MOAHIT: a web tool for visualizing tumor multi-omics data with human anatomy heatmaps

Abstract

Multi-omics data plays an important role in cancer research, helping clinicians to better explore drug targets and biomarkers. At present, there exist several databases including TCGA (the Cancer Genome Atlas) and GDSC (Genomics of Drug Sensitivity in Cancer), which contain multi-omics data on multiple cancer species, as well as various web tools for analyzing the multi-omics data, which are widely used in oncology research. Tumor heterogeneity is a widespread phenomenon, reflected by differences in multi-omics data of cancers originating from different tissues and organs. However, there is a lack of convenient analysis and visualization tools to explore tumor heterogeneity. As a result, we developed a web tool called Multi-Omics Anatomy Heatmap in Tumors (MOAHIT) based on shiny programming. This tool enables users to analyze and visualize the heterogeneity of human tissues. In addition, MOAHIT enables users to produce a beautiful human anatomy heatmap to visualize the heterogeneity between distinct cancers in the multi-omics data.

Psychiatric disorders associated with immune checkpoint inhibitors: a pharmacovigilance analysis of the FDA Adverse Event Reporting System (FAERS) database

“…The human anatomy heatmap was achieved by MOAHIT web tool. 32 Samples containing missing values were excluded during statistical analysis for each clinical characteristic (e.g., sex, age, time to onset, etc.). We controlled for multiple testing by calculating false discovery rate (FDR) value utilizing the Benjamini–Hochberg method via the ‘ P .adjust’ function from the ‘stats’ R package.…”

Adverse reactions associated with immune checkpoint inhibitors and bevacizumab: A pharmacovigilance analysis

“…Next, using multivariate logistic analysis, the adjusted OR was calculated. “forestplot” 32 was used to create a forest map, while “ggplot2,” 33 “ggpubr” 34 and “MOAHIT” 35 were employed for visualization. The clinical baseline table was implemented by the R package “table1.” All analyses and visualizations for our study were performed using R studio (version 4.1.2).…”