  Modern cell-perturbation experiments expose cells to panels of hundreds of
stimuli, such as cytokines or CRISPR guides that perform gene knockouts. These
experiments are designed to investigate whether a particular gene is
upregulated or downregulated by exposure to each treatment. However, due to
high levels of experimental noise, typical estimators of whether a gene is up-
or down-regulated make many errors. In this paper, we make two contributions.
Our first contribution is a new estimator of regulatory effect that makes use
of Gaussian processes and factor analysis to leverage auxiliary information
about similarities among treatments, such as the chemical similarity among the
drugs used to perturb cells. The new estimator typically has lower variance
than unregularized estimators, which do not use auxiliary information, but
higher bias. To assess whether this new estimator improves accuracy (i.e.,
achieves a favorable trade-off between bias and variance), we cannot simply
compute its error on heldout data as ``ground truth'' about the effects of
treatments is unavailable. Our second contribution is a novel data-splitting
method to evaluate error rates. This data-splitting method produces valid error
bounds using ``sign-valid'' estimators, which by definition have the correct
sign more often than not. Using this data-splitting method, through a series of
case studies we find that our new estimator, which leverages auxiliary
information, can yield a three-fold reduction in type S error rate.
