Language
English
Publication Date
10-1-2024
Journal
PLoS Genetics
DOI
10.1371/journal.pgen.1011423
PMID
39361716
PMCID
PMC11478871
PubMedCentral® Posted Date
10-3-2024
PubMedCentral® Full Text Version
Post-print
Abstract
Replicable signals from different yet conceptually related studies provide stronger scientific evidence and more powerful inference. We introduce STAREG, a statistical method for replicability analysis of high throughput experiments, and apply it to analyze spatial transcriptomic studies. STAREG uses summary statistics from multiple studies of high throughput experiments and models the the joint distribution of p-values accounting for the heterogeneity of different studies. It effectively controls the false discovery rate (FDR) and has higher power by information borrowing. Moreover, it provides different rankings of important genes. With the EM algorithm in combination with pool-adjacent-violator-algorithm (PAVA), STAREG is scalable to datasets with millions of genes without any tuning parameters. Analyzing two pairs of spatially resolved transcriptomic datasets, we are able to make biological discoveries that otherwise cannot be obtained by using existing methods.
Keywords
Algorithms, Gene Expression Profiling, Transcriptome, Humans, Reproducibility of Results, Animals, Models, Statistical
Published Open-Access
yes
Recommended Citation
Li, Yan; Zhou, Xiang; Chen, Rui; et al., "Stareg: Statistical Replicability Analysis of High Throughput Experiments With Applications to Spatial Transcriptomic Studies" (2024). Faculty and Staff Publications. 4370.
https://digitalcommons.library.tmc.edu/baylor_docs/4370