False Discovery Rate Control Research Articles

Sex differences in proteomics of cardiovascular disease: results from the Yale-CMD registry

Abstract Background Cardiovascular disease is the leading cause of death globally, with notable sex differences in its presentation, mechanisms, and outcomes. Purpose We investigated the serum proteomic profiles of coronary artery disease (CAD) and coronary microvascular dysfunction (CMD) patients to uncover sex-specific biological pathways and their roles in disease development. Methods Using the Yale-CMD registry's biobank, we performed a secondary analysis on adults with ischemic symptoms who underwent cardiac PET/CT and were not experiencing myocardial infarction, hemodynamic instability, acute heart failure, febrile illness, dialysis, or consent incapacity. Using PET/CT, we classified participants as controls (normal coronary flow reserve [CFR], no perfusion defect or coronary calcification), CAD (if new perfusion defect or known revascularization), or CMD (CFR <2 without defect or calcification). Using proximity extension assays (Olink® Explore 3072) in high-throughput proteomic analysis, we examined 2944 plasma proteins. Differential protein abundance analysis was performed using linear regression models adjusting for age, race, body mass index, history of diabetes, dyslipidemia, hypertension, and smoking to detect significant changes in protein abundance, with false discovery rate (FDR) control. Functional pathway analyses were conducted to identify the biological functions of the sex-differentially expressed proteins. Results Between 2014-2020, 91 patients of 190 in the registry provided blood samples for this analysis. Participants had median age 56.0 (50.0 - 62.0) years with 66% females. Among these, 48% were controls, 24% had CAD and 27% had CMD. Using linear regression adjusting for confounders, we identified significant sex differences in protein levels related to reproductive functions across all 3 cohorts (Fig. 1). In the CAD cohort, FSHB was upregulated in females, while INSL3 and EDDM3B were upregulated in males (FDR < 0.05). In the CMD cohorts, 6 proteins were differentially expressed (FDR < 0.05), with SCGB3A1 and HGFAC higher in females, and INSL3, SPINT3, EDDM3B, and KLK3 higher in males. Pathway analysis revealed that among patients with CAD, females had upregulation in immune system processes (Fig. 2A), while males showed upregulation of proteins related to endothelial regulation of blood flow (Fig. 2B). Among patients with CMD, females showed upregulation of lipid and glucose metabolism (Fig. 2C), while males showed upregulation of angiogenesis-related proteins (Fig. 2D). Conclusion We demonstrated significant sex differences in proteomic profiles among patients with CAD and CMD, underscoring the potential importance of sex-specific pathways in the pathophysiology of CVD. Our finding supports a need for large-scale validation studies to develop sex-tailored therapeutic strategies and advance precision medicine in cardiovascular health.Differential protein expression by sexPathway overrepresentation

Family therapy and EMDR after child abuse and neglect: moderating effects of child attachment style and PTSD symptoms on treatment outcome

ABSTRACT Background: Effective and appropriate care and treatment for children in order to decrease the psychosocial problems that arose after experiencing child abuse and neglect (CAN) is of vital importance, given the severity of symptomatology that may develop. Objective: The purpose of the present study was to examine whether attachment style and core cluster Posttraumatic Stress Disorder symptoms acted as moderators for treatment outcomes of a new integrative treatment model for trauma and attachment. In this treatment model, family therapy is combined with EMDR and obstacles for trauma processing are tackled first. Method: we included children, ages 6–12 years, with a history of CAN, who did not respond to evidence-based trauma treatment. Target treatment outcomes were problems in attachment, posttraumatic stress symptoms, behaviour, and emotion regulation. We conducted a multiple-baseline ABC Single-Case Experimental Design (SCED). We categorized 12 participants into four groups of attachment style and core cluster PTSD symptoms: (1) non-disorganized & re-experiencing; (2) non-disorganized & avoidance/hyperarousal; (3) disorganized & re-experiencing; & (4) disorganized & avoidance/hyperarousal. We compared the four groups with each other and across time, and the interaction between groups and effect over time. We conducted non-parametric permutation tests and estimated q-values for false discovery rate control. Results: Children with a disorganized attachment style had more severe symptomatology in general, except for posttraumatic stress symptoms. The treatment appeared more effective in targeting and successfully treating children with a non-disorganized attachment style, and specifically children with a non-disorganized attachment style and re-experiencing as core cluster PTSD symptoms. Conclusion: Our study underlines the complexity of treating children who developed a complicated combination of symptomatology after CAN and calls for the continuous development of innovative interventions.

Robust Trend Analysis in Environmental Remote Sensing: A Case Study of Cork Oak Forest Decline

We introduce a novel methodological framework for robust trend analysis (RTA) using remote sensing data to enhance the accuracy and reliability of detecting significant environmental trends. Our approach sequentially integrates the Theil–Sen (TS) slope estimator, the Contextual Mann–Kendall (CMK) test, and the false discovery rate (FDR) control. This comprehensive method addresses common challenges in trend analysis, such as handling small, noisy datasets with outliers and issues related to spatial autocorrelation, cross-correlation, and multiple testing. We applied this RTA workflow to study tree cover trends in Los Alcornocales Natural Park (Southern Spain), Europe’s largest cork oak forest, analysing interannual changes in tree cover from 2000 to 2022 using Terra MODIS MOD44B data. Our results reveal that the TS estimator provides a robust measure of trend direction and magnitude, but its effectiveness is dramatically enhanced when combined with the CMK test. This combination highlights significant trends and effectively corrects for spatial autocorrelation and cross-correlation, ensuring that genuine environmental signals are distinguished from statistical noise. Unlike previous workflows, our approach incorporates the FDR control, which successfully filtered out 29.6% of false discoveries in the case study, resulting in a more stringent assessment of true environmental trends captured by multi-temporal remotely sensed data. In the case study, we found that approximately one-third of the area exhibits significant and statistically robust declines in tree cover, with these declines being geographically clustered. Importantly, these trends correspond with relevant changes in tree cover, emphasising the ability of RTA to detect relevant environmental changes. Overall, our findings underscore the crucial importance of combining these methods, as their synergy is essential for accurately identifying and confirming robust environmental trends. The proposed RTA framework has significant implications for environmental monitoring, modelling, and management.

MSFragger-DDA+ Enhances Peptide Identification Sensitivity with Full Isolation Window Search.

Liquid chromatography-mass spectrometry (LC-MS) based proteomics, particularly in the bottom-up approach, relies on the digestion of proteins into peptides for subsequent separation and analysis. The most prevalent method for identifying peptides from data-dependent acquisition (DDA) mass spectrometry data is database search. Traditional tools typically focus on identifying a single peptide per tandem mass spectrum (MS2), often neglecting the frequent occurrence of peptide co-fragmentations leading to chimeric spectra. Here, we introduce MSFragger-DDA+, a novel database search algorithm that enhances peptide identification by detecting co-fragmented peptides with high sensitivity and speed. Utilizing MSFragger's fragment ion indexing algorithm, MSFragger-DDA+ performs a comprehensive search within the full isolation window for each MS2, followed by robust feature detection, filtering, and rescoring procedures to refine search results. Evaluation against established tools across diverse datasets demonstrated that, integrated within the FragPipe computational platform, MSFragger-DDA+ significantly increases identification sensitivity while maintaining stringent false discovery rate (FDR) control. It is also uniquely suited for wide-window acquisition (WWA) data. MSFragger-DDA+ provides an efficient and accurate solution for peptide identification, enhancing the detection of low-abundance co-fragmented peptides. Coupled with the FragPipe platform, MSFragger-DDA+ enables more comprehensive and accurate analysis of proteomics data.

An adaptive null proportion estimator for false discovery rate control

SUMMARY False discovery rate (FDR) is a commonly used criterion in multiple testing and the BenjaminiHochberg (BH) procedure is a standard approach for FDR control. To boost power, the adaptive BH procedure has been proposed by incorporating null proportion estimators, among which Storey’s estimator has gained substantial popularity. The performance of Storey’s estimator hinges on a critical hyper-parameter, where a pre-fixed configuration may lack power and existing data-driven hyper-parameters compromise the FDR control. In this work, we propose a novel class of adaptive hyper-parameters and establish the FDR control of the associated adaptive BH procedure using a martingale argument. Within this class of data-driven hyper-parameters, we further present a specific configuration designed to maximize the number of rejections and characterize its convergence to the optimal hyper-parameter under a mixture model. Our proposal exhibits significant power gains particularly in cases with a conservative null distribution common in composite null testing or a moderate proportion of weak non-nulls typically observed in biological experiments with enrichment processes.

Efficient Distributed Transfer Learning for Large‐Scale Gaussian Graphic Models

ABSTRACTA transfer learning for the large‐scale Gaussian graphical models (GGMs) is considered in a distributed architecture for fitting these target and auxiliary studies scattered across multiple sites. A distributed transfer learning algorithm, DisPower‐Trans‐CLIME, is proposed to learn the target GGMs by incorporating the data from similar and related auxiliary studies. The algorithm is communication efficient via the distributed power method for matrix decomposition. We show that DisPower‐Trans‐CLIME has a fast convergence rate comparable to the centred Trans‐CLIME algorithm. A debiased DisPower‐Trans‐CLIME is constructed and is proved to be element‐wise asymptotically normal for statistical inference. Thus, a multiple testing procedure is developed to detect edge of GGMs with false discovery rate (FDR) control. Extensive simulation experiments have been conducted to demonstrate superior numerical performance of our proposed learning algorithm on estimation and edge detection. It is also applied to infer the regional connection networks in brain regions of interest (ROIs) based on a target hospital site by leveraging the graph from multiple other hospital sites for autism spectrum disorder. We observe that the degrees of regional connectivity in the right brain are balanced, while the ones of the left brain region are extremely uneven because of the presence of multiple strong connections. However, the specific association, between the degrees of connectivity of these regions and ASD disease, is unknown.

Characterization of host cell proteins in the downstream process of plant-Based biologics using LC-MS profiling

Host cell proteins (HCPs) are process-related impurities found in biopharmaceutical products that can impair their safety and efficacy. While ELISA has traditionally been employed to quantify HCPs, LC-MS emerges as a powerful alternative for precise identification of individual HCPs. In this study, we used LC-MS for profiling HCPs from Nicotiana benthamiana-derived biopharmaceuticals. Our approach involved rigorous false discovery rate control to ensure data integrity and reliability. Comprehensive analysis revealed a systematic reduction of HCPs following purification, demonstrating the efficiency of purification processes in removing non-essential proteins. Furthermore, LC-MS enabled the identification of potential contaminants, refining purification strategies and improving product purity and integrity. Our findings highlight the potential of LC-MS as an analytical tool for HCPs analysis in biopharmaceutical development and manufacturing. By providing detailed insights into HCPs profiles and contaminants, LC-MS facilitates informed decision-making in downstream processing steps, benefiting product quality, patient safety, and the biopharmaceutical sector.

Signal quality evaluation of an in-ear EEG device in comparison to a conventional cap system.

Wearable in-ear electroencephalographic (EEG) devices hold significant promise for integrating brain monitoring technologies into real-life applications. However, despite the introduction of various in-ear EEG systems, there remains a necessity for validating these technologies against gold-standard, clinical-grade devices. This study aims to evaluate the signal quality of a newly developed mobile in-ear EEG device compared to a standard scalp EEG system among healthy volunteers during wakefulness and sleep. The study evaluated an in-ear EEG device equipped with dry electrodes in a laboratory setting, recording a single bipolar EEG channel using a cross-ear electrode configuration. Thirty healthy participants were recorded simultaneously using the in-ear EEG device and a conventional EEG cap system with 64 wet electrodes. Based on two recording protocols, one during a resting state condition involving alternating eye opening and closure with a low degree of artifact contamination and another consisting of a daytime nap, several quality measures were used for a quantitative comparison including root mean square (RMS) analysis, artifact quantification, similarities of relative spectral power (RSP), signal-to-noise ratio (SNR) based on alpha peak criteria, and cross-signal correlations of alpha activity during eyes-closed conditions and sleep activities. The statistical significance of our results was assessed through nonparametric permutation tests with False Discovery Rate (FDR) control. During the resting state, in-ear and scalp EEG signals exhibited similar fluctuations, characterized by comparable RMS values. However, intermittent signal alterations were noticed in the in-ear recordings during nap sessions, attributed to movements of the head and facial muscles. Spectral analysis indicated similar patterns between in-ear and scalp EEG, showing prominent peaks in the alpha range (8-12 Hz) during rest and in the low-frequency range during naps (particularly in the theta range of 4-7 Hz). Analysis of alpha wave characteristics during eye closures revealed smaller alpha wave amplitudes and slightly lower signal-to-noise ratio (SNR) values in the in-ear EEG compared to scalp EEG. In around 80% of cases, cross-correlation analysis between in-ear and scalp signals, using a contralateral bipolar montage of 64 scalp electrodes, revealed significant correlations with scalp EEG (p < 0.01), particularly evident in the FT11-FT12 and T7-T8 electrode derivations. Our findings support the feasibility of using in-ear EEG devices with dry-contact electrodes for brain activity monitoring, compared to a standard scalp EEG, notably for wakefulness and sleep uses. Although marginal signal degradation is associated with head and facial muscle contractions, the in-ear device offers promising applications for long-term EEG recordings, particularly in scenarios requiring enhanced comfort and user-friendliness.

A New Evaluation Metric for Quantitative Accuracy of LC-MS/MS-Based Proteomics with Data-Independent Acquisition.

Data-independent acquisition (DIA) has improved the identification and quantitation coverage of peptides and proteins in liquid chromatography-tandem mass spectrometry-based proteomics. However, different DIA data-processing tools can produce very different identification and quantitation results for the same data set. Currently, benchmarking studies of DIA tools are predominantly focused on comparing the identification results, while the quantitative accuracy of DIA measurements is acknowledged to be important but insufficiently investigated, and the absence of suitable metrics for comparing quantitative accuracy is one of the reasons. A new metric is proposed for the evaluation of quantitative accuracy to avoid the influence of differences in false discovery rate control stringency. The part of the quantitation results with high reliability was acquired from each DIA tool first, and the quantitative accuracy was evaluated by comparing quantification error rates at the same number of accurate ratios. From the results of four benchmark data sets, the proposed metric was shown to be more sensitive to discriminating the quantitative performance of DIA tools. Moreover, the DIA tools with advantages in quantitative accuracy were consistently revealed by this metric. The proposed metric can also help researchers in optimizing algorithms of the same DIA tool and sample preprocessing methods to enhance quantitative accuracy.

TWAS-GKF: a novel method for causal gene identification in transcriptome-wide association studies with knockoff inference.

Transcriptome-wide association study (TWAS) aims to identify trait-associated genes regulated by significant variants to explore the underlying biological mechanisms at a tissue-specific level. Despite the advancement of current TWAS methods to cover diverse traits, traditional approaches still face two main challenges: (i) the lack of methods that can guarantee finite-sample false discovery rate (FDR) control in identifying trait-associated genes; and (ii) the requirement for individual-level data, which is often inaccessible. To address this challenge, we propose a powerful knockoff inference method termed TWAS-GKF to identify candidate trait-associated genes with a guaranteed finite-sample FDR control. TWAS-GKF introduces the main idea of Ghostknockoff inference to generate knockoff variables using only summary statistics instead of individual-level data. In extensive studies, we demonstrate that TWAS-GKF successfully controls the finite-sample FDR under a pre-specified FDR level across all settings. We further apply TWAS-GKF to identify genes in brain cerebellum tissue from the Genotype-Tissue Expression (GTEx) v8 project associated with schizophrenia (SCZ) from the Psychiatric Genomics Consortium (PGC), and genes in liver tissue related to low-density lipoprotein cholesterol (LDL-C) from the UK Biobank, respectively. The results reveal that the majority of the identified genes are validated by Open Targets Validation Platform. The R package TWAS.GKF is publicly available at https://github.com/AnqiWang2021/TWAS.GKF.

-KIDS: A novel feature evaluation in the ultrahigh-dimensional right-censored setting, with application to Head and Neck Cancer.

Recent advances in sequencing technologies have allowed collection of massive genome-wide information that substantially enhances the diagnosis and prognosis of head and neck cancer. Identifying predictive markers for survival time is crucial for devising prognostic systems, and learning the underlying molecular driver of the cancer course. In this paper, we introduce -KIDS, a model-free feature screening procedure with false discovery rate (FDR) control for ultrahigh dimensional right-censored data, which is robust against unknown censoring mechanisms. Specifically, our two-stage procedure initially selects a set of important features with a dual screening mechanism using nonparametric reproducing-kernel-based ANOVA statistics, followed by identifying a refined set (of features) under directional FDR control through a unified knockoff procedure. The finite sample properties of our method, and its novelty (in light of existing alternatives) are evaluated via simulation studies. Furthermore, we illustrate our methodology via application to a motivating right-censored head and neck (HN) cancer survival data derived from The Cancer Genome Atlas, with further validation on a similar HN cancer data from the Gene Expression Omnibus database. The methodology can be implemented via the R package DSFDRC, available in GitHub.

False Discovery Rate Control For Structured Multiple Testing: Asymmetric Rules And Conformal Q-values

The effective utilization of structural information in data while ensuring statistical validity poses a significant challenge in false discovery rate (FDR) analyses. Conformal inference provides rigorous theory for grounding complex machine learning methods without relying on strong assumptions or highly idealized models. However, existing conformal methods have limitations in handling structured multiple testing, as their validity often requires the deployment of symmetric decision rules, which assume the exchangeability of data points and permutation-invariance of fitting algorithms. To overcome these limitations, we introduce the pseudo local index of significance (PLIS) procedure, which is capable of accommodating asymmetric rules and requires only pairwise exchangeability between the null conformity scores. We demonstrate that PLIS offers finite-sample guarantees in FDR control and the ability to assign higher weights to relevant data points. Numerical results confirm the effectiveness and robustness of PLIS and demonstrate improvements in power compared to existing model-free methods in various scenarios. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Controlling false discovery rate for mediator selection in high-dimensional data.

The need to select mediators from a high dimensional data source, such as neuroimaging data and genetic data, arises in much scientific research. In this work, we formulate a multiple-hypothesis testing framework for mediator selection from a high-dimensional candidate set, and propose a method, which extends the recent development in false discovery rate (FDR)-controlled variable selection with knockoff to select mediators with FDR control. We show that the proposed method and algorithm achieved finite sample FDR control. We present extensive simulation results to demonstrate the power and finite sample performance compared with the existing method. Lastly, we demonstrate the method for analyzing the Adolescent Brain Cognitive Development (ABCD) study, in which the proposed method selects several resting-state functional magnetic resonance imaging connectivity markers as mediators for the relationship between adverse childhood events and the crystallized composite score in the NIH toolbox.

Differential network knockoff filter with application to brain connectivity analysis.

The brain functional connectivity can typically be represented as a brain functional network, where nodes represent regions of interest (ROIs) and edges symbolize their connections. Studying group differences in brain functional connectivity can help identify brain regions and recover the brain functional network linked to neurodegenerative diseases. This process, known as differential network analysis focuses on the differences between estimated precision matrices for two groups. Current methods struggle with individual heterogeneity in measuring the brain connectivity, false discovery rate (FDR) control, and accounting for confounding factors, resulting in biased estimates and diminished power. To address these issues, we present a two-stage FDR-controlled feature selection method for differential network analysis using functional magnetic resonance imaging (fMRI) data. First, we create individual brain connectivity measures using a high-dimensional precision matrix estimation technique. Next, we devise a penalized logistic regression model that employs individual brain connectivity data and integrates a new knockoff filter for FDR control when detecting significant differential edges. Through extensive simulations, we showcase the superiority of our approach compared to other methods. Additionally, we apply our technique to fMRI data to identify differential edges between Alzheimer's disease and control groups. Our results are consistent with prior experimental studies, emphasizing the practical applicability of our method.

Analysis of mutation‐originated gain‐of‐glycosylation using mass spectrometry‐based N‐glycoproteomics

RationaleA general N‐glycoproteomics analysis pipeline has been established for characterization of mutation‐related gain‐of‐glycosylation (GoG) at intact N‐glycopeptide molecular level, generating comprehensive site and structure information of N‐glycosylation.MethodsThis study focused on mutation‐originated GoG using a mass spectrometry‐based N‐glycoproteomics analysis workflow. In brief, GoG intact N‐glycopeptide databases were built, consisting of 2701 proteins (potential GoG N‐glycosites and amino acids derived from MUTAGEN, VARIANT and VAR_SEQ in UniProt) and 6709 human N‐glycans (≤50 sequence isomers per monosaccharide composition). We employed the site‐ and structure‐specific N‐glycoproteomics workflow utilizing intact N‐glycopeptides search engine GPSeeker to identify GoG intact N‐glycopeptides from parental breast cancer stem cells (MCF‐7 CSCs) and adriamycin‐resistant breast cancer stem cells (MCF‐7/ADR CSCs).ResultsWith the criteria of spectrum‐level false discovery rate control of ≤1%, we identified 87 and 94 GoG intact N‐glycopeptides corresponding to 37 and 35 intact N‐glycoproteins from MCF‐7 CSCs and MCF‐7/ADR CSCs, respectively. Micro‐heterogeneity and macro‐heterogeneity of N‐glycosylation from GoG intact N‐glycoproteins with VAR_SEQ and VARIANT were found in both MCF‐7 CSCs and MCF‐7/ADR CSCs systems.ConclusionsThe integration of site‐ and structure‐specific N‐glycoproteomics approach, conjugating with GoG characterization, provides a universal workflow for revealing comprehensive N‐glycosite and N‐glycan structure information of GoG. The analysis of mutation‐originated GoG can be extended to GoG characterization of other N‐glycoproteome systems including complex clinical tissues and body fluids.

Mutual information for detecting multi-class biomarkers when integrating multiple bulk or single-cell transcriptomic studies.

Biomarker detection plays a pivotal role in biomedical research. Integrating omics studies from multiple cohorts can enhance statistical power, accuracy and robustness of the detection results. However, existing methods for horizontally combining omics studies are mostly designed for two-class scenarios (e.g., cases versus controls) and are not directly applicable for studies with multi-class design (e.g., samples from multiple disease subtypes, treatments, tissues, or cell types). We propose a statistical framework, namely Mutual Information Concordance Analysis (MICA), to detect biomarkers with concordant multi-class expression pattern across multiple omics studies from an information theoretic perspective. Our approach first detects biomarkers with concordant multi-class patterns across partial or all of the omics studies using a global test by mutual information. A post hoc analysis is then performed for each detected biomarkers and identify studies with concordant pattern. Extensive simulations demonstrate improved accuracy and successful false discovery rate control of MICA compared to an existing MCC method. The method is then applied to two practical scenarios: four tissues of mouse metabolism-related transcriptomic studies, and three sources of estrogen treatment expression profiles. Detected biomarkers by MICA show intriguing biological insights and functional annotations. Additionally, we implemented MICA for single-cell RNA-Seq data for tumor progression biomarkers, highlighting critical roles of ribosomal function in the tumor microenvironment of triple-negative breast cancer and underscoring the potential of MICA for detecting novel therapeutic targets. https://github.com/jianzou75/MICA.

Conformal link prediction for false discovery rate control

AbstractMost link prediction methods return estimates of the connection probability of missing edges in a graph. Such output can be used to rank the missing edges from most to least likely to be a true edge, but does not directly provide a classification into true and nonexistent. In this work, we consider the problem of identifying a set of true edges with a control of the false discovery rate (FDR). We propose a novel method based on high-level ideas from the literature on conformal inference. The graph structure induces intricate dependence in the data, which we carefully take into account, as this makes the setup different from the usual setup in conformal inference, where data exchangeability is assumed. The FDR control is empirically demonstrated for both simulated and real data.

Assessment of false discovery rate control in tandem mass spectrometry analysis using entrapment.

A pressing statistical challenge in the field of mass spectrometry proteomics is how to assess whether a given software tool provides accurate error control. Each software tool for searching such data uses its own internally implemented methodology for reporting and controlling the error. Many of these software tools are closed source, with incompletely documented methodology, and the strategies for validating the error are inconsistent across tools. In this work, we identify three different methods for validating false discovery rate (FDR) control in use in the field, one of which is invalid, one of which can only provide a lower bound rather than an upper bound, and one of which is valid but under-powered. The result is that the field has a very poor understanding of how well we are doing with respect to FDR control, particularly for the analysis of data-independent acquisition (DIA) data. We therefore propose a new, more powerful method for evaluating FDR control in this setting, and we then employ that method, along with an existing lower bounding technique, to characterize a variety of popular search tools. We find that the search tools for analysis of data-dependent acquisition (DDA) data generally seem to control the FDR at the peptide level, whereas none of the DIA search tools consistently controls the FDR at the peptide level across all the datasets we investigated. Furthermore, this problem becomes much worse when the latter tools are evaluated at the protein level. These results may have significant implications for various downstream analyses, since proper FDR control has the potential to reduce noise in discovery lists and thereby boost statistical power.

High-dimensional mediation analysis for continuous outcome with confounders using overlap weighting method in observational epigenetic study

BackgroundMediation analysis is a powerful tool to identify factors mediating the causal pathway of exposure to health outcomes. Mediation analysis has been extended to study a large number of potential mediators in high-dimensional data settings. The presence of confounding in observational studies is inevitable. Hence, it’s an essential part of high-dimensional mediation analysis (HDMA) to adjust for the potential confounders. Although the propensity score (PS) related method such as propensity score regression adjustment (PSR) and inverse probability weighting (IPW) has been proposed to tackle this problem, the characteristics with extreme propensity score distribution of the PS-based method would result in the biased estimation.MethodsIn this article, we integrated the overlapping weighting (OW) technique into HDMA workflow and proposed a concise and powerful high-dimensional mediation analysis procedure consisting of OW confounding adjustment, sure independence screening (SIS), de-biased Lasso penalization, and joint-significance testing underlying the mixture null distribution. We compared the proposed method with the existing method consisting of PS-based confounding adjustment, SIS, minimax concave penalty (MCP) variable selection, and classical joint-significance testing.ResultsSimulation studies demonstrate the proposed procedure has the best performance in mediator selection and estimation. The proposed procedure yielded the highest true positive rate, acceptable false discovery proportion level, and lower mean square error. In the empirical study based on the GSE117859 dataset in the Gene Expression Omnibus database using the proposed method, we found that smoking history may lead to the estimated natural killer (NK) cell level reduction through the mediation effect of some methylation markers, mainly including methylation sites cg13917614 in CNP gene and cg16893868 in LILRA2 gene.ConclusionsThe proposed method has higher power, sufficient false discovery rate control, and precise mediation effect estimation. Meanwhile, it is feasible to be implemented with the presence of confounders. Hence, our method is worth considering in HDMA studies.

Leveraging conformal prediction to annotate enzyme function space with limited false positives.

Machine learning (ML) is increasingly being used to guide biological discovery in biomedicine such as prioritizing promising small molecules in drug discovery. In those applications, ML models are used to predict the properties of biological systems, and researchers use these predictions to prioritize candidates as new biological hypotheses for downstream experimental validations. However, when applied to unseen situations, these models can be overconfident and produce a large number of false positives. One solution to address this issue is to quantify the model's prediction uncertainty and provide a set of hypotheses with a controlled false discovery rate (FDR) pre-specified by researchers. We propose CPEC, an ML framework for FDR-controlled biological discovery. We demonstrate its effectiveness using enzyme function annotation as a case study, simulating the discovery process of identifying the functions of less-characterized enzymes. CPEC integrates a deep learning model with a statistical tool known as conformal prediction, providing accurate and FDR-controlled function predictions for a given protein enzyme. Conformal prediction provides rigorous statistical guarantees to the predictive model and ensures that the expected FDR will not exceed a user-specified level with high probability. Evaluation experiments show that CPEC achieves reliable FDR control, better or comparable prediction performance at a lower FDR than existing methods, and accurate predictions for enzymes under-represented in the training data. We expect CPEC to be a useful tool for biological discovery applications where a high yield rate in validation experiments is desired but the experimental budget is limited.