta V <- gene.data@V X <- gene.data@X gene.name <- gene.data@gene # fit a regular FEMA (no hierarchical dependence) results <- metahdep.FEMA(theta, V, X, meta.name=gene.name, center.X=TRUE) results ### ### Example 2: glossing data ### - this produces part of Table 5 in the Stevens and Taylor JEBS paper. data(gloss) FEMA <- metahdep.FEMA(gloss.theta, gloss.V, gloss.X, center.X=TRUE) round(cbind( t(FEMA$beta.hats), t(FEMA$beta.hat.p.values)),4)### ### Example 1: gene expression data ### - this uses one gene from the HGU.prep.list object # load data and extract components for meta-analysis (for one gene) data(HGU.prep.list) gene.data <- HGU.prep.list[[7]] theta <- gene.data@theta V <- gene.data@V X <- gene.data@X gene.name <- gene.data@gene # fit a regular FEMA (no hierarchical dependence) results <- metahdep.FEMA(theta, V, X, meta.name=gene.name, center.X=TRUE) results ### ### Example 2: glossing data ### - this produces part of Table 5 in the Stevens and Taylor JEBS paper. data(gloss) FEMA <- metahdep.FEMA(gloss.theta, gloss.V, gloss.X, center.X=TRUE) round(cbind( t(FEMA$beta.hats), t(FEMA$beta.hat.p.values)),4)
This function is intended to facilitate the meta-analysis of multiple gene expression studies. This function takes the results from a number of studies and collects together the data for each gene, in preparation for a meta-analysis.
metahdep.format(ES.obj.list, newnames, min.var = 0.0001, include.row.indices = FALSE, show.warnings = FALSE, pd.verify = FALSE)metahdep.format(ES.obj.list, newnames, min.var = 0.0001, include.row.indices = FALSE, show.warnings = FALSE, pd.verify = FALSE)
ES.obj.list |
A list object containing the results from multiple gene expression studies.
Each element of the list is an object of class |
newnames |
A |
min.var |
(optional) A positive real number that acts as a lower bound on the allowed variances for any measure of differential expression. This might be used to guard against specious claims of significance due to naturally low variance. |
include.row.indices |
(optional) A logical value to determine whether or not to include the study and row indices for the data for each gene in the returned list of |
show.warnings |
(optional) A logical value to determine whether or not to display warnings in certain situations, like if a gene is expected in a particular study but not found, or if a gene is not found in any study, or on some other instances. This may sometimes cause a large number of uninteresting warnings to be displayed, and so defaults to FALSE. |
pd.verify |
(optional) A logical value to determine whether or not to check the generated variance/covariance matrix for positive-definiteness. Since this should always be the case, this would indicate a problem somewhere in the data – more specifically, in the covariance values of one of the studies in the |
Each element of the returned list is a metaprep object summarizing effect size data for a single gene. This list of metaprep gene information is passed to the metahdep() function for meta-analysis.
A list, where each element is a gene-specific object of class metaprep.
John R. Stevens, Gabriel Nicholas
Stevens J.R. and Nicholas G. (2009), metahdep: Meta-analysis of hierarchically dependent gene expression studies, Bioinformatics, 25(19):2619-2620.
See also the metahdep package vignette.
## load a pre-made list of ES.obj's and newnames data.frame. These objects hold ## data in a format suitable for use in the metahdep.format function. data(HGU.DifExp.list) data(HGU.newnames) ## now call the format function; ## this may take anywhere from several seconds to several minutes, ## depending on the speed of the computer and the number of genes under ## consideration HGU.prep.list <- metahdep.format(HGU.DifExp.list, HGU.newnames)## load a pre-made list of ES.obj's and newnames data.frame. These objects hold ## data in a format suitable for use in the metahdep.format function. data(HGU.DifExp.list) data(HGU.newnames) ## now call the format function; ## this may take anywhere from several seconds to several minutes, ## depending on the speed of the computer and the number of genes under ## consideration HGU.prep.list <- metahdep.format(HGU.DifExp.list, HGU.newnames)
Performs a meta-analysis by fitting a hierarchical Bayes linear model, allowing for hierarchical dependence.
metahdep.HBLM(theta, V, X, M = NULL, dep.groups = NULL, meta.name = "meta-analysis", center.X = FALSE, delta.split = FALSE, n = 10, m = 10, two.sided = FALSE)metahdep.HBLM(theta, V, X, M = NULL, dep.groups = NULL, meta.name = "meta-analysis", center.X = FALSE, delta.split = FALSE, n = 10, m = 10, two.sided = FALSE)
theta |
A vector of effect size estimates from multiple studies. |
V |
The variance/covariance matrix for |
X |
A matrix of covariates for |
M |
(optional) Used when |
dep.groups |
(optional) Used when |
meta.name |
(optional) A name field for bookkeeping. This can be any character string. |
center.X |
(optional) A logical value specifying whether or not to center the columns of |
delta.split |
(optional) A logical value specifying whether or not to account for hierarchical dependence (i.e., perform delta-splitting). If |
n |
(optional) An even integer telling how many steps to use when doing the numerical integration over tau, the square root of the between-study hierarchical variance. The integration is done on the log-logistic prior, split into the 4 quartiles. This number n specifies how many steps to take within each quartile. |
m |
(optional) An even integer telling how many steps to use when doing the numerical integration over varsigma (given tau), the between-study hierarchical covariance. This is only used when |
two.sided |
(optional) A logical value to determine whether to return the 2-sided p-values or default [one-sided positive] posterior probabilities for the parameter estimates. |
Takes a vector of effect size estimates, a variance/covariance matrix, and a covariate matrix, and fits a hierarchical Bayes linear model. If delta.split=TRUE, then it performs delta-splitting to account for hierarchical dependence among studies. The main parameters (beta) are given normal priors, the square root of the hierarchical variance (tau) is given a log-logistic prior, and the hierarchical covariance (varsigma) is given a uniform prior; see the Stevens and Taylor reference for details.
When a meta-analysis is to be performed for gene expression data (on a per-gene basis), the metahdep() function calls this metahdep.HBLM function for each gene separately.
A list, with the following named components:
beta.hats |
A vector of model estimates for the covariates given by |
cov.matrix |
The variance/covariance matrix for the |
beta.hat.p.values |
The p-value(s) for the |
tau.hat |
The posterior mean for tau (not tau-square). An estimate for tau-square is E(square(tau) [given data]) = tau.var + square(tau.hat) |
tau.var |
The posterior variance for tau (not tau-square). |
varsigma.hat |
The posterior mean for varsigma. |
varsigma.var |
The posterior variance for varsigma. |
tau.varsigma.cov |
The posterior covariance for tau and varsigma. |
name |
An optional name field |
John R. Stevens, Gabriel Nicholas
DuMouchel W. H. and Harris J. H. (1983), Bayes methods for combining the results of cancer studies in humans and other species, Journal of the American Statistical Association, 78(382), 293-308.
DuMouchel W.H. and Normand S.-L. (2000), Computer-modeling and graphical strategies for meta-analysis, in D. K. Stangl and D. A. Berry (Eds.), Meta-analysis in medicine and health policy, pp. 127-178. New York: Marcel Dekker.
Stevens J.R. and Doerge R.W. (2005), A Bayesian and Covariate Approach to Combine Results from Multiple Microarray Studies, Proceedings of Conference on Applied Statistics in Agriculture, pp. 133-147.
Stevens J.R. and Nicholas G. (2009), metahdep: Meta-analysis of hierarchically dependent gene expression studies, Bioinformatics, 25(19):2619-2620.
Stevens J.R. and Taylor A.M. (2009), Hierarchical Dependence in Meta-Analysis, Journal of Educational and Behavioral Statistics, 34(1):46-73.
See also the metahdep package vignette.
### ### Example 1: gene expression data ### - this uses one gene from the HGU.prep.list object # load data and extract components for meta-analysis (for one gene) data(HGU.prep.list) gene.data <- HGU.prep.list[[7]] theta <- gene.data@theta V <- gene.data@V X <- gene.data@X M <- gene.data@M dep.grps <- list(c(1:2),c(4:6)) gene.name <- gene.data@gene # fit a regular HBLM (no hierarchical dependence) results <- metahdep.HBLM(theta, V, X, meta.name=gene.name, center.X=TRUE, two.sided=TRUE) results # fit hierarchical dependence model (with delta-splitting), # using two different methods for specifying the dependence structure results.dsplitM <- metahdep.HBLM(theta, V, X, M, delta.split=TRUE, meta.name=gene.name, center.X=TRUE, two.sided=TRUE) results.dsplitM results.dsplitd <- metahdep.HBLM(theta, V, X, dep.groups=dep.grps, delta.split=TRUE, meta.name=gene.name, center.X=TRUE, two.sided=TRUE) results.dsplitd ### ### Example 2: glossing data ### - this produces part of Table 5 in the Stevens and Taylor JEBS paper. data(gloss) dep.groups <- list(c(2,3,4,5),c(10,11,12)) HBLM.ds <- metahdep.HBLM(gloss.theta, gloss.V, gloss.X, center.X=TRUE, two.sided=TRUE, delta.split=TRUE, dep.groups=dep.groups, n=20, m=20) round(cbind(HBLM.ds$beta.hats, HBLM.ds$beta.hat.p.values),4)### ### Example 1: gene expression data ### - this uses one gene from the HGU.prep.list object # load data and extract components for meta-analysis (for one gene) data(HGU.prep.list) gene.data <- HGU.prep.list[[7]] theta <- gene.data@theta V <- gene.data@V X <- gene.data@X M <- gene.data@M dep.grps <- list(c(1:2),c(4:6)) gene.name <- gene.data@gene # fit a regular HBLM (no hierarchical dependence) results <- metahdep.HBLM(theta, V, X, meta.name=gene.name, center.X=TRUE, two.sided=TRUE) results # fit hierarchical dependence model (with delta-splitting), # using two different methods for specifying the dependence structure results.dsplitM <- metahdep.HBLM(theta, V, X, M, delta.split=TRUE, meta.name=gene.name, center.X=TRUE, two.sided=TRUE) results.dsplitM results.dsplitd <- metahdep.HBLM(theta, V, X, dep.groups=dep.grps, delta.split=TRUE, meta.name=gene.name, center.X=TRUE, two.sided=TRUE) results.dsplitd ### ### Example 2: glossing data ### - this produces part of Table 5 in the Stevens and Taylor JEBS paper. data(gloss) dep.groups <- list(c(2,3,4,5),c(10,11,12)) HBLM.ds <- metahdep.HBLM(gloss.theta, gloss.V, gloss.X, center.X=TRUE, two.sided=TRUE, delta.split=TRUE, dep.groups=dep.groups, n=20, m=20) round(cbind(HBLM.ds$beta.hats, HBLM.ds$beta.hat.p.values),4)
Miscellaneous functions used internally by the metahdep package's main functions (metahdep, metahdep.FEMA, metahdep.REMA, metahdep.HBLM, and metahdep.format):
metahdep.list2dataframe |
convert list to data.frame |
LinMod.MetAn.dep.REMA |
REMA meta-analysis |
LinMod.REMA.dep |
used by LinMod.MetAn.dep.REMA to estimate parameters |
LinMod.REMA.delta.split |
REMA (with delta-splitting) |
LinMod.HBLM.fast.dep |
HBLM (no delta-splitting) |
new.LinMod.HBLM.fast.dep.delta.split |
HBLM (with delta-splitting) |
LinMod.MetAn.dep.FEMA |
FEMA |
metahdep.check.X |
check design matrix X, and drop columns if necessary |
| to make full rank | |
get.M |
create block diagonal M matrix, given dependence structure |
tr |
calculate trace of matrix |
id |
create identity matrix |
center.columns |
center all non-intercept columns of design matrix X |
mod |
mod function |
get.varsigma.v |
get varsigma values for HBLM delta-splitting model |
John R. Stevens, Gabriel Nicholas
Stevens J.R. and Nicholas G. (2009), metahdep: Meta-analysis of hierarchically dependent gene expression studies, Bioinformatics, 25(19):2619-2620.
Stevens J.R. and Taylor A.M. (2009), Hierarchical Dependence in Meta-Analysis, Journal of Educational and Behavioral Statistics, 34(1):46-73.
See also the metahdep package vignette.
## Create the M matrix for the glossing example ## - here, studies 2-5 are one hierarchically dependent group (Baumann), ## and studies 10-12 are another hierarchically dependent group (Joyce) data(gloss) dep.groups <- list(c(2:5),c(10:12)) M <- get.M(length(gloss.theta),dep.groups)## Create the M matrix for the glossing example ## - here, studies 2-5 are one hierarchically dependent group (Baumann), ## and studies 10-12 are another hierarchically dependent group (Joyce) data(gloss) dep.groups <- list(c(2:5),c(10:12)) M <- get.M(length(gloss.theta),dep.groups)
Performs a random effects linear model meta-analysis, allowing for hierarchical dependence. It returns a list containing the results.
metahdep.REMA(theta, V, X, M = NULL, dep.groups = NULL, meta.name = "meta-analysis", delta.split = FALSE, center.X = FALSE)metahdep.REMA(theta, V, X, M = NULL, dep.groups = NULL, meta.name = "meta-analysis", delta.split = FALSE, center.X = FALSE)
theta |
A vector of effect size estimates from multiple studies. |
V |
The variance/covariance matrix for |
X |
A matrix of covariates for |
M |
(optional) Used when |
dep.groups |
(optional) Used when |
meta.name |
(optional) A name field for bookkeeping. This can be any character string. |
delta.split |
(optional) A logical value specifying whether or not to account for hierarchical dependence (i.e., perform delta-splitting). If |
center.X |
(optional) A logical value specifying whether or not to center the columns of |
Takes a vector of effect size estimates, a variance/covariance matrix, and a covariate matrix, and fits a random effects linear model meta-analysis, allowing for hierarchical dependence.
If delta.split=TRUE, then it performs delta-splitting to account for hierarchical dependence among studies.
When a meta-analysis is to be performed for gene expression data (on a per-gene basis), the metahdep() function calls this metahdep.REMA() function for each gene separately.
A list, with the following named components:
beta.hats |
A vector of model estimates for the covariates given by |
cov.matrix |
The variance/covariance matrix for the |
beta.hat.p.values |
The [two-sided] p-value(s) for the |
tau2.hat |
The estimated between-study hierarchical variance tau-square, using the method of moments approach of DerSimonian and Laird. |
varsigma.hat |
(Only estimated when |
Q |
The statistic used to test for model homogeneity / model mis-specification |
Q.p.value |
The p-value for |
name |
An optional name field |
John R. Stevens, Gabriel Nicholas
DerSimonian R. and Laird N. (1986), Meta-analysis in clinical trials, Controlled Clinical Trials, 7: 177-188.
Hedges L. V. and Olkin I (1985), Statistical methods for meta-analysis, San Diego, CA: Academic Press.
Stevens J.R. and Doerge R.W. (2005), Combining Affymetrix Microarray Results, BMC Bioinformatics, 6:57.
Stevens J.R. and Taylor A.M. (2009), Hierarchical Dependence in Meta-Analysis, Journal of Educational and Behavioral Statistics, 34(1):46-73.
See also the metahdep package vignette.
### ### Example 1: gene expression data ### - this uses one gene from the HGU.prep.list object # load data and extract components for meta-analysis (for one gene) data(HGU.prep.list) gene.data <- HGU.prep.list[[7]] theta <- gene.data@theta V <- gene.data@V X <- gene.data@X M <- gene.data@M dep.grps <- list(c(1:2),c(4:6)) gene.name <- gene.data@gene # fit a regular REMA (no hierarchical dependence) results <- metahdep.REMA(theta, V, X, meta.name=gene.name) results # fit hierarchical dependence model (with delta-splitting), # using two different methods for specifying the dependence structure results.dsplitM <- metahdep.REMA(theta, V, X, M, delta.split=TRUE, meta.name=gene.name, center.X=TRUE) results.dsplitM results.dsplitd <- metahdep.REMA(theta, V, X, dep.groups=dep.grps, delta.split=TRUE, meta.name=gene.name, center.X=TRUE) results.dsplitd ### ### Example 2: glossing data ### - this produces part of Table 6 in the Stevens and Taylor JEBS paper. data(gloss) dep.groups <- list(c(2,3,4,5),c(10,11,12)) REMA.ds <- metahdep.REMA(gloss.theta, gloss.V, gloss.X, center.X=TRUE, delta.split=TRUE, dep.groups=dep.groups) round(cbind(t(REMA.ds$beta.hats), sqrt(diag(REMA.ds$cov.matrix)), t(REMA.ds$beta.hat.p.values)),4)### ### Example 1: gene expression data ### - this uses one gene from the HGU.prep.list object # load data and extract components for meta-analysis (for one gene) data(HGU.prep.list) gene.data <- HGU.prep.list[[7]] theta <- gene.data@theta V <- gene.data@V X <- gene.data@X M <- gene.data@M dep.grps <- list(c(1:2),c(4:6)) gene.name <- gene.data@gene # fit a regular REMA (no hierarchical dependence) results <- metahdep.REMA(theta, V, X, meta.name=gene.name) results # fit hierarchical dependence model (with delta-splitting), # using two different methods for specifying the dependence structure results.dsplitM <- metahdep.REMA(theta, V, X, M, delta.split=TRUE, meta.name=gene.name, center.X=TRUE) results.dsplitM results.dsplitd <- metahdep.REMA(theta, V, X, dep.groups=dep.grps, delta.split=TRUE, meta.name=gene.name, center.X=TRUE) results.dsplitd ### ### Example 2: glossing data ### - this produces part of Table 6 in the Stevens and Taylor JEBS paper. data(gloss) dep.groups <- list(c(2,3,4,5),c(10,11,12)) REMA.ds <- metahdep.REMA(gloss.theta, gloss.V, gloss.X, center.X=TRUE, delta.split=TRUE, dep.groups=dep.groups) round(cbind(t(REMA.ds$beta.hats), sqrt(diag(REMA.ds$cov.matrix)), t(REMA.ds$beta.hat.p.values)),4)
This is a class representation for the effect size estimates and other summary information for a single gene, from a collection of gene expression studies, usually constructed in preparation for a meta-analysis.
Objects can be created using the function metahdep.format and new.
theta:Object of class vector representing the gene's effect size estimates (differential expression measures) from the multiple studies.
V:Object of class matrix representing the sampling variance/covariance matrix of the gene's effect size estimates from the multiple studies.
X:Object of class matrix representing the covariate (or design) matrix for the gene. Covariate information from the multiple studies is represented here.
M:Object of class matrix representing the block diagonal hierarchical structure of effect size estimates from the multiple studies.
max.k:Object of class integer representing the size of the largest block on the diagonal of M, i.e., the size of the largest hierarchical dependence group for the gene.
row.indices:Object of class matrix with columns named Study and Row. Optionally returned by the function metahdep.format(), to see which gene (Row) in which ES.obj object (Study) produced the data recorded in each metaprep object.
gene:Object of class character representing the gene name.
replace the slot entries
Stevens J.R. and Nicholas G. (2009), metahdep: Meta-analysis of hierarchically dependent gene expression studies, Bioinformatics, 25(19):2619-2620.
See also the metahdep package vignette.
### ### See the metahdep package vignette for a full example ### data(HGU.prep.list) HGU.prep.list[[7]]### ### See the metahdep package vignette for a full example ### data(HGU.prep.list) HGU.prep.list[[7]]