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paper/paper.md

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 ---
-title: 'MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Multi-Omics'
+title: 'MolPad: An R-Shiny Package for Cluster Co-Expression Analysis in Longitudinal Microbiomics'
 tags:
   - R
   - shiny
@@ -38,11 +38,11 @@ aas-journal: Astrophysical Journal <- The name of the AAS journal.
 
 # Summary
 
-The R-shiny package MolPad provides an interactive dashboard for understanding the dynamics of longitudinal molecular co-expression in microbiome multi-omics. The main idea for addressing the issue is to first use a network to overview major patterns among their predictive relationships and then zoom into specific clusters of interest. It is designed with a focus-plu-context analysis strategy and automatically generates links to online curated annotations. The dashboard consists of a cluster-level network, a bar plot of taxonomic composition, a line plot of data modalities, and a table for each pathway, as illustrated in Fig \ref{fig:dashboard}. Plus, the package includes functions that handle the data processing for creating the dashboard. This makes it beginner-friendly for students with less R programming experience. We illustrate these methods with a case study on a longitudinal, multi-platform meta-genomics analysis for cheese communities.
+The R-shiny package MolPad provides an interactive dashboard for understanding the dynamics of longitudinal molecular co-expression in microbiomics. The main idea for addressing the issue is first to use a network to overview major patterns among their predictive relationships and then zoom into specific clusters of interest. It is designed with a focus-plu-context analysis strategy and automatically generates links to online curated annotations. The dashboard consists of a cluster-level network, a bar plot of taxonomic composition, a line plot of data modalities, and a table for each pathway, as illustrated in Fig \ref{fig:dashboard}. Plus, the package includes functions that handle the data processing for creating the dashboard. This makes it beginner-friendly for users with less R programming experience. We illustrate these methods with a case study on a longitudinal, multi-platform meta-genomics analysis for cheese communities.
 
 # Statement of need
 
-The realm of multi-omics is expanding rapidly, with numerous new studies and methodologies emerging. This highlights the need for visualizations that can account for differences across modalities. It’s also important to enable interpretations of dynamics and network structure because these have specific meanings in the genomic context. Another issue is the annotation. The special modality characteristic of multi-omics determines that each identical feature can be classified with various taxons and could have several IDs in different databases. Therefore, although the annotation is available online, it can be tedious to search for parts manually. Moreover, most present visualizations poorly evaluate longitudinal change across omics. In longitudinal data, we need to gain insight into the functioning of how individual features change and how they may influence related features. Thus, it depends upon analysis within one table and across tables. All of these have posed a challenge for unified visualization and interpretation. 
+The realm of microbiomics is expanding rapidly, with numerous new studies and methodologies emerging. This highlights the need for visualizations that can account for differences across modalities. It’s also important to enable interpretations of dynamics and network structure because these have specific meanings in the genomic context. Another issue is the annotation. The special modality characteristic of microbiomics determines that each identical feature can be classified with various taxons and could have several IDs in different databases. Therefore, although the annotation is available online, it can be tedious to search for parts manually. Moreover, most present visualizations poorly evaluate longitudinal change across omics. In longitudinal data, we need to gain insight into the functioning of how individual features change and how they may influence related features. Thus, it depends upon analysis within one table and across tables. All of these have posed a challenge for unified visualization and interpretation. 
 
 In response to the above issues, previous studies on multi-omics visualization tools have designed methods to work on complicated data. `microViz` [@microviz] provides a Shiny app for interactive exploration by pairing ordination plots and composition circular bar charts to show each taxon's prevalence and abundance. `GWENA` [@Lemoine_Scott-Boyer_Ambroise_Périn_Droit_2021] applies a network in conducting gene co‑expression analysis and extended module characterization in a single package to understand the underlying processes contributing to a disease or a phenotype. `NeVOmics` [@Zúñiga-León_Carrasco-Navarro_Fierro_2018] improved compatibility with a dynamic dashboard and facilitated the functional characterization of data from omics technologies. It also integrates Over-representation analysis methodology and network-based visualization to show the enrichment results. These methods suggest the mechanisms that improve the utility of multi-omics visualization tools under analysis.