Sample size and power calculations for causal mediation analysis: A Tutorial and Shiny App.

When designing a study for causal mediation analysis, it is crucial to conduct a power analysis to determine the sample size required to detect the causal mediation effects with sufficient power. However, the development of power analysis methods for causal mediation analysis has lagged far behind. To fill the knowledge gap, I proposed a simulation-based method and an easy-to-use web application ( https://xuqin.shinyapps.io/CausalMediationPowerAnalysis/ ) for power and sample size calculations for regression-based causal mediation analysis. By repeatedly drawing samples of a specific size from a population predefined with hypothesized models and parameter values, the method calculates the power to detect a causal mediation effect based on the proportion of the replications with a significant test result. The Monte Carlo confidence interval method is used for testing so that the sampling distributions of causal effect estimates are allowed to be asymmetric, and the power analysis runs faster than if the bootstrapping method is adopted. This also guarantees that the proposed power analysis tool is compatible with the widely used R package for causal mediation analysis, mediation, which is built upon the same estimation and inference method. In addition, users can determine the sample size required for achieving sufficient power based on power values calculated from a range of sample sizes. The method is applicable to a randomized or nonrandomized treatment, a mediator, and an outcome that can be either binary or continuous. I also provided sample size suggestions under various scenarios and a detailed guideline of app implementation to facilitate study designs.

PATH-SURVEYOR: pathway level survival enquiry for immuno-oncology and drug repurposing.

Pathway-level survival analysis offers the opportunity to examine molecular pathways and immune signatures that influence patient outcomes. However, available survival analysis algorithms are limited in pathway-level function and lack a streamlined analytical process. Here we present a comprehensive pathway-level survival analysis suite, PATH-SURVEYOR, which includes a Shiny user interface with extensive features for systematic exploration of pathways and covariates in a Cox proportional-hazard model. Moreover, our framework offers an integrative strategy for performing Hazard Ratio ranked Gene Set Enrichment Analysis and pathway clustering. As an example, we applied our tool in a combined cohort of melanoma patients treated with checkpoint inhibition (ICI) and identified several immune populations and biomarkers predictive of ICI efficacy. We also analyzed gene expression data of pediatric acute myeloid leukemia (AML) and performed an inverse association of drug targets with the patient's clinical endpoint. Our analysis derived several drug targets in high-risk KMT2A-fusion-positive patients, which were then validated in AML cell lines in the Genomics of Drug Sensitivity database. Altogether, the tool offers a comprehensive suite for pathway-level survival analysis and a user interface for exploring drug targets, molecular features, and immune populations at different resolutions.

shinyDeepDR: A user-friendly R Shiny app for predicting anti-cancer drug response using deep learning.

Advancing precision oncology requires accurate prediction of treatment response and accessible prediction models. To this end, we present shinyDeepDR, a user-friendly implementation of our innovative deep learning model, DeepDR, for predicting anti-cancer drug sensitivity. The web tool makes DeepDR more accessible to researchers without extensive programming experience. Using shinyDeepDR, users can upload mutation and/or gene expression data from a cancer sample (cell line or tumor) and perform two main functions: "Find Drug," which predicts the sample's response to 265 approved and investigational anti-cancer compounds, and "Find Sample," which searches for cell lines in the Cancer Cell Line Encyclopedia (CCLE) and tumors in The Cancer Genome Atlas (TCGA) with genomics profiles similar to those of the query sample to study potential effective treatments. shinyDeepDR provides an interactive interface to interpret prediction results and to investigate individual compounds. In conclusion, shinyDeepDR is an intuitive and free-to-use web tool for in silico anti-cancer drug screening.

DRPPM-PATH-SURVEIOR: Plug-and-Play Survival Analysis of Pathway-level Signatures and Immune Components.

Pathway-level survival analysis offers the opportunity to examine molecular pathways and immune signatures that influence patient outcomes. However, available survival analysis algorithms are limited in pathway-level function and lack a streamlined analytical process. Here we present a comprehensive pathway-level survival analysis suite, DRPPM-PATH-SURVEIOR, which includes a Shiny user interface with extensive features for systematic exploration of pathways and covariates in a Cox proportional-hazard model. Moreover, our framework offers an integrative strategy for performing Hazard Ratio ranked Gene Set Enrichment Analysis (GSEA) and pathway clustering. As an example, we applied our tool in a combined cohort of melanoma patients treated with checkpoint inhibition (ICI) and identified several immune populations and biomarkers predictive of ICI efficacy. We also analyzed gene expression data of pediatric acute myeloid leukemia (AML) and performed an inverse association of drug targets with the patient's clinical endpoint. Our analysis derived several drug targets in high-risk KMT2A-fusion-positive patients, which were then validated in AML cell lines in the Genomics of Drug Sensitivity database. Altogether, the tool offers a comprehensive suite for pathway-level survival analysis and a user interface for exploring drug targets, molecular features, and immune populations at different resolutions.

SEQUIN is an R/Shiny framework for rapid and reproducible analysis of RNA-seq data.

SEQUIN is a web-based application (app) that allows fast and intuitive analysis of RNA sequencing data derived for model organisms, tissues, and single cells. Integrated app functions enable uploading datasets, quality control, gene set enrichment, data visualization, and differential gene expression analysis. We also developed the iPSC Profiler, a practical gene module scoring tool that helps measure and compare pluripotent and differentiated cell types. Benchmarking to other commercial and non-commercial products underscored several advantages of SEQUIN. Freely available to the public, SEQUIN empowers scientists using interdisciplinary methods to investigate and present transcriptome data firsthand with state-of-the-art statistical methods. Hence, SEQUIN helps democratize and increase the throughput of interrogating biological questions using next-generation sequencing data with single-cell resolution.

The SAlzburg PEritoneal SUrface CAlculator (SAPESUCA): The First Web-Based Application for Peritoneal Surface Area Quantification.

(1) Background: Peritoneal metastasized colorectal cancer is associated with a worse prognosis. The combination of cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) showed promising results in selected patients, but standardization is lacking so far. We present the first tool enabling standardized peritoneal surface area (PSA) quantification in patients undergoing CRS and HIPEC: The SAlzburg PEritoneal SUrface CAlculator (SAPESUCA). (2) Methods: SAPESUCA was programmed using the R-Shiny framework. The application was validated in 23 consecutive colon cancer patients who received 27 closed oxaliplatin-based HIPECs between 2016 and 2020. The programming algorithm incorporates the patient's body surface area and its correlated peritoneal surface area (PSA) based on the 13 Peritoneal Cancer Index (PCI) regions. (3) Results: Patients' median age was 56 years. Median PCI was 9. SAPESUCA revealed a mean PSA of 18,613 cm2 ± 1951 of all patients before compared to 13,681 cm2 ± 2866 after CRS. The Central PCI region revealed the highest mean peritonectomy extent (1517 cm2 ± 737). The peritonectomy extent correlated significantly with PCI score and postoperative morbidity. The simulated mean oxaliplatin dose differed significantly before and after CRS (558 mg/m2 ± 58.4 vs. 409 mg/m2 ± 86.1; p < 0.0001). (4) Conclusion: SAPESUCA is the first free web-based app for standardized determination of the resected and remaining PSA after CRS. The tool enables chemotherapeutic dose adjustment to the remaining PSA.

Unequal allocation of sample/event sizes with considerations of sampling cost for testing equality, non-inferiority/superiority, and equivalence of two Poisson rates.

For non-inferiority/superiority and equivalence tests of two Poisson rates, the determination of the required number of sample sizes has been studied but the studies for the number of events to be observed are very limited. To fill the gap, the present study first is aimed toward determining the number of events to be observed for testing non-inferiority/superiority and equivalence of two Poisson rates, respectively. Also, considering the cost for each event, the second purpose is to apply an exhaustive search to find the unequal but optimal allocation of events for each group such that the budget is minimal for a user-specified power level, or the statistical power is maximal for a user-specified budget. Four R Shiny apps were developed to obtain the number of events needed for each group. A simulation study showed the proposed approach to be valid in terms of Type I error and statistical power. A comparison of the proposed approach with extant methods from various disciplines was performed, and an illustrative example of comparing the adverse reactions to the COVID-19 vaccines was demonstrated. By applying the proposed approach, researchers also can estimate the most economical number of subjects or time intervals after determining the number of events.

Free amino acids in African indigenous vegetables: Analysis with improved hydrophilic interaction ultra-high performance liquid chromatography tandem mass spectrometry and interactive machine learning.

A hydrophilic interaction (HILIC) ultra-high performance liquid chromatography (UHPLC) with triple quadrupole tandem mass spectrometry (MS/MS) method was developed and validated for the quantification of 21 free amino acids (AAs). Compared to published reports, our method renders collectively improved sensitivity with lower limit of quantification (LLOQ) at 0.5~42.19 ng/mL with 0.3 µL injection volume (or equivalently 0.15~12.6 pg injected on column), robust linear range from LLOQ up to 3521~5720 ng/mL (or 1056 ~ 1716 pg on column) and a high throughput with total time of 6 min per sample, as well as easier experimental setup, less maintenance and higher adaptation flexibility. Ammonium formate in the mobile phase, though commonly used in HILIC, was found unnecessary in our experimental setup, and its removal from mobile phase was key for significant improvement in sensitivity (4~74 times higher than with 5 mM ammonium formate). Addition of 10 (or up to100 mM) hydrochloric acid (HCl) in the sample diluent was crucial to keep response linearity for basic amino acids of histidine, lysine and arginine. Different HCl concentration (10~100 mM) in sample diluent also excreted an effect on detection sensitivity, and it is of importance to keep the final prepared sample and calibrators in the same HCl level. Leucine and isoleucine were distinguished using different transitions. Validated at seven concentration levels, accuracy was bound within 75~125%, matrix effect generally within 90~110%, and precision error mostly below 2.5%. Using this newly developed method, the free amino acids were then quantified in a total of 544 African indigenous vegetables (AIVs) samples from African nightshades (AN), Ethiopian mustards (EM), amaranths (AM) and spider plants (SP), comprising a total of 8 identified species and 43 accessions, cultivated and harvested in USA, Kenya and Tanzania over several years, 2013~2018. The AN, EM, AM and SP were distinguished based on free AAs profile using machine learning methods (ML) including principle component analysis, discriminant analysis, naïve Bayes, elastic net-regularized logistic regression, random forest and support vector machine, with prediction accuracy achieved at ca. 83~97% on the test set (train/test ratio at 7/3). An interactive ML platform was constructed using R Shiny at https://boyuan.shinyapps.io/AIV_Classifier/ for modeling train-test simulation and category prediction of unknown AIV sample(s). This new method presents a robust and rapid approach to quantifying free amino acids in plants for use in evaluating plants, biofortification, botanical authentication, safety, adulteration and with applications to nutrition, health and food product development.