arXiv subjects

Xuechan Li

Publications and source records attributed to Xuechan Li.

3 recordsLinked to original sources

arXiv2024

DART2: a robust multiple testing method to smartly leverage helpful or misleading ancillary information

In many applications of multiple testing, ancillary information is available, reflecting the hypothesis null or alternative status. Several methods have been developed to leverage this ancillary information to enhance testing power, typically requiring the ancillary information is helpful enough to ensure favorable performance. In this paper, we develop a robust and effective distance-assisted multiple testing procedure named DART2, designed to be powerful and robust regardless of the quality of ancillary information. When the ancillary information is helpful, DART2 can asymptotically control FDR while improving power; otherwise, DART2 can still control FDR and maintain power at least as high as ignoring the ancillary information. We demonstrated DART2's superior performance compared to existing methods through numerical studies under various settings. In addition, DART2 has been applied to a gene association study where we have shown its superior accuracy and robustness under two different types of ancillary information.

stat.ML↗

arXiv2021

Distance Assisted Recursive Testing

In many applications, a large number of features are collected with the goal to identify a few important ones. Sometimes, these features lie in a metric space with a known distance matrix, which partially reflects their co-importance pattern. Proper use of the distance matrix will boost the power of identifying important features. Hence, we develop a new multiple testing framework named the Distance Assisted Recursive Testing (DART). DART has two stages. In stage 1, we transform the distance matrix into an aggregation tree, where each node represents a set of features. In stage 2, based on the aggregation tree, we set up dynamic node hypotheses and perform multiple testing on the tree. All rejections are mapped back to the features. Under mild assumptions, the false discovery proportion of DART converges to the desired level in high probability converging to one. We illustrate by theory and simulations that DART has superior performance under various models compared to the existing methods. We applied DART to a clinical trial in the allogeneic stem cell transplantation study to identify the gut microbiota whose abundance will be impacted by the after-transplant care.

stat.ME↗

arXiv2021

TEAM: A Multiple Testing Algorithm on the Aggregation Tree for Flow Cytometry Analysis

In immunology studies, flow cytometry is a commonly used multivariate single-cell assay. One key goal in flow cytometry analysis is to pinpoint the immune cells responsive to certain stimuli. Statistically, this problem can be translated into comparing two protein expression probability density functions (PDFs) before and after the stimulus; the goal is to pinpoint the regions where these two pdfs differ. In this paper, we model this comparison as a multiple testing problem. First, we partition the sample space into small bins. In each bin we form a hypothesis to test the existence of differential pdfs. Second, we develop a novel multiple testing method, called TEAM (Testing on the Aggregation tree Method), to identify those bins that harbor differential pdfs while controlling the false discovery rate (FDR) under the desired level. TEAM embeds the testing procedure into an aggregation tree to test from fine- to coarse-resolution. The procedure achieves the statistical goal of pinpointing differential pdfs to the smallest possible regions. TEAM is computationally efficient, capable of analyzing large flow cytometry data sets in much shorter time compared with competing methods. We applied TEAM and competing methods on a flow cytometry data set to identify T cells responsive to the cytomeglovirus (CMV)-pp65 antigen stimulation. TEAM successfully identified the monofunctional, bifunctional, and polyfunctional T cells while the competing methods either did not finish in a reasonable time frame or provided less interpretable results. Numerical simulations and theoretical justifications demonstrate that TEAM has asymptotically valid, powerful, and robust performance. Overall, TEAM is a computationally efficient and statistically powerful algorithm that can yield meaningful biological insights in flow cytometry studies.

stat.ME↗