**modified**: Sat Jan 20 08:18:27 2018
**compiled**: `r date()`

suppressPackageStartupMessages(library(bacon)) BiocStyle::markdown() options(digits=3)

`r Biocpkg("bacon")`

can be used to remove inflation and bias often
observed in epigenome- and transcriptome-wide association
studies [@vanIterson2017].

To this end `r Biocpkg("bacon")`

constructs an empirical null
distribution using a Gibbs Sampling algorithm by fitting a
three-component normal mixture on z-scores. One component is forced,
using prior knowledge, to represent the null distribution with mean
and standard deviation representing the bias and inflation. The other
two components are necessary to capture the amount of true
associations present in the data, which we assume unknown but small.

`r Biocpkg("bacon")`

provides functionality to inspect the output of
the Gibbs Sampling algorithm, i.e., plots of traces, posterior
distributions and the mixture fit, are provided. Furthermore,
inflation- and bias-corrected test-statistics or P-values are
extracted easily. In addition, functionality for performing
fixed-effect meta-analysis are provided as well.

The function `bacon`

requires a vector or a matrix of z-scores, e.g.,
those extracted from association analyses using a linear regression
approach. For fixed-effect meta-analysis a matrix of effect-sizes and
standard-errors is required.

This vignette illustrates the use of `r Biocpkg("bacon")`

using
simulated z-scores, effect-sizes and standard errors to avoid long
run-times. If multiple sets of test-statisics or effect-sizes and
standard-errors are provided, the Gibbs Sampler algorithm can be
executed in parallel to reduce computation time using functionality
provide by `r Biocpkg("BiocParallel")`

-package.

A vector containing $5000$ z-scores is generated from a normal mixture
distribution, $90\%$ of the z-scores were drawn from a biased and
inflated null distribution, $\mathcal{N}(0.2, 1.3)$, and the remaining
z-scores from $\mathcal{N}(\mu, 1)$, where $\mu \sim \mathcal{N}(4,
1)$. The `rnormmix`

-function provided by `Bacon`

generates a vector of
random test-statistics described above optionally with different
parameters.

y <- rnormmix(5000, c(0.9, 0.2, 1.3, 1, 4, 1))

The function `bacon`

executes the Gibbs Sampler algorithm and stores
all in- and out-put in an object of class `Bacon`

. Several
accessor-functions are available to access data contained in the
`Bacon`

-object, e.g. for obtaining the estimated parameters of the
mixture fit or explicitly the bias and inflation. Actually, the latter
two are the mean and standard deviation of the null component (mu.0
and sigma.0).

bc <- bacon(y) bc estimates(bc) inflation(bc) bias(bc)

Several methods are provided to inspect the output of the Gibbs Sampler algorithm, such as traces-plots of all estimates, plots of posterior distributions, provide as a scatter plot between two parameters, and the actual fit of the three component mixture to the histogram of z-scores.

traces(bc, burnin=FALSE)

```
posteriors(bc)
```

fit(bc, n=100)

The previous three plots can be use as diagnostic tools to inspect the Gibbs sampling process.

There is also a generic plot function that can generate two types of plots; a histogram of the z-scores and a qq-plot. The histogram of the z-scores shows on top the standard normal distribution and the Gibbs Sampling estimated empirical null distribution. The quantile-quantile plot shows the $-log_{10}$ transformed P-values. Default values are raw, not controlled for bias and inflation, z-scores and P-values.

plot(bc, type="hist")

```r$ transformed P-values. Left panel using uncorrected P-values and right panel using bacon bias and inflation corrected P-values."} plot(bc, type="qq")

# Multiple sets of test-statistics # Matrices containing $5000\times6$ effect-sizes and standard errors are generated to simulated data for a fixed-effect meta-analyses. This is a toy-example just to illustrate the capabilities of `bacon` in handling multiple sets of test-statics. ```r set.seed(12345) biases <- runif(6, -0.2, 0.2) inflations <- runif(6, 1, 1.3) es <- matrix(nrow=5000, ncol=6) for(i in 1:6) es[,i] <- rnormmix(5000, c(0.9, biases[i], inflations[i], 0, 4, 1), shuffle=FALSE) se <- replicate(6, 0.8*sqrt(4/rchisq(5000,df=4))) colnames(es) <- colnames(se) <- LETTERS[1:ncol(se)] rownames(es) <- rownames(se) <- 1:5000 head(rownames(es)) head(colnames(es))

By default the function `bacon`

detects the number of cores/nodes
registered, as described in the `r Biocpkg("BiocParallel")`

, to
perform bacon in parallel. To run the vignette in general we set it
here for convenience to 1 node.

library(BiocParallel) register(MulticoreParam(1, log=TRUE)) bc <- bacon(NULL, es, se) bc knitr::kable(estimates(bc)) inflation(bc) bias(bc) knitr::kable(tstat(bc)[1:5,]) knitr::kable(pval(bc)[1:5,]) knitr::kable(se(bc)[1:5,]) knitr::kable(es(bc)[1:5,])

The accessor-function return as expected matrices of estimates. For the plotting functions an additional index of the ith study or z-score is required.

traces(bc, burnin=FALSE, index=3)

posteriors(bc, index=3)

fit(bc, n=100, index=3)

plot(bc, type="hist")

```r$ transformed P-values. Left panel using uncorrected P-values and right panel using bacon bias and inflation corrected P-values."} plot(bc, type="qq")

# Fixed-effect meta-analysis # The following code chunk shows how to perform fixed-effect meta-analysis and the inspection of results. ```r bcm <- meta(bc) head(pval(bcm)) print(topTable(bcm))

```r$ transformed P-values for each cohort and the meta-analysis P-values. Left panel using uncorrected P-values and right panel using bacon bias and inflation corrected P-values."} plot(bcm, type="qq")

# Session Info # Here is the output of `sessionInfo()` on the system on which this document was compiled: ```r sessionInfo()

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