Tissue-specific gene expression is critical in complex disease etiology, but traditional transcriptome-wide Mendelian Randomization (TWMR) often overlooks tissue heterogeneity and is constrained by limited valid instrumental variables (IVs) per tissue. We propose Multi-Tissue TWMR (MT-TWMR), which selects cis-eQTLs with consistent effects across tissues as IVs and estimates tissue-specific causal effects via a penalized regression model combining L1 regularization for sparsity with a weighted tissue-difference penalty based on tissue similarity to enable cross-tissue information sharing. Simulations under varying IV numbers, pleiotropy levels, and tissue counts showed MT-TWMR consistently achieved higher power, lower root mean square error, and better type I error control than existing univariate and multivariable MR methods, particularly when IVs were scarce. Applied to major depressive disorder and primary hypertension, MT-TWMR identified 28 and 57 causal genes respectively, with enriched signals in biologically relevant tissues and strong colocalization and pathway evidence. MT-TWMR offers an effective and interpretable framework for integrating multi-tissue eQTL data to construct tissue-specific disease gene maps.

Mendelian randomization (MR) method utilizes genetic variants as instrumental variables to infer the causal effect of an exposure on an outcome. However, the impact of rare variants on traits is often neglected, and traditional MR assumptions can be violated by correlated horizontal pleiotropy (CHP) and uncorrelated horizontal pleiotropy (UHP). To address these issues, we propose a multivariable MR approach, an extension of the standard MR framework: MVMR incorporating Rare variants Accounting for multiple Risk factors and shared horizontal plEiotropy (RARE). In the simulation studies, we demonstrate that RARE effectively detects the causal effects of exposures on outcome with accounting for the impact of rare variants on causal inference. Additionally, we apply RARE to study the effects of high density lipoprotein and low density lipoprotein on type 2 diabetes and coronary atherosclerosis, respectively, thereby illustrating its robustness and effectiveness in real data analysis.

Single-cell RNA sequencing has revealed cellular heterogeneity in complex tissues, notably benefiting research on diseases such as cancer. However, the integration of single-cell data from small samples with extensive clinical features in bulk data remains underexplored. In this study, we introduce PIPET, an algorithmic method for predicting relevant subpopulations in single-cell data based on multivariate phenotypic information from bulk data. PIPET generates feature vectors for each phenotype from differentially expressed genes in bulk data and then identifies relevant cellular subpopulations by assessing the similarity between single-cell data and these vectors. Subsequently, phenotype-related cell states can be analyzed based on these subpopulations. In simulated datasets, PIPET showed robust performance in predicting multiclassification cellular subpopulations. Application of PIPET to lung adenocarcinoma single-cell RNA sequencing data revealed cellular subpopulations with poor survival and associations with TP53 mutations. Similarly, in breast cancer single-cell data, PIPET identified cellular subpopulations associated with the PAM50 clinical subtypes and triple-negative breast cancer subtypes. Overall, PIPET effectively identified relevant cellular subpopulations in single-cell data, guided by phenotypic information from bulk data. This approach comprehensively delineates the molecular characteristics of each cellular subpopulation, offering insights into disease-related subpopulations and guiding personalized treatment strategies.

SUMMARY: Stratification of cancer patients into distinct molecular subgroups based on multi-omics data is an important issue in the context of precision medicine. Here, we present MOVICS, an R package for multi-omics integration and visualization in cancer subtyping. MOVICS provides a unified interface for 10 state-of-the-art multi-omics integrative clustering algorithms, and incorporates the most commonly used downstream analyses in cancer subtyping researches, including characterization and comparison of identified subtypes from multiple perspectives, and verification of subtypes in external cohort using two model-free approaches for multiclass prediction. MOVICS also creates feature rich customizable visualizations with minimal effort. By analysing two published breast cancer cohort, we signifies that MOVICS can serve a wide range of users and assist cancer therapy by moving away from the 'one-size-fits-all' approach to patient care. AVAILABILITY AND IMPLEMENTATION: MOVICS package and online tutorial are freely available at https://github.com/xlucpu/MOVICS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.