MOTIVATION: Despite the widely recognized importance of replicability in biological research, computational methods to quantify irreplicability and identify irreplicable instances remain underdeveloped. This article presents an efficient and robust computational framework to address this gap. RESULTS: To tackle the challenge of defining an acceptable level of intrinsic heterogeneity among replicable studies, we introduce a distinguishability criterion, ensuring that replicable effects, while potentially heterogeneous, can be distinguished from zero effects and maintain consistent directions with high probability. We implement a Bayesian model criticism approach, reporting a Bayesian P-value to identify potential irreplicable instances. Through numerical experiments, we demonstrate the efficacy of the proposed methods in detecting batch effects in high-throughput experiments and identifying instances of the publication bias. Finally, we apply the framework to multi-tissue eQTL data from the GTEx consortium, uncovering tissue-specific eQTLs that represent biological heterogeneity across tissues. AVAILABILITY AND IMPLEMENTATION: An R package DiscRep implementing our method is available on GitHub (https://github.com/PengWang96/DiscRep).

MOTIVATION: Millions of protein sequences have been generated by numerous genome and transcriptome sequencing projects. However, experimentally determining the function of the proteins is still a time consuming, low-throughput, and expensive process, leading to a large protein sequence-function gap. Therefore, it is important to develop computational methods to accurately predict protein function to fill the gap. Even though many methods have been developed to use protein sequences as input to predict function, much fewer methods leverage protein structures in protein function prediction because there was lack of accurate protein structures for most proteins until recently. RESULTS: We developed TransFun-a method using a transformer-based protein language model and 3D-equivariant graph neural networks to distill information from both protein sequences and structures to predict protein function. It extracts feature embeddings from protein sequences using a pre-trained protein language model (ESM) via transfer learning and combines them with 3D structures of proteins predicted by AlphaFold2 through equivariant graph neural networks. Benchmarked on the CAFA3 test dataset and a new test dataset, TransFun outperforms several state-of-the-art methods, indicating that the language model and 3D-equivariant graph neural networks are effective methods to leverage protein sequences and structures to improve protein function prediction. Combining TransFun predictions and sequence similarity-based predictions can further increase prediction accuracy. AVAILABILITY AND IMPLEMENTATION: The source code of TransFun is available at https://github.com/jianlin-cheng/TransFun.

MOTIVATION: Replicability is the cornerstone of scientific research. The current statistical method for high-dimensional replicability analysis either cannot control the false discovery rate (FDR) or is too conservative. RESULTS: We propose a statistical method, JUMP, for the high-dimensional replicability analysis of two studies. The input is a high-dimensional paired sequence of p-values from two studies and the test statistic is the maximum of p-values of the pair. JUMP uses four states of the p-value pairs to indicate whether they are null or non-null. Conditional on the hidden states, JUMP computes the cumulative distribution function of the maximum of p-values for each state to conservatively approximate the probability of rejection under the composite null of replicability. JUMP estimates unknown parameters and uses a step-up procedure to control FDR. By incorporating different states of composite null, JUMP achieves a substantial power gain over existing methods while controlling the FDR. Analyzing two pairs of spatially resolved transcriptomic datasets, JUMP makes biological discoveries that otherwise cannot be obtained by using existing methods. AVAILABILITY AND IMPLEMENTATION: An R package JUMP implementing the JUMP method is available on CRAN (https://CRAN.R-project.org/package=JUMP).

MOTIVATION: Next generation sequencing technologies have enabled the study of the human microbiome through direct sequencing of microbial DNA, resulting in an enormous amount of microbiome sequencing data. One unique characteristic of microbiome data is the phylogenetic tree that relates all the bacterial species. Closely related bacterial species have a tendency to exhibit a similar relationship with the environment or disease. Thus, incorporating the phylogenetic tree information can potentially improve the detection power for microbiome-wide association studies, where hundreds or thousands of tests are conducted simultaneously to identify bacterial species associated with a phenotype of interest. Despite much progress in multiple testing procedures such as false discovery rate (FDR) control, methods that take into account the phylogenetic tree are largely limited. RESULTS: We propose a new FDR control procedure that incorporates the prior structure information and apply it to microbiome data. The proposed procedure is based on a hierarchical model, where a structure-based prior distribution is designed to utilize the phylogenetic tree. By borrowing information from neighboring bacterial species, we are able to improve the statistical power of detecting associated bacterial species while controlling the FDR at desired levels. When the phylogenetic tree is mis-specified or non-informative, our procedure achieves a similar power as traditional procedures that do not take into account the tree structure. We demonstrate the performance of our method through extensive simulations and real microbiome datasets. We identified far more alcohol-drinking associated bacterial species than traditional methods. AVAILABILITY AND IMPLEMENTATION: R package StructFDR is available from CRAN. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.