MetaNet: Network analysis for multi-omics
The HTML documentation of the latest version is available at Github page.
TutorialðPlease go to https://bookdown.org/Asa12138/metanet_book/ for the full vignette.
InstallationYou can install the released version of MetaNet from CRAN with:
install.packages("MetaNet")You can install the development version of MetaNet from GitHub with:
# install.packages("devtools")
devtools::install_github("Asa12138/MetaNet")Figure 1. Overview of the MetaNet workflow and its high-efficiency computation. (A) Functional modules of MetaNet, visualized using MetaNet itself. (B) Detailed workflow of MetaNet. Green boxes indicate data objects, red boxes represent MetaNet-specific objects, and gray boxes denote core functions. (C) MetaNet logo and its available code repositories and platforms. (D) Line plots comparing memory usage and runtime for correlation-based network construction across different R packages. Error bars represent standard deviation (SD). (E) Line plots showing MetaNetâs performance on increasingly larger datasets in terms of memory usage and runtime. Error bars represent SD.
Figure 2. MetaNet supports flexible and intuitive network manipulation. (A) Initial multi-omics network constructed without annotations. (B) Annotated multi-omics network using the âc_net_setâ function. Node shape indicates omics type, color represents omics subtypes, size denotes average abundance, edge color indicates positive or negative correlation, edge type distinguishes intra- and inter-omics connections, and edge width reflects the absolute value of the correlation coefficient. (C) Subnetwork filtered from intra-omics interactions between the Microbiome and Metabolome layers using âc_net_filterâ. (D) Highlighted nodes centered on âs__Dongia_mobilisâ and its neighbors using âc_net_highlightâ. (E) Community detection and modular visualization using âc_net_moduleâ. (F) Chord diagram displaying the proportion of edges between modules. (G) Skeleton network across omics subtypes at a grouped level using âc_net_skeletonâ. (H) Operations among networks: âc_net_unionâ merges net1 and net2, âc_net_intersectâ extracts shared nodes and edges, and âc_net_differenceâ isolates net1-specific nodes and edges. All networks shown are based on simulated data and are for illustrative purposes only.
Figure 3. MetaNet enables diverse and powerful network layout strategies. (A) Application of 24 out of more than 40 built-in layout algorithms from âc_net_layoutâ on the Zachary Karate Club network. (B) Layout generated within a star using âspatstat_layoutâ. (C) Layout applied within the map of Australia using âspatstat_layoutâ. (D) Grouped network layout consisting of four subgroups arranged with âwith_fr()â, âon_grid()â, âas_polycircle(3)â, and âas_polygon(3)â within a human-body schematic. All visualization elements were rendered entirely in MetaNet without manual adjustment. (E) Modular network visualized using âg_layout_circlepackâ. (F) Three-layer modular structure visualized using âg_layout_multi_layerâ. All networks shown are based on simulated data and carry no biological interpretation.
CitationPlease cite:
Chen Peng (2025). MetaNet: A Comprehensive R Package for Network Analysis of Omics Data. R package, https://github.com/Asa12138/MetaNet.
Need helps?If you have questions/issues, please visit MetaNet homepage first. Your problems are mostly documented. If you think you found a bug, please post on github issue.
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