Author: Eric W. Bridgeford
Date: 2024-03-01

# Causal Conditional ComBat

#### Eric W. Bridgeford

#### 2024-03-01

    require(causalBatch)
    require(ggplot2)
    require(tidyr)
    n = 200

To start, we will begin with a simulation example, similar to the ones we were working in for the simulations, which you can access from:

    vignette("cb.simulations", package="causalBatch")

Let’s regenerate our working example data with some plotting code:

    # a function for plotting a scatter plot of the data
    plot.sim <- function(Ys, Ts, Xs, title="",
                         xlabel="Covariate",
                         ylabel="Outcome (1st dimension)") {
      data = data.frame(Y1=Ys[,1], Y2=Ys[,2],
                        Group=factor(Ts, levels=c(0, 1), ordered=TRUE),
                        Covariates=Xs)

      data %>%
        ggplot(aes(x=Covariates, y=Y1, color=Group)) +
        geom_point() +
        labs(title=title, x=xlabel, y=ylabel) +
        scale_x_continuous(limits = c(-1, 1)) +
        scale_color_manual(values=c(`0`="#bb0000", `1`="#0000bb"),
                           name="Group/Batch") +
        theme_bw()
    }

Next, we will generate a simulation:

    sim = cb.sims.sim_sigmoid(n=n, eff_sz=1, unbalancedness=1.5)

    plot.sim(sim$Ys, sim$Ts, sim$Xs, title="Sigmoidal Simulation")

It looks like the red group/batch tends to have outcomes exceeding the blue group/batch. We can test whether a group/batch effect can be detected. See the vignette on causal conditional distance correlation for more details, found at:

    vignette("cb.detect.caus_cdcorr", package="causalBatch")

Now to run the test and evaluate with \(\alpha = 0.05\):

    result <- cb.detect.caus_cdcorr(sim$Ys, sim$Ts, sim$Xs, R=100)
    print(sprintf("p-value: %.4f", result$Test$p.value))
    #> [1] "p-value: 0.0099"

Since the \(p\)-value is \(< \alpha\), this indicates that the data provide evidence to reject the null hypothesis in favor of the alternative (there is a causal group/batch effect). Next, we can correct for this effect using causal conditional ComBat. Note in particular that the covariates should be passed as a named data frame:

    cor.sim <- cb.correct.caus_cComBat(sim$Ys, sim$Ts, data.frame(Covar=sim$Xs),
                                       match.form="Covar")
    #> Found2batches
    #> Adjusting for1covariate(s) or covariate level(s)
    #> Standardizing Data across genes
    #> Fitting L/S model and finding priors
    #> Finding parametric adjustments
    #> Adjusting the Data

We can plot the augmented data after causal conditional ComBat has been applied, like so:

    plot.sim(cor.sim$Ys.corrected, cor.sim$Ts, cor.sim$Xs$Covar,
             title="Sigmoidal Simulation (causal cComBat corrected)")

We can see that the augmented data nearly perfectly overlaps across the two groups/batches, which can be confirmed using the causal conditional distance correlation:

    result.cor <- cb.detect.caus_cdcorr(cor.sim$Ys.corrected, cor.sim$Ts,
                                        cor.sim$Xs$Covar, R=100)
    print(sprintf("p-value: %.4f", result.cor$Test$p.value))
    #> [1] "p-value: 1.0000"

Since the \(p\)-value is \(> \alpha\), the data provide no evidence to reject the null hypothesis (that there is a causal group/batch effect) in favor of the alternative. Stated another way, the group/batch effect that was present before causal conditional ComBat correction is no longer detectable.

If we have multiple covariate values, you can specify custom formulas for the matching process using the standard R formula notation. For instance, if we had a second covariate value:

    Xs.2covar <- data.frame(Covar1=sim$Xs, Covar2=runif(n))

You could specify a formula instead like this:

    cor.sim <- cb.correct.caus_cComBat(sim$Ys, sim$Ts, Xs.2covar,
                                       match.form="Covar1 + Covar2")
    #> Found2batches
    #> Adjusting for2covariate(s) or covariate level(s)
    #> Standardizing Data across genes
    #> Fitting L/S model and finding priors
    #> Finding parametric adjustments
    #> Adjusting the Data

You can also use the standard `MatchIt::matchit()` arguments to leverage custom matching algorithms. These are passed into the function using the `match.args` argument. For instance, consider if we have a third categorical covariate:

    Xs.3covar <- cbind(data.frame(Cat.Covar=factor(rbinom(n, size=1, 0.5))),
                       Xs.2covar)

If we wanted to leverage exact matching for the categorical covariate, we could specify to perform exact matching for `Cat.Covar`, and leave nearest-neighbor matching (without replacement) for `Covar1` and `Covar2`, exactly as you would for the `MatchIt::matchit()` package:

    match.args <- list(method="nearest", exact="Cat.Covar", replace=FALSE,
                       caliper=0.1)
    cor.sim <- cb.correct.caus_cComBat(sim$Ys, sim$Ts, Xs.3covar,
                                       match.form="Covar1 + Covar2 + Cat.Covar",
                                       match.args=match.args)
    #> Found2batches
    #> Adjusting for3covariate(s) or covariate level(s)
    #> Standardizing Data across genes
    #> Fitting L/S model and finding priors
    #> Finding parametric adjustments
    #> Adjusting the Data