docs: v0.6 paper

selkamand · Aug 1, 2024 · d429509 · d429509
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diff --git a/paper/jats/paper.jats b/paper/jats/paper.jats
@@ -164,14 +164,14 @@ a Creative Commons Attribution 4.0 International License (CC BY
   et al., 2018</xref>), and genVisR
   (<xref alt="Skidmore et al., 2016" rid="ref-SkidmoreU003A2016" ref-type="bibr">Skidmore
   et al., 2016</xref>) all make static oncoplots easier to create, but
-  there is still a significant unmet need for user-friendly method of
+  there is still a significant unmet need for a user-friendly method of
   creating oncoplots with the following features:</p>
   <list list-type="bullet">
     <list-item>
       <p><bold>Interactive plots</bold>: Customizable tooltips,
       cross-selection of samples across different plots, and
       auto-copying of sample identifiers on click. This enables
-      exploration of trends in multiomic datasets as shown in
+      exploration of multiomic datasets as shown in
       <xref alt="[fig:multimodal_selection]" rid="figU003Amultimodal_selection">[fig:multimodal_selection]</xref>.</p>
     </list-item>
     <list-item>

diff --git a/paper/paper.docx b/paper/paper.docx
diff --git a/paper/paper.md b/paper/paper.md
@@ -41,9 +41,9 @@ The ggoncoplot R package generates interactive oncoplots to visualize mutational
 
 # Statement of Need
 
-Oncoplots are highly effective for visualising mutation data in cancer cohorts but are challenging to generate with the major R plotting systems (base, lattice, or ggplot2) due to their algorithmic and graphical complexity.  Simplifying the process of generating oncoplots would make them more accessible to researchers. Existing packages including ComplexHeatmap [@Gu:2022], maftools [@Mayakonda:2018], and genVisR [@Skidmore:2016] all make static oncoplots easier to create, but there is still a significant unmet need for user-friendly method of creating oncoplots with the following features: 
+Oncoplots are highly effective for visualising mutation data in cancer cohorts but are challenging to generate with the major R plotting systems (base, lattice, or ggplot2) due to their algorithmic and graphical complexity.  Simplifying the process of generating oncoplots would make them more accessible to researchers. Existing packages including ComplexHeatmap [@Gu:2022], maftools [@Mayakonda:2018], and genVisR [@Skidmore:2016] all make static oncoplots easier to create, but there is still a significant unmet need for a user-friendly method of creating oncoplots with the following features: 
 
--	**Interactive plots**: Customizable tooltips, cross-selection of samples across different plots, and auto-copying of sample identifiers on click. This enables exploration of trends in multiomic datasets as shown in \autoref{fig:multimodal_selection}.
+-	**Interactive plots**: Customizable tooltips, cross-selection of samples across different plots, and auto-copying of sample identifiers on click. This enables exploration of multiomic datasets as shown in \autoref{fig:multimodal_selection}.
 
 -	**Support for tidy datasets**: Compatibility with tidy, tabular mutation-level formats (MAF files or relational databases), typical of cancer cohort datasets. This greatly improves the range of datasets that can be quickly and easily visualised in an oncoplot.
 

diff --git a/paper/paper.pdf b/paper/paper.pdf