Data artifacts may induce errors in findings from any spatial transcriptomics platform. To provide protection from these errors, we have developed Border, Location, and edge Artifact DEtection (BLADE). BLADE is a novel collection of automated cross-platform statistical methods for detecting and removing three types of artifacts: (i) border effects, where total gene reads is modified at the border of the capture area; (ii) tissue edge effects, where total gene reads is modified at the edge of the tissue; (iii) location batch malfunctions, where there is a zone in the same location on all slides in a batch with substantially decreased sequencing depth. These artifacts are not mutually exclusive. BLADE has been applied to both Visium and CosMx data, and was used to evaluate our library of 37 10x Visium samples of liver and adipose tissue from humans and mice. Artifacts were found to be both common and impactful in those samples, indicating that artifact detection methods are critical for spatial transcriptomics quality control. Our BLADE software is publicly available.

Negative control variables are increasingly used to adjust for unmeasured confounding bias in causal inference using observational data. They are typically identified by subject matter knowledge and there is currently a severe lack of data-driven methods to find negative controls. In this paper, we present a statistical test for discovering negative controls of a special type---disconnected negative controls---that can serve as surrogates of the unmeasured confounder, and we incorporate that test into the Data-driven Automated Negative Control Estimation (DANCE) algorithm. DANCE first uses the new validation test to identify subsets of a set of candidate negative control variables that satisfy the assumptions of disconnected negative controls. It then applies a negative control method to each pair of these validated negative control variables, and aggregates the output to produce an unbiased point estimate and confidence interval for a causal effect in the presence of unmeasured confounding. We (1) prove the correctness of this validation test, and thus of DANCE; (2) demonstrate via simulation experiments that DANCE outperforms both naive analysis ignoring unmeasured confounding and negative control method with randomly selected candidate negative controls; and (3) demonstrate the effectiveness of DANCE on a challenging real-world problem.

Learning graphical conditional independence structures is an important machine learning problem and a cornerstone of causal discovery. However, the accuracy and execution time of learning algorithms generally struggle to scale to problems with hundreds of highly connected variables---for instance, recovering brain networks from fMRI data. We introduce the best order score search (BOSS) and grow-shrink trees (GSTs) for learning directed acyclic graphs (DAGs) in this paradigm. BOSS greedily searches over permutations of variables, using GSTs to construct and score DAGs from permutations. GSTs efficiently cache scores to eliminate redundant calculations. BOSS achieves state-of-the-art performance in accuracy and execution time, comparing favorably to a variety of combinatorial and gradient-based learning algorithms under a broad range of conditions. To demonstrate its practicality, we apply BOSS to two sets of resting-state fMRI data: simulated data with pseudo-empirical noise distributions derived from randomized empirical fMRI cortical signals and clinical data from 3T fMRI scans processed into cortical parcels. BOSS is available for use within the TETRAD project which includes Python and R wrappers.

Binning (a.k.a. discretization) of numerically continuous measurements is a wide-spread but controversial practice in data collection, analysis, and presentation. The consequences of binning have been evaluated for many different kinds of data analysis methods, however so far the effect of binning on causal discovery algorithms has not been directly investigated. This paper reports the results of a simulation study that examined the effect of binning on the Greedy Equivalence Search (GES) causal discovery algorithm. Our findings suggest that unbinned continuous data often result in the highest search performance, but some exceptions are identified. We also found that binned data are more sensitive to changes in sample size and tuning parameters, and identified some interactive effects between sample size, binning, and tuning parameter on performance.

Causal discovery can be a powerful tool for investigating causality when a system can be observed but is inaccessible to experiments in practice. Despite this, it is rarely used in any scientific or medical fields. One of the major hurdles preventing the field of causal discovery from having a larger impact is that it is difficult to determine when the output of a causal discovery method can be trusted in a real-world setting. Trust is especially critical when human health is on the line. In this paper, we report the results of a series of simulation studies investigating the performance of different resampling methods as indicators of confidence in discovered graph features. We found that subsampling and sampling with replacement both performed surprisingly well, suggesting that they can serve as grounds for confidence in graph features. We also found that the calibration of subsampling and sampling with replacement had different convergence properties, suggesting that one's choice of which to use should depend on the sample size.

In psychiatry, it is common to have multiple available instruments for a concept of interest. Different studies may choose to measure the same concept with different instruments for reasons such as time and resource constraints. This makes it difficult to compare findings between studies, since differences in findings may be due to the use of different instruments. In this paper, we described and demonstrated a two step process to identify a correspondence mapping between QLS and GAF, two quality of life instruments. Specifically, we applied a Markov Boundary discovery algorithm, HITON-MB, to identify the Markov Boundary of GAF using different QLS subscales and items as candidate Markov Boundary members. The mapping function is then derived by regressing the identified Markov Boundary members to GAF. We applied the two step procedure to two independ datasets to examine the QLS-GAF correspondence across different patient population and training conditions. We identified the intrapsychic foundation subscale of QLS to be a consistent Markov Boundary member of GAF. We also reported the quality of the derived mapping functions.

OBJECTIVE: The objective of this study was to assess the potential of combining graph learning methods with latent variable estimation methods for mining clinically useful information from observational clinical data sets. MATERIALS AND METHODS: The data set contained self-reported measures of psychopathology symptoms from a clinical sample receiving treatment for alcohol use disorder. We used the traditional graph learning methods: Graphical Least Absolute Shrinkage and Selection Operator, and Friedman's hill climbing algorithm; traditional latent variable estimation method factor analysis; recently developed graph learning method Greedy Fast Causal Inference; and recently developed latent variable estimation method Find One Factor Clusters. Methods were assessed qualitatively by the content of their findings. RESULTS: Recently developed graphical methods identified potential latent variables (ie, not represented in the model) influencing particular scores. Recently developed latent effect estimation methods identified plausible cross-score loadings that were not found with factor analysis. A graphical analysis of individual items identified a mistake in wording on 1 questionnaire and provided further evidence that certain scores are not reflective of indirectly measured common causes. DISCUSSION AND CONCLUSION: Our findings suggest that a combination of Greedy Fast Causal Inference and Find One Factor Clusters can enhance the evidence-based information yield from psychopathological constructs and questionnaires. Traditional methods provided some of the same information but missed other important findings. These conclusions point the way toward more informative interrogations of existing and future data sets than are commonly employed at present.

Many scientific research programs aim to learn the causal structure of real world phenomena. This learning problem is made more difficult when the target of study cannot be directly observed. One strategy commonly used by social scientists is to create measurable ``indicator'' variables that covary with the latent variables of interest. Before leveraging the indicator variables to learn about the latent variables, however, one needs a measurement model of the causal relations between the indicators and their corresponding latents. These measurement models are a special class of Bayesian networks. This paper addresses the problem of reliably inferring measurement models from measured indicators, without prior knowledge of the causal relations or the number of latent variables. We present a provably correct novel algorithm, FindOneFactorClusters (FOFC), for solving this inference problem. Compared to other state of the art algorithms, FOFC is faster, scales to larger sets of indicators, and is more reliable at small sample sizes. We also present the first correctness proofs for this problem that do not assume linearity or acyclicity among the latent variables.

Structure learning algorithms for graphical models have focused almost exclusively on stable environments in which the underlying generative process does not change; that is, they assume that the generating model is globally stationary. In real-world environments, however, such changes often occur without warning or signal. Real-world data often come from generating models that are only locally stationary. In this paper, we present LoSST, a novel, heuristic structure learning algorithm that tracks changes in graphical model structure or parameters in a dynamic, real-time manner. We show by simulation that the algorithm performs comparably to batch-mode learning when the generating graphical structure is globally stationary, and significantly better when it is only locally stationary.