Package {MetaHD}

A Multivariate Meta-Analysis Model for High-Dimensional Data

Description

The MetaHD function performs a multivariate meta-analysis for high-dimensional data, combining summary estimates obtained from multiple studies by using restricted maximum likelihood estimation. In its default settings, the function fits the fastMetaHD model, which provides a memory-efficient and computationally faster implementation of the MetaHD methodology. Assuming a meta-analysis is based on N outcomes and K studies:

Usage

MetaHD(
  Y,
  Slist,
  Psi = NULL,
  method = c("multi","REM","FEM"),
  bscov = c("unstructured","diag","none"),
  useDivideConquer = FALSE,
  DCgroups = NULL,
  parallel = FALSE,
  dendro.method = c("dynamicTreeCut", "fixedHeight", "fixedK", "optimalK"),
  dendro.height = NULL,
  dendro.k = NULL,
  est.wscor = FALSE,
  shrinkCor = TRUE,
  impute.na = FALSE,
  optim.algorithm = c("BOBYQA","hybrid","L-BFGS-B"),
  optim.maxiter = 2000,
  rigls.iter = 1,
  initPsi = NULL,
  impute.var = 10^4
)

Arguments

Details

If parallel = TRUE, the divide-and-conquer approach may be evaluated in parallel. Parallel execution is implemented using the future R package.

On Windows, users must set a future plan (e.g., future::plan(future::multisession, workers = ncores)) before calling MetaHD() in order to enable parallel computation.

On Linux and macOS, users may alternatively use future::plan(future::multicore, workers = ncores).

If no future plan is set, or if parallel = FALSE, computations are performed sequentially.

Value

A list of objects containing :

• estimate: An N-dimensional vector of the combined estimates.

• std.err: An N-dimensional vector of the associated standard errors.

• pVal: An N-dimensional vector of the p-values.

• I2.stat: I^2 statistics.

• clustering_diagnostics: A list of clustering diagnostic outputs returned when useDivideConquer = TRUE and DCgroups = NULL. Contains:

  • avg_silhouette: Overall average silhouette width across all outcomes.

  • cluster_avg: Average silhouette width per cluster.

  • n_clusters: Number of clusters identified.

  • cluster_sizes: Number of outcomes in each cluster.

  • silhouette_object: The full silhouette object of class "silhouette", which can be passed directly to plot() for visualization.

  • silhouette_by_k: A data frame with columns k and avg_silhouette, returned only when dendro.method = "optimalK", showing the average silhouette width at each candidate partition used to select the optimal number of clusters.

References

Liyanage JC, Prendergast L, Staudte R, De Livera AM (2024). MetaHD: a multivariate meta-analysis model for metabolomics data. Bioinformatics, 40(7), btae470. doi:10.1093/bioinformatics/btae470

Powell MJ (2009). The BOBYQA algorithm for bound constrained optimization without derivatives. Cambridge NA Report NA2009/06, University of Cambridge, 26, 26–46.

Sera F, Armstrong B, Blangiardo M, et al. (2019). An extended mixed-effects framework for meta-analysis. Statistics in Medicine, 38, 5429–5444.

Schaefer J, Strimmer K (2005). A shrinkage approach to large-scale covariance estimation and implications for functional genomics. Statistical Applications in Genetics and Molecular Biology, 4, 32.

Langfelder, P., Zhang, B. and Horvath, S. (2008). Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics, 24(5), pp. 719–720.

Rousseeuw, P.J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, pp. 53–65.

Examples

# CREATE INPUT DATA
input_data <- MetaHDInput(realdata)
Y <- input_data$Y
Slist <- input_data$Slist

N <- ncol(Y)
K <- nrow(Y)

Smat <- matrix(0, nrow = K, ncol = N)
for (i in 1:K) {
 Smat[i, ] <- diag(Slist[[i]])
}

# MULTIVARIATE RANDOM-EFFECTS META-ANALYSIS 
model <- MetaHD(Y = Y, Slist = Slist, method = "multi")
model$estimate
model$pVal

# UNIVARIATE RANDOM-EFFECTS META-ANALYSIS
model <- MetaHD(Y = Y, Slist = Smat, method = "REM")
model$estimate
model$pVal

# UNIVARIATE FIXED-EFFECTS META-ANALYSIS
model <- MetaHD(Y = Y, Slist = Smat, method = "FEM")
model$estimate
model$pVal

Summarise or print MetaHD results

Description

summary() returns the combined meta-analysis results from a MetaHD fit as a tidy data frame, one row per outcome. It is convenient for inspecting the results and for exporting them to a .csv file with write.csv. print() shows a concise overview of the fit.

Usage

## S3 method for class 'MetaHD'
summary(object, outcome_names = NULL, ...)

## S3 method for class 'MetaHD'
print(x, ...)

Arguments

Value

summary() returns a data frame with one row per outcome and the columns outcome, estimate, std.err, pVal and I2.stat. print() displays a concise overview and invisibly returns x.

See Also

MetaHD

Examples

Y <- simdata.1$Y
Slist <- simdata.1$Slist
model <- MetaHD(Y, Slist)

model # concise overview
results <- summary(model, outcome_names = colnames(Y))
head(results)

## Not run: 
# Export the results table to CSV
write.csv(summary(model, outcome_names = colnames(Y)),
          "metahd_results.csv", row.names = FALSE)

## End(Not run)

Creating Input Data for MetaHD When Individual-Level Data are Available

Description

The MetaHDInput function creates input data Y (treatment effects) and Slist (within-study covariance matrices) for MetaHD when individual-level data are available. Assuming that the individual-level data are in the following format, with 'study' in column 1, 'group' in column 2 and outcomes in rest of the columns, with samples in rows.

Usage

MetaHDInput(data)

Arguments

Value

A list of objects containing :

• Y: A K \times N matrix of treatment effect sizes, where K is the number of studies and N is the number of outcomes.

• Slist: A list of length K containing N \times N within-study variance-covariance matrices of the treatment effects.

Examples

# CREATE INPUT DATA
input_data <- MetaHDInput(realdata)

## treatment effect-sizes
Y <- input_data$Y
head(Y)

## within-study variance-covariance matrices
Slist <- input_data$Slist
head(Slist[[1]])

Create a MetaHD result object

Description

Constructs an object containing combined results from different meta-analysis methods, for downstream visualisation via Venn diagrams (Tav, 2025), UpSet plots (Conway et al., 2017), and ROC curves (Robin et al., 2011). The sets argument accepts numeric vectors of p-values (thresholded by alpha) or effect sizes (ranked and selected by top_n). Note that ROC curves require numeric p-values and a truth vector.

Usage

MetaHDResult(
  sets,
  outcome_names = NULL,
  truth = NULL,
  alpha = 0.05,
  top_n = NULL
)

Arguments

Details

This object is designed to be used with the S3 method plot.MetaHDResult for visualisation of MetaHD results via Venn diagrams (Tav, 2025), UpSet plots (Conway et al., 2017), and ROC curves (Robin et al., 2011).

The function supports two input modes:

1. P-value thresholding: Supply numeric p-value vectors with alpha (and optionally outcome_names). Available for Venn, UpSet, and ROC plots.

2. Top-N selection: Supply numeric vectors (e.g. effect sizes) with top_n (and optionally outcome_names). Available for Venn and UpSet plots only.

Value

An object of class "MetaHDResult" containing:

sig_df

A data frame of binary indicators (1 = significant/selected, 0 = otherwise).

sets

The original input list.

truth

The provided ground truth vector (if any).

alpha

The significance threshold used (if applicable).

top_n

The top-N value used (if applicable).

References

Tav, C. (2025). gVenn: Proportional Venn and UpSet Diagrams for Gene Sets and Genomic Regions. doi:10.18129/B9.bioc.gVenn. R package version 1.1.1, https://bioconductor.org/packages/gVenn

Conway, J.R., Lex, A., and Gehlenborg, N. (2017). UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics, 33(18), 2938–2940. doi:10.1093/bioinformatics/btx364

Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J., and Mueller, M. (2011). pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 12, 77. doi:10.1186/1471-2105-12-77

See Also

plot.MetaHDResult for Venn diagrams, UpSet plots, and ROC curves based on this object. plot_effect_heatmap for comparing pooled effect size estimates across methods. plot_correlation_heatmap for visualising correlations among outcomes.

Examples

set.seed(123)
N <- 100
truth <- rbinom(N, 1, 0.2)   # 20% of features are true signals

# Example 1: p-value threshold (default alpha = 0.05)
# Supports venn, upset, and ROC plots
res <- MetaHDResult(
  sets = list(
    method_A = runif(N)^ifelse(truth, 5, 1),
    method_B = runif(N)^ifelse(truth, 3, 1),
    method_C = runif(N)^ifelse(truth, 2, 1)
  ),
  truth = truth
)

# Example 2: top-N selection by effect size
# Supports venn and upset plots only
res2 <- MetaHDResult(
  sets = list(
    method_A = rnorm(N),
    method_B = rnorm(N),
    method_C = rnorm(N)
  ),
  outcome_names = paste0("Var", seq_len(N)),
  top_n = 10
)

P-value Combination Methods for High-Dimensional Data

Description

Combines individual p-values across multiple studies for each outcome using p-value combination methods applied independently per outcome. Includes traditional and weighted p-value combination approaches and a vote counting method.

Usage

MetaHDpval(
  pmat,
  method = c("Fisher", "Stouffer", "wZ", "Lancaster", "wFisher", "Vote counting"),
  weight = NULL,
  is.onetail = TRUE,
  eff.sign = NULL,
  alpha = 0.5
)

Arguments

Details

The MetaHDpval function offers five traditional and more recent p-value combination methods implemented using the metapro R package, as well as a vote counting method implemented using the metap R package:

• Fisher's method (Fisher, 1932), which combines logarithmically transformed p-values from individual studies for each outcome using Fisher's statistic.

• Stouffer's method (Stouffer et al., 1949), which combines inverse normal-transformed p-values derived from individual study test statistics for each outcome.

• Weighted Z-method (wZ) (Mosteller and Bush, 1954), an extension of Stouffer's method that incorporates study-specific weights, resulting in a weighted inverse normal combination.

• Lancaster's method (Lancaster, 1961), which generalizes Fisher's method by introducing weights and exploits the additive property of the \chi^2-distribution.

• Weighted Fisher's method (wFisher) (Yoon et al., 2021), which extends Fisher's method by allowing non-integer weights reflecting study-specific information (e.g., sample sizes). This approach replaces the \chi^2-distribution with the gamma distribution to accommodate non-integer degrees of freedom.

• Vote counting method (Becker, 1994), that classifies a study as positive if its p-value is less than alpha and as negative if it exceeds 1 - alpha, with studies falling in between treated as neutral and excluded. The number of positive studies is then counted, and a one-sided binomial test is applied to the non-neutral studies to obtain a combined p-value for each outcome.

Value

A numeric vector of length N containing the combined p-values for each outcome.

References

Yoon, S., Baik, B., Park, T., et al. (2021). Powerful p-value combination methods to detect incomplete association. Scientific Reports, 11, 6980. doi:10.1038/s41598-021-86465-y

Yoon, S. (2023). metapro: Robust P-Value Combination Methods (R package version 1.5.11). Comprehensive R Archive Network (CRAN). doi:10.32614/CRAN.package.metapro

Becker, B.J. (1994). Combining significance levels. In Cooper H, Hedges LV (eds.), A handbook of research synthesis, 215–230. Russell Sage, New York.

Dewey, M. (2025). metap: Meta-Analysis of Significance Values (R package version 1.13). Comprehensive R Archive Network (CRAN). doi:10.32614/CRAN.package.metap

Examples

## Example with 5 studies and 12 outcomes
set.seed(123)
pmat <- matrix(runif(15), nrow = 5, ncol = 12)
eff.sign <- matrix(sample(c(-1, 1), 60, replace = TRUE), nrow = 5, ncol = 12)
wmat <- matrix(sample(50:200, 60, replace = TRUE), nrow = 5, ncol = 12)

## Fisher's method
MetaHDpval(pmat, method = "Fisher", is.onetail = FALSE, eff.sign = eff.sign)

## Weighted Z method
MetaHDpval(pmat, method = "wZ", weight = wmat, is.onetail = FALSE, eff.sign = eff.sign)

## Vote counting
MetaHDpval(pmat, method = "Vote counting", alpha = 0.4)

Estimate the correlation matrix using observed treatment effects

Description

Estimates the correlation matrix among outcomes from observed treatment effect sizes. This is the correlation structure that MetaHD uses internally: for the divide-and-conquer clustering of outcomes, as an estimate of the within-study correlations when these are unavailable, and to estimate the between-study correlations. When the number of outcomes exceeds the number of studies (N > K), a shrinkage estimator (Schaefer and Strimmer, 2005) is used by default to obtain a well-conditioned estimate.

Usage

estimateCorMat(Y, shrinkCor = TRUE, impute.na = FALSE)

Arguments

Value

An N x N correlation matrix. When there are two or fewer studies (K \le 2), the correlations cannot be estimated and 0 is returned.

References

Schaefer, J. and Strimmer, K. (2005). A shrinkage approach to large-scale covariance estimation and implications for functional genomics. Statistical Applications in Genetics and Molecular Biology, 4, 32.

See Also

plot_correlation_heatmap for visualising a correlation matrix.

Examples

Y <- simdata.1$Y
cormat <- estimateCorMat(Y)
dim(cormat)

Plot MetaHD results

Description

Visualizes a MetaHDResult object using a Venn diagram (Tav, 2025) for a compact view of overlapping outcomes across methods, an UpSet plot (Conway et al., 2017) to show overlap of significant outcomes across methods, or ROC curves (Robin et al., 2011) to assess performance when ground truth is available. Optionally saves the plot to a file in any of several formats.

Usage

## S3 method for class 'MetaHDResult'
plot(
  x,
  type = c("upset", "venn", "ROC"),
  roc.colors = NULL,
  venn.colors = NULL,
  queries = NULL,
  highlight = NULL,
  highlight.colors = NULL,
  show.truth = TRUE,
  file = NULL,
  width = 8,
  height = 6,
  units = c("in", "cm", "mm"),
  dpi = 300,
  ...
)

Arguments

Details

Venn diagram (Tav, 2025): Displays proportional overlap of significant or selected outcomes across methods, implemented via the gVenn package. Recommended for up to three methods; for more methods use type = "upset". Requires R >= 4.5 and gVenn to be installed:

  BiocManager::install("gVenn")

Available for both input modes supported by MetaHDResult.

UpSet plot (Conway et al., 2017): Visualizes the intersection of significant or selected outcomes across meta-analysis methods, implemented via the UpSetR package. Available for both input modes supported by MetaHDResult.

ROC curves (Robin et al., 2011): Requires a ground truth vector (provided when creating the object) and displays ROC curves along with AUC values for each method, implemented via the pROC package. AUC values are computed as the area under each ROC curve and displayed in the plot legend. ROC curves require numeric p-values — they are not available when top_n was used to create the MetaHDResult object.

Value

Invisibly returns: a named list of character vectors containing the significant or top-ranked outcome names per method for type = "venn"; and a named numeric vector of AUCs for type = "ROC".

Saving the plot

To save the plot directly, pass a file path to file. The graphics device is auto-selected from the file extension, the plot is written to that file, and the plot is not also drawn to the screen. Supported extensions:

• Vector formats: .pdf, .svg

• Raster formats: .png, .jpeg/.jpg, .tiff/.tif, .bmp

Use width, height, units, and dpi to control the saved output. dpi is ignored for vector formats. The path may be absolute or relative to getwd(); missing parent directories are created automatically.

References

Tav, C. (2025). gVenn: Proportional Venn and UpSet Diagrams for Gene Sets and Genomic Regions. doi:10.18129/B9.bioc.gVenn. R package version 1.1.1, https://bioconductor.org/packages/gVenn

Conway, J.R., Lex, A., and Gehlenborg, N. (2017). UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics, 33(18), 2938–2940. doi:10.1093/bioinformatics/btx364

Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J., and Mueller, M. (2011). pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 12, 77. doi:10.1186/1471-2105-12-77

See Also

MetaHDResult for creating objects to be plotted. plot_effect_heatmap for comparing pooled effect size estimates across methods. plot_correlation_heatmap for visualising correlations among outcomes.

Examples

set.seed(123)
N <- 100
truth <- rbinom(N, 1, 0.2)   # 20% of features are true signals
res <- MetaHDResult(
  sets = list(
    method_A = runif(N)^ifelse(truth, 5, 1),
    method_B = runif(N)^ifelse(truth, 3, 1),
    method_C = runif(N)^ifelse(truth, 2, 1)
  ),
  truth = truth
)

# Example 1: simple UpSet plot 
plot(res, type = "upset")

# Example 2: highlights via the `highlight` argument
plot(res, type = "upset",
     highlight = list(c("Truth", "method_A", "method_B", "method_C")),
     highlight.colors = "darkgreen")

# Example 3: passing in-built queries
plot(res, type = "upset",
     queries = list(
       list(
         query = UpSetR::intersects,
         params = list(c("method_A", "method_B", "method_C")),
         color = "dodgerblue3",
         active = TRUE,
         query.name = "Identified by all methods"
       ),
       list(
         query = function(row) {
           row["method_A"] == 1 && sum(row) < length(row)
         },
         color = "orange",
         active = TRUE,
         query.name = "Others identified by method_A"
       )
     ),
     show.truth = FALSE)

# Example 4: ROC curves with AUC values 
# Requires numeric p-values in sets
aucs <- plot(res, type = "ROC")
print(aucs)

# Example 5: Venn diagram (Tav, 2025)
# Requires R >= 4.5 and gVenn installed
plot(res, type = "venn")

## Not run: 
# Example 6: save plots to file
plot(res, type = "venn",  file = "venn.pdf",  width = 8,  height = 7)
plot(res, type = "upset", file = "upset.pdf", width = 10, height = 6)
plot(res, type = "ROC",   file = "roc.png",   width = 8,  height = 6, dpi = 600)

## End(Not run)

Plot a heatmap of correlations among outcomes

Description

Visualises correlations among outcomes from a single covariance or correlation matrix. A covariance matrix is converted to correlations internally; a correlation matrix can be supplied directly via is.corr = TRUE (for example, the within-study or between-study correlations among outcomes). This is useful for understanding the correlation structure of the outcomes and assessing whether the multivariate meta-analysis approach is likely to offer advantages over univariate methods. This function uses the ComplexHeatmap package (Gu, 2022) for rendering.

Usage

plot_correlation_heatmap(
  mat,
  is.corr = FALSE,
  outcome_names = NULL,
  title = NULL,
  col_low = "blue",
  col_mid = "white",
  col_high = "red",
  cluster_rows = TRUE,
  cluster_columns = TRUE,
  row_km = 1,
  show_row_names = NULL,
  show_column_names = NULL,
  names_fontsize = 7,
  legend_title = "Correlation",
  file = NULL,
  width = 8,
  height = 7,
  units = c("in", "cm", "mm"),
  dpi = 300,
  ...
)

Arguments

Details

Unless is.corr = TRUE, mat is treated as a covariance matrix and converted to a correlation matrix internally using cov2cor() before plotting. The colour scale runs from col_low at -1 through col_mid at 0 to col_high at 1.

Value

Invisibly returns the Heatmap object.

Large numbers of outcomes

With many outcomes the row and column labels become unreadable and individual cells shrink to sub-pixel size. To keep the plot readable, axis labels are hidden automatically beyond 50 outcomes (override via show_row_names / show_column_names). Very large heatmaps are rasterised automatically by ComplexHeatmap to keep file size and rendering manageable.

References

Gu, Z. (2022). Complex heatmap visualization. iMeta, 1, e43. doi:10.1002/imt2.43

See Also

plot_effect_heatmap for comparing pooled effect sizes across methods. plot.MetaHDResult for Venn diagrams, UpSet plots, and ROC curves.

Examples

Y <- simdata.1$Y
Slist <- simdata.1$Slist

# Example 1: a within-study covariance matrix (converted to correlations)
plot_correlation_heatmap(Slist[[1]])

# Example 2: partition into k-means blocks
set.seed(123)  # reproducible k-means blocks
plot_correlation_heatmap(Slist[[1]], row_km = 4)

# Example 3: supply a correlation matrix directly
cormat <- estimateCorMat(Y)
plot_correlation_heatmap(cormat, is.corr = TRUE)

## Not run: 
# Example 4: save to PNG
plot_correlation_heatmap(
  Slist[[1]],
  file = "correlation_heatmap.png",
  width = 10,
  height = 8,
  dpi = 600
)

## End(Not run)

Plot dendrogram

Description

Plots the dendrogram of outcomes to help users choose an appropriate cutting height when using dendro.method = "fixedHeight" in MetaHD.

Usage

plot_dendrogram(Y, shrinkCor = TRUE, impute.na = FALSE, h = NULL, ...)

Arguments

Value

Invisibly returns the hclust object.

Plot a heatmap of pooled effect sizes across meta-analysis methods

Description

Produces a heatmap with outcomes on the y-axis and meta-analysis methods on the x-axis, allowing visual comparison of pooled effect sizes across methods. Rows (outcomes) are clustered by default to reveal patterns of agreement and disagreement across methods. This function uses the ComplexHeatmap package (Gu, 2022) for rendering.

Usage

plot_effect_heatmap(
  estimates,
  outcome_names = NULL,
  title = "Pooled Effect Sizes Across Methods",
  col_low = "blue",
  col_mid = "white",
  col_high = "red",
  cluster_rows = TRUE,
  cluster_columns = FALSE,
  row_km = 1,
  show_row_names = NULL,
  label_top_n = NULL,
  row_names_fontsize = 8,
  column_names_fontsize = 10,
  legend_title = "Pooled\nEffect Size",
  file = NULL,
  width = 8,
  height = 6,
  units = c("in", "cm", "mm"),
  dpi = 300,
  ...
)

Arguments

Details

The colour scale is centred at zero by default, with col_low indicating negative effect sizes and col_high indicating positive effect sizes. This allows easy visual identification of outcomes with consistent direction across methods as well as those where methods disagree.

Value

Invisibly returns the Heatmap object.

Large numbers of outcomes

With many outcomes per-row labels become unreadable and individual rows are squeezed to sub-pixel height. To keep the plot readable, row labels are hidden automatically beyond 50 outcomes (override via show_row_names). Alternatively, label only the key outcomes with label_top_n, which marks the outcomes with the largest absolute effect with connector lines while leaving the rest unlabelled. Very large heatmaps are rasterised automatically by ComplexHeatmap to keep file size and rendering manageable.

References

Gu, Z. (2022). Complex heatmap visualization. iMeta, 1, e43. doi:10.1002/imt2.43

See Also

plot_correlation_heatmap for visualising correlations among outcomes. plot.MetaHDResult for Venn diagrams, UpSet plots, and ROC curves.

Examples

Y <- simdata.1$Y
Slist <- simdata.1$Slist

K <- nrow(Y)
N <- ncol(Y)
Smat <- matrix(0, nrow = K, ncol = N,
               dimnames = list(rownames(Y), colnames(Y)))
for (i in 1:K) Smat[i, ] <- diag(Slist[[i]])

model_multi <- MetaHD(Y = Y, Slist = Slist, method = "multi")
model_rem <- MetaHD(Y = Y, Slist = Smat,  method = "REM")
model_fem <- MetaHD(Y = Y, Slist = Smat,  method = "FEM")

# Example 1: basic heatmap with custom colours
plot_effect_heatmap(
  estimates = list(
    fastMetaHD = model_multi$estimate,
    REM = model_rem$estimate,
    FEM = model_fem$estimate
  ),
  outcome_names = colnames(Y),
  col_low = "purple",
  col_high = "orange"
)

# Example 2: partition rows into k-means slices
set.seed(123)  # reproducible k-means slices
plot_effect_heatmap(
  estimates = list(
    fastMetaHD = model_multi$estimate,
    REM = model_rem$estimate,
    FEM = model_fem$estimate
  ),
  outcome_names = colnames(Y),
  row_km = 4
)

# Example 3: label the outcomes with the largest absolute effect with connector lines.
# useful when there are many outcomes. 
plot_effect_heatmap(
  estimates = list(
    fastMetaHD = model_multi$estimate,
    REM = model_rem$estimate,
    FEM = model_fem$estimate
  ),
  outcome_names = colnames(Y),
  label_top_n = 15
)

## Not run: 
# Example 4: save to PNG
plot_effect_heatmap(
  estimates = list(
    fastMetaHD = model_multi$estimate,
    REM = model_rem$estimate,
    FEM = model_fem$estimate
  ),
  outcome_names = colnames(Y),
  file = "effect_heatmap.png",
  width = 10,
  height = 8,
  dpi = 600
)

## End(Not run)

An Individual-Level Metabolomics Dataset

Description

This is a subset of data, publicly available on MetaboAnalyst example datasets.

Usage

realdata

Format

A data frame with 172 observations on 14 metabolites.

Examples

head(realdata)

Simulated Study-Level Complete Data

Description

A simulated dataset for demonstrating meta-analysis workflows in MetaHD. It contains the observed treatment effects, within-study covariance matrices, the associated variances of the treatment effects, individual two-sided p-values, true effect sizes, and sample sizes for each study.

Usage

simData.complete

Format

A list with six elements:

Y

A numeric matrix of dimension 10 x 200 containing the observed effect sizes. Each row corresponds to a study and each column corresponds to one of 200 outcomes.

Slist

A list of length 10 containing the within-study covariance matrices, one per study.

wsvar

A numeric matrix of dimension 10 x 200 containing the within-study variances for each study and outcome.

pvals

A numeric matrix of dimension 10 x 200 containing the two-sided p-values for each study and outcome.

true.theta

A numeric vector of length 200 containing the true effect sizes.

sample.size

An integer vector of length 10 giving the sample size of each study.

See Also

simData.missing for the version with missing values.

Simulated Study-Level Data with Missing Values

Description

A simulated dataset for demonstrating meta-analysis workflows in MetaHD when some outcomes are unobserved in some studies. The structure matches simData.complete, with missing values generated under a missing-at-random (MAR) mechanism using a logistic regression model that determines the probability of missingness based on the values of other outcomes. Missing values were introduced on ten randomly selected outcomes; for each selected outcome, approximately 50\

Usage

simData.missing

Format

A list with six elements:

Y

A numeric matrix of dimension 10 x 200 containing the observed effect sizes, with NA values where outcomes are missing. Each row corresponds to a study and each column to one of 200 outcomes.

Slist

A list of length 10 containing the within-study covariance matrices, one per study, with NA values where outcomes are missing.

wsvar

A numeric matrix of dimension 10 x 200 containing the within-study variances for each study and outcome, with NA values where outcomes are missing.

pvals

A numeric matrix of dimension 10 x 200 containing the two-sided p-values for each study and outcome, with NA values where outcomes are missing.

true.theta

A numeric vector of length 200 containing the true effect sizes.

sample.size

An integer vector of length 10 giving the sample size of each study.

See Also

simData.complete for the version without missing values.

Simulated Dataset 1 : With Complete Data

Description

This dataset consists of a list of two data frames containing treatment effect-sizes and within-study covariance matrices

Usage

simdata.1

Format

A list of data frames as follows:

Y

treatment effect sizes of the metabolites in the form of a 12 x 30 matrix, where 12 is the number of studies and 30 is the number of metabolites.

Slist

12-dimensional list of 30 x 30 matrices representing within-study variances and covariances of the treatment effects

Examples

Y <- simdata.1$Y
Slist <- simdata.1$Slist

head(Y)
head(Slist[[1]])
head(Slist[[12]])

Simulated Dataset 2 : With Data Missing-At-Random

Description

This dataset consists of a list of two data frames containing treatment effect-sizes and within-study covariance matrices with missing values

Usage

simdata.2

Format

A list of data frames as follows:

Y

treatment effect sizes of the metabolites in the form of a 12 x 30 matrix, where 12 is the number of studies and 30 is the number of metabolites.

Slist

12-dimensional list of 30 x 30 matrices representing within-study variances and covariances of the treatment effects

Examples

Y <- simdata.2$Y
Slist <- simdata.2$Slist

head(Y)
head(Slist[[1]])
head(Slist[[12]])